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 65	96.26 40	72.84 30	99.38 1	92.64 31	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 18	96.19 42	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 35	91.58 17	97.22 7	79.93 5	99.10 9	83.12 117	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 82	94.37 57	72.48 217	92.07 10	96.85 21	83.82 2	99.15 2	91.53 41	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 94	92.83 80	64.03 212	93.06 126	94.33 59	82.19 43	93.65 3	96.15 44	85.89 1	97.19 92	91.02 45	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 44	66.38 71	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 15	89.07 38	96.80 24	70.86 43	99.06 15	92.64 31	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 66	94.91 85	74.11 21	98.91 18	87.26 73	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 57	96.89 6	94.44 51	71.65 247	92.11 8	97.21 8	76.79 9	99.11 6	92.34 33	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 149	93.00 76	58.16 348	96.72 9	94.41 53	86.50 9	90.25 29	97.83 1	75.46 14	98.67 25	92.78 30	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 97	96.04 24	63.70 224	95.04 42	95.19 22	86.74 8	91.53 19	95.15 78	73.86 22	97.58 64	93.38 25	92.00 70	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 52	96.53 13	93.78 71	86.89 7	89.68 35	95.78 51	65.94 76	99.10 9	92.99 28	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	71.92 233	90.55 25	96.93 15	73.77 23	99.08 11	91.91 39	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 88	95.24 34	94.49 49	82.43 40	88.90 39	96.35 35	71.89 40	98.63 26	88.76 59	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 20	89.81 6	93.66 54	75.15 5	90.61 247	93.43 93	84.06 22	86.20 60	90.17 206	72.42 35	96.98 109	93.09 27	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 65	96.38 15	94.64 42	84.42 19	86.74 55	96.20 41	66.56 70	98.76 24	89.03 58	94.56 34	95.92 46

DPE-MVS		88.77 17	89.21 17	87.45 43	96.26 20	67.56 105	94.17 68	94.15 64	68.77 298	90.74 23	97.27 5	76.09 12	98.49 29	90.58 49	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_l_conf0.5_n_9		88.24 18	89.36 16	84.85 138	88.15 221	61.94 274	95.65 25	89.70 275	85.54 11	92.07 10	97.33 4	67.51 62	97.27 87	96.23 5	92.07 69	95.35 69

fmvsm_s_conf0.5_n_9		88.14 19	89.21 17	84.92 133	89.29 176	61.41 290	92.97 131	88.36 326	86.96 6	91.49 20	97.49 3	69.48 51	97.46 71	97.00 1	89.88 105	95.89 47

SMA-MVS		88.14 19	88.29 26	87.67 33	93.21 68	68.72 74	93.85 89	94.03 67	74.18 180	91.74 14	96.67 27	65.61 81	98.42 33	89.24 55	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 19	87.61 36	89.71 7	92.06 104	76.72 1	95.75 20	93.26 99	83.86 23	89.55 36	96.06 46	53.55 244	97.89 46	91.10 43	93.31 53	94.54 117

TSAR-MVS + MP.		88.11 22	88.64 22	86.54 74	91.73 119	68.04 92	90.36 254	93.55 85	82.89 33	91.29 21	92.89 140	72.27 37	96.03 163	87.99 63	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 23	88.93 19	85.07 128	88.43 208	61.78 277	94.73 55	91.74 172	85.87 10	91.66 16	97.50 2	64.03 102	98.33 34	96.28 4	90.08 101	95.10 85

TSAR-MVS + GP.		87.96 23	88.37 25	86.70 67	93.51 62	65.32 170	95.15 37	93.84 70	78.17 115	85.93 64	94.80 88	75.80 13	98.21 36	89.38 52	88.78 117	96.59 19

DeepC-MVS_fast	79.48 2	87.95 25	88.00 30	87.79 31	95.86 27	68.32 82	95.74 21	94.11 65	83.82 24	83.49 91	96.19 42	64.53 97	98.44 31	83.42 116	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 26	87.38 40	89.55 12	91.41 131	76.43 3	95.74 21	93.12 107	83.53 27	89.55 36	95.95 49	53.45 248	97.68 54	91.07 44	92.62 60	94.54 117

EPNet		87.84 27	88.38 24	86.23 84	93.30 65	66.05 149	95.26 33	94.84 32	87.09 5	88.06 42	94.53 94	66.79 67	97.34 80	83.89 109	91.68 76	95.29 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 28	87.77 33	87.63 38	89.24 181	71.18 24	96.57 12	92.90 116	82.70 37	87.13 50	95.27 71	64.99 87	95.80 169	89.34 53	91.80 74	95.93 45

test_fmvsm_n_1920		87.69 29	88.50 23	85.27 121	87.05 252	63.55 231	93.69 99	91.08 209	84.18 21	90.17 31	97.04 12	67.58 61	97.99 41	95.72 8	90.03 102	94.26 133

fmvsm_l_conf0.5_n_3		87.54 30	88.29 26	85.30 118	86.92 258	62.63 257	95.02 44	90.28 247	84.95 14	90.27 28	96.86 19	65.36 83	97.52 69	94.93 13	90.03 102	95.76 51

APDe-MVS		87.54 30	87.84 32	86.65 68	96.07 23	66.30 144	94.84 50	93.78 71	69.35 287	88.39 41	96.34 36	67.74 60	97.66 59	90.62 48	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 32	88.72 21	83.84 186	86.89 260	60.04 324	95.05 40	92.17 151	84.80 16	92.27 6	96.37 33	64.62 94	96.54 135	94.43 17	91.86 72	94.94 94

fmvsm_l_conf0.5_n		87.49 33	88.19 28	85.39 113	86.95 253	64.37 200	94.30 65	88.45 324	80.51 66	92.70 4	96.86 19	69.98 48	97.15 97	95.83 7	88.08 125	94.65 111

SD-MVS		87.49 33	87.49 38	87.50 42	93.60 56	68.82 71	93.90 86	92.63 131	76.86 137	87.90 44	95.76 52	66.17 73	97.63 61	89.06 57	91.48 80	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 35	88.15 29	85.30 118	87.10 250	64.19 207	94.41 60	88.14 334	80.24 74	92.54 5	96.97 14	69.52 50	97.17 93	95.89 6	88.51 120	94.56 114

dcpmvs_2		87.37 36	87.55 37	86.85 58	95.04 32	68.20 89	90.36 254	90.66 228	79.37 90	81.20 114	93.67 124	74.73 16	96.55 134	90.88 46	92.00 70	95.82 49

alignmvs		87.28 37	86.97 44	88.24 27	91.30 133	71.14 26	95.61 26	93.56 84	79.30 91	87.07 52	95.25 73	68.43 53	96.93 117	87.87 64	84.33 169	96.65 17

train_agg		87.21 38	87.42 39	86.60 70	94.18 41	67.28 112	94.16 69	93.51 87	71.87 238	85.52 69	95.33 65	68.19 55	97.27 87	89.09 56	94.90 22	95.25 80

MG-MVS		87.11 39	86.27 57	89.62 8	97.79 1	76.27 4	94.96 46	94.49 49	78.74 106	83.87 87	92.94 138	64.34 98	96.94 115	75.19 184	94.09 38	95.66 54

SF-MVS		87.03 40	87.09 42	86.84 59	92.70 86	67.45 110	93.64 102	93.76 74	70.78 271	86.25 58	96.44 32	66.98 65	97.79 50	88.68 60	94.56 34	95.28 76

fmvsm_s_conf0.5_n_3		86.88 41	87.99 31	83.58 198	87.26 245	60.74 304	93.21 123	87.94 341	84.22 20	91.70 15	97.27 5	65.91 78	95.02 209	93.95 22	90.42 97	94.99 91

CSCG		86.87 42	86.26 58	88.72 17	95.05 31	70.79 29	93.83 94	95.33 18	68.48 302	77.63 162	94.35 103	73.04 28	98.45 30	84.92 97	93.71 47	96.92 14

sasdasda		86.85 43	86.25 59	88.66 20	91.80 117	71.92 16	93.54 107	91.71 175	80.26 71	87.55 47	95.25 73	63.59 113	96.93 117	88.18 61	84.34 167	97.11 9

canonicalmvs		86.85 43	86.25 59	88.66 20	91.80 117	71.92 16	93.54 107	91.71 175	80.26 71	87.55 47	95.25 73	63.59 113	96.93 117	88.18 61	84.34 167	97.11 9

UBG		86.83 45	86.70 50	87.20 48	93.07 74	69.81 47	93.43 115	95.56 13	81.52 50	81.50 110	92.12 159	73.58 26	96.28 147	84.37 104	85.20 159	95.51 60

PHI-MVS		86.83 45	86.85 49	86.78 63	93.47 63	65.55 165	95.39 31	95.10 25	71.77 243	85.69 67	96.52 29	62.07 137	98.77 23	86.06 85	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 47	86.90 47	86.58 72	90.42 150	66.38 141	96.09 17	93.87 69	77.73 124	84.01 86	95.66 54	63.39 116	97.94 42	87.40 71	93.55 50	95.42 62
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 48	87.63 34	84.27 173	86.15 275	61.48 287	94.69 56	91.16 200	83.79 26	90.51 27	96.28 38	64.24 99	98.22 35	95.00 12	86.88 137	93.11 181

PVSNet_Blended		86.73 49	86.86 48	86.31 83	93.76 50	67.53 107	96.33 16	93.61 82	82.34 42	81.00 119	93.08 134	63.19 120	97.29 83	87.08 76	91.38 82	94.13 142

testing11		86.71 50	86.44 55	87.55 40	93.54 60	71.35 21	93.65 101	95.58 11	81.36 57	80.69 122	92.21 158	72.30 36	96.46 140	85.18 93	83.43 179	94.82 103

test_fmvsmconf_n		86.58 51	87.17 41	84.82 140	85.28 292	62.55 258	94.26 67	89.78 266	83.81 25	87.78 46	96.33 37	65.33 84	96.98 109	94.40 18	87.55 131	94.95 93

BP-MVS1		86.54 52	86.68 52	86.13 87	87.80 233	67.18 116	92.97 131	95.62 10	79.92 77	82.84 98	94.14 112	74.95 15	96.46 140	82.91 121	88.96 116	94.74 105

jason		86.40 53	86.17 61	87.11 51	86.16 274	70.54 32	95.71 24	92.19 148	82.00 45	84.58 79	94.34 104	61.86 139	95.53 191	87.76 65	90.89 90	95.27 77
jason: jason.
NormalMVS		86.39 54	86.66 53	85.60 107	92.12 101	65.95 154	94.88 47	90.83 216	84.69 17	83.67 89	94.10 113	63.16 122	96.91 121	85.31 89	91.15 86	93.93 153

fmvsm_s_conf0.5_n		86.39 54	86.91 46	84.82 140	87.36 244	63.54 232	94.74 52	90.02 259	82.52 38	90.14 32	96.92 17	62.93 127	97.84 49	95.28 11	82.26 189	93.07 184

fmvsm_s_conf0.5_n_5		86.38 56	86.94 45	84.71 151	84.67 304	63.29 237	94.04 78	89.99 261	82.88 34	87.85 45	96.03 47	62.89 129	96.36 144	94.15 19	89.95 104	94.48 123

SymmetryMVS		86.32 57	86.39 56	86.12 88	90.52 148	65.95 154	94.88 47	94.58 46	84.69 17	83.67 89	94.10 113	63.16 122	96.91 121	85.31 89	86.59 146	95.51 60

WTY-MVS		86.32 57	85.81 69	87.85 29	92.82 82	69.37 59	95.20 35	95.25 20	82.71 36	81.91 106	94.73 89	67.93 59	97.63 61	79.55 151	82.25 191	96.54 22

myMVS_eth3d28		86.31 59	86.15 62	86.78 63	93.56 58	70.49 33	92.94 134	95.28 19	82.47 39	78.70 153	92.07 161	72.45 34	95.41 193	82.11 127	85.78 155	94.44 125

MSLP-MVS++		86.27 60	85.91 68	87.35 45	92.01 108	68.97 68	95.04 42	92.70 122	79.04 101	81.50 110	96.50 31	58.98 178	96.78 125	83.49 115	93.93 41	96.29 35

VNet		86.20 61	85.65 73	87.84 30	93.92 47	69.99 39	95.73 23	95.94 7	78.43 111	86.00 63	93.07 135	58.22 185	97.00 105	85.22 91	84.33 169	96.52 23

MVS_111021_HR		86.19 62	85.80 70	87.37 44	93.17 70	69.79 48	93.99 81	93.76 74	79.08 98	78.88 149	93.99 118	62.25 136	98.15 38	85.93 86	91.15 86	94.15 141

SPE-MVS-test		86.14 63	87.01 43	83.52 199	92.63 88	59.36 336	95.49 28	91.92 161	80.09 75	85.46 71	95.53 60	61.82 141	95.77 172	86.77 80	93.37 52	95.41 63

ACMMP_NAP		86.05 64	85.80 70	86.80 62	91.58 123	67.53 107	91.79 192	93.49 90	74.93 170	84.61 78	95.30 67	59.42 169	97.92 43	86.13 83	94.92 20	94.94 94

testing99		86.01 65	85.47 75	87.63 38	93.62 55	71.25 23	93.47 113	95.23 21	80.42 69	80.60 124	91.95 166	71.73 41	96.50 138	80.02 148	82.22 192	95.13 83

ETV-MVS		86.01 65	86.11 63	85.70 104	90.21 155	67.02 123	93.43 115	91.92 161	81.21 59	84.13 85	94.07 117	60.93 149	95.63 181	89.28 54	89.81 106	94.46 124

testing91		85.93 67	85.31 79	87.78 32	93.59 57	71.47 19	93.50 110	95.08 28	80.26 71	80.53 125	91.93 167	70.43 45	96.51 137	80.32 146	82.13 194	95.37 66

