High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 10	97.13 2	95.58 11	89.33 1	85.77 71	96.26 46	72.84 32	99.38 1	92.64 33	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 105	71.10 27	97.02 3	96.04 6	88.70 2	91.57 19	96.19 48	70.12 49	98.91 21	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 169	76.68 2	97.29 1	95.35 17	82.87 37	91.58 18	97.22 8	79.93 5	99.10 9	83.12 128	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 88	94.37 61	72.48 239	92.07 11	96.85 27	83.82 2	99.15 2	91.53 46	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 110	92.83 84	64.03 235	93.06 133	94.33 63	82.19 45	93.65 3	96.15 50	85.89 1	97.19 98	91.02 50	97.75 1	96.43 31
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MGCNet		90.32 6	90.90 7	88.55 23	94.05 49	70.23 37	97.00 5	93.73 82	87.30 4	92.15 8	96.15 50	66.38 75	98.94 20	96.71 3	94.67 33	96.47 28

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 30	96.47 14	94.83 36	84.83 17	89.07 43	96.80 30	70.86 45	99.06 15	92.64 33	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 75	73.88 9	97.01 4	94.40 59	88.32 3	85.71 72	94.91 91	74.11 23	98.91 21	87.26 78	95.94 8	97.03 12
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 59	96.89 6	94.44 55	71.65 269	92.11 9	97.21 9	76.79 9	99.11 6	92.34 35	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 168	93.00 80	58.16 377	96.72 9	94.41 57	86.50 9	90.25 34	97.83 1	75.46 16	98.67 29	92.78 32	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 113	96.04 25	63.70 252	95.04 42	95.19 22	86.74 8	91.53 20	95.15 84	73.86 24	97.58 69	93.38 27	92.00 75	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 43	69.71 52	96.53 13	93.78 75	86.89 7	89.68 40	95.78 57	65.94 80	99.10 9	92.99 30	93.91 46	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 51	71.92 255	90.55 30	96.93 21	73.77 25	99.08 11	91.91 41	94.90 22	96.29 35
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
HPM-MVS++		89.37 14	89.95 13	87.64 35	95.10 31	68.23 101	95.24 34	94.49 53	82.43 42	88.90 45	96.35 41	71.89 42	98.63 30	88.76 64	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 23	89.81 6	93.66 58	75.15 5	90.61 274	93.43 97	84.06 24	86.20 66	90.17 226	72.42 37	96.98 115	93.09 29	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 75	96.38 15	94.64 46	84.42 21	86.74 61	96.20 47	66.56 74	98.76 27	89.03 63	94.56 34	95.92 49

MED-MVS		88.94 17	89.45 16	87.42 45	94.76 34	67.28 126	94.47 61	94.87 32	70.09 301	91.27 23	96.95 17	76.77 11	98.98 16	91.55 43	94.28 37	95.99 45

DPE-MVS		88.77 18	89.21 19	87.45 44	96.26 21	67.56 119	94.17 74	94.15 68	68.77 322	90.74 28	97.27 6	76.09 14	98.49 33	90.58 54	94.91 21	96.30 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TestfortrainingZip a		88.66 19	88.99 21	87.70 33	94.76 34	68.73 84	94.47 61	94.87 32	73.09 226	91.27 23	96.95 17	76.77 11	98.98 16	84.41 111	94.28 37	95.37 69

ME-MVS		88.25 20	88.55 27	87.33 50	96.33 18	67.28 126	93.93 90	94.81 37	70.09 301	88.91 44	96.95 17	70.12 49	98.73 28	91.55 43	94.28 37	95.99 45

fmvsm_l_conf0.5_n_9		88.24 21	89.36 17	84.85 157	88.15 230	61.94 302	95.65 25	89.70 298	85.54 12	92.07 11	97.33 5	67.51 66	97.27 93	96.23 5	92.07 74	95.35 73

fmvsm_s_conf0.5_n_9		88.14 22	89.21 19	84.92 152	89.29 180	61.41 319	92.97 138	88.36 353	86.96 6	91.49 21	97.49 3	69.48 54	97.46 76	97.00 1	89.88 111	95.89 50

SMA-MVS		88.14 22	88.29 31	87.67 34	93.21 72	68.72 86	93.85 96	94.03 71	74.18 200	91.74 15	96.67 33	65.61 85	98.42 37	89.24 60	96.08 7	95.88 51
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
PS-MVSNAJ		88.14 22	87.61 41	89.71 7	92.06 108	76.72 1	95.75 20	93.26 103	83.86 25	89.55 41	96.06 52	53.55 266	97.89 51	91.10 48	93.31 57	94.54 131

TSAR-MVS + MP.		88.11 25	88.64 26	86.54 88	91.73 123	68.04 105	90.36 281	93.55 89	82.89 35	91.29 22	92.89 146	72.27 39	96.03 169	87.99 68	94.77 26	95.54 62
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_11		87.99 26	89.25 18	84.23 194	89.07 188	61.60 312	94.87 49	89.06 326	85.65 11	91.09 26	97.41 4	68.26 58	97.43 80	95.07 13	92.74 64	93.66 183

fmvsm_s_conf0.5_n_8		87.96 27	88.93 22	85.07 147	88.43 217	61.78 305	94.73 56	91.74 178	85.87 10	91.66 17	97.50 2	64.03 106	98.33 38	96.28 4	90.08 107	95.10 90

TSAR-MVS + GP.		87.96 27	88.37 30	86.70 71	93.51 66	65.32 190	95.15 37	93.84 74	78.17 130	85.93 70	94.80 94	75.80 15	98.21 40	89.38 57	88.78 123	96.59 19

DeepC-MVS_fast	79.48 2	87.95 29	88.00 35	87.79 31	95.86 28	68.32 95	95.74 21	94.11 69	83.82 26	83.49 97	96.19 48	64.53 101	98.44 35	83.42 127	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_10		87.93 30	88.67 25	85.71 120	88.69 199	63.71 250	94.56 59	90.22 274	85.04 15	92.27 6	97.05 12	63.67 114	98.15 42	95.09 12	91.39 87	95.27 81

xiu_mvs_v2_base		87.92 31	87.38 45	89.55 12	91.41 135	76.43 3	95.74 21	93.12 111	83.53 29	89.55 41	95.95 55	53.45 270	97.68 59	91.07 49	92.62 65	94.54 131

EPNet		87.84 32	88.38 29	86.23 100	93.30 69	66.05 169	95.26 33	94.84 35	87.09 5	88.06 48	94.53 100	66.79 71	97.34 86	83.89 118	91.68 81	95.29 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 33	87.77 38	87.63 39	89.24 185	71.18 24	96.57 12	92.90 122	82.70 39	87.13 56	95.27 77	64.99 91	95.80 185	89.34 58	91.80 79	95.93 48

test_fmvsm_n_1920		87.69 34	88.50 28	85.27 140	87.05 265	63.55 259	93.69 106	91.08 221	84.18 23	90.17 36	97.04 14	67.58 65	97.99 46	95.72 8	90.03 108	94.26 151

fmvsm_l_conf0.5_n_3		87.54 35	88.29 31	85.30 137	86.92 276	62.63 285	95.02 44	90.28 269	84.95 16	90.27 33	96.86 25	65.36 87	97.52 74	94.93 15	90.03 108	95.76 54

APDe-MVS		87.54 35	87.84 37	86.65 74	96.07 24	66.30 164	94.84 51	93.78 75	69.35 311	88.39 47	96.34 42	67.74 64	97.66 64	90.62 53	93.44 55	96.01 44
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_6		87.50 37	88.72 24	83.84 206	86.89 278	60.04 353	95.05 40	92.17 157	84.80 18	92.27 6	96.37 39	64.62 98	96.54 141	94.43 19	91.86 77	94.94 99

fmvsm_l_conf0.5_n		87.49 38	88.19 33	85.39 131	86.95 271	64.37 222	94.30 71	88.45 351	80.51 70	92.70 4	96.86 25	69.98 51	97.15 103	95.83 7	88.08 131	94.65 124

SD-MVS		87.49 38	87.49 43	87.50 43	93.60 60	68.82 82	93.90 93	92.63 137	76.86 157	87.90 50	95.76 58	66.17 77	97.63 66	89.06 62	91.48 85	96.05 42
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
fmvsm_l_conf0.5_n_a		87.44 40	88.15 34	85.30 137	87.10 263	64.19 230	94.41 66	88.14 361	80.24 81	92.54 5	96.97 16	69.52 53	97.17 99	95.89 6	88.51 126	94.56 128

dcpmvs_2		87.37 41	87.55 42	86.85 62	95.04 33	68.20 102	90.36 281	90.66 249	79.37 105	81.20 121	93.67 130	74.73 18	96.55 140	90.88 51	92.00 75	95.82 52

alignmvs		87.28 42	86.97 49	88.24 27	91.30 137	71.14 26	95.61 26	93.56 88	79.30 106	87.07 58	95.25 79	68.43 56	96.93 123	87.87 69	84.33 182	96.65 17

train_agg		87.21 43	87.42 44	86.60 77	94.18 45	67.28 126	94.16 75	93.51 91	71.87 260	85.52 75	95.33 71	68.19 59	97.27 93	89.09 61	94.90 22	95.25 85

MG-MVS		87.11 44	86.27 62	89.62 8	97.79 1	76.27 4	94.96 46	94.49 53	78.74 121	83.87 93	92.94 144	64.34 102	96.94 121	75.19 206	94.09 42	95.66 57

SF-MVS		87.03 45	87.09 47	86.84 63	92.70 90	67.45 124	93.64 109	93.76 78	70.78 293	86.25 64	96.44 38	66.98 69	97.79 55	88.68 65	94.56 34	95.28 80

fmvsm_s_conf0.5_n_3		86.88 46	87.99 36	83.58 220	87.26 257	60.74 333	93.21 130	87.94 368	84.22 22	91.70 16	97.27 6	65.91 82	95.02 228	93.95 24	90.42 103	94.99 96

CSCG		86.87 47	86.26 63	88.72 17	95.05 32	70.79 29	93.83 101	95.33 18	68.48 326	77.63 181	94.35 109	73.04 30	98.45 34	84.92 103	93.71 51	96.92 14

sasdasda		86.85 48	86.25 64	88.66 20	91.80 121	71.92 16	93.54 114	91.71 181	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 66	84.34 180	97.11 9

canonicalmvs		86.85 48	86.25 64	88.66 20	91.80 121	71.92 16	93.54 114	91.71 181	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 66	84.34 180	97.11 9

UBG		86.83 50	86.70 55	87.20 52	93.07 78	69.81 47	93.43 122	95.56 13	81.52 52	81.50 116	92.12 167	73.58 28	96.28 153	84.37 112	85.20 169	95.51 63

PHI-MVS		86.83 50	86.85 54	86.78 67	93.47 67	65.55 185	95.39 31	95.10 25	71.77 265	85.69 73	96.52 35	62.07 143	98.77 26	86.06 91	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 52	86.90 52	86.58 80	90.42 154	66.38 161	96.09 17	93.87 73	77.73 139	84.01 92	95.66 60	63.39 121	97.94 47	87.40 76	93.55 54	95.42 65
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 53	87.63 39	84.27 192	86.15 294	61.48 316	94.69 57	91.16 208	83.79 28	90.51 32	96.28 44	64.24 103	98.22 39	95.00 14	86.88 143	93.11 201

PVSNet_Blended		86.73 54	86.86 53	86.31 99	93.76 54	67.53 121	96.33 16	93.61 86	82.34 44	81.00 127	93.08 140	63.19 125	97.29 89	87.08 82	91.38 88	94.13 160

testing11		86.71 55	86.44 60	87.55 41	93.54 64	71.35 21	93.65 108	95.58 11	81.36 59	80.69 132	92.21 165	72.30 38	96.46 146	85.18 99	83.43 197	94.82 109

test_fmvsmconf_n		86.58 56	87.17 46	84.82 159	85.28 312	62.55 286	94.26 73	89.78 289	83.81 27	87.78 52	96.33 43	65.33 88	96.98 115	94.40 20	87.55 137	94.95 98

BP-MVS1		86.54 57	86.68 57	86.13 103	87.80 245	67.18 133	92.97 138	95.62 10	79.92 86	82.84 104	94.14 118	74.95 17	96.46 146	82.91 132	88.96 122	94.74 114

jason		86.40 58	86.17 66	87.11 55	86.16 293	70.54 32	95.71 24	92.19 154	82.00 47	84.58 85	94.34 110	61.86 146	95.53 210	87.76 70	90.89 96	95.27 81
jason: jason.
NormalMVS		86.39 59	86.66 58	85.60 125	92.12 105	65.95 174	94.88 47	90.83 237	84.69 19	83.67 95	94.10 119	63.16 127	96.91 127	85.31 95	91.15 92	93.93 171

fmvsm_s_conf0.5_n		86.39 59	86.91 51	84.82 159	87.36 256	63.54 260	94.74 53	90.02 282	82.52 40	90.14 37	96.92 23	62.93 132	97.84 54	95.28 11	82.26 208	93.07 204

fmvsm_s_conf0.5_n_5		86.38 61	86.94 50	84.71 170	84.67 324	63.29 265	94.04 84	89.99 284	82.88 36	87.85 51	96.03 53	62.89 134	96.36 150	94.15 21	89.95 110	94.48 141

