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.

APDe-MVS		89.15 8	89.63 7	87.73 31	94.49 23	71.69 55	93.83 4	93.96 18	75.70 119	91.06 19	96.03 1	76.84 19	97.03 21	89.09 21	95.65 31	94.47 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 36	73.73 9	92.40 29	93.63 27	74.77 153	92.29 7	95.97 2	74.28 35	97.24 15	88.58 34	96.91 1	94.87 21
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
MED-MVS		89.78 3	90.41 3	87.89 24	94.57 18	71.43 61	93.28 12	94.36 3	77.30 62	92.25 9	95.87 3	81.59 7	97.39 11	88.15 40	96.28 16	94.85 24

TestfortrainingZip a		88.83 13	89.21 11	87.68 37	94.57 18	71.25 66	93.28 12	93.91 20	77.30 62	91.13 18	95.87 3	77.62 17	96.95 23	86.12 58	93.07 76	94.85 24

test0726					95.27 5	71.25 66	93.60 7	94.11 11	77.33 60	92.81 3	95.79 5	80.98 10

DVP-MVS		89.60 4	90.35 4	87.33 45	95.27 5	71.25 66	93.49 10	92.73 72	77.33 60	92.12 11	95.78 6	80.98 10	97.40 9	89.08 22	96.41 12	93.33 130
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
test_0728_THIRD									78.38 39	92.12 11	95.78 6	81.46 8	97.40 9	89.42 19	96.57 7	94.67 42

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 32	71.25 66	95.06 1	94.23 6	78.38 39	92.78 4	95.74 8	82.45 3	97.49 4	89.42 19	96.68 2	94.95 15

test_one_0601					95.07 7	71.46 60		94.14 10	78.27 42	92.05 13	95.74 8	80.83 12

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 74	93.57 8	94.06 15	77.24 65	93.10 1	95.72 10	82.99 1	97.44 7	89.07 25	96.63 4	94.88 19

test_241102_TWO								94.06 15	77.24 65	92.78 4	95.72 10	81.26 9	97.44 7	89.07 25	96.58 6	94.26 73

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 112	92.29 7	95.66 12	81.67 6	97.38 13	87.44 49	96.34 15	93.95 89
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
reproduce_model		87.28 35	87.39 33	86.95 55	93.10 63	71.24 71	91.60 50	93.19 42	74.69 154	88.80 35	95.61 13	70.29 85	96.44 45	86.20 57	93.08 75	93.16 142

reproduce-ours		87.47 27	87.61 27	87.07 51	93.27 55	71.60 56	91.56 54	93.19 42	74.98 144	88.96 31	95.54 14	71.20 74	96.54 42	86.28 55	93.49 71	93.06 150

our_new_method		87.47 27	87.61 27	87.07 51	93.27 55	71.60 56	91.56 54	93.19 42	74.98 144	88.96 31	95.54 14	71.20 74	96.54 42	86.28 55	93.49 71	93.06 150

lecture		88.09 17	88.59 16	86.58 63	93.26 57	69.77 98	93.70 6	94.16 8	77.13 70	89.76 27	95.52 16	72.26 56	96.27 50	86.87 51	94.65 52	93.70 105

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 83	89.48 141	67.88 156	88.59 148	89.05 243	80.19 13	90.70 20	95.40 17	74.56 30	93.92 156	91.54 2	92.07 93	95.31 6

test_241102_ONE					95.30 2	70.98 74		94.06 15	77.17 68	93.10 1	95.39 18	82.99 1	97.27 14

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 49	72.04 51	89.80 90	93.50 31	75.17 139	86.34 70	95.29 19	70.86 78	96.00 61	88.78 31	96.04 18	94.58 51
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 15	88.74 15	87.64 39	92.78 72	71.95 52	92.40 29	94.74 2	75.71 117	89.16 30	95.10 20	75.65 26	96.19 53	87.07 50	96.01 19	94.79 28

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 46	73.05 22	90.86 65	93.59 29	76.27 105	88.14 43	95.09 21	71.06 76	96.67 34	87.67 45	96.37 14	94.09 81

MED-MVS test				87.86 27	94.57 18	71.43 61	93.28 12	94.36 3	75.24 131	92.25 9	95.03 22		97.39 11	88.15 40	95.96 21	94.75 35

ME-MVS		88.98 11	89.39 8	87.75 30	94.54 21	71.43 61	91.61 49	94.25 5	76.30 104	90.62 22	95.03 22	78.06 16	97.07 20	88.15 40	95.96 21	94.75 35

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 214	87.08 267	65.21 230	89.09 124	90.21 190	79.67 20	89.98 25	95.02 24	73.17 44	91.71 277	91.30 3	91.60 101	92.34 184

MM		89.16 7	89.23 9	88.97 4	90.79 104	73.65 10	92.66 28	91.17 156	86.57 1	87.39 59	94.97 25	71.70 66	97.68 1	92.19 1	95.63 32	95.57 2

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 125	87.76 226	65.62 217	89.20 115	92.21 106	79.94 18	89.74 28	94.86 26	68.63 118	94.20 141	90.83 5	91.39 107	94.38 64

MTAPA		87.23 36	87.00 39	87.90 22	94.18 40	74.25 5	86.58 232	92.02 115	79.45 23	85.88 72	94.80 27	68.07 126	96.21 52	86.69 53	95.34 36	93.23 134

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 80	72.96 25	93.73 5	93.67 26	80.19 13	88.10 44	94.80 27	73.76 39	97.11 18	87.51 47	95.82 25	94.90 18
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 98	87.33 252	67.30 178	89.50 101	90.98 161	76.25 106	90.56 23	94.75 29	68.38 121	94.24 140	90.80 7	92.32 90	94.19 75

9.14			88.26 19		92.84 71		91.52 56	94.75 1	73.93 177	88.57 37	94.67 30	75.57 27	95.79 65	86.77 52	95.76 27

SR-MVS		86.73 43	86.67 48	86.91 56	94.11 42	72.11 49	92.37 33	92.56 83	74.50 158	86.84 66	94.65 31	67.31 134	95.77 66	84.80 69	92.85 79	92.84 164

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 47	76.73 85	84.45 97	94.52 32	69.09 109	96.70 32	84.37 75	94.83 49	94.03 84

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 41	76.78 82	84.66 92	94.52 32	68.81 115	96.65 36	84.53 73	94.90 45	94.00 86

APD-MVS		87.44 29	87.52 30	87.19 48	94.24 37	72.39 41	91.86 45	92.83 67	73.01 206	88.58 36	94.52 32	73.36 40	96.49 44	84.26 76	95.01 41	92.70 166
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 62	85.88 66	86.22 69	92.69 74	69.53 101	91.93 42	92.99 56	73.54 188	85.94 71	94.51 35	65.80 160	95.61 69	83.04 90	92.51 84	93.53 121

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 39	73.86 7	93.98 3	92.82 70	76.62 88	83.68 116	94.46 36	67.93 127	95.95 64	84.20 79	94.39 61	93.23 134

SR-MVS-dyc-post		85.77 68	85.61 73	86.23 68	93.06 65	70.63 84	91.88 43	92.27 97	73.53 189	85.69 75	94.45 37	65.00 169	95.56 71	82.75 96	91.87 97	92.50 177

RE-MVS-def			85.48 76		93.06 65	70.63 84	91.88 43	92.27 97	73.53 189	85.69 75	94.45 37	63.87 179		82.75 96	91.87 97	92.50 177

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 40	76.78 82	84.91 84	94.44 39	70.78 79	96.61 38	84.53 73	94.89 46	93.66 107

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 38	73.38 18	90.22 81	93.04 48	75.53 122	83.86 112	94.42 40	67.87 129	96.64 37	82.70 100	94.57 56	93.66 107

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 31	73.88 6	92.71 27	92.65 78	77.57 51	83.84 113	94.40 41	72.24 57	96.28 49	85.65 60	95.30 39	93.62 114
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_l_conf0.5_n_3		86.02 58	86.32 53	85.14 101	87.20 258	68.54 132	89.57 99	90.44 179	75.31 130	87.49 56	94.39 42	72.86 49	92.72 232	89.04 27	90.56 124	94.16 76

fmvsm_s_conf0.1_n_2		83.80 109	83.79 108	83.83 184	85.62 303	64.94 243	87.03 211	86.62 325	74.32 164	87.97 49	94.33 43	60.67 239	92.60 235	89.72 14	87.79 182	93.96 87

fmvsm_l_conf0.5_n_9		85.84 67	86.63 49	83.46 195	87.12 266	66.01 203	88.56 150	89.43 217	75.59 121	89.32 29	94.32 44	72.89 48	91.21 306	90.11 11	92.33 88	93.16 142

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 29	73.33 19	93.03 19	93.81 23	76.81 80	85.24 79	94.32 44	71.76 64	96.93 24	85.53 62	95.79 26	94.32 69

MGCNet		87.69 24	87.55 29	88.12 13	89.45 142	71.76 54	91.47 57	89.54 213	82.14 3	86.65 68	94.28 46	68.28 124	97.46 6	90.81 6	95.31 38	95.15 9

test_fmvsmconf0.01_n		84.73 91	84.52 93	85.34 95	80.25 423	69.03 112	89.47 102	89.65 209	73.24 200	86.98 64	94.27 47	66.62 143	93.23 202	90.26 10	89.95 136	93.78 102

HPM-MVS++		89.02 10	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 66	80.26 12	87.78 50	94.27 47	75.89 24	96.81 28	87.45 48	96.44 9	93.05 152

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 27	73.55 13	92.74 25	92.22 104	76.87 79	82.81 140	94.25 49	66.44 147	96.24 51	82.88 93	94.28 64	93.38 126

fmvsm_s_conf0.5_n_2		84.04 101	84.11 101	83.81 186	86.17 291	65.00 238	86.96 214	87.28 301	74.35 163	88.25 41	94.23 50	61.82 215	92.60 235	89.85 12	88.09 175	93.84 96

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 93	72.32 45	90.31 79	93.94 19	77.12 71	82.82 139	94.23 50	72.13 60	97.09 19	84.83 68	95.37 35	93.65 111
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_11		86.06 57	86.75 47	84.00 177	87.78 223	66.09 200	89.96 86	90.80 169	77.37 59	86.72 67	94.20 52	72.51 54	92.78 231	89.08 22	92.33 88	93.13 146

XVS		87.18 37	86.91 44	88.00 17	94.42 25	73.33 19	92.78 23	92.99 56	79.14 27	83.67 117	94.17 53	67.45 132	96.60 39	83.06 88	94.50 57	94.07 82

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 35	73.46 17	92.90 21	94.11 11	80.27 11	91.35 16	94.16 54	78.35 15	96.77 29	89.59 17	94.22 66	94.67 42
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
test_fmvsmconf0.1_n		85.61 72	85.65 72	85.50 90	82.99 379	69.39 109	89.65 95	90.29 188	73.31 196	87.77 51	94.15 55	71.72 65	93.23 202	90.31 9	90.67 122	93.89 93

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 60	92.24 79	69.03 112	89.57 99	93.39 36	77.53 55	89.79 26	94.12 56	78.98 13	96.58 41	85.66 59	95.72 28	94.58 51

HPM-MVS_fast		85.35 80	84.95 87	86.57 64	93.69 47	70.58 86	92.15 40	91.62 140	73.89 178	82.67 143	94.09 57	62.60 198	95.54 73	80.93 115	92.93 78	93.57 117

ZD-MVS					94.38 30	72.22 46		92.67 75	70.98 248	87.75 52	94.07 58	74.01 38	96.70 32	84.66 71	94.84 48

fmvsm_s_conf0.1_n_a		83.32 130	82.99 127	84.28 151	83.79 348	68.07 147	89.34 112	82.85 385	69.80 283	87.36 60	94.06 59	68.34 123	91.56 284	87.95 43	83.46 272	93.21 137

CNVR-MVS		88.93 12	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 53	80.90 7	88.06 45	94.06 59	76.43 21	96.84 26	88.48 37	95.99 20	94.34 67

test_fmvsmconf_n		85.92 63	86.04 63	85.57 89	85.03 322	69.51 102	89.62 98	90.58 174	73.42 192	87.75 52	94.02 61	72.85 50	93.24 201	90.37 8	90.75 120	93.96 87

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 25	83.77 83	96.48 8	94.88 19

PC_three_1452									68.21 323	92.02 14	94.00 63	82.09 5	95.98 63	84.58 72	96.68 2	94.95 15

SD-MVS		88.06 18	88.50 18	86.71 61	92.60 77	72.71 29	91.81 46	93.19 42	77.87 44	90.32 24	94.00 63	74.83 28	93.78 164	87.63 46	94.27 65	93.65 111
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
GST-MVS		87.42 31	87.26 34	87.89 24	94.12 41	72.97 24	92.39 31	93.43 34	76.89 78	84.68 89	93.99 65	70.67 81	96.82 27	84.18 80	95.01 41	93.90 92

test_fmvsm_n_1920		85.29 81	85.34 78	85.13 104	86.12 293	69.93 94	88.65 146	90.78 170	69.97 279	88.27 40	93.98 66	71.39 71	91.54 288	88.49 36	90.45 126	93.91 90

fmvsm_s_conf0.1_n		83.56 121	83.38 119	84.10 160	84.86 324	67.28 179	89.40 109	83.01 380	70.67 256	87.08 62	93.96 67	68.38 121	91.45 295	88.56 35	84.50 246	93.56 118

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 44	72.16 47	92.19 38	93.33 37	76.07 109	83.81 114	93.95 68	69.77 96	96.01 60	85.15 63	94.66 51	94.32 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_7		83.34 128	84.03 102	81.28 277	85.73 300	65.13 233	85.40 273	89.90 200	74.96 146	82.13 149	93.89 69	66.65 142	87.92 381	86.56 54	91.05 113	90.80 240

fmvsm_s_conf0.5_n_5		85.22 82	85.55 74	84.25 156	86.26 287	67.40 174	89.18 116	89.31 226	72.50 212	88.31 39	93.86 70	69.66 97	91.96 264	89.81 13	91.05 113	93.38 126

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 48	72.13 48	91.41 58	92.35 91	74.62 157	88.90 34	93.85 71	75.75 25	96.00 61	87.80 44	94.63 54	95.04 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		85.89 66	85.39 77	87.38 44	93.59 50	72.63 33	92.74 25	93.18 46	76.78 82	80.73 180	93.82 72	64.33 175	96.29 48	82.67 101	90.69 121	93.23 134
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
fmvsm_s_conf0.5_n_a		83.63 118	83.41 118	84.28 151	86.14 292	68.12 145	89.43 105	82.87 384	70.27 272	87.27 61	93.80 73	69.09 109	91.58 281	88.21 39	83.65 266	93.14 145

fmvsm_s_conf0.5_n_4		85.39 78	85.75 71	84.30 149	86.70 278	65.83 210	88.77 137	89.78 202	75.46 125	88.35 38	93.73 74	69.19 108	93.06 217	91.30 3	88.44 165	94.02 85

fmvsm_s_conf0.5_n		83.80 109	83.71 110	84.07 166	86.69 279	67.31 177	89.46 103	83.07 379	71.09 243	86.96 65	93.70 75	69.02 114	91.47 294	88.79 30	84.62 245	93.44 125

test_prior2							88.85 133		75.41 126	84.91 84	93.54 76	74.28 35		83.31 86	95.86 24

TestfortrainingZip				87.28 46	92.85 69	72.05 50	93.28 12	93.32 38	76.52 90	88.91 33	93.52 77	77.30 18	96.67 34		91.98 95	93.13 146

fmvsm_l_conf0.5_n		84.47 92	84.54 91	84.27 153	85.42 309	68.81 118	88.49 153	87.26 306	68.08 324	88.03 46	93.49 78	72.04 61	91.77 273	88.90 29	89.14 152	92.24 191

VDDNet		81.52 171	80.67 172	84.05 172	90.44 110	64.13 267	89.73 93	85.91 336	71.11 242	83.18 130	93.48 79	50.54 352	93.49 186	73.40 218	88.25 171	94.54 57

CDPH-MVS		85.76 69	85.29 82	87.17 49	93.49 52	71.08 72	88.58 149	92.42 88	68.32 322	84.61 94	93.48 79	72.32 55	96.15 55	79.00 148	95.43 34	94.28 72