APD-MVS		85.93 67	85.99 66	85.76 101	95.98 26	65.21 173	93.59 105	92.58 133	66.54 320	86.17 61	95.88 50	63.83 106	97.00 105	86.39 82	92.94 57	95.06 87
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 69	85.46 76	87.18 49	88.20 220	72.42 15	92.41 163	92.77 120	82.11 44	80.34 128	93.07 135	68.27 54	95.02 209	78.39 165	93.59 49	94.09 144

CS-MVS		85.80 70	86.65 54	83.27 211	92.00 109	58.92 340	95.31 32	91.86 166	79.97 76	84.82 77	95.40 63	62.26 135	95.51 192	86.11 84	92.08 68	95.37 66

fmvsm_s_conf0.5_n_a		85.75 71	86.09 64	84.72 149	85.73 285	63.58 229	93.79 95	89.32 285	81.42 55	90.21 30	96.91 18	62.41 134	97.67 56	94.48 16	80.56 216	92.90 190

test_fmvsmconf0.1_n		85.71 72	86.08 65	84.62 159	80.83 351	62.33 263	93.84 92	88.81 312	83.50 28	87.00 53	96.01 48	63.36 117	96.93 117	94.04 21	87.29 134	94.61 113

CDPH-MVS		85.71 72	85.46 76	86.46 76	94.75 34	67.19 114	93.89 87	92.83 118	70.90 267	83.09 96	95.28 69	63.62 111	97.36 78	80.63 142	94.18 37	94.84 99

casdiffmvs_mvg		85.66 74	85.18 81	87.09 52	88.22 219	69.35 60	93.74 98	91.89 164	81.47 51	80.10 130	91.45 176	64.80 92	96.35 145	87.23 74	87.69 129	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 75	85.93 67	84.68 153	82.95 334	63.48 234	94.03 80	89.46 279	81.69 48	89.86 33	96.74 25	61.85 140	97.75 52	94.74 15	82.01 196	92.81 192

MGCFI-Net		85.59 76	85.73 72	85.17 125	91.41 131	62.44 259	92.87 139	91.31 192	79.65 83	86.99 54	95.14 79	62.90 128	96.12 155	87.13 75	84.13 174	96.96 13

GDP-MVS		85.54 77	85.32 78	86.18 85	87.64 236	67.95 96	92.91 137	92.36 138	77.81 121	83.69 88	94.31 106	72.84 30	96.41 142	80.39 145	85.95 153	94.19 137

DeepC-MVS	77.85 3	85.52 78	85.24 80	86.37 80	88.80 191	66.64 135	92.15 171	93.68 80	81.07 61	76.91 173	93.64 125	62.59 131	98.44 31	85.50 87	92.84 59	94.03 148
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 79	84.87 87	86.84 59	88.25 217	69.07 64	93.04 128	91.76 171	81.27 58	80.84 121	92.07 161	64.23 100	96.06 161	84.98 96	87.43 133	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 80	85.08 83	86.06 89	93.09 73	65.65 161	93.89 87	93.41 95	73.75 191	79.94 132	94.68 91	60.61 152	98.03 40	82.63 124	93.72 46	94.52 119

fmvsm_s_conf0.5_n_7		85.24 81	86.69 51	80.91 279	84.52 309	60.10 322	93.35 118	90.35 240	83.41 29	86.54 57	96.27 39	60.50 153	90.02 363	94.84 14	90.38 98	92.61 196

MP-MVS-pluss		85.24 81	85.13 82	85.56 108	91.42 128	65.59 163	91.54 202	92.51 135	74.56 173	80.62 123	95.64 55	59.15 173	97.00 105	86.94 78	93.80 43	94.07 146
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 83	84.69 91	86.63 69	92.91 78	69.91 43	92.61 153	95.80 9	80.31 70	80.38 127	92.27 155	68.73 52	95.19 206	75.94 178	83.27 181	94.81 104

PAPR		85.15 84	84.47 92	87.18 49	96.02 25	68.29 83	91.85 190	93.00 113	76.59 148	79.03 145	95.00 80	61.59 142	97.61 63	78.16 166	89.00 115	95.63 55

fmvsm_s_conf0.5_n_2		85.06 85	85.60 74	83.44 205	86.92 258	60.53 311	94.41 60	87.31 347	83.30 30	88.72 40	96.72 26	54.28 236	97.75 52	94.07 20	84.68 166	92.04 219

MP-MVS		85.02 86	84.97 85	85.17 125	92.60 89	64.27 205	93.24 120	92.27 141	73.13 202	79.63 137	94.43 97	61.90 138	97.17 93	85.00 95	92.56 61	94.06 147
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 87	84.44 93	86.71 66	88.33 214	68.73 73	90.24 259	91.82 170	81.05 62	81.18 115	92.50 147	63.69 109	96.08 160	84.45 103	86.71 144	95.32 72

CHOSEN 1792x2688		84.98 88	83.45 108	89.57 11	89.94 160	75.14 6	92.07 177	92.32 139	81.87 46	75.68 182	88.27 238	60.18 157	98.60 27	80.46 144	90.27 100	94.96 92

MVSMamba_PlusPlus		84.97 89	83.65 102	88.93 14	90.17 156	74.04 8	87.84 315	92.69 125	62.18 358	81.47 112	87.64 252	71.47 42	96.28 147	84.69 99	94.74 31	96.47 28

viewmanbaseed2359cas		84.89 90	84.26 96	86.78 63	88.50 200	69.77 50	92.69 150	91.13 205	81.11 60	81.54 109	91.98 164	60.35 154	95.73 174	84.47 102	86.56 147	94.84 99

EIA-MVS		84.84 91	84.88 86	84.69 152	91.30 133	62.36 262	93.85 89	92.04 154	79.45 86	79.33 142	94.28 108	62.42 133	96.35 145	80.05 147	91.25 85	95.38 65

lecture		84.77 92	84.81 89	84.65 155	92.12 101	62.27 266	94.74 52	92.64 130	68.35 303	85.53 68	95.30 67	59.77 164	97.91 44	83.73 111	91.15 86	93.77 161

fmvsm_s_conf0.1_n_a		84.76 93	84.84 88	84.53 161	80.23 364	63.50 233	92.79 141	88.73 315	80.46 67	89.84 34	96.65 28	60.96 148	97.57 66	93.80 23	80.14 218	92.53 201

HFP-MVS		84.73 94	84.40 94	85.72 103	93.75 52	65.01 179	93.50 110	93.19 103	72.19 227	79.22 143	94.93 83	59.04 176	97.67 56	81.55 131	92.21 64	94.49 122

MVS		84.66 95	82.86 127	90.06 2	90.93 140	74.56 7	87.91 313	95.54 14	68.55 300	72.35 237	94.71 90	59.78 163	98.90 20	81.29 137	94.69 32	96.74 16

GST-MVS		84.63 96	84.29 95	85.66 105	92.82 82	65.27 171	93.04 128	93.13 106	73.20 200	78.89 146	94.18 111	59.41 170	97.85 48	81.45 133	92.48 63	93.86 158

EC-MVSNet		84.53 97	85.04 84	83.01 217	89.34 172	61.37 291	94.42 59	91.09 207	77.91 119	83.24 92	94.20 110	58.37 183	95.40 194	85.35 88	91.41 81	92.27 213

fmvsm_s_conf0.1_n_2		84.40 98	84.78 90	83.27 211	85.25 293	60.41 314	94.13 72	85.69 371	83.05 32	87.99 43	96.37 33	52.75 253	97.68 54	93.75 24	84.05 175	91.71 227

ACMMPR		84.37 99	84.06 97	85.28 120	93.56 58	64.37 200	93.50 110	93.15 105	72.19 227	78.85 151	94.86 86	56.69 205	97.45 72	81.55 131	92.20 65	94.02 149

region2R		84.36 100	84.03 98	85.36 116	93.54 60	64.31 203	93.43 115	92.95 114	72.16 230	78.86 150	94.84 87	56.97 200	97.53 68	81.38 135	92.11 67	94.24 135

LFMVS		84.34 101	82.73 129	89.18 13	94.76 33	73.25 11	94.99 45	91.89 164	71.90 235	82.16 105	93.49 129	47.98 304	97.05 100	82.55 125	84.82 162	97.25 8

test_yl		84.28 102	83.16 118	87.64 34	94.52 37	69.24 61	95.78 18	95.09 26	69.19 290	81.09 116	92.88 141	57.00 198	97.44 73	81.11 139	81.76 199	96.23 38

DCV-MVSNet		84.28 102	83.16 118	87.64 34	94.52 37	69.24 61	95.78 18	95.09 26	69.19 290	81.09 116	92.88 141	57.00 198	97.44 73	81.11 139	81.76 199	96.23 38

diffmvs		84.28 102	83.83 99	85.61 106	87.40 242	68.02 93	90.88 233	89.24 288	80.54 65	81.64 108	92.52 146	59.83 162	94.52 236	87.32 72	85.11 160	94.29 132
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	84.28 102	83.36 114	87.02 55	92.22 96	67.74 100	84.65 342	94.50 48	79.15 95	82.23 104	87.93 247	66.88 66	96.94 115	80.53 143	82.20 193	96.39 33

ETVMVS		84.22 106	83.71 100	85.76 101	92.58 90	68.25 87	92.45 162	95.53 15	79.54 85	79.46 139	91.64 174	70.29 46	94.18 249	69.16 247	82.76 187	94.84 99

MAR-MVS		84.18 107	83.43 109	86.44 77	96.25 21	65.93 156	94.28 66	94.27 61	74.41 175	79.16 144	95.61 56	53.99 239	98.88 22	69.62 241	93.26 54	94.50 121
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		84.16 108	83.20 117	87.05 54	91.56 124	69.82 46	89.99 268	92.05 153	77.77 123	82.84 98	86.57 269	63.93 105	96.09 157	74.91 189	89.18 112	95.25 80

CANet_DTU		84.09 109	83.52 103	85.81 98	90.30 153	66.82 130	91.87 188	89.01 303	85.27 12	86.09 62	93.74 122	47.71 310	96.98 109	77.90 168	89.78 108	93.65 164

ET-MVSNet_ETH3D		84.01 110	83.15 120	86.58 72	90.78 145	70.89 28	94.74 52	94.62 43	81.44 54	58.19 376	93.64 125	73.64 25	92.35 318	82.66 123	78.66 238	96.50 27

PVSNet_Blended_VisFu		83.97 111	83.50 105	85.39 113	90.02 158	66.59 138	93.77 96	91.73 173	77.43 132	77.08 172	89.81 214	63.77 108	96.97 112	79.67 150	88.21 123	92.60 197

MTAPA		83.91 112	83.38 113	85.50 109	91.89 115	65.16 175	81.75 372	92.23 142	75.32 165	80.53 125	95.21 76	56.06 214	97.16 96	84.86 98	92.55 62	94.18 138

XVS		83.87 113	83.47 107	85.05 129	93.22 66	63.78 217	92.92 135	92.66 127	73.99 183	78.18 156	94.31 106	55.25 220	97.41 75	79.16 155	91.58 78	93.95 151

Effi-MVS+		83.82 114	82.76 128	86.99 56	89.56 168	69.40 55	91.35 213	86.12 365	72.59 214	83.22 95	92.81 144	59.60 166	96.01 165	81.76 130	87.80 128	95.56 58

test_fmvsmvis_n_1920		83.80 115	83.48 106	84.77 144	82.51 337	63.72 222	91.37 211	83.99 389	81.42 55	77.68 161	95.74 53	58.37 183	97.58 64	93.38 25	86.87 138	93.00 187

EI-MVSNet-Vis-set		83.77 116	83.67 101	84.06 177	92.79 85	63.56 230	91.76 195	94.81 34	79.65 83	77.87 159	94.09 115	63.35 118	97.90 45	79.35 153	79.36 228	90.74 248

MVSFormer		83.75 117	82.88 126	86.37 80	89.24 181	71.18 24	89.07 291	90.69 225	65.80 325	87.13 50	94.34 104	64.99 87	92.67 304	72.83 206	91.80 74	95.27 77

CP-MVS		83.71 118	83.40 112	84.65 155	93.14 71	63.84 215	94.59 57	92.28 140	71.03 265	77.41 165	94.92 84	55.21 223	96.19 152	81.32 136	90.70 92	93.91 155

test_fmvsmconf0.01_n		83.70 119	83.52 103	84.25 174	75.26 410	61.72 281	92.17 170	87.24 349	82.36 41	84.91 76	95.41 62	55.60 218	96.83 124	92.85 29	85.87 154	94.21 136

baseline2		83.68 120	83.42 111	84.48 164	87.37 243	66.00 151	90.06 263	95.93 8	79.71 82	69.08 275	90.39 194	77.92 6	96.28 147	78.91 160	81.38 203	91.16 241

reproduce-ours		83.51 121	83.33 115	84.06 177	92.18 99	60.49 312	90.74 239	92.04 154	64.35 335	83.24 92	95.59 58	59.05 174	97.27 87	83.61 112	89.17 113	94.41 130

our_new_method		83.51 121	83.33 115	84.06 177	92.18 99	60.49 312	90.74 239	92.04 154	64.35 335	83.24 92	95.59 58	59.05 174	97.27 87	83.61 112	89.17 113	94.41 130

thisisatest0515		83.41 123	82.49 133	86.16 86	89.46 171	68.26 85	93.54 107	94.70 39	74.31 178	75.75 180	90.92 184	72.62 32	96.52 136	69.64 239	81.50 202	93.71 162