SymmetryMVS		86.32 62	86.39 61	86.12 104	90.52 152	65.95 174	94.88 47	94.58 50	84.69 19	83.67 95	94.10 119	63.16 127	96.91 127	85.31 95	86.59 152	95.51 63

WTY-MVS		86.32 62	85.81 74	87.85 29	92.82 86	69.37 61	95.20 35	95.25 20	82.71 38	81.91 112	94.73 95	67.93 63	97.63 66	79.55 171	82.25 210	96.54 22

myMVS_eth3d28		86.31 64	86.15 67	86.78 67	93.56 62	70.49 33	92.94 141	95.28 19	82.47 41	78.70 171	92.07 169	72.45 36	95.41 212	82.11 141	85.78 162	94.44 143

MSLP-MVS++		86.27 65	85.91 73	87.35 48	92.01 112	68.97 78	95.04 42	92.70 128	79.04 116	81.50 116	96.50 37	58.98 191	96.78 131	83.49 126	93.93 45	96.29 35

VNet		86.20 66	85.65 78	87.84 30	93.92 51	69.99 39	95.73 23	95.94 7	78.43 126	86.00 69	93.07 141	58.22 201	97.00 111	85.22 97	84.33 182	96.52 23

MVS_111021_HR		86.19 67	85.80 75	87.37 47	93.17 74	69.79 48	93.99 87	93.76 78	79.08 113	78.88 167	93.99 124	62.25 141	98.15 42	85.93 92	91.15 92	94.15 159

SPE-MVS-test		86.14 68	87.01 48	83.52 221	92.63 92	59.36 365	95.49 28	91.92 167	80.09 82	85.46 77	95.53 66	61.82 148	95.77 189	86.77 86	93.37 56	95.41 66

ACMMP_NAP		86.05 69	85.80 75	86.80 66	91.58 127	67.53 121	91.79 208	93.49 94	74.93 190	84.61 84	95.30 73	59.42 181	97.92 48	86.13 89	94.92 20	94.94 99

testing99		86.01 70	85.47 80	87.63 39	93.62 59	71.25 23	93.47 120	95.23 21	80.42 73	80.60 134	91.95 176	71.73 43	96.50 144	80.02 168	82.22 211	95.13 88

ETV-MVS		86.01 70	86.11 68	85.70 121	90.21 159	67.02 140	93.43 122	91.92 167	81.21 61	84.13 91	94.07 123	60.93 157	95.63 199	89.28 59	89.81 112	94.46 142

testing91		85.93 72	85.31 84	87.78 32	93.59 61	71.47 19	93.50 117	95.08 28	80.26 78	80.53 137	91.93 177	70.43 47	96.51 143	80.32 166	82.13 213	95.37 69

APD-MVS		85.93 72	85.99 71	85.76 117	95.98 27	65.21 193	93.59 112	92.58 139	66.54 345	86.17 67	95.88 56	63.83 110	97.00 111	86.39 88	92.94 61	95.06 92
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 74	85.46 81	87.18 53	88.20 229	72.42 15	92.41 175	92.77 126	82.11 46	80.34 140	93.07 141	68.27 57	95.02 228	78.39 187	93.59 53	94.09 162

CS-MVS		85.80 75	86.65 59	83.27 233	92.00 113	58.92 369	95.31 32	91.86 172	79.97 83	84.82 83	95.40 69	62.26 140	95.51 211	86.11 90	92.08 73	95.37 69

fmvsm_s_conf0.5_n_a		85.75 76	86.09 69	84.72 168	85.73 305	63.58 257	93.79 102	89.32 309	81.42 57	90.21 35	96.91 24	62.41 139	97.67 61	94.48 18	80.56 236	92.90 210

test_fmvsmconf0.1_n		85.71 77	86.08 70	84.62 178	80.83 375	62.33 291	93.84 99	88.81 338	83.50 30	87.00 59	96.01 54	63.36 122	96.93 123	94.04 23	87.29 140	94.61 126

CDPH-MVS		85.71 77	85.46 81	86.46 90	94.75 38	67.19 131	93.89 94	92.83 124	70.90 289	83.09 102	95.28 75	63.62 116	97.36 84	80.63 162	94.18 41	94.84 105

casdiffmvs_mvg		85.66 79	85.18 86	87.09 56	88.22 228	69.35 62	93.74 105	91.89 170	81.47 53	80.10 143	91.45 189	64.80 96	96.35 151	87.23 79	87.69 135	95.58 60
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.1_n		85.61 80	85.93 72	84.68 172	82.95 356	63.48 262	94.03 86	89.46 303	81.69 50	89.86 38	96.74 31	61.85 147	97.75 57	94.74 17	82.01 215	92.81 214

MGCFI-Net		85.59 81	85.73 77	85.17 144	91.41 135	62.44 287	92.87 146	91.31 198	79.65 93	86.99 60	95.14 85	62.90 133	96.12 161	87.13 81	84.13 188	96.96 13

GDP-MVS		85.54 82	85.32 83	86.18 101	87.64 248	67.95 109	92.91 144	92.36 144	77.81 136	83.69 94	94.31 112	72.84 32	96.41 148	80.39 165	85.95 159	94.19 155

DeepC-MVS	77.85 3	85.52 83	85.24 85	86.37 95	88.80 197	66.64 155	92.15 185	93.68 84	81.07 63	76.91 193	93.64 131	62.59 136	98.44 35	85.50 93	92.84 63	94.03 166
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		85.37 84	84.87 92	86.84 63	88.25 226	69.07 72	93.04 135	91.76 177	81.27 60	80.84 130	92.07 169	64.23 104	96.06 167	84.98 102	87.43 139	95.39 67
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ZNCC-MVS		85.33 85	85.08 88	86.06 105	93.09 77	65.65 181	93.89 94	93.41 99	73.75 211	79.94 145	94.68 97	60.61 162	98.03 45	82.63 136	93.72 50	94.52 133

fmvsm_s_conf0.5_n_7		85.24 86	86.69 56	80.91 308	84.52 329	60.10 351	93.35 125	90.35 262	83.41 31	86.54 63	96.27 45	60.50 163	90.02 392	94.84 16	90.38 104	92.61 218

MP-MVS-pluss		85.24 86	85.13 87	85.56 126	91.42 132	65.59 183	91.54 226	92.51 141	74.56 193	80.62 133	95.64 61	59.15 186	97.00 111	86.94 84	93.80 47	94.07 164
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 88	84.69 96	86.63 76	92.91 82	69.91 43	92.61 161	95.80 9	80.31 77	80.38 139	92.27 161	68.73 55	95.19 225	75.94 200	83.27 199	94.81 111

PAPR		85.15 89	84.47 97	87.18 53	96.02 26	68.29 96	91.85 206	93.00 117	76.59 168	79.03 163	95.00 86	61.59 149	97.61 68	78.16 188	89.00 121	95.63 58

fmvsm_s_conf0.5_n_2		85.06 90	85.60 79	83.44 227	86.92 276	60.53 340	94.41 66	87.31 376	83.30 32	88.72 46	96.72 32	54.28 258	97.75 57	94.07 22	84.68 179	92.04 241

MP-MVS		85.02 91	84.97 90	85.17 144	92.60 93	64.27 227	93.24 127	92.27 147	73.13 222	79.63 155	94.43 103	61.90 144	97.17 99	85.00 101	92.56 66	94.06 165
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 92	84.44 98	86.71 70	88.33 223	68.73 84	90.24 286	91.82 176	81.05 64	81.18 122	92.50 153	63.69 113	96.08 166	84.45 110	86.71 150	95.32 76

CHOSEN 1792x2688		84.98 93	83.45 118	89.57 11	89.94 164	75.14 6	92.07 191	92.32 145	81.87 48	75.68 202	88.27 260	60.18 167	98.60 31	80.46 164	90.27 106	94.96 97

MVSMamba_PlusPlus		84.97 94	83.65 111	88.93 14	90.17 160	74.04 8	87.84 344	92.69 131	62.18 387	81.47 118	87.64 274	71.47 44	96.28 153	84.69 105	94.74 31	96.47 28

E3new		84.94 95	84.36 100	86.69 73	89.06 189	69.31 63	92.68 158	91.29 203	80.72 67	81.03 125	92.14 166	61.89 145	95.91 173	84.59 107	85.85 161	94.86 101

viewmanbaseed2359cas		84.89 96	84.26 102	86.78 67	88.50 208	69.77 50	92.69 157	91.13 214	81.11 62	81.54 115	91.98 173	60.35 164	95.73 191	84.47 109	86.56 153	94.84 105

EIA-MVS		84.84 97	84.88 91	84.69 171	91.30 137	62.36 290	93.85 96	92.04 160	79.45 101	79.33 160	94.28 114	62.42 138	96.35 151	80.05 167	91.25 91	95.38 68

lecture		84.77 98	84.81 94	84.65 174	92.12 105	62.27 294	94.74 53	92.64 136	68.35 327	85.53 74	95.30 73	59.77 174	97.91 49	83.73 122	91.15 92	93.77 180

fmvsm_s_conf0.1_n_a		84.76 99	84.84 93	84.53 180	80.23 388	63.50 261	92.79 148	88.73 341	80.46 71	89.84 39	96.65 34	60.96 156	97.57 71	93.80 25	80.14 238	92.53 223

viewcassd2359sk11		84.74 100	84.11 103	86.64 75	88.57 202	69.20 70	92.61 161	91.23 205	80.58 68	80.85 129	91.96 174	61.39 151	95.89 175	84.28 113	85.49 166	94.82 109

HFP-MVS		84.73 101	84.40 99	85.72 119	93.75 56	65.01 199	93.50 117	93.19 107	72.19 249	79.22 161	94.93 89	59.04 189	97.67 61	81.55 151	92.21 69	94.49 140

MVS		84.66 102	82.86 140	90.06 2	90.93 144	74.56 7	87.91 342	95.54 14	68.55 324	72.35 259	94.71 96	59.78 173	98.90 23	81.29 157	94.69 32	96.74 16

GST-MVS		84.63 103	84.29 101	85.66 122	92.82 86	65.27 191	93.04 135	93.13 110	73.20 220	78.89 164	94.18 117	59.41 182	97.85 53	81.45 153	92.48 68	93.86 177

EC-MVSNet		84.53 104	85.04 89	83.01 239	89.34 176	61.37 320	94.42 65	91.09 218	77.91 134	83.24 98	94.20 116	58.37 199	95.40 213	85.35 94	91.41 86	92.27 235

E2		84.45 105	83.74 107	86.56 82	87.90 238	69.06 73	92.53 169	91.13 214	80.35 75	80.58 135	91.69 184	60.70 158	95.84 178	83.80 120	84.99 171	94.79 112

E3		84.45 105	83.74 107	86.56 82	87.90 238	69.06 73	92.53 169	91.13 214	80.35 75	80.58 135	91.69 184	60.70 158	95.84 178	83.80 120	84.99 171	94.79 112

fmvsm_s_conf0.1_n_2		84.40 107	84.78 95	83.27 233	85.25 313	60.41 343	94.13 78	85.69 401	83.05 34	87.99 49	96.37 39	52.75 275	97.68 59	93.75 26	84.05 189	91.71 249

ACMMPR		84.37 108	84.06 104	85.28 139	93.56 62	64.37 222	93.50 117	93.15 109	72.19 249	78.85 169	94.86 92	56.69 225	97.45 77	81.55 151	92.20 70	94.02 167

region2R		84.36 109	84.03 105	85.36 135	93.54 64	64.31 225	93.43 122	92.95 120	72.16 252	78.86 168	94.84 93	56.97 220	97.53 73	81.38 155	92.11 72	94.24 153

LFMVS		84.34 110	82.73 142	89.18 13	94.76 34	73.25 11	94.99 45	91.89 170	71.90 257	82.16 111	93.49 135	47.98 328	97.05 106	82.55 137	84.82 175	97.25 8

test_yl		84.28 111	83.16 131	87.64 35	94.52 41	69.24 68	95.78 18	95.09 26	69.19 314	81.09 123	92.88 147	57.00 218	97.44 78	81.11 159	81.76 219	96.23 38

DCV-MVSNet		84.28 111	83.16 131	87.64 35	94.52 41	69.24 68	95.78 18	95.09 26	69.19 314	81.09 123	92.88 147	57.00 218	97.44 78	81.11 159	81.76 219	96.23 38

diffmvs		84.28 111	83.83 106	85.61 124	87.40 254	68.02 106	90.88 259	89.24 312	80.54 69	81.64 114	92.52 152	59.83 172	94.52 257	87.32 77	85.11 170	94.29 150
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HY-MVS	76.49 5	84.28 111	83.36 124	87.02 59	92.22 100	67.74 114	84.65 373	94.50 52	79.15 110	82.23 110	87.93 269	66.88 70	96.94 121	80.53 163	82.20 212	96.39 33