NCCC		88.06 18	88.01 22	88.24 11	94.41 27	73.62 11	91.22 62	92.83 67	81.50 5	85.79 74	93.47 81	73.02 47	97.00 22	84.90 65	94.94 44	94.10 80

fmvsm_s_conf0.5_n_6		85.55 73	86.20 56	83.60 190	87.32 254	65.13 233	88.86 131	91.63 139	75.41 126	88.23 42	93.45 82	68.56 119	92.47 243	89.52 18	92.78 80	93.20 139

fmvsm_l_conf0.5_n_a		84.13 99	84.16 96	84.06 169	85.38 310	68.40 135	88.34 161	86.85 318	67.48 331	87.48 57	93.40 83	70.89 77	91.61 279	88.38 38	89.22 149	92.16 198

3Dnovator+	77.84 4	85.48 74	84.47 94	88.51 7	91.08 95	73.49 16	93.18 16	93.78 24	80.79 8	76.66 265	93.37 84	60.40 247	96.75 31	77.20 170	93.73 70	95.29 7

DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 51	72.37 43	91.26 59	93.04 48	76.62 88	84.22 104	93.36 85	71.44 70	96.76 30	80.82 117	95.33 37	94.16 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 138	82.36 140	84.96 111	91.02 97	66.40 195	88.91 129	88.11 275	77.57 51	84.39 99	93.29 86	52.19 320	93.91 157	77.05 173	88.70 160	94.57 53

test_fmvsmvis_n_1920		84.02 102	83.87 104	84.49 136	84.12 340	69.37 110	88.15 170	87.96 283	70.01 277	83.95 111	93.23 87	68.80 116	91.51 291	88.61 32	89.96 135	92.57 171

UA-Net		85.08 86	84.96 86	85.45 91	92.07 81	68.07 147	89.78 91	90.86 167	82.48 2	84.60 95	93.20 88	69.35 101	95.22 91	71.39 243	90.88 119	93.07 149

TEST9					93.26 57	72.96 25	88.75 139	91.89 123	68.44 320	85.00 82	93.10 89	74.36 34	95.41 83

train_agg		86.43 49	86.20 56	87.13 50	93.26 57	72.96 25	88.75 139	91.89 123	68.69 315	85.00 82	93.10 89	74.43 32	95.41 83	84.97 64	95.71 29	93.02 154

test_8					93.13 61	72.57 35	88.68 145	91.84 127	68.69 315	84.87 86	93.10 89	74.43 32	95.16 93

LFMVS		81.82 161	81.23 161	83.57 193	91.89 84	63.43 290	89.84 87	81.85 398	77.04 74	83.21 127	93.10 89	52.26 319	93.43 193	71.98 238	89.95 136	93.85 94

旧先验1					91.96 82	65.79 213		86.37 329			93.08 93	69.31 103			92.74 81	88.74 330

dcpmvs_2		85.63 71	86.15 60	84.06 169	91.71 86	64.94 243	86.47 236	91.87 125	73.63 184	86.60 69	93.02 94	76.57 20	91.87 271	83.36 85	92.15 91	95.35 4

testdata				79.97 314	90.90 100	64.21 265		84.71 350	59.27 435	85.40 77	92.91 95	62.02 212	89.08 362	68.95 272	91.37 108	86.63 395

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 22	72.46 40	89.82 88	93.82 22	73.07 204	84.86 87	92.89 96	76.22 22	96.33 47	84.89 67	95.13 40	94.40 63

Vis-MVSNet		83.46 124	82.80 131	85.43 92	90.25 114	68.74 123	90.30 80	90.13 193	76.33 103	80.87 177	92.89 96	61.00 234	94.20 141	72.45 235	90.97 115	93.35 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 113	83.33 121	84.92 115	93.28 54	70.86 80	92.09 41	90.38 181	68.75 314	79.57 198	92.83 98	60.60 243	93.04 220	80.92 116	91.56 104	90.86 239

3Dnovator	76.31 5	83.38 127	82.31 141	86.59 62	87.94 213	72.94 28	90.64 68	92.14 114	77.21 67	75.47 291	92.83 98	58.56 259	94.72 120	73.24 221	92.71 82	92.13 199

MSLP-MVS++		85.43 76	85.76 70	84.45 137	91.93 83	70.24 87	90.71 67	92.86 65	77.46 57	84.22 104	92.81 100	67.16 136	92.94 222	80.36 124	94.35 63	90.16 269

test2506		77.30 288	76.49 284	79.74 324	90.08 118	52.02 451	87.86 182	63.10 495	74.88 149	80.16 192	92.79 101	38.29 456	92.35 250	68.74 275	92.50 85	94.86 22

ECVR-MVS		79.61 222	79.26 215	80.67 294	90.08 118	54.69 432	87.89 180	77.44 447	74.88 149	80.27 189	92.79 101	48.96 377	92.45 244	68.55 276	92.50 85	94.86 22

Casviewmamba		86.09 56	86.04 63	86.24 67	88.17 199	68.05 149	89.44 104	92.79 71	80.30 10	84.71 88	92.78 103	72.83 51	95.05 102	82.81 94	90.57 123	95.62 1

test1111		79.43 229	79.18 218	80.15 309	89.99 123	53.31 445	87.33 203	77.05 451	75.04 142	80.23 191	92.77 104	48.97 376	92.33 252	68.87 273	92.40 87	94.81 27

MG-MVS		83.41 125	83.45 117	83.28 203	92.74 73	62.28 318	88.17 168	89.50 215	75.22 133	81.49 161	92.74 105	66.75 141	95.11 97	72.85 225	91.58 103	92.45 181

casdiffmvs_mvg		85.99 60	86.09 62	85.70 83	87.65 234	67.22 183	88.69 144	93.04 48	79.64 22	85.33 78	92.54 106	73.30 41	94.50 129	83.49 84	91.14 112	95.37 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
hybridcas		85.11 84	85.18 83	84.90 117	87.47 246	65.68 215	88.53 152	92.38 89	77.91 43	84.27 103	92.48 107	72.19 58	93.88 161	80.37 123	90.97 115	95.15 9

patch_mono-2		83.65 116	84.54 91	80.99 286	90.06 122	65.83 210	84.21 310	88.74 262	71.60 231	85.01 81	92.44 108	74.51 31	83.50 428	82.15 103	92.15 91	93.64 113

casdiffmvs		85.11 84	85.14 84	85.01 109	87.20 258	65.77 214	87.75 184	92.83 67	77.84 45	84.36 102	92.38 109	72.15 59	93.93 155	81.27 112	90.48 125	95.33 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS		86.69 44	86.95 42	85.90 80	90.76 105	67.57 167	92.83 22	93.30 39	79.67 20	84.57 96	92.27 110	71.47 69	95.02 104	84.24 78	93.46 73	95.13 11

E5new		84.22 94	84.12 97	84.51 132	87.60 236	65.36 225	87.45 194	92.31 93	76.51 91	83.53 120	92.26 111	69.25 106	93.50 184	79.88 132	88.26 167	94.69 37

E6new		84.22 94	84.12 97	84.52 130	87.60 236	65.36 225	87.45 194	92.30 95	76.51 91	83.53 120	92.26 111	69.26 104	93.49 186	79.88 132	88.26 167	94.69 37

E6		84.22 94	84.12 97	84.52 130	87.60 236	65.36 225	87.45 194	92.30 95	76.51 91	83.53 120	92.26 111	69.26 104	93.49 186	79.88 132	88.26 167	94.69 37

E5		84.22 94	84.12 97	84.51 132	87.60 236	65.36 225	87.45 194	92.31 93	76.51 91	83.53 120	92.26 111	69.25 106	93.50 184	79.88 132	88.26 167	94.69 37

baseline		84.93 88	84.98 85	84.80 122	87.30 256	65.39 223	87.30 204	92.88 64	77.62 49	84.04 109	92.26 111	71.81 63	93.96 149	81.31 110	90.30 128	95.03 13

NormalMVS		86.29 54	85.88 66	87.52 41	93.26 57	72.47 38	91.65 47	92.19 109	79.31 25	84.39 99	92.18 116	64.64 172	95.53 74	80.70 120	94.65 52	94.56 55

SymmetryMVS		85.38 79	84.81 88	87.07 51	91.47 89	72.47 38	91.65 47	88.06 279	79.31 25	84.39 99	92.18 116	64.64 172	95.53 74	80.70 120	90.91 118	93.21 137

QAPM		80.88 184	79.50 207	85.03 107	88.01 211	68.97 116	91.59 51	92.00 117	66.63 345	75.15 309	92.16 118	57.70 266	95.45 78	63.52 315	88.76 158	90.66 248

IS-MVSNet		83.15 133	82.81 130	84.18 158	89.94 125	63.30 292	91.59 51	88.46 272	79.04 31	79.49 199	92.16 118	65.10 166	94.28 135	67.71 282	91.86 99	94.95 15

viewmacassd2359aftdt		83.76 112	83.66 112	84.07 166	86.59 282	64.56 253	86.88 219	91.82 128	75.72 116	83.34 126	92.15 120	68.24 125	92.88 225	79.05 144	89.15 151	94.77 30

BP-MVS1		84.32 93	83.71 110	86.17 70	87.84 218	67.85 157	89.38 110	89.64 210	77.73 47	83.98 110	92.12 121	56.89 277	95.43 80	84.03 81	91.75 100	95.24 8

E4		84.10 100	83.99 103	84.45 137	87.58 244	64.99 239	86.54 234	92.25 100	76.38 100	83.37 125	92.09 122	69.88 94	93.58 173	79.78 137	88.03 178	94.77 30

新几何1				83.42 198	93.13 61	70.71 82		85.48 342	57.43 454	81.80 155	91.98 123	63.28 183	92.27 253	64.60 310	92.99 77	87.27 373

OpenMVS	72.83 10	79.77 220	78.33 236	84.09 164	85.17 315	69.91 95	90.57 69	90.97 162	66.70 339	72.17 356	91.91 124	54.70 296	93.96 149	61.81 347	90.95 117	88.41 339

PHI-MVS		86.43 49	86.17 59	87.24 47	90.88 101	70.96 76	92.27 37	94.07 14	72.45 213	85.22 80	91.90 125	69.47 99	96.42 46	83.28 87	95.94 23	94.35 66

VNet		82.21 151	82.41 138	81.62 266	90.82 102	60.93 343	84.47 299	89.78 202	76.36 102	84.07 108	91.88 126	64.71 171	90.26 338	70.68 251	88.89 154	93.66 107

EC-MVSNet		86.01 59	86.38 52	84.91 116	89.31 151	66.27 198	92.32 35	93.63 27	79.37 24	84.17 106	91.88 126	69.04 113	95.43 80	83.93 82	93.77 69	93.01 155

GDP-MVS		83.52 122	82.64 134	86.16 71	88.14 202	68.45 134	89.13 122	92.69 73	72.82 210	83.71 115	91.86 128	55.69 286	95.35 89	80.03 129	89.74 140	94.69 37

KinetiMVS		83.31 131	82.61 135	85.39 94	87.08 267	67.56 168	88.06 172	91.65 138	77.80 46	82.21 148	91.79 129	57.27 272	94.07 147	77.77 163	89.89 138	94.56 55

E2		84.00 103	83.87 104	84.39 140	87.70 231	64.95 240	86.40 241	92.23 101	75.85 113	83.21 127	91.78 130	70.09 89	93.55 178	79.52 141	88.05 176	94.66 45

E3		84.00 103	83.87 104	84.39 140	87.70 231	64.95 240	86.40 241	92.23 101	75.85 113	83.21 127	91.78 130	70.09 89	93.55 178	79.52 141	88.05 176	94.66 45

OPM-MVS		83.50 123	82.95 128	85.14 101	88.79 176	70.95 77	89.13 122	91.52 144	77.55 54	80.96 174	91.75 132	60.71 237	94.50 129	79.67 139	86.51 208	89.97 285
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
casdiffseed414692147		83.62 119	83.02 125	85.40 93	87.31 255	67.50 170	88.70 143	91.72 134	76.97 75	82.77 141	91.72 133	66.85 140	93.71 171	73.06 223	88.12 174	94.98 14

MVSMamba_PlusPlus		85.99 60	85.96 65	86.05 75	91.09 94	67.64 164	89.63 97	92.65 78	72.89 209	84.64 93	91.71 134	71.85 62	96.03 57	84.77 70	94.45 60	94.49 59

viewmanbaseed2359cas		83.66 115	83.55 115	84.00 177	86.81 274	64.53 254	86.65 229	91.75 133	74.89 148	83.15 132	91.68 135	68.74 117	92.83 229	79.02 146	89.24 148	94.63 48

XVG-OURS-SEG-HR		80.81 187	79.76 198	83.96 181	85.60 304	68.78 120	83.54 331	90.50 177	70.66 259	76.71 264	91.66 136	60.69 238	91.26 301	76.94 174	81.58 296	91.83 204

EPNet		83.72 114	82.92 129	86.14 74	84.22 338	69.48 103	91.05 64	85.27 343	81.30 6	76.83 260	91.65 137	66.09 154	95.56 71	76.00 189	93.85 68	93.38 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 142	81.97 153	84.85 119	88.75 178	67.42 172	87.98 174	90.87 166	74.92 147	79.72 196	91.65 137	62.19 208	93.96 149	75.26 200	86.42 209	93.16 142

viewdifsd2359ckpt07		82.83 141	82.78 133	82.99 221	86.51 284	62.58 309	85.09 281	90.83 168	75.22 133	82.28 145	91.63 139	69.43 100	92.03 260	77.71 164	86.32 211	94.34 67

BridgeMVS		86.78 42	86.99 40	86.15 72	91.24 92	67.61 165	90.51 70	92.90 63	77.26 64	87.44 58	91.63 139	71.27 73	96.06 56	85.62 61	95.01 41	94.78 29

test222					91.50 88	68.26 139	84.16 313	83.20 377	54.63 466	79.74 195	91.63 139	58.97 255			91.42 106	86.77 390

MVS_111021_HR		85.14 83	84.75 89	86.32 66	91.65 87	72.70 30	85.98 254	90.33 185	76.11 108	82.08 150	91.61 142	71.36 72	94.17 144	81.02 114	92.58 83	92.08 200

原ACMM1				84.35 144	93.01 67	68.79 119		92.44 85	63.96 387	81.09 169	91.57 143	66.06 155	95.45 78	67.19 289	94.82 50	88.81 325

viewcassd2359sk11		83.89 106	83.74 109	84.34 145	87.76 226	64.91 247	86.30 245	92.22 104	75.47 124	83.04 133	91.52 144	70.15 87	93.53 181	79.26 143	87.96 179	94.57 53

LPG-MVS_test		82.08 154	81.27 160	84.50 134	89.23 156	68.76 121	90.22 81	91.94 121	75.37 128	76.64 266	91.51 145	54.29 299	94.91 106	78.44 154	83.78 259	89.83 290

LGP-MVS_train				84.50 134	89.23 156	68.76 121		91.94 121	75.37 128	76.64 266	91.51 145	54.29 299	94.91 106	78.44 154	83.78 259	89.83 290

XVG-OURS		80.41 204	79.23 216	83.97 180	85.64 302	69.02 114	83.03 347	90.39 180	71.09 243	77.63 242	91.49 147	54.62 298	91.35 298	75.71 192	83.47 271	91.54 215

alignmvs		85.48 74	85.32 80	85.96 79	89.51 137	69.47 104	89.74 92	92.47 84	76.17 107	87.73 54	91.46 148	70.32 84	93.78 164	81.51 106	88.95 153	94.63 48

CANet		86.45 48	86.10 61	87.51 42	90.09 117	70.94 78	89.70 94	92.59 82	81.78 4	81.32 164	91.43 149	70.34 83	97.23 16	84.26 76	93.36 74	94.37 65

h-mvs33		83.15 133	82.19 144	86.02 78	90.56 107	70.85 81	88.15 170	89.16 237	76.02 110	84.67 90	91.39 150	61.54 220	95.50 76	82.71 98	75.48 379	91.72 211

MGCFI-Net		85.06 87	85.51 75	83.70 188	89.42 143	63.01 299	89.43 105	92.62 81	76.43 95	87.53 55	91.34 151	72.82 52	93.42 194	81.28 111	88.74 159	94.66 45

nrg030		83.88 107	83.53 116	84.96 111	86.77 276	69.28 111	90.46 75	92.67 75	74.79 152	82.95 134	91.33 152	72.70 53	93.09 215	80.79 119	79.28 328	92.50 177