PVSNet_BlendedMVS		83.38 124	83.43 109	83.22 213	93.76 50	67.53 107	94.06 74	93.61 82	79.13 96	81.00 119	85.14 287	63.19 120	97.29 83	87.08 76	73.91 276	84.83 348

test2506		83.29 125	82.92 125	84.37 168	88.39 211	63.18 243	92.01 180	91.35 191	77.66 126	78.49 155	91.42 177	64.58 96	95.09 208	73.19 202	89.23 110	94.85 96

PGM-MVS		83.25 126	82.70 130	84.92 133	92.81 84	64.07 211	90.44 249	92.20 146	71.28 259	77.23 169	94.43 97	55.17 224	97.31 82	79.33 154	91.38 82	93.37 171

HPM-MVS		83.25 126	82.95 124	84.17 175	92.25 95	62.88 252	90.91 230	91.86 166	70.30 276	77.12 170	93.96 119	56.75 203	96.28 147	82.04 128	91.34 84	93.34 172
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 128	82.96 122	83.73 190	92.02 105	59.74 328	90.37 253	92.08 152	63.70 342	82.86 97	95.48 61	58.62 180	97.17 93	83.06 118	88.42 121	94.26 133

EI-MVSNet-UG-set		83.14 129	82.96 122	83.67 195	92.28 94	63.19 242	91.38 210	94.68 40	79.22 93	76.60 175	93.75 121	62.64 130	97.76 51	78.07 167	78.01 241	90.05 257

testing3-2		83.11 130	83.15 120	82.98 218	91.92 112	64.01 213	94.39 63	95.37 16	78.32 112	75.53 187	90.06 212	73.18 27	93.18 283	74.34 194	75.27 265	91.77 226

VDD-MVS		83.06 131	81.81 143	86.81 61	90.86 143	67.70 101	95.40 30	91.50 186	75.46 160	81.78 107	92.34 154	40.09 354	97.13 98	86.85 79	82.04 195	95.60 56

h-mvs33		83.01 132	82.56 132	84.35 169	89.34 172	62.02 270	92.72 144	93.76 74	81.45 52	82.73 101	92.25 157	60.11 158	97.13 98	87.69 66	62.96 357	93.91 155

PAPM_NR		82.97 133	81.84 142	86.37 80	94.10 44	66.76 133	87.66 319	92.84 117	69.96 280	74.07 210	93.57 127	63.10 125	97.50 70	70.66 234	90.58 94	94.85 96

mPP-MVS		82.96 134	82.44 134	84.52 162	92.83 80	62.92 250	92.76 142	91.85 168	71.52 255	75.61 185	94.24 109	53.48 247	96.99 108	78.97 158	90.73 91	93.64 165

SR-MVS		82.81 135	82.58 131	83.50 202	93.35 64	61.16 294	92.23 168	91.28 197	64.48 334	81.27 113	95.28 69	53.71 243	95.86 167	82.87 122	88.77 118	93.49 169

DP-MVS Recon		82.73 136	81.65 144	85.98 91	97.31 4	67.06 119	95.15 37	91.99 158	69.08 295	76.50 177	93.89 120	54.48 232	98.20 37	70.76 232	85.66 157	92.69 193

CLD-MVS		82.73 136	82.35 136	83.86 185	87.90 228	67.65 103	95.45 29	92.18 149	85.06 13	72.58 228	92.27 155	52.46 256	95.78 170	84.18 105	79.06 233	88.16 285
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 138	82.38 135	83.73 190	89.25 178	59.58 331	92.24 167	94.89 31	77.96 117	79.86 133	92.38 152	56.70 204	97.05 100	77.26 171	80.86 211	94.55 115

3Dnovator	73.91 6	82.69 139	80.82 157	88.31 26	89.57 167	71.26 22	92.60 154	94.39 56	78.84 103	67.89 296	92.48 150	48.42 299	98.52 28	68.80 252	94.40 36	95.15 82

RRT-MVS		82.61 140	81.16 148	86.96 57	91.10 137	68.75 72	87.70 318	92.20 146	76.97 135	72.68 224	87.10 263	51.30 270	96.41 142	83.56 114	87.84 127	95.74 52

viewmambaseed2359dif		82.60 141	81.91 141	84.67 154	85.83 282	66.09 148	90.50 248	89.01 303	75.46 160	79.64 136	92.01 163	59.51 167	94.38 240	82.99 120	82.26 189	93.54 167

MVSTER		82.47 142	82.05 137	83.74 188	92.68 87	69.01 66	91.90 187	93.21 100	79.83 78	72.14 238	85.71 282	74.72 17	94.72 223	75.72 180	72.49 286	87.50 292

TESTMET0.1,1		82.41 143	81.98 140	83.72 192	88.08 222	63.74 219	92.70 146	93.77 73	79.30 91	77.61 163	87.57 254	58.19 186	94.08 254	73.91 196	86.68 145	93.33 174

CostFormer		82.33 144	81.15 149	85.86 96	89.01 186	68.46 79	82.39 369	93.01 111	75.59 158	80.25 129	81.57 332	72.03 39	94.96 213	79.06 157	77.48 249	94.16 140

API-MVS		82.28 145	80.53 166	87.54 41	96.13 22	70.59 31	93.63 103	91.04 213	65.72 327	75.45 188	92.83 143	56.11 213	98.89 21	64.10 300	89.75 109	93.15 179

IB-MVS	77.80 4	82.18 146	80.46 168	87.35 45	89.14 183	70.28 36	95.59 27	95.17 24	78.85 102	70.19 263	85.82 280	70.66 44	97.67 56	72.19 218	66.52 327	94.09 144
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		82.16 147	81.12 150	85.26 122	86.42 267	68.72 74	92.59 156	90.44 237	73.12 203	84.20 82	94.36 99	38.04 367	95.73 174	84.12 106	86.81 139	91.33 234

xiu_mvs_v1_base		82.16 147	81.12 150	85.26 122	86.42 267	68.72 74	92.59 156	90.44 237	73.12 203	84.20 82	94.36 99	38.04 367	95.73 174	84.12 106	86.81 139	91.33 234

xiu_mvs_v1_base_debi		82.16 147	81.12 150	85.26 122	86.42 267	68.72 74	92.59 156	90.44 237	73.12 203	84.20 82	94.36 99	38.04 367	95.73 174	84.12 106	86.81 139	91.33 234

3Dnovator+	73.60 7	82.10 150	80.60 164	86.60 70	90.89 142	66.80 132	95.20 35	93.44 92	74.05 182	67.42 303	92.49 149	49.46 289	97.65 60	70.80 231	91.68 76	95.33 70

MVS_111021_LR		82.02 151	81.52 145	83.51 201	88.42 209	62.88 252	89.77 271	88.93 308	76.78 140	75.55 186	93.10 132	50.31 278	95.38 196	83.82 110	87.02 136	92.26 214

PMMVS		81.98 152	82.04 138	81.78 253	89.76 164	56.17 368	91.13 226	90.69 225	77.96 117	80.09 131	93.57 127	46.33 323	94.99 212	81.41 134	87.46 132	94.17 139

baseline1		81.84 153	81.03 154	84.28 172	91.60 122	66.62 136	91.08 227	91.66 180	81.87 46	74.86 196	91.67 173	69.98 48	94.92 216	71.76 221	64.75 344	91.29 239

EPP-MVSNet		81.79 154	81.52 145	82.61 228	88.77 192	60.21 320	93.02 130	93.66 81	68.52 301	72.90 222	90.39 194	72.19 38	94.96 213	74.93 188	79.29 231	92.67 194

WBMVS		81.67 155	80.98 156	83.72 192	93.07 74	69.40 55	94.33 64	93.05 109	76.84 138	72.05 240	84.14 298	74.49 19	93.88 268	72.76 209	68.09 315	87.88 287

test_vis1_n_1920		81.66 156	82.01 139	80.64 282	82.24 339	55.09 377	94.76 51	86.87 353	81.67 49	84.40 81	94.63 92	38.17 364	94.67 227	91.98 38	83.34 180	92.16 217

APD-MVS_3200maxsize		81.64 157	81.32 147	82.59 230	92.36 92	58.74 342	91.39 208	91.01 214	63.35 346	79.72 135	94.62 93	51.82 259	96.14 154	79.71 149	87.93 126	92.89 191

mvsmamba		81.55 158	80.72 159	84.03 181	91.42 128	66.93 128	83.08 361	89.13 296	78.55 110	67.50 301	87.02 264	51.79 261	90.07 362	87.48 69	90.49 96	95.10 85

ACMMP		81.49 159	80.67 161	83.93 183	91.71 120	62.90 251	92.13 172	92.22 145	71.79 242	71.68 246	93.49 129	50.32 277	96.96 113	78.47 164	84.22 173	91.93 224
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		81.43 160	80.11 170	85.38 115	86.60 263	65.47 169	92.90 138	93.54 86	75.33 164	77.31 167	90.39 194	46.81 315	96.75 126	71.65 224	86.46 150	93.93 153

CDS-MVSNet		81.43 160	80.74 158	83.52 199	86.26 271	64.45 194	92.09 175	90.65 229	75.83 156	73.95 212	89.81 214	63.97 104	92.91 294	71.27 225	82.82 184	93.20 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 162	79.99 174	85.46 110	90.39 152	68.40 80	86.88 330	90.61 230	74.41 175	70.31 262	84.67 292	63.79 107	92.32 320	73.13 203	85.70 156	95.67 53

ECVR-MVS		81.29 163	80.38 169	84.01 182	88.39 211	61.96 272	92.56 159	86.79 355	77.66 126	76.63 174	91.42 177	46.34 322	95.24 205	74.36 193	89.23 110	94.85 96

guyue		81.23 164	80.57 165	83.21 215	86.64 261	61.85 275	92.52 160	92.78 119	78.69 107	74.92 195	89.42 218	50.07 281	95.35 197	80.79 141	79.31 230	92.42 203

icg_test_0403		81.19 165	79.88 176	85.13 127	88.54 195	64.75 184	88.84 296	90.80 219	76.73 143	75.21 191	90.18 200	54.22 237	96.21 151	73.47 198	80.95 206	94.43 126

thisisatest0530		81.15 166	80.07 171	84.39 167	88.26 216	65.63 162	91.40 206	94.62 43	71.27 260	70.93 253	89.18 223	72.47 33	96.04 162	65.62 289	76.89 256	91.49 230

Fast-Effi-MVS+		81.14 167	80.01 173	84.51 163	90.24 154	65.86 157	94.12 73	89.15 294	73.81 190	75.37 190	88.26 239	57.26 193	94.53 235	66.97 274	84.92 161	93.15 179

HQP-MVS		81.14 167	80.64 162	82.64 227	87.54 238	63.66 227	94.06 74	91.70 178	79.80 79	74.18 203	90.30 197	51.63 264	95.61 183	77.63 169	78.90 234	88.63 276

hse-mvs2		81.12 169	81.11 153	81.16 267	86.52 266	57.48 357	89.40 282	91.16 200	81.45 52	82.73 101	90.49 192	60.11 158	94.58 228	87.69 66	60.41 384	91.41 233

SR-MVS-dyc-post		81.06 170	80.70 160	82.15 244	92.02 105	58.56 345	90.90 231	90.45 233	62.76 353	78.89 146	94.46 95	51.26 271	95.61 183	78.77 162	86.77 142	92.28 210

HyFIR lowres test		81.03 171	79.56 183	85.43 111	87.81 232	68.11 91	90.18 260	90.01 260	70.65 273	72.95 221	86.06 276	63.61 112	94.50 237	75.01 187	79.75 222	93.67 163

nrg030		80.93 172	79.86 177	84.13 176	83.69 323	68.83 70	93.23 121	91.20 198	75.55 159	75.06 193	88.22 242	63.04 126	94.74 222	81.88 129	66.88 324	88.82 274

Vis-MVSNet		80.92 173	79.98 175	83.74 188	88.48 205	61.80 276	93.44 114	88.26 333	73.96 186	77.73 160	91.76 170	49.94 283	94.76 220	65.84 286	90.37 99	94.65 111
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 174	80.02 172	83.33 206	87.87 229	60.76 302	92.62 152	86.86 354	77.86 120	75.73 181	91.39 179	46.35 321	94.70 226	72.79 208	88.68 119	94.52 119

UWE-MVS		80.81 175	81.01 155	80.20 292	89.33 174	57.05 362	91.91 186	94.71 38	75.67 157	75.01 194	89.37 219	63.13 124	91.44 345	67.19 271	82.80 186	92.12 218

icg_test_0407		80.80 176	79.39 189	85.00 132	88.54 195	64.75 184	88.40 304	90.80 219	76.73 143	73.95 212	90.18 200	51.55 266	95.81 168	73.47 198	80.95 206	94.43 126

1314		80.70 177	78.95 197	85.94 93	87.77 235	67.56 105	87.91 313	92.55 134	72.17 229	67.44 302	93.09 133	50.27 279	97.04 103	71.68 223	87.64 130	93.23 176

AstraMVS		80.66 178	79.79 179	83.28 210	85.07 299	61.64 283	92.19 169	90.58 231	79.40 88	74.77 198	90.18 200	45.93 327	95.61 183	83.04 119	76.96 255	92.60 197

tpmrst		80.57 179	79.14 195	84.84 139	90.10 157	68.28 84	81.70 373	89.72 273	77.63 128	75.96 179	79.54 364	64.94 89	92.71 301	75.43 182	77.28 252	93.55 166