ETVMVS		84.22 115	83.71 109	85.76 117	92.58 94	68.25 100	92.45 173	95.53 15	79.54 100	79.46 157	91.64 187	70.29 48	94.18 271	69.16 269	82.76 205	94.84 105

MAR-MVS		84.18 116	83.43 119	86.44 92	96.25 22	65.93 176	94.28 72	94.27 65	74.41 195	79.16 162	95.61 62	53.99 261	98.88 25	69.62 263	93.26 58	94.50 139
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVS_Test		84.16 117	83.20 128	87.05 58	91.56 128	69.82 46	89.99 295	92.05 159	77.77 138	82.84 104	86.57 291	63.93 109	96.09 163	74.91 211	89.18 118	95.25 85

CANet_DTU		84.09 118	83.52 112	85.81 114	90.30 157	66.82 149	91.87 204	89.01 329	85.27 13	86.09 68	93.74 128	47.71 334	96.98 115	77.90 190	89.78 114	93.65 184

viewdifsd2359ckpt13		84.08 119	83.21 127	86.70 71	88.49 212	69.55 55	92.25 179	91.14 212	79.71 91	79.73 152	91.72 183	58.83 192	95.89 175	82.06 142	84.99 171	94.66 123

viewmacassd2359aftdt		84.03 120	83.18 130	86.59 79	86.76 279	69.44 56	92.44 174	90.85 236	80.38 74	80.78 131	91.33 195	58.54 196	95.62 201	82.15 140	85.41 167	94.72 117

ET-MVSNet_ETH3D		84.01 121	83.15 133	86.58 80	90.78 149	70.89 28	94.74 53	94.62 47	81.44 56	58.19 405	93.64 131	73.64 27	92.35 347	82.66 135	78.66 258	96.50 27

E4		84.00 122	83.19 129	86.46 90	86.99 266	68.85 80	92.39 176	90.99 230	79.94 84	80.17 142	91.36 194	59.73 175	95.79 186	82.87 133	84.22 186	94.74 114

diffmvs_AUTHOR		83.97 123	83.49 115	85.39 131	86.09 295	67.83 111	90.76 264	89.05 327	79.94 84	81.43 119	92.23 164	59.53 178	94.42 260	87.18 80	85.22 168	93.92 173

PVSNet_Blended_VisFu		83.97 123	83.50 114	85.39 131	90.02 162	66.59 158	93.77 103	91.73 179	77.43 148	77.08 192	89.81 236	63.77 112	96.97 118	79.67 170	88.21 129	92.60 219

MTAPA		83.91 125	83.38 123	85.50 127	91.89 119	65.16 195	81.75 405	92.23 148	75.32 185	80.53 137	95.21 82	56.06 234	97.16 102	84.86 104	92.55 67	94.18 156

XVS		83.87 126	83.47 117	85.05 148	93.22 70	63.78 244	92.92 142	92.66 133	73.99 203	78.18 175	94.31 112	55.25 240	97.41 81	79.16 177	91.58 83	93.95 169

Effi-MVS+		83.82 127	82.76 141	86.99 60	89.56 172	69.40 57	91.35 239	86.12 395	72.59 236	83.22 101	92.81 150	59.60 177	96.01 171	81.76 150	87.80 134	95.56 61

test_fmvsmvis_n_1920		83.80 128	83.48 116	84.77 163	82.51 359	63.72 249	91.37 235	83.99 419	81.42 57	77.68 180	95.74 59	58.37 199	97.58 69	93.38 27	86.87 144	93.00 207

EI-MVSNet-Vis-set		83.77 129	83.67 110	84.06 197	92.79 89	63.56 258	91.76 213	94.81 37	79.65 93	77.87 178	94.09 121	63.35 123	97.90 50	79.35 175	79.36 248	90.74 270

MVSFormer		83.75 130	82.88 139	86.37 95	89.24 185	71.18 24	89.07 320	90.69 246	65.80 354	87.13 56	94.34 110	64.99 91	92.67 333	72.83 228	91.80 79	95.27 81

CP-MVS		83.71 131	83.40 122	84.65 174	93.14 75	63.84 242	94.59 58	92.28 146	71.03 287	77.41 185	94.92 90	55.21 243	96.19 158	81.32 156	90.70 98	93.91 174

test_fmvsmconf0.01_n		83.70 132	83.52 112	84.25 193	75.26 437	61.72 309	92.17 184	87.24 378	82.36 43	84.91 82	95.41 68	55.60 238	96.83 130	92.85 31	85.87 160	94.21 154

baseline2		83.68 133	83.42 121	84.48 183	87.37 255	66.00 171	90.06 290	95.93 8	79.71 91	69.08 297	90.39 214	77.92 6	96.28 153	78.91 182	81.38 223	91.16 263

E5new		83.62 134	82.65 144	86.55 84	86.98 267	69.28 66	91.69 217	90.96 231	79.61 95	79.80 147	91.25 197	58.04 204	95.84 178	81.83 148	83.66 194	94.52 133

E6new		83.62 134	82.65 144	86.55 84	86.98 267	69.29 64	91.69 217	90.95 233	79.60 98	79.80 147	91.25 197	58.04 204	95.84 178	81.84 146	83.67 192	94.52 133

E6		83.62 134	82.65 144	86.55 84	86.98 267	69.29 64	91.69 217	90.95 233	79.60 98	79.80 147	91.25 197	58.04 204	95.84 178	81.84 146	83.67 192	94.52 133

E5		83.62 134	82.65 144	86.55 84	86.98 267	69.28 66	91.69 217	90.96 231	79.61 95	79.80 147	91.25 197	58.04 204	95.84 178	81.83 148	83.66 194	94.52 133

viewdifsd2359ckpt09		83.52 138	82.57 149	86.37 95	88.02 235	68.47 91	91.78 210	89.63 299	79.61 95	78.56 173	92.00 172	59.28 184	95.96 172	81.94 144	82.35 206	94.69 118

reproduce-ours		83.51 139	83.33 125	84.06 197	92.18 103	60.49 341	90.74 266	92.04 160	64.35 364	83.24 98	95.59 64	59.05 187	97.27 93	83.61 123	89.17 119	94.41 148

our_new_method		83.51 139	83.33 125	84.06 197	92.18 103	60.49 341	90.74 266	92.04 160	64.35 364	83.24 98	95.59 64	59.05 187	97.27 93	83.61 123	89.17 119	94.41 148

thisisatest0515		83.41 141	82.49 151	86.16 102	89.46 175	68.26 98	93.54 114	94.70 43	74.31 198	75.75 200	90.92 204	72.62 34	96.52 142	69.64 261	81.50 222	93.71 181

PVSNet_BlendedMVS		83.38 142	83.43 119	83.22 235	93.76 54	67.53 121	94.06 80	93.61 86	79.13 111	81.00 127	85.14 309	63.19 125	97.29 89	87.08 82	73.91 296	84.83 377

test2506		83.29 143	82.92 138	84.37 187	88.39 220	63.18 271	92.01 194	91.35 197	77.66 141	78.49 174	91.42 190	64.58 100	95.09 227	73.19 224	89.23 116	94.85 102

PGM-MVS		83.25 144	82.70 143	84.92 152	92.81 88	64.07 234	90.44 276	92.20 152	71.28 281	77.23 189	94.43 103	55.17 244	97.31 88	79.33 176	91.38 88	93.37 191

HPM-MVS		83.25 144	82.95 137	84.17 195	92.25 99	62.88 280	90.91 256	91.86 172	70.30 298	77.12 190	93.96 125	56.75 223	96.28 153	82.04 143	91.34 90	93.34 192
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 146	82.96 135	83.73 212	92.02 109	59.74 357	90.37 280	92.08 158	63.70 371	82.86 103	95.48 67	58.62 194	97.17 99	83.06 129	88.42 127	94.26 151

EI-MVSNet-UG-set		83.14 147	82.96 135	83.67 217	92.28 98	63.19 270	91.38 234	94.68 44	79.22 108	76.60 195	93.75 127	62.64 135	97.76 56	78.07 189	78.01 261	90.05 279

testing3-2		83.11 148	83.15 133	82.98 240	91.92 116	64.01 237	94.39 69	95.37 16	78.32 127	75.53 207	90.06 232	73.18 29	93.18 312	74.34 216	75.27 285	91.77 248

VDD-MVS		83.06 149	81.81 162	86.81 65	90.86 147	67.70 115	95.40 30	91.50 192	75.46 180	81.78 113	92.34 160	40.09 382	97.13 104	86.85 85	82.04 214	95.60 59

h-mvs33		83.01 150	82.56 150	84.35 188	89.34 176	62.02 298	92.72 151	93.76 78	81.45 54	82.73 107	92.25 163	60.11 168	97.13 104	87.69 71	62.96 382	93.91 174

PAPM_NR		82.97 151	81.84 161	86.37 95	94.10 48	66.76 152	87.66 348	92.84 123	69.96 304	74.07 232	93.57 133	63.10 130	97.50 75	70.66 256	90.58 100	94.85 102

mPP-MVS		82.96 152	82.44 152	84.52 181	92.83 84	62.92 278	92.76 149	91.85 174	71.52 277	75.61 205	94.24 115	53.48 269	96.99 114	78.97 180	90.73 97	93.64 185

viewdifsd2359ckpt07		82.95 153	82.04 156	85.66 122	87.19 260	66.73 153	91.56 225	90.39 261	77.58 144	77.58 184	91.19 201	58.57 195	95.65 198	82.32 138	82.01 215	94.60 127

SR-MVS		82.81 154	82.58 148	83.50 224	93.35 68	61.16 323	92.23 182	91.28 204	64.48 363	81.27 120	95.28 75	53.71 265	95.86 177	82.87 133	88.77 124	93.49 189

DP-MVS Recon		82.73 155	81.65 163	85.98 107	97.31 4	67.06 136	95.15 37	91.99 164	69.08 319	76.50 197	93.89 126	54.48 254	98.20 41	70.76 254	85.66 164	92.69 215

CLD-MVS		82.73 155	82.35 154	83.86 205	87.90 238	67.65 117	95.45 29	92.18 155	85.06 14	72.58 250	92.27 161	52.46 278	95.78 187	84.18 114	79.06 253	88.16 307
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 157	82.38 153	83.73 212	89.25 182	59.58 360	92.24 181	94.89 31	77.96 132	79.86 146	92.38 158	56.70 224	97.05 106	77.26 193	80.86 231	94.55 129

3Dnovator	73.91 6	82.69 158	80.82 176	88.31 26	89.57 171	71.26 22	92.60 163	94.39 60	78.84 118	67.89 320	92.48 156	48.42 323	98.52 32	68.80 274	94.40 36	95.15 87

RRT-MVS		82.61 159	81.16 167	86.96 61	91.10 141	68.75 83	87.70 347	92.20 152	76.97 155	72.68 246	87.10 285	51.30 292	96.41 148	83.56 125	87.84 133	95.74 55

viewmambaseed2359dif		82.60 160	81.91 160	84.67 173	85.83 302	66.09 168	90.50 275	89.01 329	75.46 180	79.64 154	92.01 171	59.51 179	94.38 262	82.99 131	82.26 208	93.54 187

MVSTER		82.47 161	82.05 155	83.74 210	92.68 91	69.01 76	91.90 203	93.21 104	79.83 87	72.14 260	85.71 304	74.72 19	94.72 242	75.72 202	72.49 306	87.50 314

TESTMET0.1,1		82.41 162	81.98 159	83.72 214	88.08 231	63.74 246	92.70 153	93.77 77	79.30 106	77.61 182	87.57 276	58.19 202	94.08 276	73.91 218	86.68 151	93.33 194

CostFormer		82.33 163	81.15 168	85.86 112	89.01 192	68.46 92	82.39 402	93.01 115	75.59 178	80.25 141	81.57 356	72.03 41	94.96 232	79.06 179	77.48 269	94.16 158

API-MVS		82.28 164	80.53 185	87.54 42	96.13 23	70.59 31	93.63 110	91.04 227	65.72 356	75.45 208	92.83 149	56.11 233	98.89 24	64.10 329	89.75 115	93.15 199

IB-MVS	77.80 4	82.18 165	80.46 187	87.35 48	89.14 187	70.28 36	95.59 27	95.17 24	78.85 117	70.19 285	85.82 302	70.66 46	97.67 61	72.19 240	66.52 351	94.09 162
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
xiu_mvs_v1_base_debu		82.16 166	81.12 169	85.26 141	86.42 286	68.72 86	92.59 165	90.44 258	73.12 223	84.20 88	94.36 105	38.04 395	95.73 191	84.12 115	86.81 145	91.33 256

xiu_mvs_v1_base		82.16 166	81.12 169	85.26 141	86.42 286	68.72 86	92.59 165	90.44 258	73.12 223	84.20 88	94.36 105	38.04 395	95.73 191	84.12 115	86.81 145	91.33 256

xiu_mvs_v1_base_debi		82.16 166	81.12 169	85.26 141	86.42 286	68.72 86	92.59 165	90.44 258	73.12 223	84.20 88	94.36 105	38.04 395	95.73 191	84.12 115	86.81 145	91.33 256