E3new		83.78 111	83.60 114	84.31 147	87.76 226	64.89 248	86.24 248	92.20 107	75.15 140	82.87 136	91.23 153	70.11 88	93.52 183	79.05 144	87.79 182	94.51 58

sasdasda		85.91 64	85.87 68	86.04 76	89.84 127	69.44 107	90.45 76	93.00 53	76.70 86	88.01 47	91.23 153	73.28 42	93.91 157	81.50 107	88.80 156	94.77 30

canonicalmvs		85.91 64	85.87 68	86.04 76	89.84 127	69.44 107	90.45 76	93.00 53	76.70 86	88.01 47	91.23 153	73.28 42	93.91 157	81.50 107	88.80 156	94.77 30

DPM-MVS		84.93 88	84.29 95	86.84 57	90.20 115	73.04 23	87.12 208	93.04 48	69.80 283	82.85 138	91.22 156	73.06 46	96.02 59	76.72 182	94.63 54	91.46 221

Anonymous202405211		78.25 260	77.01 270	81.99 259	91.03 96	60.67 350	84.77 288	83.90 363	70.65 260	80.00 193	91.20 157	41.08 438	91.43 296	65.21 304	85.26 236	93.85 94

SPE-MVS-test		86.29 54	86.48 51	85.71 82	91.02 97	67.21 184	92.36 34	93.78 24	78.97 34	83.51 124	91.20 157	70.65 82	95.15 94	81.96 104	94.89 46	94.77 30

Anonymous20240529		80.19 214	78.89 224	84.10 160	90.60 106	64.75 251	88.95 128	90.90 164	65.97 354	80.59 183	91.17 159	49.97 359	93.73 170	69.16 270	82.70 284	93.81 98

viewmamba		82.38 147	82.11 145	83.19 209	83.30 361	64.26 264	84.62 295	89.16 237	75.24 131	80.97 173	91.10 160	67.12 137	91.63 278	81.36 109	86.13 217	93.67 106

EPP-MVSNet		83.40 126	83.02 125	84.57 128	90.13 116	64.47 259	92.32 35	90.73 171	74.45 161	79.35 204	91.10 160	69.05 112	95.12 95	72.78 226	87.22 193	94.13 78

TAPA-MVS	73.13 9	79.15 238	77.94 243	82.79 235	89.59 133	62.99 303	88.16 169	91.51 145	65.77 355	77.14 257	91.09 162	60.91 235	93.21 204	50.26 438	87.05 197	92.17 197
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 51	86.19 58	87.07 51	92.91 68	72.48 37	90.81 66	93.56 30	73.95 174	83.16 131	91.07 163	75.94 23	95.19 92	79.94 131	94.38 62	93.55 119

FIs		82.07 155	82.42 137	81.04 285	88.80 175	58.34 375	88.26 165	93.49 32	76.93 77	78.47 222	91.04 164	69.92 93	92.34 251	69.87 263	84.97 238	92.44 182

MVS_111021_LR		82.61 144	82.11 145	84.11 159	88.82 170	71.58 58	85.15 278	86.16 333	74.69 154	80.47 187	91.04 164	62.29 205	90.55 333	80.33 126	90.08 133	90.20 268

DP-MVS Recon		83.11 136	82.09 148	86.15 72	94.44 24	70.92 79	88.79 136	92.20 107	70.53 261	79.17 206	91.03 166	64.12 177	96.03 57	68.39 279	90.14 131	91.50 217

HQP_MVS		83.64 117	83.14 122	85.14 101	90.08 118	68.71 125	91.25 60	92.44 85	79.12 29	78.92 210	91.00 167	60.42 245	95.38 85	78.71 152	86.32 211	91.33 222

plane_prior4											91.00 167

FC-MVSNet-test		81.52 171	82.02 151	80.03 311	88.42 191	55.97 416	87.95 176	93.42 35	77.10 72	77.38 246	90.98 169	69.96 92	91.79 272	68.46 278	84.50 246	92.33 185

diffmvs_AUTHOR		82.38 147	82.27 143	82.73 240	83.26 363	63.80 274	83.89 318	89.76 204	73.35 195	82.37 144	90.84 170	66.25 150	90.79 325	82.77 95	87.93 180	93.59 116

Vis-MVSNet (Re-imp)		78.36 259	78.45 231	78.07 362	88.64 182	51.78 457	86.70 227	79.63 429	74.14 171	75.11 310	90.83 171	61.29 228	89.75 348	58.10 385	91.60 101	92.69 168

balanced_ft_v1		83.98 105	83.64 113	85.03 107	89.76 130	65.86 209	88.31 163	91.71 135	74.41 162	80.41 188	90.82 172	62.90 196	94.90 108	83.04 90	91.37 108	94.32 69

hybridnocas07		81.44 174	81.13 163	82.37 249	82.13 396	63.11 298	83.45 332	88.74 262	72.54 211	80.71 181	90.73 173	65.14 165	90.74 330	80.35 125	86.41 210	93.27 133

114514_t		80.68 195	79.51 206	84.20 157	94.09 43	67.27 180	89.64 96	91.11 159	58.75 442	74.08 328	90.72 174	58.10 262	95.04 103	69.70 264	89.42 146	90.30 265

viewdifsd2359ckpt13		82.91 139	82.29 142	84.77 123	86.96 270	66.90 191	87.47 191	91.62 140	72.19 218	81.68 158	90.71 175	66.92 139	93.28 197	75.90 190	87.15 195	94.12 79

viewdifsd2359ckpt09		83.34 128	82.55 136	85.70 83	87.64 235	67.72 162	88.43 154	91.68 137	71.91 225	81.65 159	90.68 176	67.10 138	94.75 118	76.17 185	87.70 185	94.62 50

PAPM_NR		83.02 137	82.41 138	84.82 120	92.47 78	66.37 196	87.93 178	91.80 129	73.82 179	77.32 248	90.66 177	67.90 128	94.90 108	70.37 254	89.48 145	93.19 140

onestephybrid01		82.22 150	81.81 156	83.46 195	83.16 369	64.93 246	84.64 294	89.19 236	73.95 174	81.48 162	90.63 178	66.00 158	91.92 268	80.33 126	86.93 199	93.53 121

viewdifsd2359ckpt11		80.37 208	79.73 199	82.30 251	83.70 352	62.39 313	84.20 311	86.67 321	73.22 201	80.90 175	90.62 179	63.00 194	91.56 284	76.81 179	78.44 335	92.95 159

viewmsd2359difaftdt		80.37 208	79.73 199	82.30 251	83.70 352	62.39 313	84.20 311	86.67 321	73.22 201	80.90 175	90.62 179	63.00 194	91.56 284	76.81 179	78.44 335	92.95 159

LS3D		76.95 294	74.82 313	83.37 201	90.45 109	67.36 176	89.15 121	86.94 315	61.87 414	69.52 386	90.61 181	51.71 335	94.53 127	46.38 460	86.71 205	88.21 345

AstraMVS		80.81 187	80.14 188	82.80 232	86.05 295	63.96 269	86.46 237	85.90 337	73.71 182	80.85 178	90.56 182	54.06 303	91.57 283	79.72 138	83.97 257	92.86 162

PRO-TEST		82.16 152	82.06 149	82.45 246	89.49 140	58.24 377	84.07 317	91.34 150	75.05 141	73.21 340	90.55 183	62.05 211	95.60 70	81.23 113	91.56 104	93.51 123

VPNet		78.69 251	78.66 227	78.76 345	88.31 194	55.72 420	84.45 302	86.63 324	76.79 81	78.26 226	90.55 183	59.30 253	89.70 350	66.63 293	77.05 352	90.88 238

UniMVSNet_ETH3D		79.10 240	78.24 238	81.70 265	86.85 272	60.24 358	87.28 205	88.79 255	74.25 168	76.84 259	90.53 185	49.48 366	91.56 284	67.98 280	82.15 288	93.29 131

dtuplus		80.04 216	79.40 209	81.97 260	83.08 371	62.61 308	83.63 327	87.98 281	67.47 332	81.02 171	90.50 186	64.86 170	90.77 328	71.28 245	84.76 242	92.53 174

ACMP	74.13 6	81.51 173	80.57 175	84.36 143	89.42 143	68.69 128	89.97 85	91.50 148	74.46 160	75.04 313	90.41 187	53.82 305	94.54 126	77.56 166	82.91 279	89.86 289
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
hybrid		81.05 181	80.66 173	82.22 253	81.97 398	62.99 303	83.42 333	88.68 265	70.76 254	80.56 184	90.40 188	64.49 174	90.48 334	79.57 140	86.06 219	93.19 140

SSM_0407		81.58 168	80.48 178	84.87 118	88.81 171	67.96 152	87.37 200	89.25 231	71.06 245	79.48 200	90.39 189	59.57 250	94.48 131	72.45 235	85.93 224	92.18 194

SSM_0404		81.91 158	80.84 170	85.13 104	89.24 155	68.26 139	87.84 183	89.25 231	71.06 245	80.62 182	90.39 189	59.57 250	94.65 124	72.45 235	87.19 194	92.47 180

viewmambaseed2359dif		80.41 204	79.84 196	82.12 254	82.95 381	62.50 312	83.39 334	88.06 279	67.11 334	80.98 172	90.31 191	66.20 152	91.01 315	74.62 204	84.90 239	92.86 162

RRT-MVS		82.60 146	82.10 147	84.10 160	87.98 212	62.94 305	87.45 194	91.27 152	77.42 58	79.85 194	90.28 192	56.62 280	94.70 122	79.87 136	88.15 173	94.67 42

PCF-MVS	73.52 7	80.38 206	78.84 225	85.01 109	87.71 229	68.99 115	83.65 324	91.46 149	63.00 396	77.77 240	90.28 192	66.10 153	95.09 101	61.40 352	88.22 172	90.94 237
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 132	68.32 137					90.24 194

HQP-MVS		82.61 144	82.02 151	84.37 142	89.33 148	66.98 187	89.17 117	92.19 109	76.41 96	77.23 251	90.23 195	60.17 248	95.11 97	77.47 167	85.99 222	91.03 232

PS-MVSNAJss		82.07 155	81.31 159	84.34 145	86.51 284	67.27 180	89.27 113	91.51 145	71.75 226	79.37 203	90.22 196	63.15 189	94.27 136	77.69 165	82.36 287	91.49 218

TSAR-MVS + GP.		85.71 70	85.33 79	86.84 57	91.34 90	72.50 36	89.07 125	87.28 301	76.41 96	85.80 73	90.22 196	74.15 37	95.37 88	81.82 105	91.88 96	92.65 170

SDMVSNet		80.38 206	80.18 185	80.99 286	89.03 165	64.94 243	80.45 387	89.40 218	75.19 137	76.61 268	89.98 198	60.61 242	87.69 385	76.83 178	83.55 268	90.33 263

sd_testset		77.70 279	77.40 263	78.60 348	89.03 165	60.02 360	79.00 409	85.83 338	75.19 137	76.61 268	89.98 198	54.81 291	85.46 410	62.63 333	83.55 268	90.33 263

TranMVSNet+NR-MVSNet		80.84 185	80.31 182	82.42 247	87.85 217	62.33 316	87.74 185	91.33 151	80.55 9	77.99 234	89.86 200	65.23 164	92.62 233	67.05 291	75.24 389	92.30 187

diffmvs		82.10 153	81.88 154	82.76 238	83.00 375	63.78 276	83.68 323	89.76 204	72.94 207	82.02 151	89.85 201	65.96 159	90.79 325	82.38 102	87.30 192	93.71 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Elysia		81.53 169	80.16 186	85.62 86	85.51 306	68.25 141	88.84 134	92.19 109	71.31 236	80.50 185	89.83 202	46.89 388	94.82 113	76.85 175	89.57 142	93.80 100

StellarMVS		81.53 169	80.16 186	85.62 86	85.51 306	68.25 141	88.84 134	92.19 109	71.31 236	80.50 185	89.83 202	46.89 388	94.82 113	76.85 175	89.57 142	93.80 100

mamba_0408		79.37 234	77.52 260	84.93 114	88.81 171	67.96 152	65.03 491	88.66 266	70.96 249	79.48 200	89.80 204	58.69 256	94.65 124	70.35 255	85.93 224	92.18 194

SSM_04072		77.67 281	77.52 260	78.12 360	88.81 171	67.96 152	65.03 491	88.66 266	70.96 249	79.48 200	89.80 204	58.69 256	74.23 485	70.35 255	85.93 224	92.18 194

BH-RMVSNet		79.61 222	78.44 232	83.14 212	89.38 147	65.93 206	84.95 285	87.15 309	73.56 187	78.19 228	89.79 206	56.67 279	93.36 195	59.53 368	86.74 204	90.13 271

GeoE		81.71 163	81.01 167	83.80 187	89.51 137	64.45 260	88.97 127	88.73 264	71.27 239	78.63 216	89.76 207	66.32 149	93.20 207	69.89 262	86.02 221	93.74 103

guyue		81.13 179	80.64 174	82.60 243	86.52 283	63.92 272	86.69 228	87.73 291	73.97 173	80.83 179	89.69 208	56.70 278	91.33 300	78.26 161	85.40 235	92.54 173

AdaColmap		80.58 202	79.42 208	84.06 169	93.09 64	68.91 117	89.36 111	88.97 249	69.27 296	75.70 287	89.69 208	57.20 274	95.77 66	63.06 324	88.41 166	87.50 363

ACMM	73.20 8	80.78 194	79.84 196	83.58 192	89.31 151	68.37 136	89.99 84	91.60 142	70.28 271	77.25 249	89.66 210	53.37 310	93.53 181	74.24 210	82.85 280	88.85 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 266	76.79 277	81.97 260	90.40 111	71.07 73	87.59 188	84.55 353	66.03 352	72.38 353	89.64 211	57.56 268	86.04 402	59.61 367	83.35 273	88.79 326

test_yl		81.17 177	80.47 179	83.24 206	89.13 160	63.62 277	86.21 249	89.95 198	72.43 216	81.78 156	89.61 212	57.50 269	93.58 173	70.75 249	86.90 200	92.52 175

DCV-MVSNet		81.17 177	80.47 179	83.24 206	89.13 160	63.62 277	86.21 249	89.95 198	72.43 216	81.78 156	89.61 212	57.50 269	93.58 173	70.75 249	86.90 200	92.52 175

EI-MVSNet-Vis-set		84.19 98	83.81 107	85.31 96	88.18 198	67.85 157	87.66 186	89.73 207	80.05 16	82.95 134	89.59 214	70.74 80	94.82 113	80.66 122	84.72 243	93.28 132

PAPR		81.66 166	80.89 169	83.99 179	90.27 113	64.00 268	86.76 226	91.77 132	68.84 313	77.13 258	89.50 215	67.63 130	94.88 111	67.55 284	88.52 163	93.09 148

jajsoiax		79.29 235	77.96 242	83.27 204	84.68 329	66.57 194	89.25 114	90.16 192	69.20 301	75.46 293	89.49 216	45.75 405	93.13 213	76.84 177	80.80 306	90.11 273

MVSFormer		82.85 140	82.05 150	85.24 98	87.35 247	70.21 88	90.50 72	90.38 181	68.55 317	81.32 164	89.47 217	61.68 217	93.46 191	78.98 149	90.26 129	92.05 201

jason		81.39 175	80.29 183	84.70 126	86.63 281	69.90 96	85.95 255	86.77 319	63.24 392	81.07 170	89.47 217	61.08 233	92.15 257	78.33 157	90.07 134	92.05 201
jason: jason.
mvs_tets		79.13 239	77.77 252	83.22 208	84.70 328	66.37 196	89.17 117	90.19 191	69.38 293	75.40 296	89.46 219	44.17 417	93.15 211	76.78 181	80.70 308	90.14 270

UGNet		80.83 186	79.59 205	84.54 129	88.04 208	68.09 146	89.42 107	88.16 274	76.95 76	76.22 277	89.46 219	49.30 371	93.94 152	68.48 277	90.31 127	91.60 212
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
VPA-MVSNet		80.60 199	80.55 176	80.76 292	88.07 207	60.80 346	86.86 220	91.58 143	75.67 120	80.24 190	89.45 221	63.34 182	90.25 339	70.51 253	79.22 329	91.23 225

MVS_Test		83.15 133	83.06 124	83.41 200	86.86 271	63.21 294	86.11 252	92.00 117	74.31 165	82.87 136	89.44 222	70.03 91	93.21 204	77.39 169	88.50 164	93.81 98