1112_ss		80.56 180	79.83 178	82.77 222	88.65 193	60.78 300	92.29 165	88.36 326	72.58 215	72.46 234	94.95 81	65.09 86	93.42 280	66.38 280	77.71 243	94.10 143

VDDNet		80.50 181	78.26 205	87.21 47	86.19 272	69.79 48	94.48 58	91.31 192	60.42 374	79.34 141	90.91 185	38.48 362	96.56 133	82.16 126	81.05 205	95.27 77

BH-w/o		80.49 182	79.30 191	84.05 180	90.83 144	64.36 202	93.60 104	89.42 282	74.35 177	69.09 274	90.15 208	55.23 222	95.61 183	64.61 297	86.43 151	92.17 216

test_cas_vis1_n_1920		80.45 183	80.61 163	79.97 301	78.25 391	57.01 364	94.04 78	88.33 328	79.06 100	82.81 100	93.70 123	38.65 359	91.63 336	90.82 47	79.81 220	91.27 240

icg_test_0407_2		80.38 184	79.22 193	83.88 184	88.54 195	64.75 184	86.79 331	90.80 219	76.73 143	73.95 212	90.18 200	51.55 266	92.45 313	73.47 198	80.95 206	94.43 126

TAMVS		80.37 185	79.45 186	83.13 216	85.14 296	63.37 235	91.23 220	90.76 224	74.81 172	72.65 226	88.49 232	60.63 151	92.95 289	69.41 243	81.95 197	93.08 183

HQP_MVS		80.34 186	79.75 180	82.12 246	86.94 254	62.42 260	93.13 124	91.31 192	78.81 104	72.53 229	89.14 225	50.66 274	95.55 189	76.74 172	78.53 239	88.39 282

SDMVSNet		80.26 187	78.88 198	84.40 166	89.25 178	67.63 104	85.35 338	93.02 110	76.77 141	70.84 254	87.12 261	47.95 307	96.09 157	85.04 94	74.55 267	89.48 267

HPM-MVS_fast		80.25 188	79.55 185	82.33 236	91.55 125	59.95 325	91.32 215	89.16 293	65.23 331	74.71 200	93.07 135	47.81 309	95.74 173	74.87 191	88.23 122	91.31 238

ab-mvs		80.18 189	78.31 204	85.80 99	88.44 207	65.49 168	83.00 364	92.67 126	71.82 241	77.36 166	85.01 288	54.50 229	96.59 130	76.35 177	75.63 263	95.32 72

IS-MVSNet		80.14 190	79.41 187	82.33 236	87.91 227	60.08 323	91.97 184	88.27 331	72.90 210	71.44 250	91.73 172	61.44 143	93.66 275	62.47 314	86.53 148	93.24 175

test-LLR		80.10 191	79.56 183	81.72 255	86.93 256	61.17 292	92.70 146	91.54 183	71.51 256	75.62 183	86.94 265	53.83 240	92.38 315	72.21 216	84.76 164	91.60 228

PVSNet	73.49 8	80.05 192	78.63 200	84.31 170	90.92 141	64.97 180	92.47 161	91.05 212	79.18 94	72.43 235	90.51 191	37.05 379	94.06 256	68.06 258	86.00 152	93.90 157

UA-Net		80.02 193	79.65 181	81.11 270	89.33 174	57.72 352	86.33 335	89.00 307	77.44 131	81.01 118	89.15 224	59.33 171	95.90 166	61.01 321	84.28 171	89.73 263

test-mter		79.96 194	79.38 190	81.72 255	86.93 256	61.17 292	92.70 146	91.54 183	73.85 188	75.62 183	86.94 265	49.84 285	92.38 315	72.21 216	84.76 164	91.60 228

QAPM		79.95 195	77.39 224	87.64 34	89.63 166	71.41 20	93.30 119	93.70 79	65.34 330	67.39 305	91.75 171	47.83 308	98.96 16	57.71 337	89.81 106	92.54 200

UGNet		79.87 196	78.68 199	83.45 204	89.96 159	61.51 285	92.13 172	90.79 223	76.83 139	78.85 151	86.33 273	38.16 365	96.17 153	67.93 261	87.17 135	92.67 194
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		79.80 197	77.95 211	85.34 117	88.28 215	68.26 85	81.56 375	91.42 189	70.11 278	77.59 164	80.50 350	67.40 63	94.26 247	67.34 268	77.35 250	93.51 168

thres200		79.66 198	78.33 203	83.66 196	92.54 91	65.82 159	93.06 126	96.31 3	74.90 171	73.30 218	88.66 230	59.67 165	95.61 183	47.84 380	78.67 237	89.56 266

CPTT-MVS		79.59 199	79.16 194	80.89 280	91.54 126	59.80 327	92.10 174	88.54 323	60.42 374	72.96 220	93.28 131	48.27 300	92.80 298	78.89 161	86.50 149	90.06 256

Test_1112_low_res		79.56 200	78.60 201	82.43 232	88.24 218	60.39 316	92.09 175	87.99 338	72.10 231	71.84 242	87.42 256	64.62 94	93.04 285	65.80 287	77.30 251	93.85 159

tttt0517		79.50 201	78.53 202	82.41 235	87.22 247	61.43 289	89.75 272	94.76 35	69.29 288	67.91 294	88.06 246	72.92 29	95.63 181	62.91 310	73.90 277	90.16 255

reproduce_monomvs		79.49 202	79.11 196	80.64 282	92.91 78	61.47 288	91.17 225	93.28 98	83.09 31	64.04 335	82.38 318	66.19 72	94.57 230	81.19 138	57.71 392	85.88 331

FIs		79.47 203	79.41 187	79.67 309	85.95 278	59.40 333	91.68 199	93.94 68	78.06 116	68.96 280	88.28 237	66.61 69	91.77 332	66.20 283	74.99 266	87.82 288

mamba_0404		79.46 204	77.65 214	84.91 135	88.37 213	67.04 121	89.59 273	87.03 350	67.99 306	75.45 188	89.32 220	47.98 304	95.34 199	71.23 226	81.90 198	92.34 206

BH-RMVSNet		79.46 204	77.65 214	84.89 136	91.68 121	65.66 160	93.55 106	88.09 336	72.93 207	73.37 217	91.12 183	46.20 325	96.12 155	56.28 343	85.61 158	92.91 189

PCF-MVS	73.15 9	79.29 206	77.63 216	84.29 171	86.06 276	65.96 153	87.03 326	91.10 206	69.86 282	69.79 270	90.64 187	57.54 192	96.59 130	64.37 299	82.29 188	90.32 253
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 207	79.57 182	78.24 330	88.46 206	52.29 388	90.41 251	89.12 297	74.24 179	69.13 273	91.91 168	65.77 79	90.09 361	59.00 333	88.09 124	92.33 207

114514_t		79.17 208	77.67 213	83.68 194	95.32 29	65.53 166	92.85 140	91.60 182	63.49 344	67.92 293	90.63 189	46.65 318	95.72 179	67.01 273	83.54 178	89.79 261

FA-MVS(test-final)		79.12 209	77.23 226	84.81 143	90.54 147	63.98 214	81.35 378	91.71 175	71.09 264	74.85 197	82.94 311	52.85 251	97.05 100	67.97 259	81.73 201	93.41 170

mamba_test_0407		79.09 210	77.21 227	84.75 147	88.50 200	66.98 124	89.21 287	87.03 350	67.99 306	74.12 207	89.32 220	47.98 304	95.29 204	71.23 226	79.52 223	91.98 221

VPA-MVSNet		79.03 211	78.00 209	82.11 249	85.95 278	64.48 193	93.22 122	94.66 41	75.05 169	74.04 211	84.95 289	52.17 258	93.52 277	74.90 190	67.04 323	88.32 284

OPM-MVS		79.00 212	78.09 207	81.73 254	83.52 326	63.83 216	91.64 201	90.30 245	76.36 152	71.97 241	89.93 213	46.30 324	95.17 207	75.10 185	77.70 244	86.19 319
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 213	78.22 206	81.25 264	85.33 289	62.73 255	89.53 279	93.21 100	72.39 222	72.14 238	90.13 209	60.99 146	94.72 223	67.73 263	72.49 286	86.29 316

AdaColmap		78.94 214	77.00 231	84.76 146	96.34 17	65.86 157	92.66 151	87.97 340	62.18 358	70.56 256	92.37 153	43.53 339	97.35 79	64.50 298	82.86 183	91.05 243

GeoE		78.90 215	77.43 220	83.29 209	88.95 187	62.02 270	92.31 164	86.23 361	70.24 277	71.34 251	89.27 222	54.43 233	94.04 259	63.31 306	80.81 213	93.81 160

miper_enhance_ethall		78.86 216	77.97 210	81.54 259	88.00 226	65.17 174	91.41 204	89.15 294	75.19 167	68.79 283	83.98 301	67.17 64	92.82 296	72.73 210	65.30 334	86.62 313

VPNet		78.82 217	77.53 219	82.70 225	84.52 309	66.44 140	93.93 84	92.23 142	80.46 67	72.60 227	88.38 236	49.18 293	93.13 284	72.47 214	63.97 354	88.55 279

EPNet_dtu		78.80 218	79.26 192	77.43 338	88.06 223	49.71 404	91.96 185	91.95 160	77.67 125	76.56 176	91.28 181	58.51 181	90.20 359	56.37 342	80.95 206	92.39 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 219	77.43 220	82.88 220	92.21 97	64.49 191	92.05 178	96.28 4	73.48 197	71.75 244	88.26 239	60.07 160	95.32 200	45.16 393	77.58 246	88.83 272

TR-MVS		78.77 220	77.37 225	82.95 219	90.49 149	60.88 298	93.67 100	90.07 255	70.08 279	74.51 201	91.37 180	45.69 328	95.70 180	60.12 327	80.32 217	92.29 209

thres400		78.68 221	77.43 220	82.43 232	92.21 97	64.49 191	92.05 178	96.28 4	73.48 197	71.75 244	88.26 239	60.07 160	95.32 200	45.16 393	77.58 246	87.48 293

BH-untuned		78.68 221	77.08 228	83.48 203	89.84 161	63.74 219	92.70 146	88.59 321	71.57 253	66.83 312	88.65 231	51.75 262	95.39 195	59.03 332	84.77 163	91.32 237

OMC-MVS		78.67 223	77.91 212	80.95 277	85.76 284	57.40 359	88.49 302	88.67 318	73.85 188	72.43 235	92.10 160	49.29 292	94.55 234	72.73 210	77.89 242	90.91 247

tpm		78.58 224	77.03 229	83.22 213	85.94 280	64.56 189	83.21 360	91.14 204	78.31 113	73.67 215	79.68 362	64.01 103	92.09 326	66.07 284	71.26 296	93.03 185

OpenMVS	70.45 11	78.54 225	75.92 248	86.41 79	85.93 281	71.68 18	92.74 143	92.51 135	66.49 321	64.56 329	91.96 165	43.88 338	98.10 39	54.61 348	90.65 93	89.44 269

EPMVS		78.49 226	75.98 247	86.02 90	91.21 135	69.68 53	80.23 387	91.20 198	75.25 166	72.48 233	78.11 373	54.65 228	93.69 274	57.66 338	83.04 182	94.69 107

AUN-MVS		78.37 227	77.43 220	81.17 266	86.60 263	57.45 358	89.46 281	91.16 200	74.11 181	74.40 202	90.49 192	55.52 219	94.57 230	74.73 192	60.43 383	91.48 231

thres100view900		78.37 227	77.01 230	82.46 231	91.89 115	63.21 241	91.19 224	96.33 1	72.28 225	70.45 259	87.89 248	60.31 155	95.32 200	45.16 393	77.58 246	88.83 272

GA-MVS		78.33 229	76.23 243	84.65 155	83.65 324	66.30 144	91.44 203	90.14 253	76.01 154	70.32 261	84.02 300	42.50 343	94.72 223	70.98 229	77.00 254	92.94 188

cascas		78.18 230	75.77 250	85.41 112	87.14 249	69.11 63	92.96 133	91.15 203	66.71 319	70.47 257	86.07 275	37.49 373	96.48 139	70.15 237	79.80 221	90.65 249

UniMVSNet_NR-MVSNet		78.15 231	77.55 218	79.98 299	84.46 312	60.26 318	92.25 166	93.20 102	77.50 130	68.88 281	86.61 268	66.10 74	92.13 324	66.38 280	62.55 361	87.54 291

LuminaMVS		78.14 232	76.66 235	82.60 229	80.82 352	64.64 188	89.33 283	90.45 233	68.25 304	74.73 199	85.51 284	41.15 349	94.14 250	78.96 159	80.69 215	89.04 270

ICG_test_0404		78.11 233	76.29 242	83.59 197	88.54 195	64.75 184	84.63 343	90.80 219	76.73 143	61.16 357	90.18 200	40.17 353	91.58 338	73.47 198	80.95 206	94.43 126

thres600view7		78.00 234	76.66 235	82.03 251	91.93 111	63.69 225	91.30 216	96.33 1	72.43 220	70.46 258	87.89 248	60.31 155	94.92 216	42.64 405	76.64 257	87.48 293

FC-MVSNet-test		77.99 235	78.08 208	77.70 333	84.89 302	55.51 374	90.27 257	93.75 77	76.87 136	66.80 313	87.59 253	65.71 80	90.23 358	62.89 311	73.94 275	87.37 296