3Dnovator+	73.60 7	82.10 169	80.60 183	86.60 77	90.89 146	66.80 151	95.20 35	93.44 96	74.05 202	67.42 327	92.49 155	49.46 313	97.65 65	70.80 253	91.68 81	95.33 74

MVS_111021_LR		82.02 170	81.52 164	83.51 223	88.42 218	62.88 280	89.77 298	88.93 334	76.78 160	75.55 206	93.10 138	50.31 302	95.38 215	83.82 119	87.02 142	92.26 236

PMMVS		81.98 171	82.04 156	81.78 277	89.76 168	56.17 398	91.13 252	90.69 246	77.96 132	80.09 144	93.57 133	46.33 351	94.99 231	81.41 154	87.46 138	94.17 157

baseline1		81.84 172	81.03 173	84.28 191	91.60 126	66.62 156	91.08 253	91.66 186	81.87 48	74.86 218	91.67 186	69.98 51	94.92 235	71.76 243	64.75 368	91.29 261

EPP-MVSNet		81.79 173	81.52 164	82.61 250	88.77 198	60.21 349	93.02 137	93.66 85	68.52 325	72.90 244	90.39 214	72.19 40	94.96 232	74.93 210	79.29 251	92.67 216

WBMVS		81.67 174	80.98 175	83.72 214	93.07 78	69.40 57	94.33 70	93.05 113	76.84 158	72.05 262	84.14 322	74.49 21	93.88 290	72.76 231	68.09 337	87.88 309

test_vis1_n_1920		81.66 175	82.01 158	80.64 311	82.24 361	55.09 407	94.76 52	86.87 382	81.67 51	84.40 87	94.63 98	38.17 392	94.67 248	91.98 40	83.34 198	92.16 239

APD-MVS_3200maxsize		81.64 176	81.32 166	82.59 252	92.36 96	58.74 371	91.39 232	91.01 229	63.35 375	79.72 153	94.62 99	51.82 281	96.14 160	79.71 169	87.93 132	92.89 211

mvsmamba		81.55 177	80.72 178	84.03 201	91.42 132	66.93 147	83.08 393	89.13 320	78.55 125	67.50 325	87.02 286	51.79 283	90.07 391	87.48 74	90.49 102	95.10 90

ACMMP		81.49 178	80.67 180	83.93 203	91.71 124	62.90 279	92.13 186	92.22 151	71.79 264	71.68 268	93.49 135	50.32 301	96.96 119	78.47 186	84.22 186	91.93 246
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
KinetiMVS		81.43 179	80.11 189	85.38 134	86.60 282	65.47 189	92.90 145	93.54 90	75.33 184	77.31 187	90.39 214	46.81 342	96.75 132	71.65 246	86.46 156	93.93 171

CDS-MVSNet		81.43 179	80.74 177	83.52 221	86.26 290	64.45 216	92.09 189	90.65 250	75.83 176	73.95 234	89.81 236	63.97 108	92.91 323	71.27 247	82.82 202	93.20 198
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 181	79.99 193	85.46 128	90.39 156	68.40 93	86.88 359	90.61 251	74.41 195	70.31 284	84.67 314	63.79 111	92.32 349	73.13 225	85.70 163	95.67 56

ECVR-MVS		81.29 182	80.38 188	84.01 202	88.39 220	61.96 300	92.56 168	86.79 384	77.66 141	76.63 194	91.42 190	46.34 350	95.24 224	74.36 215	89.23 116	94.85 102

guyue		81.23 183	80.57 184	83.21 237	86.64 280	61.85 303	92.52 171	92.78 125	78.69 122	74.92 217	89.42 240	50.07 305	95.35 216	80.79 161	79.31 250	92.42 225

IMVS_0403		81.19 184	79.88 195	85.13 146	88.54 203	64.75 204	88.84 325	90.80 240	76.73 163	75.21 211	90.18 220	54.22 259	96.21 157	73.47 220	80.95 226	94.43 144

thisisatest0530		81.15 185	80.07 190	84.39 186	88.26 225	65.63 182	91.40 230	94.62 47	71.27 282	70.93 275	89.18 245	72.47 35	96.04 168	65.62 315	76.89 276	91.49 252

Fast-Effi-MVS+		81.14 186	80.01 192	84.51 182	90.24 158	65.86 177	94.12 79	89.15 318	73.81 210	75.37 210	88.26 261	57.26 213	94.53 256	66.97 298	84.92 174	93.15 199

HQP-MVS		81.14 186	80.64 181	82.64 249	87.54 250	63.66 255	94.06 80	91.70 184	79.80 88	74.18 225	90.30 217	51.63 286	95.61 202	77.63 191	78.90 254	88.63 298

hse-mvs2		81.12 188	81.11 172	81.16 296	86.52 285	57.48 386	89.40 311	91.16 208	81.45 54	82.73 107	90.49 212	60.11 168	94.58 249	87.69 71	60.41 409	91.41 255

SR-MVS-dyc-post		81.06 189	80.70 179	82.15 268	92.02 109	58.56 374	90.90 257	90.45 254	62.76 382	78.89 164	94.46 101	51.26 293	95.61 202	78.77 184	86.77 148	92.28 232

HyFIR lowres test		81.03 190	79.56 202	85.43 129	87.81 244	68.11 104	90.18 287	90.01 283	70.65 295	72.95 243	86.06 298	63.61 117	94.50 258	75.01 209	79.75 242	93.67 182

nrg030		80.93 191	79.86 196	84.13 196	83.69 345	68.83 81	93.23 128	91.20 206	75.55 179	75.06 213	88.22 264	63.04 131	94.74 241	81.88 145	66.88 348	88.82 296

Vis-MVSNet		80.92 192	79.98 194	83.74 210	88.48 214	61.80 304	93.44 121	88.26 360	73.96 206	77.73 179	91.76 180	49.94 307	94.76 239	65.84 310	90.37 105	94.65 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 193	80.02 191	83.33 228	87.87 241	60.76 331	92.62 160	86.86 383	77.86 135	75.73 201	91.39 192	46.35 349	94.70 247	72.79 230	88.68 125	94.52 133

UWE-MVS		80.81 194	81.01 174	80.20 321	89.33 178	57.05 392	91.91 202	94.71 42	75.67 177	75.01 214	89.37 241	63.13 129	91.44 374	67.19 295	82.80 204	92.12 240

IMVS_0407		80.80 195	79.39 208	85.00 151	88.54 203	64.75 204	88.40 333	90.80 240	76.73 163	73.95 234	90.18 220	51.55 288	95.81 184	73.47 220	80.95 226	94.43 144

1314		80.70 196	78.95 216	85.94 109	87.77 247	67.56 119	87.91 342	92.55 140	72.17 251	67.44 326	93.09 139	50.27 303	97.04 109	71.68 245	87.64 136	93.23 196

AstraMVS		80.66 197	79.79 198	83.28 232	85.07 319	61.64 311	92.19 183	90.58 252	79.40 103	74.77 220	90.18 220	45.93 355	95.61 202	83.04 130	76.96 275	92.60 219

tpmrst		80.57 198	79.14 214	84.84 158	90.10 161	68.28 97	81.70 406	89.72 296	77.63 143	75.96 199	79.54 388	64.94 93	92.71 330	75.43 204	77.28 272	93.55 186

1112_ss		80.56 199	79.83 197	82.77 244	88.65 200	60.78 329	92.29 178	88.36 353	72.58 237	72.46 256	94.95 87	65.09 90	93.42 307	66.38 304	77.71 263	94.10 161

VDDNet		80.50 200	78.26 224	87.21 51	86.19 291	69.79 48	94.48 60	91.31 198	60.42 403	79.34 159	90.91 205	38.48 390	96.56 139	82.16 139	81.05 225	95.27 81

BH-w/o		80.49 201	79.30 210	84.05 200	90.83 148	64.36 224	93.60 111	89.42 306	74.35 197	69.09 296	90.15 228	55.23 242	95.61 202	64.61 324	86.43 157	92.17 238

test_cas_vis1_n_1920		80.45 202	80.61 182	79.97 330	78.25 415	57.01 394	94.04 84	88.33 355	79.06 115	82.81 106	93.70 129	38.65 387	91.63 365	90.82 52	79.81 240	91.27 262

icg_test_0407_2		80.38 203	79.22 212	83.88 204	88.54 203	64.75 204	86.79 360	90.80 240	76.73 163	73.95 234	90.18 220	51.55 288	92.45 342	73.47 220	80.95 226	94.43 144

TAMVS		80.37 204	79.45 205	83.13 238	85.14 316	63.37 263	91.23 246	90.76 245	74.81 192	72.65 248	88.49 254	60.63 161	92.95 318	69.41 265	81.95 217	93.08 203

HQP_MVS		80.34 205	79.75 199	82.12 270	86.94 272	62.42 288	93.13 131	91.31 198	78.81 119	72.53 251	89.14 247	50.66 298	95.55 208	76.74 194	78.53 259	88.39 304

SDMVSNet		80.26 206	78.88 217	84.40 185	89.25 182	67.63 118	85.35 369	93.02 114	76.77 161	70.84 276	87.12 283	47.95 331	96.09 163	85.04 100	74.55 287	89.48 289

HPM-MVS_fast		80.25 207	79.55 204	82.33 260	91.55 129	59.95 354	91.32 241	89.16 317	65.23 360	74.71 222	93.07 141	47.81 333	95.74 190	74.87 213	88.23 128	91.31 260

ab-mvs		80.18 208	78.31 223	85.80 115	88.44 216	65.49 188	83.00 396	92.67 132	71.82 263	77.36 186	85.01 310	54.50 251	96.59 136	76.35 199	75.63 283	95.32 76

IS-MVSNet		80.14 209	79.41 206	82.33 260	87.91 237	60.08 352	91.97 198	88.27 358	72.90 232	71.44 272	91.73 182	61.44 150	93.66 299	62.47 343	86.53 154	93.24 195

test-LLR		80.10 210	79.56 202	81.72 279	86.93 274	61.17 321	92.70 153	91.54 189	71.51 278	75.62 203	86.94 287	53.83 262	92.38 344	72.21 238	84.76 177	91.60 250

PVSNet	73.49 8	80.05 211	78.63 219	84.31 189	90.92 145	64.97 200	92.47 172	91.05 226	79.18 109	72.43 257	90.51 211	37.05 407	94.06 278	68.06 282	86.00 158	93.90 176

UA-Net		80.02 212	79.65 200	81.11 299	89.33 178	57.72 381	86.33 365	89.00 333	77.44 147	81.01 126	89.15 246	59.33 183	95.90 174	61.01 350	84.28 184	89.73 285

test-mter		79.96 213	79.38 209	81.72 279	86.93 274	61.17 321	92.70 153	91.54 189	73.85 208	75.62 203	86.94 287	49.84 309	92.38 344	72.21 238	84.76 177	91.60 250

QAPM		79.95 214	77.39 245	87.64 35	89.63 170	71.41 20	93.30 126	93.70 83	65.34 359	67.39 329	91.75 181	47.83 332	98.96 19	57.71 366	89.81 112	92.54 222

UGNet		79.87 215	78.68 218	83.45 226	89.96 163	61.51 314	92.13 186	90.79 244	76.83 159	78.85 169	86.33 295	38.16 393	96.17 159	67.93 285	87.17 141	92.67 216
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
tpm2		79.80 216	77.95 231	85.34 136	88.28 224	68.26 98	81.56 408	91.42 195	70.11 300	77.59 183	80.50 374	67.40 67	94.26 269	67.34 292	77.35 270	93.51 188

thres200		79.66 217	78.33 222	83.66 218	92.54 95	65.82 179	93.06 133	96.31 3	74.90 191	73.30 240	88.66 252	59.67 176	95.61 202	47.84 410	78.67 257	89.56 288

CPTT-MVS		79.59 218	79.16 213	80.89 309	91.54 130	59.80 356	92.10 188	88.54 350	60.42 403	72.96 242	93.28 137	48.27 324	92.80 327	78.89 183	86.50 155	90.06 278

Test_1112_low_res		79.56 219	78.60 220	82.43 254	88.24 227	60.39 345	92.09 189	87.99 365	72.10 253	71.84 264	87.42 278	64.62 98	93.04 314	65.80 311	77.30 271	93.85 178

tttt0517		79.50 220	78.53 221	82.41 257	87.22 259	61.43 318	89.75 299	94.76 39	69.29 312	67.91 318	88.06 268	72.92 31	95.63 199	62.91 339	73.90 297	90.16 277

reproduce_monomvs		79.49 221	79.11 215	80.64 311	92.91 82	61.47 317	91.17 251	93.28 102	83.09 33	64.04 359	82.38 342	66.19 76	94.57 251	81.19 158	57.71 417	85.88 360

FIs		79.47 222	79.41 206	79.67 338	85.95 298	59.40 362	91.68 221	93.94 72	78.06 131	68.96 302	88.28 259	66.61 73	91.77 361	66.20 307	74.99 286	87.82 310