EI-MVSNet-UG-set		83.81 108	83.38 119	85.09 106	87.87 216	67.53 169	87.44 199	89.66 208	79.74 19	82.23 147	89.41 223	70.24 86	94.74 119	79.95 130	83.92 258	92.99 157

RPSCF		73.23 355	71.46 355	78.54 351	82.50 390	59.85 361	82.18 356	82.84 386	58.96 438	71.15 368	89.41 223	45.48 409	84.77 417	58.82 377	71.83 419	91.02 234

UniMVSNet_NR-MVSNet		81.88 159	81.54 158	82.92 225	88.46 188	63.46 288	87.13 207	92.37 90	80.19 13	78.38 223	89.14 225	71.66 68	93.05 218	70.05 259	76.46 362	92.25 189

tttt0517		79.40 231	77.91 244	83.90 183	88.10 205	63.84 273	88.37 160	84.05 361	71.45 234	76.78 262	89.12 226	49.93 362	94.89 110	70.18 258	83.18 277	92.96 158

DU-MVS		81.12 180	80.52 177	82.90 226	87.80 220	63.46 288	87.02 212	91.87 125	79.01 32	78.38 223	89.07 227	65.02 167	93.05 218	70.05 259	76.46 362	92.20 192

NR-MVSNet		80.23 212	79.38 210	82.78 236	87.80 220	63.34 291	86.31 244	91.09 160	79.01 32	72.17 356	89.07 227	67.20 135	92.81 230	66.08 298	75.65 375	92.20 192

icg_test_0407_2		78.92 246	78.93 223	78.90 343	87.13 261	63.59 281	76.58 437	89.33 221	70.51 262	77.82 236	89.03 229	61.84 213	81.38 444	72.56 231	85.56 231	91.74 207

IMVS_0407		80.61 197	79.90 194	82.75 239	87.13 261	63.59 281	85.33 274	89.33 221	70.51 262	77.82 236	89.03 229	61.84 213	92.91 223	72.56 231	85.56 231	91.74 207

IMVS_0404		77.16 290	76.42 287	79.37 334	87.13 261	63.59 281	77.12 434	89.33 221	70.51 262	66.22 434	89.03 229	50.36 354	82.78 433	72.56 231	85.56 231	91.74 207

IMVS_0403		80.80 190	80.12 189	82.87 228	87.13 261	63.59 281	85.19 275	89.33 221	70.51 262	78.49 220	89.03 229	63.26 185	93.27 199	72.56 231	85.56 231	91.74 207

DELS-MVS		85.41 77	85.30 81	85.77 81	88.49 186	67.93 155	85.52 272	93.44 33	78.70 35	83.63 119	89.03 229	74.57 29	95.71 68	80.26 128	94.04 67	93.66 107
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
mvsmamba		80.60 199	79.38 210	84.27 153	89.74 131	67.24 182	87.47 191	86.95 314	70.02 276	75.38 297	88.93 234	51.24 342	92.56 238	75.47 198	89.22 149	93.00 156

baseline1		76.98 293	76.75 280	77.66 370	88.13 203	55.66 421	85.12 279	81.89 396	73.04 205	76.79 261	88.90 235	62.43 203	87.78 384	63.30 319	71.18 423	89.55 299

DP-MVS		76.78 296	74.57 316	83.42 198	93.29 53	69.46 106	88.55 151	83.70 365	63.98 386	70.20 374	88.89 236	54.01 304	94.80 116	46.66 457	81.88 293	86.01 405

ab-mvs		79.51 225	78.97 222	81.14 282	88.46 188	60.91 344	83.84 319	89.24 233	70.36 267	79.03 207	88.87 237	63.23 187	90.21 340	65.12 305	82.57 285	92.28 188

PEN-MVS		77.73 276	77.69 256	77.84 366	87.07 269	53.91 439	87.91 179	91.18 155	77.56 53	73.14 341	88.82 238	61.23 229	89.17 360	59.95 363	72.37 413	90.43 258

tt0805		78.73 249	77.83 248	81.43 271	85.17 315	60.30 357	89.41 108	90.90 164	71.21 240	77.17 256	88.73 239	46.38 394	93.21 204	72.57 229	78.96 330	90.79 241

test_djsdf		80.30 211	79.32 213	83.27 204	83.98 344	65.37 224	90.50 72	90.38 181	68.55 317	76.19 278	88.70 240	56.44 281	93.46 191	78.98 149	80.14 316	90.97 235

PAPM		77.68 280	76.40 288	81.51 269	87.29 257	61.85 325	83.78 320	89.59 212	64.74 373	71.23 366	88.70 240	62.59 199	93.66 172	52.66 422	87.03 198	89.01 315

DTE-MVSNet		76.99 292	76.80 276	77.54 375	86.24 288	53.06 449	87.52 189	90.66 172	77.08 73	72.50 350	88.67 242	60.48 244	89.52 352	57.33 392	70.74 425	90.05 280

PS-CasMVS		78.01 270	78.09 240	77.77 368	87.71 229	54.39 436	88.02 173	91.22 153	77.50 56	73.26 338	88.64 243	60.73 236	88.41 376	61.88 345	73.88 402	90.53 254

cdsmvs_eth3d_5k		19.96 484	26.61 479	0.00 535	0.00 559	0.00 561	0.00 546	89.26 230	0.00 553	0.00 555	88.61 244	61.62 219	0.00 555	0.00 553	0.00 553	0.00 550

lupinMVS		81.39 175	80.27 184	84.76 124	87.35 247	70.21 88	85.55 268	86.41 327	62.85 399	81.32 164	88.61 244	61.68 217	92.24 255	78.41 156	90.26 129	91.83 204

F-COLMAP		76.38 308	74.33 322	82.50 245	89.28 153	66.95 190	88.41 156	89.03 244	64.05 384	66.83 423	88.61 244	46.78 390	92.89 224	57.48 389	78.55 332	87.67 355

mvs_anonymous		79.42 230	79.11 219	80.34 302	84.45 335	57.97 382	82.59 349	87.62 293	67.40 333	76.17 281	88.56 247	68.47 120	89.59 351	70.65 252	86.05 220	93.47 124

CP-MVSNet		78.22 261	78.34 235	77.84 366	87.83 219	54.54 434	87.94 177	91.17 156	77.65 48	73.48 336	88.49 248	62.24 207	88.43 375	62.19 340	74.07 398	90.55 253

PVSNet_Blended_VisFu		82.62 143	81.83 155	84.96 111	90.80 103	69.76 99	88.74 141	91.70 136	69.39 292	78.96 208	88.46 249	65.47 162	94.87 112	74.42 207	88.57 161	90.24 267

CANet_DTU		80.61 197	79.87 195	82.83 229	85.60 304	63.17 297	87.36 201	88.65 268	76.37 101	75.88 284	88.44 250	53.51 308	93.07 216	73.30 219	89.74 140	92.25 189

PLC	70.83 11	78.05 268	76.37 289	83.08 216	91.88 85	67.80 159	88.19 167	89.46 216	64.33 380	69.87 383	88.38 251	53.66 306	93.58 173	58.86 376	82.73 282	87.86 352
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 226	79.22 217	80.27 304	88.79 176	58.35 374	85.06 282	88.61 270	78.56 36	77.65 241	88.34 252	63.81 181	90.66 332	64.98 307	77.22 350	91.80 206

XXY-MVS		75.41 322	75.56 298	74.96 401	83.59 355	57.82 386	80.59 384	83.87 364	66.54 346	74.93 316	88.31 253	63.24 186	80.09 450	62.16 341	76.85 356	86.97 385

Effi-MVS+		83.62 119	83.08 123	85.24 98	88.38 192	67.45 171	88.89 130	89.15 239	75.50 123	82.27 146	88.28 254	69.61 98	94.45 132	77.81 162	87.84 181	93.84 96

API-MVS		81.99 157	81.23 161	84.26 155	90.94 99	70.18 93	91.10 63	89.32 225	71.51 233	78.66 215	88.28 254	65.26 163	95.10 100	64.74 309	91.23 111	87.51 362

thisisatest0530		79.40 231	77.76 253	84.31 147	87.69 233	65.10 236	87.36 201	84.26 359	70.04 275	77.42 245	88.26 256	49.94 360	94.79 117	70.20 257	84.70 244	93.03 153

hse-mvs2		81.72 162	80.94 168	84.07 166	88.72 179	67.68 163	85.87 258	87.26 306	76.02 110	84.67 90	88.22 257	61.54 220	93.48 189	82.71 98	73.44 407	91.06 230

xiu_mvs_v1_base_debu		80.80 190	79.72 201	84.03 174	87.35 247	70.19 90	85.56 265	88.77 256	69.06 305	81.83 152	88.16 258	50.91 345	92.85 226	78.29 158	87.56 186	89.06 310

xiu_mvs_v1_base		80.80 190	79.72 201	84.03 174	87.35 247	70.19 90	85.56 265	88.77 256	69.06 305	81.83 152	88.16 258	50.91 345	92.85 226	78.29 158	87.56 186	89.06 310

xiu_mvs_v1_base_debi		80.80 190	79.72 201	84.03 174	87.35 247	70.19 90	85.56 265	88.77 256	69.06 305	81.83 152	88.16 258	50.91 345	92.85 226	78.29 158	87.56 186	89.06 310

UniMVSNet (Re)		81.60 167	81.11 164	83.09 214	88.38 192	64.41 261	87.60 187	93.02 52	78.42 38	78.56 218	88.16 258	69.78 95	93.26 200	69.58 266	76.49 361	91.60 212

AUN-MVS		79.21 237	77.60 258	84.05 172	88.71 180	67.61 165	85.84 260	87.26 306	69.08 304	77.23 251	88.14 262	53.20 312	93.47 190	75.50 197	73.45 406	91.06 230

Anonymous20231211		78.97 244	77.69 256	82.81 231	90.54 108	64.29 263	90.11 83	91.51 145	65.01 371	76.16 282	88.13 263	50.56 351	93.03 221	69.68 265	77.56 348	91.11 228

pm-mvs1		77.25 289	76.68 282	78.93 342	84.22 338	58.62 372	86.41 238	88.36 273	71.37 235	73.31 337	88.01 264	61.22 230	89.15 361	64.24 313	73.01 410	89.03 314

LuminaMVS		80.68 195	79.62 204	83.83 184	85.07 321	68.01 151	86.99 213	88.83 253	70.36 267	81.38 163	87.99 265	50.11 357	92.51 242	79.02 146	86.89 202	90.97 235

SD_0403		74.65 330	74.77 314	74.29 410	86.20 290	47.42 477	83.71 322	85.12 345	69.30 295	68.50 400	87.95 266	59.40 252	86.05 401	49.38 442	83.35 273	89.40 302

LTVRE_ROB	69.57 13	76.25 309	74.54 318	81.41 272	88.60 183	64.38 262	79.24 404	89.12 242	70.76 254	69.79 385	87.86 267	49.09 374	93.20 207	56.21 404	80.16 314	86.65 394
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
testing3-2		75.12 327	75.19 309	74.91 402	90.40 111	45.09 488	80.29 390	78.42 439	78.37 41	76.54 270	87.75 268	44.36 415	87.28 390	57.04 395	83.49 270	92.37 183

WTY-MVS		75.65 317	75.68 295	75.57 392	86.40 286	56.82 401	77.92 427	82.40 389	65.10 368	76.18 279	87.72 269	63.13 192	80.90 447	60.31 361	81.96 291	89.00 317

TAMVS		78.89 247	77.51 262	83.03 219	87.80 220	67.79 160	84.72 289	85.05 348	67.63 327	76.75 263	87.70 270	62.25 206	90.82 324	58.53 380	87.13 196	90.49 256

BH-untuned		79.47 227	78.60 228	82.05 257	89.19 158	65.91 207	86.07 253	88.52 271	72.18 219	75.42 295	87.69 271	61.15 231	93.54 180	60.38 360	86.83 203	86.70 392

COLMAP_ROB	66.92 17	73.01 358	70.41 376	80.81 291	87.13 261	65.63 216	88.30 164	84.19 360	62.96 397	63.80 454	87.69 271	38.04 457	92.56 238	46.66 457	74.91 392	84.24 434
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 333	72.42 346	79.80 319	83.76 350	59.59 365	85.92 257	86.64 323	66.39 347	66.96 421	87.58 273	39.46 447	91.60 280	65.76 301	69.27 431	88.22 344

FA-MVS(test-final)		80.96 183	79.91 193	84.10 160	88.30 195	65.01 237	84.55 298	90.01 196	73.25 199	79.61 197	87.57 274	58.35 261	94.72 120	71.29 244	86.25 214	92.56 172

Baseline_NR-MVSNet		78.15 265	78.33 236	77.61 372	85.79 298	56.21 414	86.78 224	85.76 339	73.60 186	77.93 235	87.57 274	65.02 167	88.99 363	67.14 290	75.33 386	87.63 356

WR-MVS_H		78.51 256	78.49 230	78.56 350	88.02 209	56.38 410	88.43 154	92.67 75	77.14 69	73.89 330	87.55 276	66.25 150	89.24 358	58.92 375	73.55 405	90.06 279

EI-MVSNet		80.52 203	79.98 191	82.12 254	84.28 336	63.19 296	86.41 238	88.95 250	74.18 170	78.69 213	87.54 277	66.62 143	92.43 245	72.57 229	80.57 310	90.74 245

CVMVSNet		72.99 359	72.58 344	74.25 411	84.28 336	50.85 465	86.41 238	83.45 371	44.56 485	73.23 339	87.54 277	49.38 368	85.70 405	65.90 299	78.44 335	86.19 400

ACMH+	68.96 14	76.01 313	74.01 324	82.03 258	88.60 183	65.31 229	88.86 131	87.55 294	70.25 273	67.75 409	87.47 279	41.27 436	93.19 209	58.37 382	75.94 372	87.60 357

TransMVSNet (Re)		75.39 324	74.56 317	77.86 365	85.50 308	57.10 398	86.78 224	86.09 335	72.17 220	71.53 363	87.34 280	63.01 193	89.31 356	56.84 398	61.83 467	87.17 377

GBi-Net		78.40 257	77.40 263	81.40 273	87.60 236	63.01 299	88.39 157	89.28 227	71.63 228	75.34 299	87.28 281	54.80 292	91.11 307	62.72 329	79.57 320	90.09 275

test1		78.40 257	77.40 263	81.40 273	87.60 236	63.01 299	88.39 157	89.28 227	71.63 228	75.34 299	87.28 281	54.80 292	91.11 307	62.72 329	79.57 320	90.09 275

FMVSNet2		78.20 263	77.21 267	81.20 280	87.60 236	62.89 306	87.47 191	89.02 245	71.63 228	75.29 305	87.28 281	54.80 292	91.10 310	62.38 337	79.38 326	89.61 297

FMVSNet1		77.44 284	76.12 291	81.40 273	86.81 274	63.01 299	88.39 157	89.28 227	70.49 266	74.39 325	87.28 281	49.06 375	91.11 307	60.91 356	78.52 333	90.09 275

v2v482		80.23 212	79.29 214	83.05 218	83.62 354	64.14 266	87.04 210	89.97 197	73.61 185	78.18 229	87.22 285	61.10 232	93.82 162	76.11 186	76.78 358	91.18 226

ITE_SJBPF				78.22 357	81.77 402	60.57 352		83.30 372	69.25 298	67.54 411	87.20 286	36.33 465	87.28 390	54.34 413	74.62 395	86.80 389

anonymousdsp		78.60 253	77.15 268	82.98 223	80.51 421	67.08 185	87.24 206	89.53 214	65.66 357	75.16 308	87.19 287	52.52 314	92.25 254	77.17 171	79.34 327	89.61 297

MVSTER		79.01 242	77.88 247	82.38 248	83.07 372	64.80 250	84.08 316	88.95 250	69.01 308	78.69 213	87.17 288	54.70 296	92.43 245	74.69 203	80.57 310	89.89 288

thres100view900		76.50 300	75.55 299	79.33 335	89.52 136	56.99 399	85.83 261	83.23 374	73.94 176	76.32 275	87.12 289	51.89 331	91.95 265	48.33 448	83.75 262	89.07 308

thres600view7		76.50 300	75.44 300	79.68 327	89.40 145	57.16 396	85.53 270	83.23 374	73.79 180	76.26 276	87.09 290	51.89 331	91.89 269	48.05 453	83.72 265	90.00 281