Anonymous202405211		77.96 236	75.33 256	85.87 95	93.73 53	64.52 190	94.85 49	85.36 374	62.52 356	76.11 178	90.18 200	29.43 410	97.29 83	68.51 254	77.24 253	95.81 50

cl22		77.94 237	76.78 233	81.42 261	87.57 237	64.93 182	90.67 242	88.86 311	72.45 219	67.63 300	82.68 315	64.07 101	92.91 294	71.79 219	65.30 334	86.44 314

XXY-MVS		77.94 237	76.44 238	82.43 232	82.60 336	64.44 195	92.01 180	91.83 169	73.59 196	70.00 266	85.82 280	54.43 233	94.76 220	69.63 240	68.02 317	88.10 286

MS-PatchMatch		77.90 239	76.50 237	82.12 246	85.99 277	69.95 42	91.75 197	92.70 122	73.97 185	62.58 352	84.44 296	41.11 350	95.78 170	63.76 303	92.17 66	80.62 396

FMVSNet3		77.73 240	76.04 246	82.80 221	91.20 136	68.99 67	91.87 188	91.99 158	73.35 199	67.04 308	83.19 310	56.62 206	92.14 323	59.80 329	69.34 303	87.28 299

VortexMVS		77.62 241	76.44 238	81.13 268	88.58 194	63.73 221	91.24 219	91.30 196	77.81 121	65.76 318	81.97 324	49.69 287	93.72 272	76.40 176	65.26 337	85.94 329

miper_ehance_all_eth		77.60 242	76.44 238	81.09 274	85.70 286	64.41 198	90.65 243	88.64 320	72.31 223	67.37 306	82.52 316	64.77 93	92.64 307	70.67 233	65.30 334	86.24 318

UniMVSNet (Re)		77.58 243	76.78 233	79.98 299	84.11 318	60.80 299	91.76 195	93.17 104	76.56 149	69.93 269	84.78 291	63.32 119	92.36 317	64.89 296	62.51 363	86.78 307

PatchmatchNet		77.46 244	74.63 263	85.96 92	89.55 169	70.35 35	79.97 392	89.55 277	72.23 226	70.94 252	76.91 385	57.03 196	92.79 299	54.27 350	81.17 204	94.74 105
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 245	75.65 252	82.73 223	80.38 360	67.13 118	91.85 190	90.23 250	75.09 168	69.37 271	83.39 307	53.79 242	94.44 238	71.77 220	65.00 341	86.63 312

CHOSEN 280x420		77.35 246	76.95 232	78.55 325	87.07 251	62.68 256	69.71 424	82.95 396	68.80 297	71.48 249	87.27 260	66.03 75	84.00 409	76.47 175	82.81 185	88.95 271

PS-MVSNAJss		77.26 247	76.31 241	80.13 294	80.64 356	59.16 338	90.63 246	91.06 211	72.80 211	68.58 287	84.57 294	53.55 244	93.96 264	72.97 204	71.96 290	87.27 300

gg-mvs-nofinetune		77.18 248	74.31 270	85.80 99	91.42 128	68.36 81	71.78 418	94.72 37	49.61 417	77.12 170	45.92 446	77.41 8	93.98 263	67.62 264	93.16 55	95.05 88

WB-MVSnew		77.14 249	76.18 245	80.01 298	86.18 273	63.24 239	91.26 217	94.11 65	71.72 245	73.52 216	87.29 259	45.14 333	93.00 287	56.98 340	79.42 226	83.80 357

MVP-Stereo		77.12 250	76.23 243	79.79 306	81.72 344	66.34 143	89.29 284	90.88 215	70.56 274	62.01 355	82.88 312	49.34 290	94.13 251	65.55 291	93.80 43	78.88 411
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 251	75.37 254	82.20 242	89.25 178	62.11 269	82.06 370	89.09 299	76.77 141	70.84 254	87.12 261	41.43 348	95.01 211	67.23 270	74.55 267	89.48 267

MonoMVSNet		76.99 252	75.08 259	82.73 223	83.32 328	63.24 239	86.47 334	86.37 357	79.08 98	66.31 316	79.30 366	49.80 286	91.72 333	79.37 152	65.70 332	93.23 176

dmvs_re		76.93 253	75.36 255	81.61 257	87.78 234	60.71 306	80.00 391	87.99 338	79.42 87	69.02 277	89.47 217	46.77 316	94.32 241	63.38 305	74.45 270	89.81 260

X-MVStestdata		76.86 254	74.13 276	85.05 129	93.22 66	63.78 217	92.92 135	92.66 127	73.99 183	78.18 156	10.19 461	55.25 220	97.41 75	79.16 155	91.58 78	93.95 151

DU-MVS		76.86 254	75.84 249	79.91 302	82.96 332	60.26 318	91.26 217	91.54 183	76.46 151	68.88 281	86.35 271	56.16 211	92.13 324	66.38 280	62.55 361	87.35 297

Anonymous20240529		76.84 256	74.15 275	84.88 137	91.02 138	64.95 181	93.84 92	91.09 207	53.57 405	73.00 219	87.42 256	35.91 383	97.32 81	69.14 248	72.41 288	92.36 205

UWE-MVS-28		76.83 257	77.60 217	74.51 367	84.58 308	50.34 400	88.22 307	94.60 45	74.46 174	66.66 314	88.98 229	62.53 132	85.50 401	57.55 339	80.80 214	87.69 290

c3_l		76.83 257	75.47 253	80.93 278	85.02 300	64.18 208	90.39 252	88.11 335	71.66 246	66.65 315	81.64 330	63.58 115	92.56 308	69.31 245	62.86 358	86.04 324

WR-MVS		76.76 259	75.74 251	79.82 305	84.60 306	62.27 266	92.60 154	92.51 135	76.06 153	67.87 297	85.34 285	56.76 202	90.24 357	62.20 315	63.69 356	86.94 305

v1144		76.73 260	74.88 260	82.27 238	80.23 364	66.60 137	91.68 199	90.21 252	73.69 193	69.06 276	81.89 325	52.73 254	94.40 239	69.21 246	65.23 338	85.80 332

IterMVS-LS		76.49 261	75.18 258	80.43 286	84.49 311	62.74 254	90.64 244	88.80 313	72.40 221	65.16 324	81.72 328	60.98 147	92.27 321	67.74 262	64.65 346	86.29 316
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 262	74.55 266	82.19 243	79.14 378	67.82 98	90.26 258	89.42 282	73.75 191	68.63 286	81.89 325	51.31 269	94.09 253	71.69 222	64.84 342	84.66 349

Elysia		76.45 263	74.17 273	83.30 207	80.43 358	64.12 209	89.58 274	90.83 216	61.78 366	72.53 229	85.92 278	34.30 390	94.81 218	68.10 256	84.01 176	90.97 244

StellarMVS		76.45 263	74.17 273	83.30 207	80.43 358	64.12 209	89.58 274	90.83 216	61.78 366	72.53 229	85.92 278	34.30 390	94.81 218	68.10 256	84.01 176	90.97 244

mamba_0408		76.22 265	73.37 287	84.77 144	88.50 200	66.98 124	58.80 445	86.18 363	69.12 293	74.12 207	89.01 227	47.50 311	95.35 197	67.57 265	79.52 223	91.98 221

v148		76.19 266	74.47 268	81.36 262	80.05 366	64.44 195	91.75 197	90.23 250	73.68 194	67.13 307	80.84 345	55.92 216	93.86 271	68.95 250	61.73 372	85.76 335

Effi-MVS+-dtu		76.14 267	75.28 257	78.72 324	83.22 329	55.17 376	89.87 269	87.78 342	75.42 162	67.98 292	81.43 334	45.08 334	92.52 310	75.08 186	71.63 291	88.48 280

cl____		76.07 268	74.67 261	80.28 289	85.15 295	61.76 279	90.12 261	88.73 315	71.16 261	65.43 321	81.57 332	61.15 144	92.95 289	66.54 277	62.17 365	86.13 322

DIV-MVS_self_test		76.07 268	74.67 261	80.28 289	85.14 296	61.75 280	90.12 261	88.73 315	71.16 261	65.42 322	81.60 331	61.15 144	92.94 293	66.54 277	62.16 367	86.14 320

FMVSNet2		76.07 268	74.01 278	82.26 240	88.85 188	67.66 102	91.33 214	91.61 181	70.84 268	65.98 317	82.25 320	48.03 301	92.00 328	58.46 334	68.73 311	87.10 302

v144192		76.05 271	74.03 277	82.12 246	79.50 372	66.55 139	91.39 208	89.71 274	72.30 224	68.17 290	81.33 337	51.75 262	94.03 261	67.94 260	64.19 349	85.77 333

NR-MVSNet		76.05 271	74.59 264	80.44 285	82.96 332	62.18 268	90.83 235	91.73 173	77.12 134	60.96 359	86.35 271	59.28 172	91.80 331	60.74 322	61.34 376	87.35 297

v1192		75.98 273	73.92 279	82.15 244	79.73 368	66.24 146	91.22 221	89.75 268	72.67 213	68.49 288	81.42 335	49.86 284	94.27 245	67.08 272	65.02 340	85.95 327

FE-MVS		75.97 274	73.02 293	84.82 140	89.78 162	65.56 164	77.44 403	91.07 210	64.55 333	72.66 225	79.85 360	46.05 326	96.69 128	54.97 347	80.82 212	92.21 215

eth_miper_zixun_eth		75.96 275	74.40 269	80.66 281	84.66 305	63.02 245	89.28 285	88.27 331	71.88 237	65.73 319	81.65 329	59.45 168	92.81 297	68.13 255	60.53 381	86.14 320

TranMVSNet+NR-MVSNet		75.86 276	74.52 267	79.89 303	82.44 338	60.64 309	91.37 211	91.37 190	76.63 147	67.65 299	86.21 274	52.37 257	91.55 339	61.84 317	60.81 379	87.48 293

SCA		75.82 277	72.76 296	85.01 131	86.63 262	70.08 38	81.06 380	89.19 291	71.60 252	70.01 265	77.09 383	45.53 329	90.25 354	60.43 324	73.27 279	94.68 108

LPG-MVS_test		75.82 277	74.58 265	79.56 313	84.31 315	59.37 334	90.44 249	89.73 271	69.49 285	64.86 325	88.42 234	38.65 359	94.30 243	72.56 212	72.76 283	85.01 346

GBi-Net		75.65 279	73.83 280	81.10 271	88.85 188	65.11 176	90.01 265	90.32 241	70.84 268	67.04 308	80.25 355	48.03 301	91.54 340	59.80 329	69.34 303	86.64 309

test1		75.65 279	73.83 280	81.10 271	88.85 188	65.11 176	90.01 265	90.32 241	70.84 268	67.04 308	80.25 355	48.03 301	91.54 340	59.80 329	69.34 303	86.64 309

v1921920		75.63 281	73.49 285	82.06 250	79.38 373	66.35 142	91.07 229	89.48 278	71.98 232	67.99 291	81.22 340	49.16 295	93.90 267	66.56 276	64.56 347	85.92 330

ACMP	71.68 10	75.58 282	74.23 272	79.62 311	84.97 301	59.64 329	90.80 236	89.07 301	70.39 275	62.95 348	87.30 258	38.28 363	93.87 269	72.89 205	71.45 294	85.36 342
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 283	73.26 291	81.61 257	80.67 355	66.82 130	89.54 278	89.27 287	71.65 247	63.30 343	80.30 354	54.99 226	94.06 256	67.33 269	62.33 364	83.94 355

tpm cat1		75.30 284	72.21 305	84.58 160	88.52 199	67.77 99	78.16 401	88.02 337	61.88 364	68.45 289	76.37 389	60.65 150	94.03 261	53.77 353	74.11 273	91.93 224

PLC	68.80 14	75.23 285	73.68 283	79.86 304	92.93 77	58.68 343	90.64 244	88.30 329	60.90 371	64.43 333	90.53 190	42.38 344	94.57 230	56.52 341	76.54 258	86.33 315
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 286	72.98 294	81.88 252	79.20 375	66.00 151	90.75 238	89.11 298	71.63 251	67.41 304	81.22 340	47.36 313	93.87 269	65.46 292	64.72 345	85.77 333

Fast-Effi-MVS+-dtu		75.04 287	73.37 287	80.07 295	80.86 350	59.52 332	91.20 223	85.38 373	71.90 235	65.20 323	84.84 290	41.46 347	92.97 288	66.50 279	72.96 282	87.73 289

dp		75.01 288	72.09 306	83.76 187	89.28 177	66.22 147	79.96 393	89.75 268	71.16 261	67.80 298	77.19 382	51.81 260	92.54 309	50.39 363	71.44 295	92.51 202

TAPA-MVS	70.22 12	74.94 289	73.53 284	79.17 319	90.40 151	52.07 389	89.19 289	89.61 276	62.69 355	70.07 264	92.67 145	48.89 298	94.32 241	38.26 419	79.97 219	91.12 242
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 290	73.32 290	79.74 308	86.53 265	60.31 317	89.03 294	92.70 122	78.61 109	68.98 279	83.34 308	41.93 346	92.23 322	52.77 357	65.97 330	86.69 308

mamba_test_0407_2		74.86 291	73.37 287	79.35 316	88.50 200	66.98 124	58.80 445	86.18 363	69.12 293	74.12 207	89.01 227	47.50 311	79.09 430	67.57 265	79.52 223	91.98 221

v10		74.77 292	72.54 302	81.46 260	80.33 362	66.71 134	89.15 290	89.08 300	70.94 266	63.08 346	79.86 359	52.52 255	94.04 259	65.70 288	62.17 365	83.64 358

XVG-OURS-SEG-HR		74.70 293	73.08 292	79.57 312	78.25 391	57.33 360	80.49 383	87.32 345	63.22 348	68.76 284	90.12 211	44.89 335	91.59 337	70.55 235	74.09 274	89.79 261