SSM_0404		79.46 223	77.65 235	84.91 154	88.37 222	67.04 138	89.59 300	87.03 379	67.99 330	75.45 208	89.32 242	47.98 328	95.34 218	71.23 248	81.90 218	92.34 228

BH-RMVSNet		79.46 223	77.65 235	84.89 155	91.68 125	65.66 180	93.55 113	88.09 363	72.93 229	73.37 239	91.12 203	46.20 353	96.12 161	56.28 372	85.61 165	92.91 209

viewdifsd2359ckpt11		79.42 225	77.95 231	83.81 207	83.87 342	63.85 240	89.54 305	87.38 372	77.39 150	74.94 215	89.95 233	51.11 294	94.72 242	79.52 172	67.90 340	92.88 212

viewmsd2359difaftdt		79.42 225	77.96 230	83.81 207	83.88 341	63.85 240	89.54 305	87.38 372	77.39 150	74.94 215	89.95 233	51.11 294	94.72 242	79.52 172	67.90 340	92.88 212

PCF-MVS	73.15 9	79.29 227	77.63 237	84.29 190	86.06 296	65.96 173	87.03 355	91.10 217	69.86 306	69.79 292	90.64 207	57.54 212	96.59 136	64.37 328	82.29 207	90.32 275
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 228	79.57 201	78.24 359	88.46 215	52.29 418	90.41 278	89.12 321	74.24 199	69.13 295	91.91 178	65.77 83	90.09 390	59.00 362	88.09 130	92.33 229

114514_t		79.17 229	77.67 234	83.68 216	95.32 30	65.53 186	92.85 147	91.60 188	63.49 373	67.92 317	90.63 209	46.65 346	95.72 196	67.01 297	83.54 196	89.79 283

FA-MVS(test-final)		79.12 230	77.23 247	84.81 162	90.54 151	63.98 239	81.35 411	91.71 181	71.09 286	74.85 219	82.94 335	52.85 273	97.05 106	67.97 283	81.73 221	93.41 190

SSM_0407		79.09 231	77.21 248	84.75 166	88.50 208	66.98 143	89.21 316	87.03 379	67.99 330	74.12 229	89.32 242	47.98 328	95.29 223	71.23 248	79.52 243	91.98 243

VPA-MVSNet		79.03 232	78.00 228	82.11 273	85.95 298	64.48 215	93.22 129	94.66 45	75.05 189	74.04 233	84.95 311	52.17 280	93.52 301	74.90 212	67.04 347	88.32 306

OPM-MVS		79.00 233	78.09 226	81.73 278	83.52 348	63.83 243	91.64 223	90.30 267	76.36 172	71.97 263	89.93 235	46.30 352	95.17 226	75.10 207	77.70 264	86.19 348
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 234	78.22 225	81.25 293	85.33 309	62.73 283	89.53 308	93.21 104	72.39 244	72.14 260	90.13 229	60.99 154	94.72 242	67.73 287	72.49 306	86.29 345

AdaColmap		78.94 235	77.00 252	84.76 165	96.34 17	65.86 177	92.66 159	87.97 367	62.18 387	70.56 278	92.37 159	43.53 367	97.35 85	64.50 327	82.86 201	91.05 265

GeoE		78.90 236	77.43 241	83.29 231	88.95 193	62.02 298	92.31 177	86.23 391	70.24 299	71.34 273	89.27 244	54.43 255	94.04 281	63.31 335	80.81 233	93.81 179

miper_enhance_ethall		78.86 237	77.97 229	81.54 285	88.00 236	65.17 194	91.41 228	89.15 318	75.19 187	68.79 305	83.98 325	67.17 68	92.82 325	72.73 232	65.30 358	86.62 337

VPNet		78.82 238	77.53 240	82.70 247	84.52 329	66.44 160	93.93 90	92.23 148	80.46 71	72.60 249	88.38 258	49.18 317	93.13 313	72.47 236	63.97 378	88.55 301

EPNet_dtu		78.80 239	79.26 211	77.43 367	88.06 232	49.71 435	91.96 199	91.95 166	77.67 140	76.56 196	91.28 196	58.51 197	90.20 388	56.37 371	80.95 226	92.39 226
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 240	77.43 241	82.88 242	92.21 101	64.49 213	92.05 192	96.28 4	73.48 217	71.75 266	88.26 261	60.07 170	95.32 219	45.16 423	77.58 266	88.83 294

TR-MVS		78.77 241	77.37 246	82.95 241	90.49 153	60.88 327	93.67 107	90.07 278	70.08 303	74.51 223	91.37 193	45.69 356	95.70 197	60.12 356	80.32 237	92.29 231

thres400		78.68 242	77.43 241	82.43 254	92.21 101	64.49 213	92.05 192	96.28 4	73.48 217	71.75 266	88.26 261	60.07 170	95.32 219	45.16 423	77.58 266	87.48 315

BH-untuned		78.68 242	77.08 249	83.48 225	89.84 165	63.74 246	92.70 153	88.59 347	71.57 275	66.83 336	88.65 253	51.75 284	95.39 214	59.03 361	84.77 176	91.32 259

OMC-MVS		78.67 244	77.91 233	80.95 306	85.76 304	57.40 388	88.49 331	88.67 344	73.85 208	72.43 257	92.10 168	49.29 316	94.55 255	72.73 232	77.89 262	90.91 269

tpm		78.58 245	77.03 250	83.22 235	85.94 300	64.56 211	83.21 392	91.14 212	78.31 128	73.67 237	79.68 386	64.01 107	92.09 355	66.07 308	71.26 316	93.03 205

OpenMVS	70.45 11	78.54 246	75.92 271	86.41 94	85.93 301	71.68 18	92.74 150	92.51 141	66.49 346	64.56 353	91.96 174	43.88 366	98.10 44	54.61 377	90.65 99	89.44 291

EPMVS		78.49 247	75.98 270	86.02 106	91.21 139	69.68 53	80.23 420	91.20 206	75.25 186	72.48 255	78.11 397	54.65 250	93.69 298	57.66 367	83.04 200	94.69 118

AUN-MVS		78.37 248	77.43 241	81.17 295	86.60 282	57.45 387	89.46 310	91.16 208	74.11 201	74.40 224	90.49 212	55.52 239	94.57 251	74.73 214	60.43 408	91.48 253

thres100view900		78.37 248	77.01 251	82.46 253	91.89 119	63.21 269	91.19 250	96.33 1	72.28 247	70.45 281	87.89 270	60.31 165	95.32 219	45.16 423	77.58 266	88.83 294

GA-MVS		78.33 250	76.23 266	84.65 174	83.65 346	66.30 164	91.44 227	90.14 276	76.01 174	70.32 283	84.02 324	42.50 371	94.72 242	70.98 251	77.00 274	92.94 208

cascas		78.18 251	75.77 273	85.41 130	87.14 262	69.11 71	92.96 140	91.15 211	66.71 344	70.47 279	86.07 297	37.49 401	96.48 145	70.15 259	79.80 241	90.65 271

UniMVSNet_NR-MVSNet		78.15 252	77.55 239	79.98 328	84.46 332	60.26 347	92.25 179	93.20 106	77.50 146	68.88 303	86.61 290	66.10 78	92.13 353	66.38 304	62.55 386	87.54 313

LuminaMVS		78.14 253	76.66 256	82.60 251	80.82 376	64.64 210	89.33 312	90.45 254	68.25 328	74.73 221	85.51 306	41.15 377	94.14 272	78.96 181	80.69 235	89.04 292

IMVS_0404		78.11 254	76.29 265	83.59 219	88.54 203	64.75 204	84.63 374	90.80 240	76.73 163	61.16 382	90.18 220	40.17 381	91.58 367	73.47 220	80.95 226	94.43 144

thres600view7		78.00 255	76.66 256	82.03 275	91.93 115	63.69 253	91.30 242	96.33 1	72.43 242	70.46 280	87.89 270	60.31 165	94.92 235	42.64 435	76.64 277	87.48 315

FC-MVSNet-test		77.99 256	78.08 227	77.70 362	84.89 322	55.51 404	90.27 284	93.75 81	76.87 156	66.80 337	87.59 275	65.71 84	90.23 387	62.89 340	73.94 295	87.37 318

Anonymous202405211		77.96 257	75.33 279	85.87 111	93.73 57	64.52 212	94.85 50	85.36 404	62.52 385	76.11 198	90.18 220	29.43 440	97.29 89	68.51 276	77.24 273	95.81 53

cl22		77.94 258	76.78 254	81.42 287	87.57 249	64.93 202	90.67 269	88.86 337	72.45 241	67.63 324	82.68 339	64.07 105	92.91 323	71.79 241	65.30 358	86.44 339

XXY-MVS		77.94 258	76.44 259	82.43 254	82.60 358	64.44 217	92.01 194	91.83 175	73.59 216	70.00 288	85.82 302	54.43 255	94.76 239	69.63 262	68.02 339	88.10 308

MS-PatchMatch		77.90 260	76.50 258	82.12 270	85.99 297	69.95 42	91.75 215	92.70 128	73.97 205	62.58 376	84.44 318	41.11 378	95.78 187	63.76 332	92.17 71	80.62 426

usedtu_dtu_shiyan1		77.89 261	76.39 262	82.40 258	81.92 366	67.01 141	91.94 200	93.00 117	77.01 153	68.44 312	84.15 320	54.78 248	93.25 309	65.76 312	70.53 319	86.94 327

FE-MVSNET3		77.89 261	76.39 262	82.40 258	81.92 366	67.01 141	91.94 200	93.00 117	77.01 153	68.44 312	84.15 320	54.78 248	93.25 309	65.76 312	70.53 319	86.94 327

FMVSNet3		77.73 263	76.04 269	82.80 243	91.20 140	68.99 77	91.87 204	91.99 164	73.35 219	67.04 332	83.19 334	56.62 226	92.14 352	59.80 358	69.34 325	87.28 321

VortexMVS		77.62 264	76.44 259	81.13 297	88.58 201	63.73 248	91.24 245	91.30 202	77.81 136	65.76 342	81.97 348	49.69 311	93.72 294	76.40 198	65.26 361	85.94 358

miper_ehance_all_eth		77.60 265	76.44 259	81.09 303	85.70 306	64.41 220	90.65 270	88.64 346	72.31 245	67.37 330	82.52 340	64.77 97	92.64 336	70.67 255	65.30 358	86.24 347

UniMVSNet (Re)		77.58 266	76.78 254	79.98 328	84.11 338	60.80 328	91.76 213	93.17 108	76.56 169	69.93 291	84.78 313	63.32 124	92.36 346	64.89 322	62.51 388	86.78 331

PatchmatchNet		77.46 267	74.63 286	85.96 108	89.55 173	70.35 35	79.97 425	89.55 301	72.23 248	70.94 274	76.91 410	57.03 216	92.79 328	54.27 379	81.17 224	94.74 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 268	75.65 275	82.73 245	80.38 384	67.13 135	91.85 206	90.23 272	75.09 188	69.37 293	83.39 331	53.79 264	94.44 259	71.77 242	65.00 365	86.63 336

CHOSEN 280x420		77.35 269	76.95 253	78.55 354	87.07 264	62.68 284	69.71 457	82.95 427	68.80 321	71.48 271	87.27 282	66.03 79	84.00 440	76.47 197	82.81 203	88.95 293

PS-MVSNAJss		77.26 270	76.31 264	80.13 323	80.64 380	59.16 367	90.63 273	91.06 223	72.80 233	68.58 309	84.57 316	53.55 266	93.96 286	72.97 226	71.96 310	87.27 322

gg-mvs-nofinetune		77.18 271	74.31 293	85.80 115	91.42 132	68.36 94	71.78 451	94.72 41	49.61 448	77.12 190	45.92 478	77.41 8	93.98 285	67.62 288	93.16 59	95.05 93

WB-MVSnew		77.14 272	76.18 268	80.01 327	86.18 292	63.24 267	91.26 243	94.11 69	71.72 267	73.52 238	87.29 281	45.14 361	93.00 316	56.98 369	79.42 246	83.80 386

MVP-Stereo		77.12 273	76.23 266	79.79 335	81.72 368	66.34 163	89.29 313	90.88 235	70.56 296	62.01 379	82.88 336	49.34 314	94.13 273	65.55 317	93.80 47	78.88 442
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 274	75.37 277	82.20 266	89.25 182	62.11 297	82.06 403	89.09 323	76.77 161	70.84 276	87.12 283	41.43 376	95.01 230	67.23 294	74.55 287	89.48 289

MonoMVSNet		76.99 275	75.08 282	82.73 245	83.32 350	63.24 267	86.47 364	86.37 387	79.08 113	66.31 340	79.30 390	49.80 310	91.72 362	79.37 174	65.70 356	93.23 196

dmvs_re		76.93 276	75.36 278	81.61 283	87.78 246	60.71 335	80.00 424	87.99 365	79.42 102	69.02 299	89.47 239	46.77 344	94.32 263	63.38 334	74.45 290	89.81 282