XVG-ACMP-BASELINE		76.11 311	74.27 323	81.62 266	83.20 366	64.67 252	83.60 328	89.75 206	69.75 286	71.85 359	87.09 290	32.78 472	92.11 258	69.99 261	80.43 312	88.09 347

HY-MVS	69.67 12	77.95 271	77.15 268	80.36 301	87.57 245	60.21 359	83.37 336	87.78 290	66.11 349	75.37 298	87.06 292	63.27 184	90.48 334	61.38 353	82.43 286	90.40 260

CHOSEN 1792x2688		77.63 282	75.69 294	83.44 197	89.98 124	68.58 131	78.70 414	87.50 296	56.38 459	75.80 286	86.84 293	58.67 258	91.40 297	61.58 350	85.75 229	90.34 262

v8		79.97 219	79.02 221	82.80 232	84.09 341	64.50 258	87.96 175	90.29 188	74.13 172	75.24 306	86.81 294	62.88 197	93.89 160	74.39 208	75.40 384	90.00 281

AllTest		70.96 380	68.09 396	79.58 330	85.15 317	63.62 277	84.58 297	79.83 426	62.31 408	60.32 468	86.73 295	32.02 473	88.96 366	50.28 436	71.57 421	86.15 401

TestCases				79.58 330	85.15 317	63.62 277		79.83 426	62.31 408	60.32 468	86.73 295	32.02 473	88.96 366	50.28 436	71.57 421	86.15 401

LCM-MVSNet-Re		77.05 291	76.94 273	77.36 376	87.20 258	51.60 458	80.06 393	80.46 416	75.20 136	67.69 410	86.72 297	62.48 201	88.98 364	63.44 317	89.25 147	91.51 216

1112_ss		77.40 286	76.43 286	80.32 303	89.11 164	60.41 356	83.65 324	87.72 292	62.13 411	73.05 342	86.72 297	62.58 200	89.97 344	62.11 343	80.80 306	90.59 252

ab-mvs-re		7.23 495	9.64 495	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	86.72 297	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

IterMVS-LS		80.06 215	79.38 210	82.11 256	85.89 296	63.20 295	86.79 223	89.34 220	74.19 169	75.45 294	86.72 297	66.62 143	92.39 247	72.58 228	76.86 355	90.75 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 314	73.93 326	81.77 264	88.71 180	66.61 193	88.62 147	89.01 246	69.81 282	66.78 424	86.70 301	41.95 433	91.51 291	55.64 405	78.14 341	87.17 377
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 307	75.44 300	79.27 336	89.28 153	58.09 378	81.69 364	87.07 312	59.53 433	72.48 351	86.67 302	61.30 227	89.33 355	60.81 358	80.15 315	90.41 259

FMVSNet3		77.88 273	76.85 275	80.97 288	86.84 273	62.36 315	86.52 235	88.77 256	71.13 241	75.34 299	86.66 303	54.07 302	91.10 310	62.72 329	79.57 320	89.45 301

pmmvs6		74.69 329	73.39 333	78.61 347	81.38 410	57.48 393	86.64 230	87.95 284	64.99 372	70.18 375	86.61 304	50.43 353	89.52 352	62.12 342	70.18 428	88.83 324

ET-MVSNet_ETH3D		78.63 252	76.63 283	84.64 127	86.73 277	69.47 104	85.01 283	84.61 352	69.54 290	66.51 431	86.59 305	50.16 356	91.75 274	76.26 184	84.24 254	92.69 168

testgi		66.67 425	66.53 421	67.08 462	75.62 467	41.69 498	75.93 440	76.50 454	66.11 349	65.20 444	86.59 305	35.72 467	74.71 482	43.71 471	73.38 408	84.84 428

CLD-MVS		82.31 149	81.65 157	84.29 150	88.47 187	67.73 161	85.81 262	92.35 91	75.78 115	78.33 225	86.58 307	64.01 178	94.35 133	76.05 188	87.48 189	90.79 241
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 221	78.67 226	82.97 224	84.06 342	64.95 240	87.88 181	90.62 173	73.11 203	75.11 310	86.56 308	61.46 223	94.05 148	73.68 213	75.55 377	89.90 287

CDS-MVSNet		79.07 241	77.70 255	83.17 211	87.60 236	68.23 143	84.40 307	86.20 332	67.49 330	76.36 274	86.54 309	61.54 220	90.79 325	61.86 346	87.33 191	90.49 256
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 164	81.05 165	83.60 190	89.15 159	68.03 150	84.46 301	90.02 195	70.67 256	81.30 167	86.53 310	63.17 188	94.19 143	75.60 195	88.54 162	88.57 335

TR-MVS		77.44 284	76.18 290	81.20 280	88.24 196	63.24 293	84.61 296	86.40 328	67.55 329	77.81 238	86.48 311	54.10 301	93.15 211	57.75 388	82.72 283	87.20 375

EIA-MVS		83.31 131	82.80 131	84.82 120	89.59 133	65.59 218	88.21 166	92.68 74	74.66 156	78.96 208	86.42 312	69.06 111	95.26 90	75.54 196	90.09 132	93.62 114

tfpn200view9		76.42 306	75.37 304	79.55 332	89.13 160	57.65 390	85.17 276	83.60 366	73.41 193	76.45 271	86.39 313	52.12 321	91.95 265	48.33 448	83.75 262	89.07 308

thres400		76.50 300	75.37 304	79.86 317	89.13 160	57.65 390	85.17 276	83.60 366	73.41 193	76.45 271	86.39 313	52.12 321	91.95 265	48.33 448	83.75 262	90.00 281

v7n		78.97 244	77.58 259	83.14 212	83.45 358	65.51 219	88.32 162	91.21 154	73.69 183	72.41 352	86.32 315	57.93 263	93.81 163	69.18 269	75.65 375	90.11 273

dtuonly		69.95 397	69.98 380	69.85 447	73.09 483	49.46 472	74.55 455	76.40 455	57.56 453	67.82 407	86.31 316	50.89 349	74.23 485	61.46 351	81.71 295	85.86 411

MAR-MVS		81.84 160	80.70 171	85.27 97	91.32 91	71.53 59	89.82 88	90.92 163	69.77 285	78.50 219	86.21 317	62.36 204	94.52 128	65.36 303	92.05 94	89.77 293
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
v1144		80.03 217	79.03 220	83.01 220	83.78 349	64.51 256	87.11 209	90.57 176	71.96 224	78.08 232	86.20 318	61.41 224	93.94 152	74.93 202	77.23 349	90.60 251

test_vis1_n_1920		75.52 319	75.78 293	74.75 406	79.84 430	57.44 394	83.26 338	85.52 341	62.83 400	79.34 205	86.17 319	45.10 410	79.71 451	78.75 151	81.21 300	87.10 383

V42		79.38 233	78.24 238	82.83 229	81.10 415	65.50 220	85.55 268	89.82 201	71.57 232	78.21 227	86.12 320	60.66 240	93.18 210	75.64 193	75.46 381	89.81 292

PVSNet_BlendedMVS		80.60 199	80.02 190	82.36 250	88.85 167	65.40 221	86.16 251	92.00 117	69.34 294	78.11 230	86.09 321	66.02 156	94.27 136	71.52 240	82.06 290	87.39 365

v1192		79.59 224	78.43 233	83.07 217	83.55 356	64.52 255	86.93 217	90.58 174	70.83 251	77.78 239	85.90 322	59.15 254	93.94 152	73.96 212	77.19 351	90.76 243

SixPastTwentyTwo		73.37 348	71.26 361	79.70 326	85.08 320	57.89 384	85.57 264	83.56 368	71.03 247	65.66 437	85.88 323	42.10 431	92.57 237	59.11 373	63.34 461	88.65 332

EPNet_dtu		75.46 320	74.86 312	77.23 379	82.57 389	54.60 433	86.89 218	83.09 378	71.64 227	66.25 433	85.86 324	55.99 284	88.04 380	54.92 410	86.55 207	89.05 313
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 343	73.64 331	73.51 419	82.80 383	55.01 429	76.12 439	81.69 399	62.47 406	74.68 320	85.85 325	57.32 271	78.11 458	60.86 357	80.93 302	87.39 365

ETV-MVS		84.90 90	84.67 90	85.59 88	89.39 146	68.66 129	88.74 141	92.64 80	79.97 17	84.10 107	85.71 326	69.32 102	95.38 85	80.82 117	91.37 108	92.72 165

test_cas_vis1_n_1920		73.76 341	73.74 330	73.81 417	75.90 463	59.77 362	80.51 385	82.40 389	58.30 444	81.62 160	85.69 327	44.35 416	76.41 469	76.29 183	78.61 331	85.23 420

v1240		78.99 243	77.78 251	82.64 241	83.21 365	63.54 285	86.62 231	90.30 187	69.74 288	77.33 247	85.68 328	57.04 275	93.76 167	73.13 222	76.92 353	90.62 249

v144192		79.47 227	78.37 234	82.78 236	83.35 359	63.96 269	86.96 214	90.36 184	69.99 278	77.50 243	85.67 329	60.66 240	93.77 166	74.27 209	76.58 359	90.62 249

tfpnnormal		74.39 331	73.16 337	78.08 361	86.10 294	58.05 379	84.65 293	87.53 295	70.32 270	71.22 367	85.63 330	54.97 290	89.86 345	43.03 474	75.02 391	86.32 397

PS-MVSNAJ		81.69 164	81.02 166	83.70 188	89.51 137	68.21 144	84.28 309	90.09 194	70.79 252	81.26 168	85.62 331	63.15 189	94.29 134	75.62 194	88.87 155	88.59 334

SSC-MVS3.2		73.35 351	73.39 333	73.23 420	85.30 313	49.01 473	74.58 454	81.57 400	75.21 135	73.68 333	85.58 332	52.53 313	82.05 439	54.33 414	77.69 346	88.63 333

v1921920		79.22 236	78.03 241	82.80 232	83.30 361	63.94 271	86.80 222	90.33 185	69.91 281	77.48 244	85.53 333	58.44 260	93.75 168	73.60 214	76.85 356	90.71 247

test_0402		72.79 364	70.44 375	79.84 318	88.13 203	65.99 205	85.93 256	84.29 357	65.57 358	67.40 417	85.49 334	46.92 387	92.61 234	35.88 489	74.38 397	80.94 465

v148		78.72 250	77.80 250	81.47 270	82.73 385	61.96 324	86.30 245	88.08 277	73.26 198	76.18 279	85.47 335	62.46 202	92.36 249	71.92 239	73.82 403	90.09 275

USDC		70.33 390	68.37 391	76.21 386	80.60 419	56.23 413	79.19 406	86.49 326	60.89 419	61.29 463	85.47 335	31.78 475	89.47 354	53.37 419	76.21 370	82.94 451

VortexMVS		78.57 255	77.89 246	80.59 295	85.89 296	62.76 307	85.61 263	89.62 211	72.06 222	74.99 314	85.38 337	55.94 285	90.77 328	74.99 201	76.58 359	88.23 343

MVP-Stereo		76.12 310	74.46 320	81.13 283	85.37 311	69.79 97	84.42 306	87.95 284	65.03 370	67.46 414	85.33 338	53.28 311	91.73 276	58.01 386	83.27 275	81.85 460
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 264	76.99 272	81.78 263	85.66 301	66.99 186	84.66 291	90.47 178	55.08 465	72.02 358	85.27 339	63.83 180	94.11 146	66.10 297	89.80 139	84.24 434

DIV-MVS_self_test		77.72 277	76.76 278	80.58 296	82.48 392	60.48 354	83.09 343	87.86 287	69.22 299	74.38 326	85.24 340	62.10 209	91.53 289	71.09 246	75.40 384	89.74 294

FE-MVS		77.78 275	75.68 295	84.08 165	88.09 206	66.00 204	83.13 341	87.79 289	68.42 321	78.01 233	85.23 341	45.50 408	95.12 95	59.11 373	85.83 228	91.11 228

cl____		77.72 277	76.76 278	80.58 296	82.49 391	60.48 354	83.09 343	87.87 286	69.22 299	74.38 326	85.22 342	62.10 209	91.53 289	71.09 246	75.41 383	89.73 295

HyFIR lowres test		77.53 283	75.40 302	83.94 182	89.59 133	66.62 192	80.36 388	88.64 269	56.29 460	76.45 271	85.17 343	57.64 267	93.28 197	61.34 354	83.10 278	91.91 203

pmmvs4		74.03 339	71.91 350	80.39 299	81.96 399	68.32 137	81.45 368	82.14 394	59.32 434	69.87 383	85.13 344	52.40 317	88.13 379	60.21 362	74.74 394	84.73 430

TDRefinement		67.49 417	64.34 429	76.92 381	73.47 479	61.07 339	84.86 287	82.98 382	59.77 430	58.30 475	85.13 344	26.06 484	87.89 382	47.92 454	60.59 473	81.81 461

Fast-Effi-MVS+		80.81 187	79.92 192	83.47 194	88.85 167	64.51 256	85.53 270	89.39 219	70.79 252	78.49 220	85.06 346	67.54 131	93.58 173	67.03 292	86.58 206	92.32 186

PVSNet_Blended		80.98 182	80.34 181	82.90 226	88.85 167	65.40 221	84.43 304	92.00 117	67.62 328	78.11 230	85.05 347	66.02 156	94.27 136	71.52 240	89.50 144	89.01 315

ttmdpeth		59.91 446	57.10 450	68.34 457	67.13 495	46.65 482	74.64 453	67.41 485	48.30 480	62.52 461	85.04 348	20.40 494	75.93 474	42.55 477	45.90 496	82.44 454

test_fmvs1_n		70.86 383	70.24 378	72.73 428	72.51 487	55.28 426	81.27 373	79.71 428	51.49 476	78.73 212	84.87 349	27.54 483	77.02 463	76.06 187	79.97 318	85.88 409

WBMVS		73.43 345	72.81 341	75.28 398	87.91 214	50.99 464	78.59 417	81.31 405	65.51 361	74.47 324	84.83 350	46.39 393	86.68 394	58.41 381	77.86 342	88.17 346

CMPMVS	51.72 21	70.19 392	68.16 394	76.28 385	73.15 482	57.55 392	79.47 401	83.92 362	48.02 481	56.48 481	84.81 351	43.13 423	86.42 398	62.67 332	81.81 294	84.89 427
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 411	67.61 408	71.31 440	78.51 445	47.01 480	84.47 299	84.27 358	42.27 488	66.44 432	84.79 352	40.44 441	83.76 423	58.76 378	68.54 436	83.17 445

BH-w/o		78.21 262	77.33 266	80.84 290	88.81 171	65.13 233	84.87 286	87.85 288	69.75 286	74.52 323	84.74 353	61.34 226	93.11 214	58.24 384	85.84 227	84.27 433

pmmvs5		71.55 375	70.20 379	75.61 391	77.83 452	56.39 409	81.74 361	80.89 407	57.76 449	67.46 414	84.49 354	49.26 372	85.32 412	57.08 394	75.29 387	85.11 424

reproduce_monomvs		75.40 323	74.38 321	78.46 355	83.92 346	57.80 387	83.78 320	86.94 315	73.47 191	72.25 355	84.47 355	38.74 452	89.27 357	75.32 199	70.53 426	88.31 340

thres200		75.55 318	74.47 319	78.82 344	87.78 223	57.85 385	83.07 345	83.51 369	72.44 215	75.84 285	84.42 356	52.08 324	91.75 274	47.41 455	83.64 267	86.86 387

test_fmvs1		70.93 381	70.52 373	72.16 431	73.71 475	55.05 428	80.82 376	78.77 437	51.21 477	78.58 217	84.41 357	31.20 477	76.94 464	75.88 191	80.12 317	84.47 432

testing3		68.56 410	67.67 407	71.22 441	87.33 252	42.87 493	83.06 346	71.54 473	70.36 267	69.08 392	84.38 358	30.33 479	85.69 406	37.50 487	75.45 382	85.09 425

test_fmvs2		68.35 414	67.48 410	70.98 443	69.50 491	51.95 453	80.05 394	76.38 456	49.33 479	74.65 321	84.38 358	23.30 492	75.40 480	74.51 206	75.17 390	85.60 413

eth_miper_zixun_eth		77.92 272	76.69 281	81.61 268	83.00 375	61.98 323	83.15 340	89.20 235	69.52 291	74.86 317	84.35 360	61.76 216	92.56 238	71.50 242	72.89 411	90.28 266

myMVS_eth3d28		73.62 342	73.53 332	73.90 416	88.20 197	47.41 478	78.06 424	79.37 431	74.29 167	73.98 329	84.29 361	44.67 411	83.54 427	51.47 428	87.39 190	90.74 245

testing91		76.54 298	75.66 297	79.18 339	88.43 190	55.89 417	81.08 374	83.00 381	73.76 181	75.34 299	84.29 361	46.20 399	90.07 342	64.33 311	84.50 246	91.58 214

c3_l		78.75 248	77.91 244	81.26 278	82.89 382	61.56 330	84.09 315	89.13 241	69.97 279	75.56 289	84.29 361	66.36 148	92.09 259	73.47 217	75.48 379	90.12 272

testing99		76.09 312	75.12 311	79.00 340	88.16 200	55.50 423	80.79 378	81.40 403	73.30 197	75.17 307	84.27 364	44.48 414	90.02 343	64.28 312	84.22 255	91.48 219