ACMM	69.62 13	74.34 294	72.73 298	79.17 319	84.25 317	57.87 350	90.36 254	89.93 262	63.17 350	65.64 320	86.04 277	37.79 371	94.10 252	65.89 285	71.52 293	85.55 338
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 295	72.30 304	80.32 287	91.49 127	61.66 282	90.85 234	80.72 402	56.67 397	63.85 338	90.64 187	46.75 317	90.84 348	53.79 352	75.99 262	88.47 281

XVG-OURS		74.25 296	72.46 303	79.63 310	78.45 389	57.59 356	80.33 385	87.39 344	63.86 340	68.76 284	89.62 216	40.50 352	91.72 333	69.00 249	74.25 272	89.58 264

test_fmvs1		74.07 297	73.69 282	75.22 358	78.91 382	47.34 417	89.06 293	74.69 418	63.68 343	79.41 140	91.59 175	24.36 421	87.77 384	85.22 91	76.26 260	90.55 252

CVMVSNet		74.04 298	74.27 271	73.33 377	85.33 289	43.94 431	89.53 279	88.39 325	54.33 404	70.37 260	90.13 209	49.17 294	84.05 407	61.83 318	79.36 228	91.99 220

Baseline_NR-MVSNet		73.99 299	72.83 295	77.48 337	80.78 353	59.29 337	91.79 192	84.55 382	68.85 296	68.99 278	80.70 346	56.16 211	92.04 327	62.67 312	60.98 378	81.11 390

pmmvs4		73.92 300	71.81 310	80.25 291	79.17 376	65.24 172	87.43 322	87.26 348	67.64 312	63.46 341	83.91 302	48.96 297	91.53 343	62.94 309	65.49 333	83.96 354

D2MVS		73.80 301	72.02 307	79.15 321	79.15 377	62.97 246	88.58 301	90.07 255	72.94 206	59.22 369	78.30 370	42.31 345	92.70 303	65.59 290	72.00 289	81.79 385

SD_0403		73.79 302	73.48 286	74.69 364	85.33 289	45.56 427	83.80 350	85.57 372	76.55 150	62.96 347	88.45 233	50.62 276	87.59 388	48.80 373	79.28 232	90.92 246

CR-MVSNet		73.79 302	70.82 318	82.70 225	83.15 330	67.96 94	70.25 421	84.00 387	73.67 195	69.97 267	72.41 406	57.82 189	89.48 367	52.99 356	73.13 280	90.64 250

test_djsdf		73.76 304	72.56 301	77.39 339	77.00 403	53.93 382	89.07 291	90.69 225	65.80 325	63.92 336	82.03 323	43.14 342	92.67 304	72.83 206	68.53 312	85.57 337

pmmvs5		73.35 305	71.52 312	78.86 323	78.64 386	60.61 310	91.08 227	86.90 352	67.69 309	63.32 342	83.64 303	44.33 337	90.53 351	62.04 316	66.02 329	85.46 340

Anonymous20231211		73.08 306	70.39 322	81.13 268	90.62 146	63.33 236	91.40 206	90.06 257	51.84 410	64.46 332	80.67 348	36.49 381	94.07 255	63.83 302	64.17 350	85.98 326

tt0805		73.07 307	70.73 319	80.07 295	78.37 390	57.05 362	87.78 316	92.18 149	61.23 370	67.04 308	86.49 270	31.35 403	94.58 228	65.06 295	67.12 322	88.57 278

miper_lstm_enhance		73.05 308	71.73 311	77.03 344	83.80 321	58.32 347	81.76 371	88.88 309	69.80 283	61.01 358	78.23 372	57.19 194	87.51 389	65.34 293	59.53 386	85.27 345

jajsoiax		73.05 308	71.51 313	77.67 334	77.46 400	54.83 378	88.81 297	90.04 258	69.13 292	62.85 350	83.51 305	31.16 404	92.75 300	70.83 230	69.80 299	85.43 341

LCM-MVSNet-Re		72.93 310	71.84 309	76.18 353	88.49 204	48.02 412	80.07 390	70.17 433	73.96 186	52.25 403	80.09 358	49.98 282	88.24 378	67.35 267	84.23 172	92.28 210

pm-mvs1		72.89 311	71.09 315	78.26 329	79.10 379	57.62 354	90.80 236	89.30 286	67.66 310	62.91 349	81.78 327	49.11 296	92.95 289	60.29 326	58.89 389	84.22 353

tpmvs		72.88 312	69.76 328	82.22 241	90.98 139	67.05 120	78.22 400	88.30 329	63.10 351	64.35 334	74.98 396	55.09 225	94.27 245	43.25 399	69.57 302	85.34 343

test0.0.03 1		72.76 313	72.71 299	72.88 381	80.25 363	47.99 413	91.22 221	89.45 280	71.51 256	62.51 353	87.66 251	53.83 240	85.06 403	50.16 365	67.84 320	85.58 336

UniMVSNet_ETH3D		72.74 314	70.53 321	79.36 315	78.62 387	56.64 366	85.01 340	89.20 290	63.77 341	64.84 327	84.44 296	34.05 392	91.86 330	63.94 301	70.89 298	89.57 265

mvs_tets		72.71 315	71.11 314	77.52 335	77.41 401	54.52 380	88.45 303	89.76 267	68.76 299	62.70 351	83.26 309	29.49 409	92.71 301	70.51 236	69.62 301	85.34 343

FMVSNet1		72.71 315	69.91 326	81.10 271	83.60 325	65.11 176	90.01 265	90.32 241	63.92 339	63.56 340	80.25 355	36.35 382	91.54 340	54.46 349	66.75 325	86.64 309

test_fmvs1_n		72.69 317	71.92 308	74.99 362	71.15 423	47.08 419	87.34 324	75.67 413	63.48 345	78.08 158	91.17 182	20.16 435	87.87 381	84.65 100	75.57 264	90.01 258

IterMVS		72.65 318	70.83 316	78.09 331	82.17 340	62.96 247	87.64 320	86.28 359	71.56 254	60.44 362	78.85 368	45.42 331	86.66 393	63.30 307	61.83 369	84.65 350
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 319	72.74 297	72.10 389	87.87 229	49.45 406	88.07 309	89.01 303	72.91 208	63.11 344	88.10 243	63.63 110	85.54 398	32.73 434	69.23 306	81.32 388

PatchMatch-RL		72.06 320	69.98 323	78.28 328	89.51 170	55.70 373	83.49 353	83.39 394	61.24 369	63.72 339	82.76 313	34.77 387	93.03 286	53.37 355	77.59 245	86.12 323

PVSNet_0	68.08 15	71.81 321	68.32 337	82.27 238	84.68 303	62.31 265	88.68 299	90.31 244	75.84 155	57.93 381	80.65 349	37.85 370	94.19 248	69.94 238	29.05 449	90.31 254

MIMVSNet		71.64 322	68.44 335	81.23 265	81.97 343	64.44 195	73.05 415	88.80 313	69.67 284	64.59 328	74.79 398	32.79 395	87.82 382	53.99 351	76.35 259	91.42 232

test_vis1_n		71.63 323	70.73 319	74.31 371	69.63 429	47.29 418	86.91 328	72.11 426	63.21 349	75.18 192	90.17 206	20.40 433	85.76 397	84.59 101	74.42 271	89.87 259

IterMVS-SCA-FT		71.55 324	69.97 324	76.32 351	81.48 346	60.67 308	87.64 320	85.99 366	66.17 323	59.50 367	78.88 367	45.53 329	83.65 411	62.58 313	61.93 368	84.63 352

v7n		71.31 325	68.65 332	79.28 317	76.40 405	60.77 301	86.71 332	89.45 280	64.17 338	58.77 374	78.24 371	44.59 336	93.54 276	57.76 336	61.75 371	83.52 361

anonymousdsp		71.14 326	69.37 330	76.45 350	72.95 418	54.71 379	84.19 347	88.88 309	61.92 363	62.15 354	79.77 361	38.14 366	91.44 345	68.90 251	67.45 321	83.21 367

F-COLMAP		70.66 327	68.44 335	77.32 340	86.37 270	55.91 371	88.00 311	86.32 358	56.94 395	57.28 385	88.07 245	33.58 393	92.49 311	51.02 360	68.37 313	83.55 359

WR-MVS_H		70.59 328	69.94 325	72.53 383	81.03 349	51.43 393	87.35 323	92.03 157	67.38 313	60.23 364	80.70 346	55.84 217	83.45 413	46.33 388	58.58 391	82.72 374

CP-MVSNet		70.50 329	69.91 326	72.26 386	80.71 354	51.00 397	87.23 325	90.30 245	67.84 308	59.64 366	82.69 314	50.23 280	82.30 421	51.28 359	59.28 387	83.46 363

RPMNet		70.42 330	65.68 351	84.63 158	83.15 330	67.96 94	70.25 421	90.45 233	46.83 426	69.97 267	65.10 429	56.48 210	95.30 203	35.79 424	73.13 280	90.64 250

testing3		70.38 331	70.83 316	69.03 401	85.82 283	43.93 432	90.72 241	90.56 232	68.06 305	60.24 363	86.82 267	64.83 91	84.12 405	26.33 442	64.10 351	79.04 409

tfpnnormal		70.10 332	67.36 341	78.32 327	83.45 327	60.97 297	88.85 295	92.77 120	64.85 332	60.83 360	78.53 369	43.52 340	93.48 278	31.73 437	61.70 373	80.52 397

TransMVSNet (Re)		70.07 333	67.66 339	77.31 341	80.62 357	59.13 339	91.78 194	84.94 378	65.97 324	60.08 365	80.44 351	50.78 273	91.87 329	48.84 372	45.46 422	80.94 392

CL-MVSNet_self_test		69.92 334	68.09 338	75.41 356	73.25 417	55.90 372	90.05 264	89.90 263	69.96 280	61.96 356	76.54 386	51.05 272	87.64 385	49.51 369	50.59 412	82.70 376

DP-MVS		69.90 335	66.48 343	80.14 293	95.36 28	62.93 248	89.56 276	76.11 411	50.27 416	57.69 383	85.23 286	39.68 355	95.73 174	33.35 429	71.05 297	81.78 386

PS-CasMVS		69.86 336	69.13 331	72.07 390	80.35 361	50.57 399	87.02 327	89.75 268	67.27 314	59.19 370	82.28 319	46.58 319	82.24 422	50.69 362	59.02 388	83.39 365

Syy-MVS		69.65 337	69.52 329	70.03 397	87.87 229	43.21 433	88.07 309	89.01 303	72.91 208	63.11 344	88.10 243	45.28 332	85.54 398	22.07 447	69.23 306	81.32 388

MSDG		69.54 338	65.73 350	80.96 276	85.11 298	63.71 223	84.19 347	83.28 395	56.95 394	54.50 392	84.03 299	31.50 401	96.03 163	42.87 403	69.13 308	83.14 369

PEN-MVS		69.46 339	68.56 333	72.17 388	79.27 374	49.71 404	86.90 329	89.24 288	67.24 317	59.08 371	82.51 317	47.23 314	83.54 412	48.42 375	57.12 393	83.25 366

LS3D		69.17 340	66.40 345	77.50 336	91.92 112	56.12 369	85.12 339	80.37 404	46.96 424	56.50 387	87.51 255	37.25 374	93.71 273	32.52 436	79.40 227	82.68 377

PatchT		69.11 341	65.37 355	80.32 287	82.07 342	63.68 226	67.96 431	87.62 343	50.86 414	69.37 271	65.18 428	57.09 195	88.53 374	41.59 408	66.60 326	88.74 275

KD-MVS_2432*1600		69.03 342	66.37 346	77.01 345	85.56 287	61.06 295	81.44 376	90.25 248	67.27 314	58.00 379	76.53 387	54.49 230	87.63 386	48.04 377	35.77 440	82.34 380

miper_refine_blended		69.03 342	66.37 346	77.01 345	85.56 287	61.06 295	81.44 376	90.25 248	67.27 314	58.00 379	76.53 387	54.49 230	87.63 386	48.04 377	35.77 440	82.34 380

mvsany_test1		68.77 344	68.56 333	69.39 399	73.57 416	45.88 426	80.93 381	60.88 447	59.65 380	71.56 247	90.26 199	43.22 341	75.05 434	74.26 195	62.70 360	87.25 301

ACMH	63.93 17	68.62 345	64.81 357	80.03 297	85.22 294	63.25 238	87.72 317	84.66 380	60.83 372	51.57 407	79.43 365	27.29 416	94.96 213	41.76 406	64.84 342	81.88 384
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 346	65.41 354	77.96 332	78.69 385	62.93 248	89.86 270	89.17 292	60.55 373	50.27 412	77.73 377	22.60 429	94.06 256	47.18 384	72.65 285	76.88 422

ADS-MVSNet		68.54 347	64.38 364	81.03 275	88.06 223	66.90 129	68.01 429	84.02 386	57.57 388	64.48 330	69.87 416	38.68 357	89.21 369	40.87 410	67.89 318	86.97 303

DTE-MVSNet		68.46 348	67.33 342	71.87 392	77.94 395	49.00 410	86.16 336	88.58 322	66.36 322	58.19 376	82.21 321	46.36 320	83.87 410	44.97 396	55.17 400	82.73 373

mmtdpeth		68.33 349	66.37 346	74.21 372	82.81 335	51.73 390	84.34 345	80.42 403	67.01 318	71.56 247	68.58 420	30.52 407	92.35 318	75.89 179	36.21 438	78.56 416

our_test_3		68.29 350	64.69 359	79.11 322	78.92 380	64.85 183	88.40 304	85.06 376	60.32 376	52.68 401	76.12 391	40.81 351	89.80 366	44.25 398	55.65 398	82.67 378