X-MVStestdata		76.86 277	74.13 299	85.05 148	93.22 70	63.78 244	92.92 142	92.66 133	73.99 203	78.18 175	10.19 493	55.25 240	97.41 81	79.16 177	91.58 83	93.95 169

DU-MVS		76.86 277	75.84 272	79.91 331	82.96 354	60.26 347	91.26 243	91.54 189	76.46 171	68.88 303	86.35 293	56.16 231	92.13 353	66.38 304	62.55 386	87.35 319

Anonymous20240529		76.84 279	74.15 298	84.88 156	91.02 142	64.95 201	93.84 99	91.09 218	53.57 436	73.00 241	87.42 278	35.91 411	97.32 87	69.14 270	72.41 308	92.36 227

UWE-MVS-28		76.83 280	77.60 238	74.51 397	84.58 328	50.34 431	88.22 336	94.60 49	74.46 194	66.66 338	88.98 251	62.53 137	85.50 432	57.55 368	80.80 234	87.69 312

c3_l		76.83 280	75.47 276	80.93 307	85.02 320	64.18 231	90.39 279	88.11 362	71.66 268	66.65 339	81.64 354	63.58 120	92.56 337	69.31 267	62.86 383	86.04 353

WR-MVS		76.76 282	75.74 274	79.82 334	84.60 326	62.27 294	92.60 163	92.51 141	76.06 173	67.87 321	85.34 307	56.76 222	90.24 386	62.20 344	63.69 380	86.94 327

v1144		76.73 283	74.88 283	82.27 262	80.23 388	66.60 157	91.68 221	90.21 275	73.69 213	69.06 298	81.89 349	52.73 276	94.40 261	69.21 268	65.23 362	85.80 361

IterMVS-LS		76.49 284	75.18 281	80.43 315	84.49 331	62.74 282	90.64 271	88.80 339	72.40 243	65.16 348	81.72 352	60.98 155	92.27 350	67.74 286	64.65 370	86.29 345
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 285	74.55 289	82.19 267	79.14 402	67.82 112	90.26 285	89.42 306	73.75 211	68.63 308	81.89 349	51.31 291	94.09 275	71.69 244	64.84 366	84.66 378

Elysia		76.45 286	74.17 296	83.30 229	80.43 382	64.12 232	89.58 301	90.83 237	61.78 395	72.53 251	85.92 300	34.30 418	94.81 237	68.10 280	84.01 190	90.97 266

StellarMVS		76.45 286	74.17 296	83.30 229	80.43 382	64.12 232	89.58 301	90.83 237	61.78 395	72.53 251	85.92 300	34.30 418	94.81 237	68.10 280	84.01 190	90.97 266

mamba_0408		76.22 288	73.37 310	84.77 163	88.50 208	66.98 143	58.80 478	86.18 393	69.12 317	74.12 229	89.01 249	47.50 335	95.35 216	67.57 289	79.52 243	91.98 243

v148		76.19 289	74.47 291	81.36 289	80.05 390	64.44 217	91.75 215	90.23 272	73.68 214	67.13 331	80.84 369	55.92 236	93.86 293	68.95 272	61.73 397	85.76 364

Effi-MVS+-dtu		76.14 290	75.28 280	78.72 353	83.22 351	55.17 406	89.87 296	87.78 369	75.42 182	67.98 316	81.43 358	45.08 362	92.52 339	75.08 208	71.63 311	88.48 302

cl____		76.07 291	74.67 284	80.28 318	85.15 315	61.76 307	90.12 288	88.73 341	71.16 283	65.43 345	81.57 356	61.15 152	92.95 318	66.54 301	62.17 390	86.13 351

DIV-MVS_self_test		76.07 291	74.67 284	80.28 318	85.14 316	61.75 308	90.12 288	88.73 341	71.16 283	65.42 346	81.60 355	61.15 152	92.94 322	66.54 301	62.16 392	86.14 349

FMVSNet2		76.07 291	74.01 301	82.26 264	88.85 194	67.66 116	91.33 240	91.61 187	70.84 290	65.98 341	82.25 344	48.03 325	92.00 357	58.46 363	68.73 333	87.10 324

v144192		76.05 294	74.03 300	82.12 270	79.50 396	66.55 159	91.39 232	89.71 297	72.30 246	68.17 314	81.33 361	51.75 284	94.03 283	67.94 284	64.19 373	85.77 362

NR-MVSNet		76.05 294	74.59 287	80.44 314	82.96 354	62.18 296	90.83 261	91.73 179	77.12 152	60.96 384	86.35 293	59.28 184	91.80 360	60.74 351	61.34 401	87.35 319

v1192		75.98 296	73.92 302	82.15 268	79.73 392	66.24 166	91.22 247	89.75 291	72.67 235	68.49 310	81.42 359	49.86 308	94.27 267	67.08 296	65.02 364	85.95 356

FE-MVS		75.97 297	73.02 316	84.82 159	89.78 166	65.56 184	77.44 436	91.07 222	64.55 362	72.66 247	79.85 384	46.05 354	96.69 134	54.97 376	80.82 232	92.21 237

eth_miper_zixun_eth		75.96 298	74.40 292	80.66 310	84.66 325	63.02 273	89.28 314	88.27 358	71.88 259	65.73 343	81.65 353	59.45 180	92.81 326	68.13 279	60.53 406	86.14 349

TranMVSNet+NR-MVSNet		75.86 299	74.52 290	79.89 332	82.44 360	60.64 338	91.37 235	91.37 196	76.63 167	67.65 323	86.21 296	52.37 279	91.55 368	61.84 346	60.81 404	87.48 315

SCA		75.82 300	72.76 320	85.01 150	86.63 281	70.08 38	81.06 413	89.19 315	71.60 274	70.01 287	77.09 408	45.53 357	90.25 383	60.43 353	73.27 299	94.68 120

LPG-MVS_test		75.82 300	74.58 288	79.56 342	84.31 335	59.37 363	90.44 276	89.73 294	69.49 309	64.86 349	88.42 256	38.65 387	94.30 265	72.56 234	72.76 303	85.01 375

GBi-Net		75.65 302	73.83 303	81.10 300	88.85 194	65.11 196	90.01 292	90.32 263	70.84 290	67.04 332	80.25 379	48.03 325	91.54 369	59.80 358	69.34 325	86.64 333

test1		75.65 302	73.83 303	81.10 300	88.85 194	65.11 196	90.01 292	90.32 263	70.84 290	67.04 332	80.25 379	48.03 325	91.54 369	59.80 358	69.34 325	86.64 333

v1921920		75.63 304	73.49 308	82.06 274	79.38 397	66.35 162	91.07 255	89.48 302	71.98 254	67.99 315	81.22 364	49.16 319	93.90 289	66.56 300	64.56 371	85.92 359

ACMP	71.68 10	75.58 305	74.23 295	79.62 340	84.97 321	59.64 358	90.80 262	89.07 325	70.39 297	62.95 372	87.30 280	38.28 391	93.87 291	72.89 227	71.45 314	85.36 371
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 306	73.26 314	81.61 283	80.67 379	66.82 149	89.54 305	89.27 311	71.65 269	63.30 367	80.30 378	54.99 246	94.06 278	67.33 293	62.33 389	83.94 384

tpm cat1		75.30 307	72.21 329	84.58 179	88.52 207	67.77 113	78.16 434	88.02 364	61.88 393	68.45 311	76.37 418	60.65 160	94.03 283	53.77 383	74.11 293	91.93 246

PLC	68.80 14	75.23 308	73.68 306	79.86 333	92.93 81	58.68 372	90.64 271	88.30 356	60.90 400	64.43 357	90.53 210	42.38 372	94.57 251	56.52 370	76.54 278	86.33 344
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 309	72.98 318	81.88 276	79.20 399	66.00 171	90.75 265	89.11 322	71.63 273	67.41 328	81.22 364	47.36 337	93.87 291	65.46 318	64.72 369	85.77 362

blend_shiyan4		75.18 310	73.00 317	81.69 281	75.62 434	64.75 204	91.78 210	91.06 223	65.89 353	61.35 381	77.39 402	62.16 142	93.71 295	68.18 277	63.60 381	86.61 338

Fast-Effi-MVS+-dtu		75.04 311	73.37 310	80.07 324	80.86 374	59.52 361	91.20 249	85.38 403	71.90 257	65.20 347	84.84 312	41.46 375	92.97 317	66.50 303	72.96 302	87.73 311

dp		75.01 312	72.09 330	83.76 209	89.28 181	66.22 167	79.96 426	89.75 291	71.16 283	67.80 322	77.19 407	51.81 282	92.54 338	50.39 393	71.44 315	92.51 224

TAPA-MVS	70.22 12	74.94 313	73.53 307	79.17 348	90.40 155	52.07 419	89.19 318	89.61 300	62.69 384	70.07 286	92.67 151	48.89 322	94.32 263	38.26 450	79.97 239	91.12 264
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 314	73.32 313	79.74 337	86.53 284	60.31 346	89.03 323	92.70 128	78.61 124	68.98 301	83.34 332	41.93 374	92.23 351	52.77 387	65.97 354	86.69 332

SSM_04072		74.86 315	73.37 310	79.35 345	88.50 208	66.98 143	58.80 478	86.18 393	69.12 317	74.12 229	89.01 249	47.50 335	79.09 462	67.57 289	79.52 243	91.98 243

v10		74.77 316	72.54 326	81.46 286	80.33 386	66.71 154	89.15 319	89.08 324	70.94 288	63.08 370	79.86 383	52.52 277	94.04 281	65.70 314	62.17 390	83.64 387

XVG-OURS-SEG-HR		74.70 317	73.08 315	79.57 341	78.25 415	57.33 389	80.49 416	87.32 374	63.22 377	68.76 306	90.12 231	44.89 363	91.59 366	70.55 257	74.09 294	89.79 283

ACMM	69.62 13	74.34 318	72.73 322	79.17 348	84.25 337	57.87 379	90.36 281	89.93 285	63.17 379	65.64 344	86.04 299	37.79 399	94.10 274	65.89 309	71.52 313	85.55 367
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 319	72.30 328	80.32 316	91.49 131	61.66 310	90.85 260	80.72 433	56.67 427	63.85 362	90.64 207	46.75 345	90.84 377	53.79 382	75.99 282	88.47 303

XVG-OURS		74.25 320	72.46 327	79.63 339	78.45 413	57.59 385	80.33 418	87.39 371	63.86 369	68.76 306	89.62 238	40.50 380	91.72 362	69.00 271	74.25 292	89.58 286

test_fmvs1		74.07 321	73.69 305	75.22 387	78.91 406	47.34 448	89.06 322	74.69 450	63.68 372	79.41 158	91.59 188	24.36 451	87.77 413	85.22 97	76.26 280	90.55 274

CVMVSNet		74.04 322	74.27 294	73.33 407	85.33 309	43.94 462	89.53 308	88.39 352	54.33 435	70.37 282	90.13 229	49.17 318	84.05 438	61.83 347	79.36 248	91.99 242

Baseline_NR-MVSNet		73.99 323	72.83 319	77.48 366	80.78 377	59.29 366	91.79 208	84.55 412	68.85 320	68.99 300	80.70 370	56.16 231	92.04 356	62.67 341	60.98 403	81.11 420

pmmvs4		73.92 324	71.81 334	80.25 320	79.17 400	65.24 192	87.43 351	87.26 377	67.64 337	63.46 365	83.91 326	48.96 321	91.53 372	62.94 338	65.49 357	83.96 383

D2MVS		73.80 325	72.02 331	79.15 350	79.15 401	62.97 274	88.58 330	90.07 278	72.94 228	59.22 398	78.30 394	42.31 373	92.70 332	65.59 316	72.00 309	81.79 415

SD_0403		73.79 326	73.48 309	74.69 394	85.33 309	45.56 458	83.80 381	85.57 402	76.55 170	62.96 371	88.45 255	50.62 300	87.59 417	48.80 403	79.28 252	90.92 268

CR-MVSNet		73.79 326	70.82 342	82.70 247	83.15 352	67.96 107	70.25 454	84.00 417	73.67 215	69.97 289	72.41 435	57.82 209	89.48 396	52.99 386	73.13 300	90.64 272

test_djsdf		73.76 328	72.56 325	77.39 368	77.00 427	53.93 412	89.07 320	90.69 246	65.80 354	63.92 360	82.03 347	43.14 370	92.67 333	72.83 228	68.53 334	85.57 366

pmmvs5		73.35 329	71.52 336	78.86 352	78.64 410	60.61 339	91.08 253	86.90 381	67.69 334	63.32 366	83.64 327	44.33 365	90.53 380	62.04 345	66.02 353	85.46 369