UWE-MVS		72.13 372	71.49 354	74.03 414	86.66 280	47.70 475	81.40 370	76.89 453	63.60 390	75.59 288	84.22 365	39.94 444	85.62 407	48.98 445	86.13 217	88.77 327

usedtu_dtu_shiyan1		76.43 304	75.32 306	79.76 322	83.00 375	60.72 347	81.74 361	88.76 260	68.99 309	72.98 343	84.19 366	56.41 282	90.27 336	62.39 335	79.40 324	88.31 340

FE-MVSNET3		76.43 304	75.32 306	79.76 322	83.00 375	60.72 347	81.74 361	88.76 260	68.99 309	72.98 343	84.19 366	56.41 282	90.27 336	62.39 335	79.40 324	88.31 340

Fast-Effi-MVS+-dtu		78.02 269	76.49 284	82.62 242	83.16 369	66.96 189	86.94 216	87.45 298	72.45 213	71.49 364	84.17 368	54.79 295	91.58 281	67.61 283	80.31 313	89.30 306

IterMVS-SCA-FT		75.43 321	73.87 328	80.11 310	82.69 386	64.85 249	81.57 366	83.47 370	69.16 302	70.49 371	84.15 369	51.95 327	88.15 378	69.23 268	72.14 417	87.34 370

1314		76.53 299	75.30 308	80.21 307	83.93 345	62.32 317	84.66 291	88.81 254	60.23 425	70.16 377	84.07 370	55.30 289	90.73 331	67.37 286	83.21 276	87.59 359

cl22		78.07 267	77.01 270	81.23 279	82.37 394	61.83 326	83.55 329	87.98 281	68.96 311	75.06 312	83.87 371	61.40 225	91.88 270	73.53 215	76.39 364	89.98 284

EG-PatchMatch MVS		74.04 337	71.82 351	80.71 293	84.92 323	67.42 172	85.86 259	88.08 277	66.04 351	64.22 449	83.85 372	35.10 468	92.56 238	57.44 390	80.83 305	82.16 458

thisisatest0515		77.33 287	75.38 303	83.18 210	85.27 314	63.80 274	82.11 357	83.27 373	65.06 369	75.91 283	83.84 373	49.54 365	94.27 136	67.24 288	86.19 215	91.48 219

test20.03		67.45 418	66.95 417	68.94 451	75.48 468	44.84 489	77.50 430	77.67 443	66.66 340	63.01 456	83.80 374	47.02 386	78.40 456	42.53 478	68.86 435	83.58 442

miper_ehance_all_eth		78.59 254	77.76 253	81.08 284	82.66 387	61.56 330	83.65 324	89.15 239	68.87 312	75.55 290	83.79 375	66.49 146	92.03 260	73.25 220	76.39 364	89.64 296

MSDG		73.36 350	70.99 365	80.49 298	84.51 334	65.80 212	80.71 382	86.13 334	65.70 356	65.46 439	83.74 376	44.60 412	90.91 321	51.13 431	76.89 354	84.74 429

MonoMVSNet		76.49 303	75.80 292	78.58 349	81.55 406	58.45 373	86.36 243	86.22 331	74.87 151	74.73 319	83.73 377	51.79 334	88.73 369	70.78 248	72.15 416	88.55 336

testing11		75.14 326	74.01 324	78.53 352	88.16 200	56.38 410	80.74 381	80.42 418	70.67 256	72.69 349	83.72 378	43.61 421	89.86 345	62.29 339	83.76 261	89.36 304

IterMVS		74.29 332	72.94 340	78.35 356	81.53 407	63.49 287	81.58 365	82.49 388	68.06 325	69.99 380	83.69 379	51.66 336	85.54 408	65.85 300	71.64 420	86.01 405
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 367	71.71 352	74.35 409	82.19 395	52.00 452	79.22 405	77.29 449	64.56 375	72.95 345	83.68 380	51.35 337	83.26 431	58.33 383	75.80 373	87.81 353

UWE-MVS-28		65.32 432	64.93 426	66.49 463	78.70 443	38.55 501	77.86 428	64.39 493	62.00 413	64.13 450	83.60 381	41.44 434	76.00 473	31.39 494	80.89 303	84.92 426

sc_t1		72.19 371	69.51 383	80.23 306	84.81 325	61.09 338	84.68 290	80.22 423	60.70 421	71.27 365	83.58 382	36.59 463	89.24 358	60.41 359	63.31 462	90.37 261

testing222		74.04 337	72.66 343	78.19 358	87.89 215	55.36 424	81.06 375	79.20 434	71.30 238	74.65 321	83.57 383	39.11 451	88.67 371	51.43 430	85.75 229	90.53 254

Effi-MVS+-dtu		80.03 217	78.57 229	84.42 139	85.13 319	68.74 123	88.77 137	88.10 276	74.99 143	74.97 315	83.49 384	57.27 272	93.36 195	73.53 215	80.88 304	91.18 226

baseline2		75.70 316	73.83 329	81.30 276	83.26 363	61.79 327	82.57 350	80.65 411	66.81 336	66.88 422	83.42 385	57.86 265	92.19 256	63.47 316	79.57 320	89.91 286

mvs5depth		69.45 402	67.45 411	75.46 396	73.93 473	55.83 418	79.19 406	83.23 374	66.89 335	71.63 362	83.32 386	33.69 471	85.09 413	59.81 365	55.34 483	85.46 416

TinyColmap		67.30 420	64.81 427	74.76 405	81.92 401	56.68 405	80.29 390	81.49 402	60.33 423	56.27 483	83.22 387	24.77 488	87.66 386	45.52 466	69.47 430	79.95 470

mvsany_test1		62.30 442	61.26 446	65.41 465	69.52 490	54.86 431	66.86 483	49.78 506	46.65 482	68.50 400	83.21 388	49.15 373	66.28 498	56.93 397	60.77 471	75.11 481

test_vis1_n		69.85 400	69.21 386	71.77 434	72.66 486	55.27 427	81.48 367	76.21 457	52.03 473	75.30 304	83.20 389	28.97 480	76.22 471	74.60 205	78.41 339	83.81 440

CostFormer		75.24 325	73.90 327	79.27 336	82.65 388	58.27 376	80.80 377	82.73 387	61.57 415	75.33 303	83.13 390	55.52 287	91.07 313	64.98 307	78.34 340	88.45 337

MVStest1		56.63 450	52.76 456	68.25 458	61.67 501	53.25 447	71.67 464	68.90 483	38.59 493	50.59 490	83.05 391	25.08 486	70.66 492	36.76 488	38.56 497	80.83 466

WB-MVSnew		71.96 374	71.65 353	72.89 426	84.67 332	51.88 455	82.29 354	77.57 444	62.31 408	73.67 334	83.00 392	53.49 309	81.10 446	45.75 465	82.13 289	85.70 412

ETVMVS		72.25 370	71.05 364	75.84 388	87.77 225	51.91 454	79.39 402	74.98 461	69.26 297	73.71 332	82.95 393	40.82 440	86.14 400	46.17 461	84.43 251	89.47 300

miper_lstm_enhance		74.11 336	73.11 338	77.13 380	80.11 426	59.62 364	72.23 462	86.92 317	66.76 338	70.40 372	82.92 394	56.93 276	82.92 432	69.06 271	72.63 412	88.87 322

GA-MVS		76.87 295	75.17 310	81.97 260	82.75 384	62.58 309	81.44 369	86.35 330	72.16 221	74.74 318	82.89 395	46.20 399	92.02 262	68.85 274	81.09 301	91.30 224

K. test v3		71.19 377	68.51 390	79.21 338	83.04 374	57.78 388	84.35 308	76.91 452	72.90 208	62.99 457	82.86 396	39.27 448	91.09 312	61.65 349	52.66 486	88.75 328

MS-PatchMatch		73.83 340	72.67 342	77.30 378	83.87 347	66.02 202	81.82 359	84.66 351	61.37 418	68.61 396	82.82 397	47.29 383	88.21 377	59.27 370	84.32 253	77.68 476

lessismore_v0				78.97 341	81.01 416	57.15 397		65.99 488		61.16 464	82.82 397	39.12 450	91.34 299	59.67 366	46.92 493	88.43 338

D2MVS		74.82 328	73.21 336	79.64 329	79.81 431	62.56 311	80.34 389	87.35 300	64.37 379	68.86 393	82.66 399	46.37 395	90.10 341	67.91 281	81.24 299	86.25 398

Anonymous20231206		68.60 408	67.80 404	71.02 442	80.23 424	50.75 466	78.30 422	80.47 415	56.79 457	66.11 435	82.63 400	46.35 396	78.95 454	43.62 472	75.70 374	83.36 444

MIMVSNet		70.69 385	69.30 384	74.88 403	84.52 333	56.35 412	75.87 443	79.42 430	64.59 374	67.76 408	82.41 401	41.10 437	81.54 442	46.64 459	81.34 297	86.75 391

UBG		73.08 357	72.27 348	75.51 394	88.02 209	51.29 462	78.35 421	77.38 448	65.52 359	73.87 331	82.36 402	45.55 406	86.48 397	55.02 409	84.39 252	88.75 328

OpenMVS_ROB	64.09 19	70.56 387	68.19 393	77.65 371	80.26 422	59.41 368	85.01 283	82.96 383	58.76 441	65.43 440	82.33 403	37.63 459	91.23 303	45.34 469	76.03 371	82.32 455

miper_enhance_ethall		77.87 274	76.86 274	80.92 289	81.65 403	61.38 334	82.68 348	88.98 247	65.52 359	75.47 291	82.30 404	65.76 161	92.00 263	72.95 224	76.39 364	89.39 303

test0.0.03 1		68.00 416	67.69 406	68.90 452	77.55 457	47.43 476	75.70 444	72.95 472	66.66 340	66.56 427	82.29 405	48.06 380	75.87 475	44.97 470	74.51 396	83.41 443

PVSNet	64.34 18	72.08 373	70.87 368	75.69 390	86.21 289	56.44 408	74.37 456	80.73 410	62.06 412	70.17 376	82.23 406	42.86 425	83.31 430	54.77 411	84.45 250	87.32 371

MIMVSNet1		68.58 409	66.78 420	73.98 415	80.07 427	51.82 456	80.77 379	84.37 354	64.40 378	59.75 471	82.16 407	36.47 464	83.63 425	42.73 475	70.33 427	86.48 396

CL-MVSNet_self_test		72.37 367	71.46 355	75.09 400	79.49 437	53.53 441	80.76 380	85.01 349	69.12 303	70.51 370	82.05 408	57.92 264	84.13 421	52.27 424	66.00 448	87.60 357

tpm2		73.26 353	71.46 355	78.63 346	83.34 360	56.71 404	80.65 383	80.40 419	56.63 458	73.55 335	82.02 409	51.80 333	91.24 302	56.35 403	78.42 338	87.95 349

PatchMatch-RL		72.38 366	70.90 367	76.80 383	88.60 183	67.38 175	79.53 400	76.17 458	62.75 402	69.36 388	82.00 410	45.51 407	84.89 416	53.62 417	80.58 309	78.12 475

FE-MVSNET2		72.88 363	71.28 359	77.67 369	78.30 448	57.78 388	84.43 304	88.92 252	69.56 289	64.61 446	81.67 411	46.73 392	88.54 374	59.33 369	67.99 440	86.69 393

FMVSNet5		69.50 401	67.96 398	74.15 412	82.97 380	55.35 425	80.01 395	82.12 395	62.56 405	63.02 455	81.53 412	36.92 461	81.92 440	48.42 447	74.06 399	85.17 423

CR-MVSNet		73.37 348	71.27 360	79.67 328	81.32 413	65.19 231	75.92 441	80.30 421	59.92 429	72.73 347	81.19 413	52.50 315	86.69 393	59.84 364	77.71 344	87.11 381

Patchmtry		70.74 384	69.16 387	75.49 395	80.72 417	54.07 438	74.94 452	80.30 421	58.34 443	70.01 378	81.19 413	52.50 315	86.54 395	53.37 419	71.09 424	85.87 410

IB-MVS	68.01 15	75.85 315	73.36 335	83.31 202	84.76 327	66.03 201	83.38 335	85.06 347	70.21 274	69.40 387	81.05 415	45.76 404	94.66 123	65.10 306	75.49 378	89.25 307
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
cascas		76.72 297	74.64 315	82.99 221	85.78 299	65.88 208	82.33 353	89.21 234	60.85 420	72.74 346	81.02 416	47.28 384	93.75 168	67.48 285	85.02 237	89.34 305

LF4IMVS		64.02 438	62.19 441	69.50 449	70.90 488	53.29 446	76.13 438	77.18 450	52.65 471	58.59 473	80.98 417	23.55 491	76.52 467	53.06 421	66.66 444	78.68 473

Anonymous20240521		68.80 407	67.22 415	73.55 418	74.33 471	54.11 437	83.18 339	85.61 340	58.15 445	61.68 462	80.94 418	30.71 478	81.27 445	57.00 396	73.34 409	85.28 419

gm-plane-assit					81.40 409	53.83 440			62.72 403		80.94 418		92.39 247	63.40 318

UnsupCasMVSNet_eth		67.33 419	65.99 423	71.37 437	73.48 478	51.47 460	75.16 448	85.19 344	65.20 365	60.78 465	80.93 420	42.35 427	77.20 462	57.12 393	53.69 485	85.44 417

dmvs_re		71.14 378	70.58 372	72.80 427	81.96 399	59.68 363	75.60 445	79.34 432	68.55 317	69.27 391	80.72 421	49.42 367	76.54 466	52.56 423	77.79 343	82.19 457

MDTV_nov1_ep13			69.97 381		83.18 367	53.48 442	77.10 435	80.18 425	60.45 422	69.33 389	80.44 422	48.89 378	86.90 392	51.60 427	78.51 334

pmmvs-eth3d		70.50 388	67.83 403	78.52 353	77.37 459	66.18 199	81.82 359	81.51 401	58.90 439	63.90 453	80.42 423	42.69 426	86.28 399	58.56 379	65.30 457	83.11 447

tt0320		70.49 389	68.03 397	77.89 364	84.78 326	59.12 369	83.55 329	80.44 417	58.13 446	67.43 416	80.41 424	39.26 449	87.54 387	55.12 407	63.18 463	86.99 384

mmtdpeth		74.16 335	73.01 339	77.60 374	83.72 351	61.13 336	85.10 280	85.10 346	72.06 222	77.21 255	80.33 425	43.84 419	85.75 404	77.14 172	52.61 487	85.91 408

tt0320-xc		70.11 393	67.45 411	78.07 362	85.33 312	59.51 367	83.28 337	78.96 436	58.77 440	67.10 420	80.28 426	36.73 462	87.42 388	56.83 399	59.77 475	87.29 372

PM-MVS		66.41 427	64.14 430	73.20 423	73.92 474	56.45 407	78.97 410	64.96 492	63.88 388	64.72 445	80.24 427	19.84 496	83.44 429	66.24 294	64.52 459	79.71 471

SCA		74.22 334	72.33 347	79.91 315	84.05 343	62.17 319	79.96 396	79.29 433	66.30 348	72.38 353	80.13 428	51.95 327	88.60 372	59.25 371	77.67 347	88.96 319

Patchmatch-test		64.82 435	63.24 436	69.57 448	79.42 438	49.82 470	63.49 495	69.05 481	51.98 474	59.95 470	80.13 428	50.91 345	70.98 491	40.66 481	73.57 404	87.90 351

tpmrst		72.39 365	72.13 349	73.18 424	80.54 420	49.91 469	79.91 397	79.08 435	63.11 394	71.69 361	79.95 430	55.32 288	82.77 434	65.66 302	73.89 401	86.87 386