Patchmatch-RL test		68.17 351	64.49 362	79.19 318	71.22 422	53.93 382	70.07 423	71.54 430	69.22 289	56.79 386	62.89 433	56.58 207	88.61 371	69.53 242	52.61 407	95.03 90

XVG-ACMP-BASELINE		68.04 352	65.53 353	75.56 355	74.06 415	52.37 387	78.43 397	85.88 367	62.03 361	58.91 373	81.21 342	20.38 434	91.15 347	60.69 323	68.18 314	83.16 368

FMVSNet5		68.04 352	65.66 352	75.18 360	84.43 313	57.89 349	83.54 352	86.26 360	61.83 365	53.64 398	73.30 401	37.15 377	85.08 402	48.99 371	61.77 370	82.56 379

ppachtmachnet_test		67.72 354	63.70 366	79.77 307	78.92 380	66.04 150	88.68 299	82.90 397	60.11 378	55.45 389	75.96 392	39.19 356	90.55 350	39.53 414	52.55 408	82.71 375

ACMH+	65.35 16	67.65 355	64.55 360	76.96 347	84.59 307	57.10 361	88.08 308	80.79 401	58.59 386	53.00 400	81.09 344	26.63 418	92.95 289	46.51 386	61.69 374	80.82 393

pmmvs6		67.57 356	64.76 358	76.00 354	72.82 420	53.37 384	88.71 298	86.78 356	53.19 406	57.58 384	78.03 374	35.33 386	92.41 314	55.56 345	54.88 402	82.21 382

Anonymous20231206		67.53 357	65.78 349	72.79 382	74.95 411	47.59 415	88.23 306	87.32 345	61.75 368	58.07 378	77.29 380	37.79 371	87.29 391	42.91 401	63.71 355	83.48 362

Patchmtry		67.53 357	63.93 365	78.34 326	82.12 341	64.38 199	68.72 426	84.00 387	48.23 423	59.24 368	72.41 406	57.82 189	89.27 368	46.10 389	56.68 397	81.36 387

USDC		67.43 359	64.51 361	76.19 352	77.94 395	55.29 375	78.38 398	85.00 377	73.17 201	48.36 420	80.37 352	21.23 431	92.48 312	52.15 358	64.02 353	80.81 394

ADS-MVSNet2		66.90 360	63.44 368	77.26 342	88.06 223	60.70 307	68.01 429	75.56 415	57.57 388	64.48 330	69.87 416	38.68 357	84.10 406	40.87 410	67.89 318	86.97 303

CMPMVS	48.56 21	66.77 361	64.41 363	73.84 374	70.65 426	50.31 401	77.79 402	85.73 370	45.54 429	44.76 430	82.14 322	35.40 385	90.14 360	63.18 308	74.54 269	81.07 391
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 362	62.92 371	76.80 349	76.51 404	57.77 351	89.22 286	83.41 393	55.48 401	53.86 396	77.84 375	26.28 419	93.95 265	34.90 426	68.76 310	78.68 414

LTVRE_ROB	59.60 19	66.27 363	63.54 367	74.45 368	84.00 320	51.55 392	67.08 433	83.53 391	58.78 384	54.94 391	80.31 353	34.54 388	93.23 282	40.64 412	68.03 316	78.58 415
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		66.06 364	62.45 374	76.88 348	81.42 348	54.45 381	57.49 447	88.67 318	49.36 418	63.86 337	46.86 445	56.06 214	90.25 354	49.53 368	68.83 309	85.95 327

Patchmatch-test		65.86 365	60.94 380	80.62 284	83.75 322	58.83 341	58.91 444	75.26 417	44.50 432	50.95 411	77.09 383	58.81 179	87.90 380	35.13 425	64.03 352	95.12 84

UnsupCasMVSNet_eth		65.79 366	63.10 369	73.88 373	70.71 425	50.29 402	81.09 379	89.88 264	72.58 215	49.25 417	74.77 399	32.57 397	87.43 390	55.96 344	41.04 430	83.90 356

test_fmvs2		65.78 367	64.84 356	68.60 403	66.54 435	41.71 435	83.27 357	69.81 434	54.38 403	67.91 294	84.54 295	15.35 440	81.22 426	75.65 181	66.16 328	82.88 370

dmvs_testset		65.55 368	66.45 344	62.86 415	79.87 367	22.35 461	76.55 405	71.74 428	77.42 133	55.85 388	87.77 250	51.39 268	80.69 427	31.51 440	65.92 331	85.55 338

pmmvs-eth3d		65.53 369	62.32 375	75.19 359	69.39 430	59.59 330	82.80 365	83.43 392	62.52 356	51.30 409	72.49 404	32.86 394	87.16 392	55.32 346	50.73 411	78.83 412

mamv4		65.18 370	67.43 340	58.44 419	77.88 397	49.36 409	69.40 425	70.99 432	48.31 422	57.78 382	85.53 283	59.01 177	51.88 457	73.67 197	64.32 348	74.07 427

SixPastTwentyTwo		64.92 371	61.78 378	74.34 370	78.74 384	49.76 403	83.42 356	79.51 407	62.86 352	50.27 412	77.35 378	30.92 406	90.49 352	45.89 390	47.06 417	82.78 371

OurMVSNet-221017-0		64.68 372	62.17 376	72.21 387	76.08 408	47.35 416	80.67 382	81.02 400	56.19 398	51.60 406	79.66 363	27.05 417	88.56 373	53.60 354	53.63 405	80.71 395

test_0402		64.54 373	61.09 379	74.92 363	84.10 319	60.75 303	87.95 312	79.71 406	52.03 408	52.41 402	77.20 381	32.21 399	91.64 335	23.14 445	61.03 377	72.36 433

testgi		64.48 374	62.87 372	69.31 400	71.24 421	40.62 438	85.49 337	79.92 405	65.36 329	54.18 394	83.49 306	23.74 424	84.55 404	41.60 407	60.79 380	82.77 372

RPSCF		64.24 375	61.98 377	71.01 395	76.10 407	45.00 428	75.83 410	75.94 412	46.94 425	58.96 372	84.59 293	31.40 402	82.00 423	47.76 382	60.33 385	86.04 324

EU-MVSNet		64.01 376	63.01 370	67.02 409	74.40 414	38.86 444	83.27 357	86.19 362	45.11 430	54.27 393	81.15 343	36.91 380	80.01 429	48.79 374	57.02 394	82.19 383

test20.03		63.83 377	62.65 373	67.38 408	70.58 427	39.94 440	86.57 333	84.17 384	63.29 347	51.86 405	77.30 379	37.09 378	82.47 419	38.87 418	54.13 404	79.73 403

sc_t1		63.81 378	59.39 386	77.10 343	77.62 398	56.03 370	84.32 346	73.56 422	46.66 427	58.22 375	73.06 402	23.28 427	90.62 349	50.93 361	46.84 418	84.64 351

MDA-MVSNet_test_wron		63.78 379	60.16 382	74.64 365	78.15 393	60.41 314	83.49 353	84.03 385	56.17 400	39.17 440	71.59 412	37.22 375	83.24 416	42.87 403	48.73 414	80.26 400

YYNet1		63.76 380	60.14 383	74.62 366	78.06 394	60.19 321	83.46 355	83.99 389	56.18 399	39.25 439	71.56 413	37.18 376	83.34 414	42.90 402	48.70 415	80.32 399

K. test v3		63.09 381	59.61 385	73.53 376	76.26 406	49.38 408	83.27 357	77.15 410	64.35 335	47.77 422	72.32 408	28.73 411	87.79 383	49.93 367	36.69 437	83.41 364

COLMAP_ROB	57.96 20	62.98 382	59.65 384	72.98 380	81.44 347	53.00 386	83.75 351	75.53 416	48.34 421	48.81 419	81.40 336	24.14 422	90.30 353	32.95 431	60.52 382	75.65 425
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 383	59.08 387	71.10 394	67.19 433	48.72 411	83.91 349	85.23 375	50.38 415	47.84 421	71.22 415	20.74 432	85.51 400	46.47 387	58.75 390	79.06 408

tt0320		61.85 384	57.45 393	75.03 361	77.49 399	57.60 355	82.74 366	73.65 421	43.65 436	53.65 397	68.18 422	25.47 420	88.66 370	45.56 392	46.68 419	78.81 413

AllTest		61.66 385	58.06 389	72.46 384	79.57 369	51.42 394	80.17 388	68.61 436	51.25 412	45.88 424	81.23 338	19.86 436	86.58 394	38.98 416	57.01 395	79.39 405

UnsupCasMVSNet_bld		61.60 386	57.71 390	73.29 378	68.73 431	51.64 391	78.61 396	89.05 302	57.20 393	46.11 423	61.96 436	28.70 412	88.60 372	50.08 366	38.90 435	79.63 404

MDA-MVSNet-bldmvs		61.54 387	57.70 391	73.05 379	79.53 371	57.00 365	83.08 361	81.23 399	57.57 388	34.91 444	72.45 405	32.79 395	86.26 396	35.81 423	41.95 428	75.89 424

tt0320-xc		61.51 388	56.89 396	75.37 357	78.50 388	58.61 344	82.61 367	71.27 431	44.31 433	53.17 399	68.03 424	23.38 425	88.46 375	47.77 381	43.00 427	79.03 410

mvs5depth		61.03 389	57.65 392	71.18 393	67.16 434	47.04 421	72.74 416	77.49 408	57.47 391	60.52 361	72.53 403	22.84 428	88.38 376	49.15 370	38.94 434	78.11 419

KD-MVS_self_test		60.87 390	58.60 388	67.68 406	66.13 436	39.93 441	75.63 412	84.70 379	57.32 392	49.57 415	68.45 421	29.55 408	82.87 417	48.09 376	47.94 416	80.25 401

kuosan		60.86 391	60.24 381	62.71 416	81.57 345	46.43 423	75.70 411	85.88 367	57.98 387	48.95 418	69.53 418	58.42 182	76.53 432	28.25 441	35.87 439	65.15 440

TinyColmap		60.32 392	56.42 399	72.00 391	78.78 383	53.18 385	78.36 399	75.64 414	52.30 407	41.59 438	75.82 394	14.76 443	88.35 377	35.84 422	54.71 403	74.46 426

MVS-HIRNet		60.25 393	55.55 400	74.35 369	84.37 314	56.57 367	71.64 419	74.11 419	34.44 443	45.54 428	42.24 451	31.11 405	89.81 364	40.36 413	76.10 261	76.67 423

MIMVSNet1		60.16 394	57.33 394	68.67 402	69.71 428	44.13 430	78.92 395	84.21 383	55.05 402	44.63 431	71.85 410	23.91 423	81.54 425	32.63 435	55.03 401	80.35 398

PM-MVS		59.40 395	56.59 397	67.84 404	63.63 439	41.86 434	76.76 404	63.22 444	59.01 383	51.07 410	72.27 409	11.72 447	83.25 415	61.34 319	50.28 413	78.39 417

new-patchmatchnet		59.30 396	56.48 398	67.79 405	65.86 437	44.19 429	82.47 368	81.77 398	59.94 379	43.65 434	66.20 427	27.67 415	81.68 424	39.34 415	41.40 429	77.50 421

test_vis1_rt		59.09 397	57.31 395	64.43 412	68.44 432	46.02 425	83.05 363	48.63 456	51.96 409	49.57 415	63.86 432	16.30 438	80.20 428	71.21 228	62.79 359	67.07 439

test_fmvs3		56.82 398	54.86 402	62.69 417	53.59 450	35.47 447	75.87 409	65.64 441	43.91 434	55.10 390	71.43 414	6.91 455	74.40 437	68.64 253	52.63 406	78.20 418

DSMNet-mixed		56.78 399	54.44 403	63.79 413	63.21 440	29.44 456	64.43 436	64.10 443	42.12 440	51.32 408	71.60 411	31.76 400	75.04 435	36.23 421	65.20 339	86.87 306

pmmvs3		55.51 400	51.50 406	67.53 407	57.90 448	50.93 398	80.37 384	73.66 420	40.63 441	44.15 433	64.75 430	16.30 438	78.97 431	44.77 397	40.98 432	72.69 431

TDRefinement		55.28 401	51.58 405	66.39 410	59.53 447	46.15 424	76.23 407	72.80 423	44.60 431	42.49 436	76.28 390	15.29 441	82.39 420	33.20 430	43.75 424	70.62 435

dongtai		55.18 402	55.46 401	54.34 427	76.03 409	36.88 445	76.07 408	84.61 381	51.28 411	43.41 435	64.61 431	56.56 208	67.81 445	18.09 450	28.50 450	58.32 443

LF4IMVS		54.01 403	52.12 404	59.69 418	62.41 442	39.91 442	68.59 427	68.28 438	42.96 438	44.55 432	75.18 395	14.09 445	68.39 444	41.36 409	51.68 409	70.78 434

ttmdpeth		53.34 404	49.96 407	63.45 414	62.07 444	40.04 439	72.06 417	65.64 441	42.54 439	51.88 404	77.79 376	13.94 446	76.48 433	32.93 432	30.82 448	73.84 428

MVStest1		51.35 405	46.89 409	64.74 411	65.06 438	51.10 396	67.33 432	72.58 424	30.20 447	35.30 442	74.82 397	27.70 414	69.89 442	24.44 444	24.57 451	73.22 429