Anonymous20231211		73.08 330	70.39 346	81.13 297	90.62 150	63.33 264	91.40 230	90.06 280	51.84 441	64.46 356	80.67 372	36.49 409	94.07 277	63.83 331	64.17 374	85.98 355

tt0805		73.07 331	70.73 343	80.07 324	78.37 414	57.05 392	87.78 345	92.18 155	61.23 399	67.04 332	86.49 292	31.35 432	94.58 249	65.06 321	67.12 346	88.57 300

miper_lstm_enhance		73.05 332	71.73 335	77.03 373	83.80 343	58.32 376	81.76 404	88.88 335	69.80 307	61.01 383	78.23 396	57.19 214	87.51 419	65.34 319	59.53 411	85.27 374

jajsoiax		73.05 332	71.51 337	77.67 363	77.46 424	54.83 408	88.81 326	90.04 281	69.13 316	62.85 374	83.51 329	31.16 433	92.75 329	70.83 252	69.80 321	85.43 370

LCM-MVSNet-Re		72.93 334	71.84 333	76.18 382	88.49 212	48.02 443	80.07 423	70.17 465	73.96 206	52.25 432	80.09 382	49.98 306	88.24 407	67.35 291	84.23 185	92.28 232

pm-mvs1		72.89 335	71.09 339	78.26 358	79.10 403	57.62 383	90.80 262	89.30 310	67.66 335	62.91 373	81.78 351	49.11 320	92.95 318	60.29 355	58.89 414	84.22 382

tpmvs		72.88 336	69.76 352	82.22 265	90.98 143	67.05 137	78.22 433	88.30 356	63.10 380	64.35 358	74.98 425	55.09 245	94.27 267	43.25 429	69.57 324	85.34 372

test0.0.03 1		72.76 337	72.71 323	72.88 411	80.25 387	47.99 444	91.22 247	89.45 304	71.51 278	62.51 377	87.66 273	53.83 262	85.06 434	50.16 395	67.84 344	85.58 365

UniMVSNet_ETH3D		72.74 338	70.53 345	79.36 344	78.62 411	56.64 396	85.01 371	89.20 314	63.77 370	64.84 351	84.44 318	34.05 420	91.86 359	63.94 330	70.89 318	89.57 287

mvs_tets		72.71 339	71.11 338	77.52 364	77.41 425	54.52 410	88.45 332	89.76 290	68.76 323	62.70 375	83.26 333	29.49 439	92.71 330	70.51 258	69.62 323	85.34 372

FMVSNet1		72.71 339	69.91 350	81.10 300	83.60 347	65.11 196	90.01 292	90.32 263	63.92 368	63.56 364	80.25 379	36.35 410	91.54 369	54.46 378	66.75 349	86.64 333

test_fmvs1_n		72.69 341	71.92 332	74.99 392	71.15 452	47.08 450	87.34 353	75.67 445	63.48 374	78.08 177	91.17 202	20.16 465	87.87 410	84.65 106	75.57 284	90.01 280

IterMVS		72.65 342	70.83 340	78.09 360	82.17 362	62.96 275	87.64 349	86.28 389	71.56 276	60.44 389	78.85 392	45.42 359	86.66 423	63.30 336	61.83 394	84.65 379
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 343	72.74 321	72.10 419	87.87 241	49.45 437	88.07 338	89.01 329	72.91 230	63.11 368	88.10 265	63.63 115	85.54 429	32.73 466	69.23 328	81.32 418

FE-blended-shiyan7		72.42 344	69.43 354	81.37 288	75.39 435	64.24 229	91.58 224	91.09 218	66.36 347	60.64 386	76.86 411	47.20 339	93.47 303	64.80 323	50.98 438	86.40 340

blended_shiyan8		72.26 345	69.25 357	81.29 291	75.23 439	64.03 235	91.36 238	91.04 227	66.11 351	60.42 390	76.73 414	46.79 343	93.45 305	64.58 326	51.00 437	86.37 342

blended_shiyan6		72.26 345	69.26 356	81.27 292	75.24 438	64.00 238	91.37 235	91.06 223	66.12 350	60.34 391	76.75 413	46.82 341	93.45 305	64.61 324	50.98 438	86.37 342

PatchMatch-RL		72.06 347	69.98 347	78.28 357	89.51 174	55.70 403	83.49 385	83.39 425	61.24 398	63.72 363	82.76 337	34.77 415	93.03 315	53.37 385	77.59 265	86.12 352

PVSNet_0	68.08 15	71.81 348	68.32 364	82.27 262	84.68 323	62.31 293	88.68 328	90.31 266	75.84 175	57.93 410	80.65 373	37.85 398	94.19 270	69.94 260	29.05 481	90.31 276

MIMVSNet		71.64 349	68.44 362	81.23 294	81.97 365	64.44 217	73.05 448	88.80 339	69.67 308	64.59 352	74.79 427	32.79 424	87.82 411	53.99 380	76.35 279	91.42 254

test_vis1_n		71.63 350	70.73 343	74.31 401	69.63 459	47.29 449	86.91 357	72.11 458	63.21 378	75.18 212	90.17 226	20.40 463	85.76 428	84.59 107	74.42 291	89.87 281

IterMVS-SCA-FT		71.55 351	69.97 348	76.32 380	81.48 370	60.67 337	87.64 349	85.99 396	66.17 349	59.50 396	78.88 391	45.53 357	83.65 442	62.58 342	61.93 393	84.63 381

v7n		71.31 352	68.65 359	79.28 346	76.40 429	60.77 330	86.71 361	89.45 304	64.17 367	58.77 403	78.24 395	44.59 364	93.54 300	57.76 365	61.75 396	83.52 390

anonymousdsp		71.14 353	69.37 355	76.45 379	72.95 447	54.71 409	84.19 378	88.88 335	61.92 392	62.15 378	79.77 385	38.14 394	91.44 374	68.90 273	67.45 345	83.21 396

usedtu_blend_shiyan5		71.06 354	67.54 367	81.62 282	75.39 435	64.75 204	85.67 367	86.47 386	56.48 428	60.64 386	76.85 412	47.20 339	93.71 295	68.18 277	50.98 438	86.40 340

F-COLMAP		70.66 355	68.44 362	77.32 369	86.37 289	55.91 401	88.00 340	86.32 388	56.94 425	57.28 414	88.07 267	33.58 422	92.49 340	51.02 390	68.37 335	83.55 388

WR-MVS_H		70.59 356	69.94 349	72.53 413	81.03 373	51.43 423	87.35 352	92.03 163	67.38 338	60.23 393	80.70 370	55.84 237	83.45 445	46.33 418	58.58 416	82.72 403

CP-MVSNet		70.50 357	69.91 350	72.26 416	80.71 378	51.00 427	87.23 354	90.30 267	67.84 333	59.64 395	82.69 338	50.23 304	82.30 453	51.28 389	59.28 412	83.46 392

RPMNet		70.42 358	65.68 379	84.63 177	83.15 352	67.96 107	70.25 454	90.45 254	46.83 457	69.97 289	65.10 460	56.48 230	95.30 222	35.79 455	73.13 300	90.64 272

testing3		70.38 359	70.83 340	69.03 433	85.82 303	43.93 463	90.72 268	90.56 253	68.06 329	60.24 392	86.82 289	64.83 95	84.12 436	26.33 474	64.10 375	79.04 440

tfpnnormal		70.10 360	67.36 369	78.32 356	83.45 349	60.97 326	88.85 324	92.77 126	64.85 361	60.83 385	78.53 393	43.52 368	93.48 302	31.73 469	61.70 398	80.52 427

TransMVSNet (Re)		70.07 361	67.66 366	77.31 370	80.62 381	59.13 368	91.78 210	84.94 408	65.97 352	60.08 394	80.44 375	50.78 297	91.87 358	48.84 402	45.46 454	80.94 422

CL-MVSNet_self_test		69.92 362	68.09 365	75.41 385	73.25 446	55.90 402	90.05 291	89.90 286	69.96 304	61.96 380	76.54 415	51.05 296	87.64 414	49.51 399	50.59 442	82.70 405

DP-MVS		69.90 363	66.48 371	80.14 322	95.36 29	62.93 276	89.56 303	76.11 443	50.27 447	57.69 412	85.23 308	39.68 383	95.73 191	33.35 460	71.05 317	81.78 416

PS-CasMVS		69.86 364	69.13 358	72.07 420	80.35 385	50.57 430	87.02 356	89.75 291	67.27 339	59.19 399	82.28 343	46.58 347	82.24 454	50.69 392	59.02 413	83.39 394

Syy-MVS		69.65 365	69.52 353	70.03 428	87.87 241	43.21 464	88.07 338	89.01 329	72.91 230	63.11 368	88.10 265	45.28 360	85.54 429	22.07 479	69.23 328	81.32 418

MSDG		69.54 366	65.73 378	80.96 305	85.11 318	63.71 250	84.19 378	83.28 426	56.95 424	54.50 421	84.03 323	31.50 430	96.03 169	42.87 433	69.13 330	83.14 398

PEN-MVS		69.46 367	68.56 360	72.17 418	79.27 398	49.71 435	86.90 358	89.24 312	67.24 342	59.08 400	82.51 341	47.23 338	83.54 444	48.42 405	57.12 418	83.25 395

LS3D		69.17 368	66.40 373	77.50 365	91.92 116	56.12 399	85.12 370	80.37 435	46.96 455	56.50 416	87.51 277	37.25 402	93.71 295	32.52 468	79.40 247	82.68 406

PatchT		69.11 369	65.37 383	80.32 316	82.07 364	63.68 254	67.96 464	87.62 370	50.86 445	69.37 293	65.18 459	57.09 215	88.53 403	41.59 439	66.60 350	88.74 297

KD-MVS_2432*1600		69.03 370	66.37 374	77.01 374	85.56 307	61.06 324	81.44 409	90.25 270	67.27 339	58.00 408	76.53 416	54.49 252	87.63 415	48.04 407	35.77 472	82.34 409

miper_refine_blended		69.03 370	66.37 374	77.01 374	85.56 307	61.06 324	81.44 409	90.25 270	67.27 339	58.00 408	76.53 416	54.49 252	87.63 415	48.04 407	35.77 472	82.34 409

mvsany_test1		68.77 372	68.56 360	69.39 431	73.57 445	45.88 457	80.93 414	60.88 479	59.65 409	71.56 269	90.26 219	43.22 369	75.05 466	74.26 217	62.70 385	87.25 323

ACMH	63.93 17	68.62 373	64.81 385	80.03 326	85.22 314	63.25 266	87.72 346	84.66 410	60.83 401	51.57 436	79.43 389	27.29 446	94.96 232	41.76 437	64.84 366	81.88 414
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 374	65.41 382	77.96 361	78.69 409	62.93 276	89.86 297	89.17 316	60.55 402	50.27 442	77.73 401	22.60 459	94.06 278	47.18 414	72.65 305	76.88 454

ADS-MVSNet		68.54 375	64.38 392	81.03 304	88.06 232	66.90 148	68.01 462	84.02 416	57.57 418	64.48 354	69.87 447	38.68 385	89.21 398	40.87 441	67.89 342	86.97 325

DTE-MVSNet		68.46 376	67.33 370	71.87 422	77.94 419	49.00 441	86.16 366	88.58 348	66.36 347	58.19 405	82.21 345	46.36 348	83.87 441	44.97 426	55.17 425	82.73 402

mmtdpeth		68.33 377	66.37 374	74.21 402	82.81 357	51.73 420	84.34 376	80.42 434	67.01 343	71.56 269	68.58 451	30.52 437	92.35 347	75.89 201	36.21 470	78.56 447

our_test_3		68.29 378	64.69 387	79.11 351	78.92 404	64.85 203	88.40 333	85.06 406	60.32 405	52.68 430	76.12 420	40.81 379	89.80 395	44.25 428	55.65 423	82.67 407

Patchmatch-RL test		68.17 379	64.49 390	79.19 347	71.22 451	53.93 412	70.07 456	71.54 462	69.22 313	56.79 415	62.89 464	56.58 227	88.61 400	69.53 264	52.61 433	95.03 95

XVG-ACMP-BASELINE		68.04 380	65.53 381	75.56 384	74.06 444	52.37 417	78.43 430	85.88 397	62.03 390	58.91 402	81.21 366	20.38 464	91.15 376	60.69 352	68.18 336	83.16 397

FMVSNet5		68.04 380	65.66 380	75.18 389	84.43 333	57.89 378	83.54 383	86.26 390	61.83 394	53.64 427	73.30 430	37.15 405	85.08 433	48.99 401	61.77 395	82.56 408

ppachtmachnet_test		67.72 382	63.70 395	79.77 336	78.92 404	66.04 170	88.68 328	82.90 428	60.11 407	55.45 418	75.96 421	39.19 384	90.55 379	39.53 445	52.55 434	82.71 404

ACMH+	65.35 16	67.65 383	64.55 388	76.96 376	84.59 327	57.10 391	88.08 337	80.79 432	58.59 416	53.00 429	81.09 368	26.63 448	92.95 318	46.51 416	61.69 399	80.82 423

pmmvs6		67.57 384	64.76 386	76.00 383	72.82 449	53.37 414	88.71 327	86.78 385	53.19 437	57.58 413	78.03 398	35.33 414	92.41 343	55.56 374	54.88 427	82.21 411

Anonymous20231206		67.53 385	65.78 377	72.79 412	74.95 440	47.59 446	88.23 335	87.32 374	61.75 397	58.07 407	77.29 405	37.79 399	87.29 421	42.91 431	63.71 379	83.48 391