DSMNet-mixed		57.77 449	56.90 451	60.38 471	67.70 493	35.61 505	69.18 475	53.97 504	32.30 503	57.49 478	79.88 431	40.39 442	68.57 497	38.78 485	72.37 413	76.97 477

MDA-MVSNet-bldmvs		66.68 424	63.66 434	75.75 389	79.28 440	60.56 353	73.92 458	78.35 440	64.43 376	50.13 491	79.87 432	44.02 418	83.67 424	46.10 462	56.86 477	83.03 449

PatchmatchNet		73.12 356	71.33 358	78.49 354	83.18 367	60.85 345	79.63 399	78.57 438	64.13 381	71.73 360	79.81 433	51.20 343	85.97 403	57.40 391	76.36 369	88.66 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVSNET		67.25 421	65.33 425	73.02 425	75.86 464	52.54 450	80.26 392	80.56 413	63.80 389	60.39 466	79.70 434	41.41 435	84.66 419	43.34 473	62.62 465	81.86 459

Syy-MVS		68.05 415	67.85 401	68.67 455	84.68 329	40.97 499	78.62 415	73.08 470	66.65 343	66.74 425	79.46 435	52.11 323	82.30 437	32.89 492	76.38 367	82.75 452

myMVS_eth3d		67.02 422	66.29 422	69.21 450	84.68 329	42.58 494	78.62 415	73.08 470	66.65 343	66.74 425	79.46 435	31.53 476	82.30 437	39.43 484	76.38 367	82.75 452

ppachtmachnet_test		70.04 394	67.34 413	78.14 359	79.80 432	61.13 336	79.19 406	80.59 412	59.16 436	65.27 441	79.29 437	46.75 391	87.29 389	49.33 443	66.72 443	86.00 407

EPMVS		69.02 405	68.16 394	71.59 435	79.61 435	49.80 471	77.40 431	66.93 486	62.82 401	70.01 378	79.05 438	45.79 403	77.86 460	56.58 401	75.26 388	87.13 380

PMMVS		69.34 403	68.67 389	71.35 439	75.67 466	62.03 322	75.17 447	73.46 468	50.00 478	68.68 394	79.05 438	52.07 325	78.13 457	61.16 355	82.77 281	73.90 483

test-LLR		72.94 360	72.43 345	74.48 407	81.35 411	58.04 380	78.38 418	77.46 445	66.66 340	69.95 381	79.00 440	48.06 380	79.24 452	66.13 295	84.83 240	86.15 401

test-mter		71.41 376	70.39 377	74.48 407	81.35 411	58.04 380	78.38 418	77.46 445	60.32 424	69.95 381	79.00 440	36.08 466	79.24 452	66.13 295	84.83 240	86.15 401

KD-MVS_self_test		68.81 406	67.59 409	72.46 430	74.29 472	45.45 483	77.93 426	87.00 313	63.12 393	63.99 452	78.99 442	42.32 428	84.77 417	56.55 402	64.09 460	87.16 379

test_fmvs3		63.36 440	61.82 442	67.98 459	62.51 500	46.96 481	77.37 432	74.03 467	45.24 484	67.50 412	78.79 443	12.16 504	72.98 490	72.77 227	66.02 447	83.99 438

KD-MVS_2432*1600		66.22 429	63.89 432	73.21 421	75.47 469	53.42 443	70.76 469	84.35 355	64.10 382	66.52 429	78.52 444	34.55 469	84.98 414	50.40 434	50.33 490	81.23 463

miper_refine_blended		66.22 429	63.89 432	73.21 421	75.47 469	53.42 443	70.76 469	84.35 355	64.10 382	66.52 429	78.52 444	34.55 469	84.98 414	50.40 434	50.33 490	81.23 463

tpmvs		71.09 379	69.29 385	76.49 384	82.04 397	56.04 415	78.92 412	81.37 404	64.05 384	67.18 419	78.28 446	49.74 364	89.77 347	49.67 441	72.37 413	83.67 441

our_test_3		69.14 404	67.00 416	75.57 392	79.80 432	58.80 370	77.96 425	77.81 442	59.55 432	62.90 458	78.25 447	47.43 382	83.97 422	51.71 426	67.58 442	83.93 439

MDA-MVSNet_test_wron		65.03 433	62.92 437	71.37 437	75.93 462	56.73 402	69.09 478	74.73 464	57.28 455	54.03 486	77.89 448	45.88 401	74.39 484	49.89 440	61.55 469	82.99 450

YYNet1		65.03 433	62.91 438	71.38 436	75.85 465	56.60 406	69.12 477	74.66 466	57.28 455	54.12 485	77.87 449	45.85 402	74.48 483	49.95 439	61.52 470	83.05 448

ambc				75.24 399	73.16 481	50.51 467	63.05 496	87.47 297		64.28 448	77.81 450	17.80 498	89.73 349	57.88 387	60.64 472	85.49 415

tpm cat1		70.57 386	68.31 392	77.35 377	82.41 393	57.95 383	78.08 423	80.22 423	52.04 472	68.54 399	77.66 451	52.00 326	87.84 383	51.77 425	72.07 418	86.25 398

blended_shiyan6		73.38 346	71.17 362	80.01 313	78.36 446	61.48 333	82.43 351	87.27 304	65.40 363	68.56 398	77.55 452	51.94 329	91.01 315	63.27 321	65.76 450	87.55 360

blended_shiyan8		73.38 346	71.17 362	80.02 312	78.36 446	61.51 332	82.43 351	87.28 301	65.40 363	68.61 396	77.53 453	51.91 330	91.00 318	63.28 320	65.76 450	87.53 361

dp		66.80 423	65.43 424	70.90 444	79.74 434	48.82 474	75.12 450	74.77 463	59.61 431	64.08 451	77.23 454	42.89 424	80.72 448	48.86 446	66.58 445	83.16 446

TESTMET0.1,1		69.89 399	69.00 388	72.55 429	79.27 441	56.85 400	78.38 418	74.71 465	57.64 450	68.09 403	77.19 455	37.75 458	76.70 465	63.92 314	84.09 256	84.10 437

CHOSEN 280x420		66.51 426	64.71 428	71.90 433	81.45 408	63.52 286	57.98 500	68.95 482	53.57 468	62.59 459	76.70 456	46.22 398	75.29 481	55.25 406	79.68 319	76.88 478

PatchT		68.46 412	67.85 401	70.29 445	80.70 418	43.93 491	72.47 461	74.88 462	60.15 426	70.55 369	76.57 457	49.94 360	81.59 441	50.58 432	74.83 393	85.34 418

mvsany_test3		53.99 453	51.45 458	61.61 470	55.51 505	44.74 490	63.52 494	45.41 510	43.69 487	58.11 476	76.45 458	17.99 497	63.76 501	54.77 411	47.59 492	76.34 479

RPMNet		73.51 344	70.49 374	82.58 244	81.32 413	65.19 231	75.92 441	92.27 97	57.60 451	72.73 347	76.45 458	52.30 318	95.43 80	48.14 452	77.71 344	87.11 381

dtuonlycased		68.45 413	67.29 414	71.92 432	80.18 425	54.90 430	79.76 398	80.38 420	60.11 427	62.57 460	76.44 460	49.34 369	82.31 436	55.05 408	61.77 468	78.53 474

blend_shiyan4		72.29 369	69.65 382	80.21 307	78.24 449	62.16 320	82.29 354	87.27 304	65.41 362	68.43 402	76.42 461	39.91 445	91.23 303	63.21 322	65.66 455	87.22 374

wanda-best-256-512		72.94 360	70.66 370	79.79 320	77.80 453	61.03 341	81.31 371	87.15 309	65.18 366	68.09 403	76.28 462	51.32 338	90.97 319	63.06 324	65.76 450	87.35 367

FE-blended-shiyan7		72.94 360	70.66 370	79.79 320	77.80 453	61.03 341	81.31 371	87.15 309	65.18 366	68.09 403	76.28 462	51.32 338	90.97 319	63.06 324	65.76 450	87.35 367

usedtu_blend_shiyan5		73.29 352	70.96 366	80.25 305	77.80 453	62.16 320	84.44 303	87.38 299	64.41 377	68.09 403	76.28 462	51.32 338	91.23 303	63.21 322	65.76 450	87.35 367

gbinet_0.2-2-1-0.02		73.24 354	70.86 369	80.39 299	78.03 451	61.62 329	83.10 342	86.69 320	65.98 353	69.29 390	76.15 465	49.77 363	91.51 291	62.75 328	66.00 448	88.03 348

dmvs_testset		62.63 441	64.11 431	58.19 473	78.55 444	24.76 515	75.28 446	65.94 489	67.91 326	60.34 467	76.01 466	53.56 307	73.94 488	31.79 493	67.65 441	75.88 480

ADS-MVSNet2		66.20 431	63.33 435	74.82 404	79.92 428	58.75 371	67.55 481	75.19 460	53.37 469	65.25 442	75.86 467	42.32 428	80.53 449	41.57 479	68.91 433	85.18 421

ADS-MVSNet		64.36 437	62.88 439	68.78 454	79.92 428	47.17 479	67.55 481	71.18 474	53.37 469	65.25 442	75.86 467	42.32 428	73.99 487	41.57 479	68.91 433	85.18 421

EGC-MVSNET		52.07 459	47.05 463	67.14 461	83.51 357	60.71 349	80.50 386	67.75 484	0.07 550	0.43 552	75.85 469	24.26 489	81.54 442	28.82 496	62.25 466	59.16 496

new-patchmatchnet		61.73 443	61.73 443	61.70 469	72.74 485	24.50 516	69.16 476	78.03 441	61.40 416	56.72 480	75.53 470	38.42 454	76.48 468	45.95 463	57.67 476	84.13 436

N_pmnet		52.79 457	53.26 455	51.40 485	78.99 442	7.68 532	69.52 473	3.89 531	51.63 475	57.01 479	74.98 471	40.83 439	65.96 499	37.78 486	64.67 458	80.56 469

usedtu_dtu_shiyan2		64.75 436	61.63 444	74.10 413	70.64 489	53.18 448	82.10 358	81.27 406	56.22 461	56.39 482	74.67 472	27.94 482	83.56 426	42.71 476	62.73 464	85.57 414

WB-MVS		54.94 451	54.72 452	55.60 480	73.50 477	20.90 518	74.27 457	61.19 497	59.16 436	50.61 489	74.15 473	47.19 385	75.78 476	17.31 512	35.07 499	70.12 488

patchmatchnet-post											74.00 474	51.12 344	88.60 372

GG-mvs-BLEND				75.38 397	81.59 405	55.80 419	79.32 403	69.63 478		67.19 418	73.67 475	43.24 422	88.90 368	50.41 433	84.50 246	81.45 462

SSC-MVS		53.88 454	53.59 454	54.75 483	72.87 484	19.59 519	73.84 459	60.53 499	57.58 452	49.18 493	73.45 476	46.34 397	75.47 479	16.20 515	32.28 501	69.20 489

Patchmatch-RL test		70.24 391	67.78 405	77.61 372	77.43 458	59.57 366	71.16 466	70.33 475	62.94 398	68.65 395	72.77 477	50.62 350	85.49 409	69.58 266	66.58 445	87.77 354

FPMVS		53.68 455	51.64 457	59.81 472	65.08 497	51.03 463	69.48 474	69.58 479	41.46 489	40.67 499	72.32 478	16.46 500	70.00 495	24.24 505	65.42 456	58.40 498

UnsupCasMVSNet_bld		63.70 439	61.53 445	70.21 446	73.69 476	51.39 461	72.82 460	81.89 396	55.63 463	57.81 477	71.80 479	38.67 453	78.61 455	49.26 444	52.21 488	80.63 467

APD_test1		53.31 456	49.93 461	63.42 468	65.68 496	50.13 468	71.59 465	66.90 487	34.43 499	40.58 500	71.56 480	8.65 509	76.27 470	34.64 491	55.36 482	63.86 494

ArgMatch-SfM		44.04 468	39.87 473	56.58 476	50.92 513	36.22 504	59.86 498	27.68 516	33.67 501	42.15 498	71.07 481	3.10 516	59.10 503	45.79 464	24.54 505	74.41 482

test_f		52.09 458	50.82 459	55.90 478	53.82 508	42.31 497	59.42 499	58.31 502	36.45 496	56.12 484	70.96 482	12.18 503	57.79 505	53.51 418	56.57 479	67.60 490

PVSNet_0	57.27 20	61.67 444	59.27 447	68.85 453	79.61 435	57.44 394	68.01 479	73.44 469	55.93 462	58.54 474	70.41 483	44.58 413	77.55 461	47.01 456	35.91 498	71.55 487

ArgMatch-Sym		43.72 469	39.92 472	55.10 482	52.36 511	37.56 503	61.93 497	23.00 518	35.80 498	43.62 496	70.22 484	3.22 515	55.93 507	45.35 468	23.80 507	71.81 486

pmmvs3		57.79 448	54.26 453	68.37 456	64.02 499	56.72 403	75.12 450	65.17 490	40.20 490	52.93 487	69.86 485	20.36 495	75.48 478	45.45 467	55.25 484	72.90 485

0.4-1-1-0.1		70.93 381	67.94 400	79.91 315	79.35 439	61.27 335	78.95 411	82.19 393	63.36 391	67.50 412	69.40 486	39.83 446	91.04 314	62.44 334	68.40 437	87.40 364

0.3-1-1-0.015		70.03 395	66.80 419	79.72 325	78.18 450	61.07 339	77.63 429	82.32 392	62.65 404	65.50 438	67.29 487	37.62 460	90.91 321	61.99 344	68.04 439	87.19 376

0.4-1-1-0.2		70.01 396	66.86 418	79.44 333	77.61 456	60.64 351	76.77 436	82.34 391	62.40 407	65.91 436	66.65 488	40.05 443	90.83 323	61.77 348	68.24 438	86.86 387

test_vis1_rt		60.28 445	58.42 448	65.84 464	67.25 494	55.60 422	70.44 471	60.94 498	44.33 486	59.00 472	66.64 489	24.91 487	68.67 496	62.80 327	69.48 429	73.25 484

new_pmnet		50.91 460	50.29 460	52.78 484	68.58 492	34.94 507	63.71 493	56.63 503	39.73 491	44.95 494	65.47 490	21.93 493	58.48 504	34.98 490	56.62 478	64.92 492

gg-mvs-nofinetune		69.95 397	67.96 398	75.94 387	83.07 372	54.51 435	77.23 433	70.29 476	63.11 394	70.32 373	62.33 491	43.62 420	88.69 370	53.88 416	87.76 184	84.62 431

JIA-IIPM		66.32 428	62.82 440	76.82 382	77.09 460	61.72 328	65.34 489	75.38 459	58.04 448	64.51 447	62.32 492	42.05 432	86.51 396	51.45 429	69.22 432	82.21 456

LCM-MVSNet		54.25 452	49.68 462	67.97 460	53.73 509	45.28 486	66.85 484	80.78 409	35.96 497	39.45 501	62.23 493	8.70 508	78.06 459	48.24 451	51.20 489	80.57 468

PMMVS2		40.82 470	38.86 474	46.69 486	53.84 507	16.45 523	48.61 503	49.92 505	37.49 494	31.67 502	60.97 494	8.14 510	56.42 506	28.42 497	30.72 502	67.19 491

testf1		45.72 463	41.96 467	57.00 474	56.90 503	45.32 484	66.14 486	59.26 500	26.19 504	30.89 504	60.96 495	4.14 512	70.64 493	26.39 503	46.73 494	55.04 500

APD_test2		45.72 463	41.96 467	57.00 474	56.90 503	45.32 484	66.14 486	59.26 500	26.19 504	30.89 504	60.96 495	4.14 512	70.64 493	26.39 503	46.73 494	55.04 500

RoMa-SfM		28.67 477	25.38 481	38.54 490	32.61 521	22.48 517	40.24 505	7.23 525	21.81 509	26.66 508	60.46 497	0.96 521	41.72 514	26.47 502	11.95 517	51.40 503

DenseAffine		31.97 472	28.22 478	43.21 489	43.10 516	27.10 510	46.21 504	11.36 521	24.92 506	27.70 506	58.81 498	1.09 520	46.50 513	26.95 500	13.85 516	56.02 499