N_pmnet		50.55 406	49.11 408	54.88 425	77.17 402	4.02 469	84.36 344	2.00 467	48.59 419	45.86 426	68.82 419	32.22 398	82.80 418	31.58 438	51.38 410	77.81 420

new_pmnet		49.31 407	46.44 410	57.93 420	62.84 441	40.74 437	68.47 428	62.96 445	36.48 442	35.09 443	57.81 440	14.97 442	72.18 439	32.86 433	46.44 420	60.88 442

mvsany_test3		48.86 408	46.35 411	56.41 421	46.00 456	31.67 452	62.26 438	47.25 457	43.71 435	45.54 428	68.15 423	10.84 448	64.44 453	57.95 335	35.44 442	73.13 430

test_f		46.58 409	43.45 413	55.96 422	45.18 457	32.05 451	61.18 439	49.49 455	33.39 444	42.05 437	62.48 435	7.00 454	65.56 449	47.08 385	43.21 426	70.27 436

WB-MVS		46.23 410	44.94 412	50.11 430	62.13 443	21.23 463	76.48 406	55.49 449	45.89 428	35.78 441	61.44 438	35.54 384	72.83 438	9.96 457	21.75 452	56.27 445

FPMVS		45.64 411	43.10 415	53.23 428	51.42 453	36.46 446	64.97 435	71.91 427	29.13 448	27.53 448	61.55 437	9.83 450	65.01 451	16.00 454	55.58 399	58.22 444

SSC-MVS		44.51 412	43.35 414	47.99 434	61.01 446	18.90 465	74.12 414	54.36 450	43.42 437	34.10 445	60.02 439	34.42 389	70.39 441	9.14 459	19.57 453	54.68 446

EGC-MVSNET		42.35 413	38.09 416	55.11 424	74.57 412	46.62 422	71.63 420	55.77 448	0.04 462	0.24 463	62.70 434	14.24 444	74.91 436	17.59 451	46.06 421	43.80 448

LCM-MVSNet		40.54 414	35.79 419	54.76 426	36.92 463	30.81 453	51.41 450	69.02 435	22.07 450	24.63 450	45.37 447	4.56 459	65.81 448	33.67 428	34.50 443	67.67 437

APD_test1		40.50 415	37.31 418	50.09 431	51.88 451	35.27 448	59.45 443	52.59 452	21.64 451	26.12 449	57.80 441	4.56 459	66.56 447	22.64 446	39.09 433	48.43 447

test_vis3_rt		40.46 416	37.79 417	48.47 433	44.49 458	33.35 450	66.56 434	32.84 464	32.39 445	29.65 446	39.13 454	3.91 462	68.65 443	50.17 364	40.99 431	43.40 449

ANet_high		40.27 417	35.20 420	55.47 423	34.74 464	34.47 449	63.84 437	71.56 429	48.42 420	18.80 453	41.08 452	9.52 451	64.45 452	20.18 448	8.66 460	67.49 438

test_method		38.59 418	35.16 421	48.89 432	54.33 449	21.35 462	45.32 453	53.71 451	7.41 459	28.74 447	51.62 443	8.70 452	52.87 456	33.73 427	32.89 444	72.47 432

PMMVS2		37.93 419	33.61 422	50.92 429	46.31 455	24.76 459	60.55 442	50.05 453	28.94 449	20.93 451	47.59 444	4.41 461	65.13 450	25.14 443	18.55 455	62.87 441

Gipuma		34.91 420	31.44 423	45.30 435	70.99 424	39.64 443	19.85 457	72.56 425	20.10 453	16.16 457	21.47 458	5.08 458	71.16 440	13.07 455	43.70 425	25.08 455
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 421	29.47 424	42.67 437	41.89 460	30.81 453	52.07 448	43.45 458	15.45 454	18.52 454	44.82 448	2.12 463	58.38 454	16.05 452	30.87 446	38.83 450

APD_test2		32.77 421	29.47 424	42.67 437	41.89 460	30.81 453	52.07 448	43.45 458	15.45 454	18.52 454	44.82 448	2.12 463	58.38 454	16.05 452	30.87 446	38.83 450

PMVS	26.43 22	31.84 423	28.16 426	42.89 436	25.87 466	27.58 457	50.92 451	49.78 454	21.37 452	14.17 458	40.81 453	2.01 465	66.62 446	9.61 458	38.88 436	34.49 454
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 424	24.00 428	26.45 441	43.74 459	18.44 466	60.86 440	39.66 460	15.11 456	9.53 460	22.10 457	6.52 456	46.94 459	8.31 460	10.14 457	13.98 457

MVE	24.84 23	24.35 425	19.77 431	38.09 439	34.56 465	26.92 458	26.57 455	38.87 462	11.73 458	11.37 459	27.44 455	1.37 466	50.42 458	11.41 456	14.60 456	36.93 452
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 426	23.20 430	25.46 442	41.52 462	16.90 467	60.56 441	38.79 463	14.62 457	8.99 461	20.24 460	7.35 453	45.82 460	7.25 461	9.46 458	13.64 458

tmp_tt		22.26 427	23.75 429	17.80 443	5.23 467	12.06 468	35.26 454	39.48 461	2.82 461	18.94 452	44.20 450	22.23 430	24.64 462	36.30 420	9.31 459	16.69 456

cdsmvs_eth3d_5k		19.86 428	26.47 427	0.00 447	0.00 470	0.00 472	0.00 458	93.45 91	0.00 465	0.00 466	95.27 71	49.56 288	0.00 466	0.00 465	0.00 463	0.00 462

wuyk23d		11.30 429	10.95 432	12.33 444	48.05 454	19.89 464	25.89 456	1.92 468	3.58 460	3.12 462	1.37 462	0.64 467	15.77 463	6.23 462	7.77 461	1.35 459

ab-mvs-re		7.91 430	10.55 433	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	94.95 81	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

testmvs		7.23 431	9.62 434	0.06 446	0.04 468	0.02 471	84.98 341	0.02 469	0.03 463	0.18 464	1.21 463	0.01 469	0.02 464	0.14 463	0.01 462	0.13 461

test123		6.92 432	9.21 435	0.08 445	0.03 469	0.05 470	81.65 374	0.01 470	0.02 464	0.14 465	0.85 464	0.03 468	0.02 464	0.12 464	0.00 463	0.16 460

pcd_1.5k_mvsjas		4.46 433	5.95 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	53.55 244	0.00 466	0.00 465	0.00 463	0.00 462

mmdepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

monomultidepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

test_blank		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

uanet_test		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

DCPMVS		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

sosnet-low-res		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

sosnet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

uncertanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

Regformer		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

uanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 463	0.00 462

WAC-MVS						49.45 406								31.56 439

FOURS1					93.95 46	61.77 278	93.96 82	91.92 161	62.14 360	86.57 56

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 36	94.77 26	96.51 24

PC_three_1452									80.91 63	94.07 2	96.83 23	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 12		95.05 29					99.07 13	92.01 36	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 51		94.18 62	71.42 258	90.67 24	96.85 21	74.45 20

eth-test2					0.00 470

eth-test					0.00 470

ZD-MVS					96.63 9	65.50 167		93.50 89	70.74 272	85.26 74	95.19 77	64.92 90	97.29 83	87.51 68	93.01 56

RE-MVS-def			80.48 167		92.02 105	58.56 345	90.90 231	90.45 233	62.76 353	78.89 146	94.46 95	49.30 291		78.77 162	86.77 142	92.28 210

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 64	95.28 1				92.34 33	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 13	83.82 2	99.15 2	95.72 8	97.63 3	97.62 2

test_241102_TWO								94.41 53	71.65 247	92.07 10	97.21 8	74.58 18	99.11 6	92.34 33	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 57		94.44 51	71.65 247	92.11 8	97.05 11	76.79 9	99.11 6

9.14			87.63 34		93.86 48		94.41 60	94.18 62	72.76 212	86.21 59	96.51 30	66.64 68	97.88 47	90.08 50	94.04 39

save fliter					93.84 49	67.89 97	95.05 40	92.66 127	78.19 114

test_0728_THIRD									72.48 217	90.55 25	96.93 15	76.24 11	99.08 11	91.53 41	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 39	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 59	71.92 233	91.89 13	97.11 10	73.77 23

GSMVS																94.68 108

test_part2					96.29 19	68.16 90				90.78 22

sam_mvs1												57.85 188				94.68 108

sam_mvs												54.91 227

ambc				69.61 398	61.38 445	41.35 436	49.07 452	85.86 369		50.18 414	66.40 426	10.16 449	88.14 379	45.73 391	44.20 423	79.32 407

MTGPA								92.23 142

test_post1							78.95 394				20.70 459	53.05 249	91.50 344	60.43 324

test_post											23.01 456	56.49 209	92.67 304

patchmatchnet-post											67.62 425	57.62 191	90.25 354

GG-mvs-BLEND				86.53 75	91.91 114	69.67 54	75.02 413	94.75 36		78.67 154	90.85 186	77.91 7	94.56 233	72.25 215	93.74 45	95.36 68

MTMP							93.77 96	32.52 465

gm-plane-assit					88.42 209	67.04 121			78.62 108		91.83 169		97.37 77	76.57 174

test9_res														89.41 51	94.96 19	95.29 74

TEST9					94.18 41	67.28 112	94.16 69	93.51 87	71.75 244	85.52 69	95.33 65	68.01 57	97.27 87

test_8					94.19 40	67.19 114	94.15 71	93.42 94	71.87 238	85.38 72	95.35 64	68.19 55	96.95 114

agg_prior2														86.41 81	94.75 30	95.33 70

agg_prior					94.16 43	66.97 127		93.31 97		84.49 80			96.75 126

TestCases				72.46 384	79.57 369	51.42 394		68.61 436	51.25 412	45.88 424	81.23 338	19.86 436	86.58 394	38.98 416	57.01 395	79.39 405

test_prior4						67.18 116	93.92 85

test_prior2							95.10 39		75.40 163	85.25 75	95.61 56	67.94 58		87.47 70	94.77 26

test_prior				86.42 78	94.71 35	67.35 111		93.10 108					96.84 123			95.05 88

旧先验2							92.00 183		59.37 382	87.54 49			93.47 279	75.39 183

新几何2							91.41 204

新几何1				84.73 148	92.32 93	64.28 204		91.46 188	59.56 381	79.77 134	92.90 139	56.95 201	96.57 132	63.40 304	92.91 58	93.34 172

旧先验1					91.94 110	60.74 304		91.50 186			94.36 99	65.23 85			91.84 73	94.55 115

无先验							92.71 145	92.61 132	62.03 361				97.01 104	66.63 275		93.97 150

原ACMM2							92.01 180

原ACMM1				84.42 165	93.21 68	64.27 205		93.40 96	65.39 328	79.51 138	92.50 147	58.11 187	96.69 128	65.27 294	93.96 40	92.32 208

test222					89.77 163	61.60 284	89.55 277	89.42 282	56.83 396	77.28 168	92.43 151	52.76 252			91.14 89	93.09 182

testdata2													96.09 157	61.26 320

segment_acmp												65.94 76

testdata				81.34 263	89.02 185	57.72 352		89.84 265	58.65 385	85.32 73	94.09 115	57.03 196	93.28 281	69.34 244	90.56 95	93.03 185

testdata1							89.21 287		77.55 129

test12				87.09 52	94.60 36	68.86 69		92.91 115		82.67 103		65.44 82	97.55 67		93.69 48	94.84 99

plane_prior7					86.94 254	61.51 285

plane_prior6					87.23 246	62.32 264						50.66 274

plane_prior5								91.31 192					95.55 189	76.74 172	78.53 239	88.39 282

plane_prior4											89.14 225

plane_prior3						61.95 273			79.09 97	72.53 229

plane_prior2							93.13 124		78.81 104

plane_prior1					87.15 248

plane_prior						62.42 260	93.85 89		79.38 89						78.80 236

n2								0.00 471

nn								0.00 471

door-mid								66.01 440

lessismore_v0				73.72 375	72.93 419	47.83 414		61.72 446		45.86 426	73.76 400	28.63 413	89.81 364	47.75 383	31.37 445	83.53 360

LGP-MVS_train				79.56 313	84.31 315	59.37 334		89.73 271	69.49 285	64.86 325	88.42 234	38.65 359	94.30 243	72.56 212	72.76 283	85.01 346

test11								93.01 111

door								66.57 439

HQP5-MVS						63.66 227

HQP-NCC					87.54 238		94.06 74		79.80 79	74.18 203

ACMP_Plane					87.54 238		94.06 74		79.80 79	74.18 203

BP-MVS														77.63 169

HQP4-MVS										74.18 203			95.61 183			88.63 276

HQP3-MVS								91.70 178							78.90 234

HQP2-MVS												51.63 264

NP-MVS					87.41 241	63.04 244					90.30 197

MDTV_nov1_ep13_2view						59.90 326	80.13 389		67.65 311	72.79 223		54.33 235		59.83 328		92.58 199

MDTV_nov1_ep13			72.61 300		89.06 184	68.48 78	80.33 385	90.11 254	71.84 240	71.81 243	75.92 393	53.01 250	93.92 266	48.04 377	73.38 278

ACMMP++_ref															71.63 291

ACMMP++															69.72 300

Test By Simon												54.21 238

ITE_SJBPF				70.43 396	74.44 413	47.06 420		77.32 409	60.16 377	54.04 395	83.53 304	23.30 426	84.01 408	43.07 400	61.58 375	80.21 402

DeepMVS_CX				34.71 440	51.45 452	24.73 460		28.48 466	31.46 446	17.49 456	52.75 442	5.80 457	42.60 461	18.18 449	19.42 454	36.81 453

High-resolution multi-view benchmark