MVS-HIRNet		59.14 447	57.67 449	63.57 467	81.65 403	43.50 492	71.73 463	65.06 491	39.59 492	51.43 488	57.73 499	38.34 455	82.58 435	39.53 482	73.95 400	64.62 493

ANet_high		50.57 461	46.10 465	63.99 466	48.67 514	39.13 500	70.99 468	80.85 408	61.39 417	31.18 503	57.70 500	17.02 499	73.65 489	31.22 495	15.89 513	79.18 472

PMVS	37.38 22	44.16 467	40.28 471	55.82 479	40.82 517	42.54 496	65.12 490	63.99 494	34.43 499	24.48 509	57.12 501	3.92 514	76.17 472	17.10 513	55.52 481	48.75 504
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LoFTR		27.52 478	24.27 482	37.29 493	34.75 520	19.27 520	33.78 509	21.60 519	12.42 516	21.61 514	56.59 502	0.91 522	40.37 515	13.94 517	22.80 509	52.22 502

dongtai		45.42 465	45.38 466	45.55 487	73.36 480	26.85 513	67.72 480	34.19 512	54.15 467	49.65 492	56.41 503	25.43 485	62.94 502	19.45 510	28.09 503	46.86 507

DKM		25.67 479	23.01 483	33.64 496	32.08 522	19.25 521	37.50 507	5.52 527	18.67 510	23.58 512	55.44 504	0.64 527	34.02 516	23.95 506	9.73 519	47.66 506

RoMa-HiRes		21.63 482	19.64 487	27.59 499	22.40 526	14.25 525	29.71 513	4.10 529	15.42 514	21.09 515	54.77 505	0.72 525	28.87 519	21.01 508	7.52 524	39.65 510

test_vis3_rt		49.26 462	47.02 464	56.00 477	54.30 506	45.27 487	66.76 485	48.08 507	36.83 495	44.38 495	53.20 506	7.17 511	64.07 500	56.77 400	55.66 480	58.65 497

test_method		31.52 474	29.28 477	38.23 491	27.03 524	6.50 535	20.94 516	62.21 496	4.05 524	22.35 513	52.50 507	13.33 501	47.58 510	27.04 499	34.04 500	60.62 495

PDCNetPlus		24.75 480	22.46 484	31.64 497	35.53 519	17.00 522	32.00 511	9.46 522	18.43 511	18.56 519	51.31 508	1.65 518	33.00 518	26.51 501	8.70 521	44.91 508

DKM-HiRes		20.87 483	19.15 488	26.02 501	25.34 525	14.13 526	29.63 514	3.62 534	14.53 515	20.13 516	50.55 509	0.47 535	24.22 523	20.96 509	7.15 525	39.70 509

kuosan		39.70 471	40.40 470	37.58 492	64.52 498	26.98 511	65.62 488	33.02 513	46.12 483	42.79 497	48.99 510	24.10 490	46.56 512	12.16 520	26.30 504	39.20 511

MASt3R-SfM		13.55 489	13.93 490	12.41 507	10.54 535	5.97 536	16.61 518	6.07 526	4.50 522	16.53 520	48.67 511	0.73 524	9.44 528	11.56 521	10.18 518	21.81 521

DeepMVS_CX				27.40 500	40.17 518	26.90 512		24.59 517	17.44 513	23.95 510	48.61 512	9.77 506	26.48 520	18.06 511	24.47 506	28.83 517

MatchFormer		22.13 481	19.86 486	28.93 498	28.66 523	15.74 524	31.91 512	17.10 520	7.75 517	18.87 517	47.50 513	0.62 529	33.92 517	7.49 525	18.87 510	37.14 513

PMatch-SfM		14.15 488	12.67 491	18.59 505	12.84 531	7.03 533	17.41 517	2.28 536	6.63 519	12.96 523	43.56 514	0.09 551	16.11 525	13.90 518	4.38 534	32.63 516

MVE	26.22 23	30.37 476	25.89 480	43.81 488	44.55 515	35.46 506	28.87 515	39.07 511	18.20 512	18.58 518	40.18 515	2.68 517	47.37 511	17.07 514	23.78 508	48.60 505
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ELoFTR		14.23 487	11.56 492	22.24 502	11.02 532	6.56 534	13.59 521	7.57 524	5.55 520	11.96 525	39.09 516	0.21 539	24.93 521	9.43 524	5.66 528	35.22 514

PMatch-Up-SfM		10.76 491	9.99 494	13.09 506	9.50 538	4.83 537	12.94 523	1.40 543	4.65 521	10.16 526	37.54 517	0.07 554	10.94 527	10.71 522	2.92 545	23.50 518

Gipuma		45.18 466	41.86 469	55.16 481	77.03 461	51.52 459	32.50 510	80.52 414	32.46 502	27.12 507	35.02 518	9.52 507	75.50 477	22.31 507	60.21 474	38.45 512
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 473	30.64 475	35.15 494	52.87 510	27.67 509	57.09 501	47.86 508	24.64 507	16.40 521	33.05 519	11.23 505	54.90 508	14.46 516	18.15 511	22.87 519

GLUNet-SfM		12.90 490	10.00 493	21.62 503	13.58 530	8.30 530	10.19 524	9.30 523	4.31 523	12.18 524	30.90 520	0.50 533	22.76 524	4.89 526	4.14 535	33.79 515

EMVS		30.81 475	29.65 476	34.27 495	50.96 512	25.95 514	56.58 502	46.80 509	24.01 508	15.53 522	30.68 521	12.47 502	54.43 509	12.81 519	17.05 512	22.43 520

tmp_tt		18.61 485	21.40 485	10.23 508	4.82 553	10.11 527	34.70 508	30.74 515	1.48 528	23.91 511	26.07 522	28.42 481	13.41 526	27.12 498	15.35 514	7.17 528

ALIKED-LG		8.61 492	8.70 496	8.33 509	20.63 527	8.70 529	15.50 519	4.61 528	2.19 525	5.84 528	18.70 523	0.80 523	8.06 529	1.03 534	8.97 520	8.25 522

ALIKED-MNN		7.86 493	7.83 499	7.97 510	19.40 528	8.86 528	14.48 520	3.90 530	1.59 526	4.74 533	16.49 524	0.59 530	7.65 530	0.91 535	8.34 523	7.39 525

ALIKED-NN		7.51 494	7.61 500	7.21 511	18.26 529	8.10 531	13.45 522	3.88 532	1.50 527	4.87 531	16.47 525	0.64 527	7.00 531	0.88 536	8.50 522	6.52 530

XFeat-MNN		4.39 499	4.49 502	4.10 512	2.88 555	1.91 550	5.86 530	2.57 535	1.06 530	5.04 529	13.99 526	0.43 537	4.47 532	2.00 528	6.55 526	5.92 531

SP-DiffGlue		4.29 500	4.46 503	3.77 516	3.68 554	2.12 544	5.97 529	2.22 537	1.10 529	4.89 530	13.93 527	0.66 526	1.95 538	2.47 527	5.24 529	7.22 527

X-MVStestdata		80.37 208	77.83 248	88.00 17	94.42 25	73.33 19	92.78 23	92.99 56	79.14 27	83.67 117	12.47 528	67.45 132	96.60 39	83.06 88	94.50 57	94.07 82

XFeat-NN		3.78 505	3.96 508	3.23 518	2.65 556	1.53 555	4.99 531	1.92 541	0.81 535	4.77 532	12.37 529	0.38 538	3.39 533	1.64 529	6.13 527	4.77 532

SP-LightGlue		4.27 501	4.41 504	3.86 513	10.99 533	1.99 547	8.19 525	2.06 539	0.98 532	2.37 535	8.29 530	0.56 531	2.10 535	1.27 530	4.99 530	7.48 524

SP-SuperGlue		4.24 502	4.38 505	3.81 515	10.75 534	2.00 546	8.18 526	2.09 538	1.00 531	2.41 534	8.29 530	0.56 531	2.05 537	1.27 530	4.91 531	7.39 525

SP-MNN		4.14 503	4.24 506	3.82 514	10.32 536	1.83 551	8.11 527	1.99 540	0.82 534	2.23 536	8.27 532	0.47 535	2.14 534	1.20 532	4.77 532	7.49 523

SP-NN		4.00 504	4.12 507	3.63 517	9.92 537	1.81 552	7.94 528	1.90 542	0.86 533	2.15 537	8.00 533	0.50 533	2.09 536	1.20 532	4.63 533	6.98 529

SIFT-NN		2.77 506	2.92 509	2.34 519	8.70 539	3.08 538	4.46 532	1.01 545	0.68 536	1.46 538	5.49 534	0.16 540	1.65 539	0.26 537	4.04 536	2.27 534

test_post											5.46 535	50.36 354	84.24 420

test_post1							78.90 413				5.43 536	48.81 379	85.44 411	59.25 371

SIFT-MNN		2.63 507	2.75 510	2.25 520	8.10 540	2.84 539	4.08 533	1.02 544	0.68 536	1.28 539	5.34 537	0.15 541	1.64 540	0.26 537	3.88 538	2.27 534

SIFT-NN-UMatch		2.26 511	2.39 514	1.89 525	6.21 548	2.08 545	3.76 535	0.83 548	0.66 538	1.04 543	5.09 538	0.14 542	1.52 543	0.23 540	3.51 540	2.07 538

SIFT-NN-CMatch		2.31 510	2.41 513	2.00 523	6.59 546	2.34 543	3.48 537	0.83 548	0.65 539	1.28 539	5.09 538	0.14 542	1.52 543	0.23 540	3.41 541	2.14 536

SIFT-NN-NCMNet		2.52 508	2.64 511	2.14 521	7.53 542	2.74 540	4.00 534	0.98 546	0.65 539	1.24 541	5.08 540	0.14 542	1.60 541	0.23 540	3.94 537	2.07 538

SIFT-ConvMatch		2.25 512	2.37 515	1.90 524	7.29 543	2.37 542	3.21 540	0.75 550	0.65 539	1.03 544	4.91 541	0.12 548	1.51 545	0.22 543	3.13 543	1.81 541

SIFT-UMatch		2.16 513	2.30 516	1.72 527	6.99 544	1.97 549	3.32 538	0.70 552	0.64 543	0.91 545	4.86 542	0.12 548	1.49 546	0.22 543	2.97 544	1.72 543

SIFT-NCM-Cal		2.40 509	2.52 512	2.05 522	7.74 541	2.54 541	3.75 536	0.84 547	0.65 539	0.89 546	4.78 543	0.13 545	1.60 541	0.19 548	3.71 539	2.01 540

SIFT-NN-PointCN		2.07 514	2.18 517	1.74 526	5.75 549	1.65 554	3.27 539	0.73 551	0.60 546	1.07 542	4.62 544	0.13 545	1.43 547	0.21 545	3.22 542	2.12 537

SIFT-UM-Cal		1.97 516	2.12 519	1.52 529	6.57 547	1.67 553	2.93 541	0.57 555	0.62 545	0.83 548	4.55 545	0.11 550	1.37 549	0.20 547	2.69 547	1.53 546

SIFT-CM-Cal		2.02 515	2.13 518	1.67 528	6.79 545	1.99 547	2.79 542	0.64 553	0.63 544	0.87 547	4.48 546	0.13 545	1.41 548	0.19 548	2.70 546	1.61 545

SIFT-PCN-Cal		1.72 517	1.82 521	1.39 530	5.64 550	1.19 557	2.39 544	0.53 556	0.55 548	0.72 549	3.90 547	0.09 551	1.22 551	0.17 550	2.42 549	1.76 542

SIFT-PointCN		1.72 517	1.83 520	1.36 531	5.55 551	1.22 556	2.59 543	0.59 554	0.55 548	0.71 550	3.77 548	0.08 553	1.24 550	0.17 550	2.48 548	1.63 544

SIFT-NCMNet		1.44 519	1.56 522	1.08 532	5.14 552	1.07 558	1.97 545	0.32 557	0.56 547	0.64 551	3.23 549	0.07 554	1.01 552	0.14 552	1.95 550	1.15 547

wuyk23d		16.82 486	15.94 489	19.46 504	58.74 502	31.45 508	39.22 506	3.74 533	6.84 518	6.04 527	2.70 550	1.27 519	24.29 522	10.54 523	14.40 515	2.63 533

testmvs		6.04 497	8.02 498	0.10 534	0.08 557	0.03 560	69.74 472	0.04 558	0.05 551	0.31 553	1.68 551	0.02 557	0.04 553	0.24 539	0.02 551	0.25 549

test123		6.12 496	8.11 497	0.14 533	0.06 558	0.09 559	71.05 467	0.03 559	0.04 552	0.25 554	1.30 552	0.05 556	0.03 554	0.21 545	0.01 552	0.29 548

mmdepth		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

monomultidepth		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

test_blank		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uanet_test		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

DCPMVS		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

pcd_1.5k_mvsjas		5.26 498	7.02 501	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	63.15 189	0.00 555	0.00 553	0.00 553	0.00 550

sosnet-low-res		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

sosnet		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uncertanet		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

Regformer		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

uanet		0.00 520	0.00 523	0.00 535	0.00 559	0.00 561	0.00 546	0.00 560	0.00 553	0.00 555	0.00 553	0.00 558	0.00 555	0.00 553	0.00 553	0.00 550

test-260524					94.58 16	71.43 61		94.16 8		90.64 21		78.62 14	97.13 17	88.60 33	96.28 16

WAC-MVS						42.58 494								39.46 483

FOURS1					95.00 10	72.39 41	95.06 1	93.84 21	74.49 159	91.30 17

MSC_two_6792asdad				89.16 1	94.34 32	75.53 2		92.99 56					97.53 2	89.67 15	96.44 9	94.41 61

No_MVS				89.16 1	94.34 32	75.53 2		92.99 56					97.53 2	89.67 15	96.44 9	94.41 61

eth-test2					0.00 559

eth-test					0.00 559

IU-MVS					95.30 2	71.25 66		92.95 62	66.81 336	92.39 6				88.94 28	96.63 4	94.85 24

save fliter					93.80 45	72.35 44	90.47 74	91.17 156	74.31 165

test_0728_SECOND				87.71 35	95.34 1	71.43 61	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 74

GSMVS																88.96 319

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs1												51.32 338				88.96 319

sam_mvs												50.01 358

MTGPA								92.02 115

MTMP							92.18 39	32.83 514

test9_res														84.90 65	95.70 30	92.87 161

agg_prior2														82.91 92	95.45 33	92.70 166

agg_prior					92.85 69	71.94 53		91.78 131		84.41 98			94.93 105

test_prior4						72.60 34	89.01 126

test_prior				86.33 65	92.61 76	69.59 100		92.97 61					95.48 77			93.91 90

旧先验2							86.56 233		58.10 447	87.04 63			88.98 364	74.07 211

新几何2							86.29 247

无先验							87.48 190	88.98 247	60.00 428				94.12 145	67.28 287		88.97 318

原ACMM2							86.86 220

testdata2													91.01 315	62.37 338

segment_acmp												73.08 45

testdata1							84.14 314		75.71 117

test12				86.80 59	92.63 75	70.70 83		91.79 130		82.71 142		71.67 67	96.16 54		94.50 57	93.54 120

plane_prior7					90.08 118	68.51 133

plane_prior6					89.84 127	68.70 127						60.42 245

plane_prior5								92.44 85					95.38 85	78.71 152	86.32 211	91.33 222

plane_prior3						68.60 130			78.44 37	78.92 210

plane_prior2							91.25 60		79.12 29

plane_prior1					89.90 126

plane_prior						68.71 125	90.38 78		77.62 49						86.16 216

n2								0.00 560

nn								0.00 560

door-mid								69.98 477

test11								92.23 101

door								69.44 480

HQP5-MVS						66.98 187

HQP-NCC					89.33 148		89.17 117		76.41 96	77.23 251

ACMP_Plane					89.33 148		89.17 117		76.41 96	77.23 251

BP-MVS														77.47 167

HQP4-MVS										77.24 250			95.11 97			91.03 232

HQP3-MVS								92.19 109							85.99 222

HQP2-MVS												60.17 248

MDTV_nov1_ep13_2view						37.79 502	75.16 448		55.10 464	66.53 428		49.34 369		53.98 415		87.94 350

ACMMP++_ref															81.95 292

ACMMP++															81.25 298

Test By Simon												64.33 175

High-resolution multi-view benchmark