Patchmtry		67.53 385	63.93 394	78.34 355	82.12 363	64.38 221	68.72 459	84.00 417	48.23 454	59.24 397	72.41 435	57.82 209	89.27 397	46.10 419	56.68 422	81.36 417

USDC		67.43 387	64.51 389	76.19 381	77.94 419	55.29 405	78.38 431	85.00 407	73.17 221	48.36 450	80.37 376	21.23 461	92.48 341	52.15 388	64.02 377	80.81 424

ADS-MVSNet2		66.90 388	63.44 397	77.26 371	88.06 232	60.70 336	68.01 462	75.56 447	57.57 418	64.48 354	69.87 447	38.68 385	84.10 437	40.87 441	67.89 342	86.97 325

FE-MVSNET2		66.80 389	64.06 393	75.03 390	69.84 457	57.11 390	86.57 362	88.57 349	67.94 332	50.97 440	72.16 439	33.79 421	87.55 418	53.94 381	52.74 431	80.45 428

CMPMVS	48.56 21	66.77 390	64.41 391	73.84 404	70.65 455	50.31 432	77.79 435	85.73 400	45.54 460	44.76 461	82.14 346	35.40 413	90.14 389	63.18 337	74.54 289	81.07 421
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 391	62.92 400	76.80 378	76.51 428	57.77 380	89.22 315	83.41 424	55.48 432	53.86 425	77.84 399	26.28 449	93.95 287	34.90 457	68.76 332	78.68 445

LTVRE_ROB	59.60 19	66.27 392	63.54 396	74.45 398	84.00 340	51.55 422	67.08 466	83.53 422	58.78 414	54.94 420	80.31 377	34.54 416	93.23 311	40.64 443	68.03 338	78.58 446
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
JIA-IIPM		66.06 393	62.45 403	76.88 377	81.42 372	54.45 411	57.49 480	88.67 344	49.36 449	63.86 361	46.86 477	56.06 234	90.25 383	49.53 398	68.83 331	85.95 356

Patchmatch-test		65.86 394	60.94 409	80.62 313	83.75 344	58.83 370	58.91 477	75.26 449	44.50 464	50.95 441	77.09 408	58.81 193	87.90 409	35.13 456	64.03 376	95.12 89

UnsupCasMVSNet_eth		65.79 395	63.10 398	73.88 403	70.71 454	50.29 433	81.09 412	89.88 287	72.58 237	49.25 447	74.77 428	32.57 426	87.43 420	55.96 373	41.04 462	83.90 385

test_fmvs2		65.78 396	64.84 384	68.60 435	66.54 466	41.71 467	83.27 389	69.81 466	54.38 434	67.91 318	84.54 317	15.35 471	81.22 458	75.65 203	66.16 352	82.88 399

dmvs_testset		65.55 397	66.45 372	62.86 447	79.87 391	22.35 493	76.55 438	71.74 460	77.42 149	55.85 417	87.77 272	51.39 290	80.69 459	31.51 472	65.92 355	85.55 367

pmmvs-eth3d		65.53 398	62.32 404	75.19 388	69.39 460	59.59 359	82.80 397	83.43 423	62.52 385	51.30 438	72.49 433	32.86 423	87.16 422	55.32 375	50.73 441	78.83 443

mamv4		65.18 399	67.43 368	58.44 451	77.88 421	49.36 440	69.40 458	70.99 464	48.31 453	57.78 411	85.53 305	59.01 190	51.88 489	73.67 219	64.32 372	74.07 459

SixPastTwentyTwo		64.92 400	61.78 407	74.34 400	78.74 408	49.76 434	83.42 388	79.51 438	62.86 381	50.27 442	77.35 403	30.92 435	90.49 381	45.89 420	47.06 448	82.78 400

OurMVSNet-221017-0		64.68 401	62.17 405	72.21 417	76.08 432	47.35 447	80.67 415	81.02 431	56.19 429	51.60 435	79.66 387	27.05 447	88.56 402	53.60 384	53.63 430	80.71 425

test_0402		64.54 402	61.09 408	74.92 393	84.10 339	60.75 332	87.95 341	79.71 437	52.03 439	52.41 431	77.20 406	32.21 428	91.64 364	23.14 477	61.03 402	72.36 465

testgi		64.48 403	62.87 401	69.31 432	71.24 450	40.62 470	85.49 368	79.92 436	65.36 358	54.18 423	83.49 330	23.74 454	84.55 435	41.60 438	60.79 405	82.77 401

RPSCF		64.24 404	61.98 406	71.01 425	76.10 431	45.00 459	75.83 443	75.94 444	46.94 456	58.96 401	84.59 315	31.40 431	82.00 455	47.76 412	60.33 410	86.04 353

EU-MVSNet		64.01 405	63.01 399	67.02 441	74.40 443	38.86 476	83.27 389	86.19 392	45.11 462	54.27 422	81.15 367	36.91 408	80.01 461	48.79 404	57.02 419	82.19 412

test20.03		63.83 406	62.65 402	67.38 440	70.58 456	39.94 472	86.57 362	84.17 414	63.29 376	51.86 434	77.30 404	37.09 406	82.47 451	38.87 449	54.13 429	79.73 434

sc_t1		63.81 407	59.39 415	77.10 372	77.62 422	56.03 400	84.32 377	73.56 454	46.66 458	58.22 404	73.06 431	23.28 457	90.62 378	50.93 391	46.84 449	84.64 380

MDA-MVSNet_test_wron		63.78 408	60.16 411	74.64 395	78.15 417	60.41 343	83.49 385	84.03 415	56.17 431	39.17 472	71.59 442	37.22 403	83.24 448	42.87 433	48.73 444	80.26 431

YYNet1		63.76 409	60.14 412	74.62 396	78.06 418	60.19 350	83.46 387	83.99 419	56.18 430	39.25 471	71.56 443	37.18 404	83.34 446	42.90 432	48.70 445	80.32 430

K. test v3		63.09 410	59.61 414	73.53 406	76.26 430	49.38 439	83.27 389	77.15 441	64.35 364	47.77 452	72.32 437	28.73 441	87.79 412	49.93 397	36.69 469	83.41 393

COLMAP_ROB	57.96 20	62.98 411	59.65 413	72.98 410	81.44 371	53.00 416	83.75 382	75.53 448	48.34 452	48.81 449	81.40 360	24.14 452	90.30 382	32.95 463	60.52 407	75.65 457
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 412	59.08 416	71.10 424	67.19 464	48.72 442	83.91 380	85.23 405	50.38 446	47.84 451	71.22 445	20.74 462	85.51 431	46.47 417	58.75 415	79.06 439

tt0320		61.85 413	57.45 422	75.03 390	77.49 423	57.60 384	82.74 398	73.65 453	43.65 468	53.65 426	68.18 453	25.47 450	88.66 399	45.56 422	46.68 450	78.81 444

AllTest		61.66 414	58.06 418	72.46 414	79.57 393	51.42 424	80.17 421	68.61 468	51.25 443	45.88 455	81.23 362	19.86 466	86.58 424	38.98 447	57.01 420	79.39 436

UnsupCasMVSNet_bld		61.60 415	57.71 419	73.29 408	68.73 461	51.64 421	78.61 429	89.05 327	57.20 423	46.11 454	61.96 468	28.70 442	88.60 401	50.08 396	38.90 467	79.63 435

MDA-MVSNet-bldmvs		61.54 416	57.70 420	73.05 409	79.53 395	57.00 395	83.08 393	81.23 430	57.57 418	34.91 476	72.45 434	32.79 424	86.26 426	35.81 454	41.95 460	75.89 456

tt0320-xc		61.51 417	56.89 426	75.37 386	78.50 412	58.61 373	82.61 400	71.27 463	44.31 465	53.17 428	68.03 455	23.38 455	88.46 404	47.77 411	43.00 459	79.03 441

mvs5depth		61.03 418	57.65 421	71.18 423	67.16 465	47.04 452	72.74 449	77.49 439	57.47 421	60.52 388	72.53 432	22.84 458	88.38 405	49.15 400	38.94 466	78.11 450

KD-MVS_self_test		60.87 419	58.60 417	67.68 438	66.13 467	39.93 473	75.63 445	84.70 409	57.32 422	49.57 445	68.45 452	29.55 438	82.87 449	48.09 406	47.94 446	80.25 432

kuosan		60.86 420	60.24 410	62.71 448	81.57 369	46.43 454	75.70 444	85.88 397	57.98 417	48.95 448	69.53 449	58.42 198	76.53 464	28.25 473	35.87 471	65.15 472

FE-MVSNET		60.52 421	57.18 425	70.53 426	67.53 463	50.68 429	82.62 399	76.28 442	59.33 412	46.71 453	71.10 446	30.54 436	83.61 443	33.15 462	47.37 447	77.29 453

TinyColmap		60.32 422	56.42 429	72.00 421	78.78 407	53.18 415	78.36 432	75.64 446	52.30 438	41.59 470	75.82 423	14.76 474	88.35 406	35.84 453	54.71 428	74.46 458

MVS-HIRNet		60.25 423	55.55 430	74.35 399	84.37 334	56.57 397	71.64 452	74.11 451	34.44 475	45.54 459	42.24 483	31.11 434	89.81 393	40.36 444	76.10 281	76.67 455

MIMVSNet1		60.16 424	57.33 423	68.67 434	69.71 458	44.13 461	78.92 428	84.21 413	55.05 433	44.63 462	71.85 440	23.91 453	81.54 457	32.63 467	55.03 426	80.35 429

PM-MVS		59.40 425	56.59 427	67.84 436	63.63 470	41.86 465	76.76 437	63.22 476	59.01 413	51.07 439	72.27 438	11.72 478	83.25 447	61.34 348	50.28 443	78.39 448

new-patchmatchnet		59.30 426	56.48 428	67.79 437	65.86 468	44.19 460	82.47 401	81.77 429	59.94 408	43.65 466	66.20 458	27.67 445	81.68 456	39.34 446	41.40 461	77.50 452

test_vis1_rt		59.09 427	57.31 424	64.43 444	68.44 462	46.02 456	83.05 395	48.63 488	51.96 440	49.57 445	63.86 463	16.30 469	80.20 460	71.21 250	62.79 384	67.07 471

usedtu_dtu_shiyan2		57.76 428	53.69 434	69.95 429	57.60 480	41.80 466	83.50 384	83.67 421	45.26 461	43.79 465	62.82 465	17.63 468	85.93 427	42.56 436	46.40 452	82.12 413

test_fmvs3		56.82 429	54.86 432	62.69 449	53.59 482	35.47 479	75.87 442	65.64 473	43.91 466	55.10 419	71.43 444	6.91 486	74.40 469	68.64 275	52.63 432	78.20 449

DSMNet-mixed		56.78 430	54.44 433	63.79 445	63.21 471	29.44 488	64.43 469	64.10 475	42.12 472	51.32 437	71.60 441	31.76 429	75.04 467	36.23 452	65.20 363	86.87 330

pmmvs3		55.51 431	51.50 437	67.53 439	57.90 479	50.93 428	80.37 417	73.66 452	40.63 473	44.15 464	64.75 461	16.30 469	78.97 463	44.77 427	40.98 464	72.69 463

TDRefinement		55.28 432	51.58 436	66.39 442	59.53 478	46.15 455	76.23 440	72.80 455	44.60 463	42.49 468	76.28 419	15.29 472	82.39 452	33.20 461	43.75 456	70.62 467

dongtai		55.18 433	55.46 431	54.34 459	76.03 433	36.88 477	76.07 441	84.61 411	51.28 442	43.41 467	64.61 462	56.56 228	67.81 477	18.09 482	28.50 482	58.32 475

LF4IMVS		54.01 434	52.12 435	59.69 450	62.41 473	39.91 474	68.59 460	68.28 470	42.96 470	44.55 463	75.18 424	14.09 476	68.39 476	41.36 440	51.68 435	70.78 466

ttmdpeth		53.34 435	49.96 438	63.45 446	62.07 475	40.04 471	72.06 450	65.64 473	42.54 471	51.88 433	77.79 400	13.94 477	76.48 465	32.93 464	30.82 480	73.84 460

MVStest1		51.35 436	46.89 440	64.74 443	65.06 469	51.10 426	67.33 465	72.58 456	30.20 479	35.30 474	74.82 426	27.70 444	69.89 474	24.44 476	24.57 483	73.22 461

N_pmnet		50.55 437	49.11 439	54.88 457	77.17 426	4.02 501	84.36 375	2.00 499	48.59 450	45.86 457	68.82 450	32.22 427	82.80 450	31.58 470	51.38 436	77.81 451

new_pmnet		49.31 438	46.44 441	57.93 452	62.84 472	40.74 469	68.47 461	62.96 477	36.48 474	35.09 475	57.81 472	14.97 473	72.18 471	32.86 465	46.44 451	60.88 474

mvsany_test3		48.86 439	46.35 442	56.41 453	46.00 488	31.67 484	62.26 471	47.25 489	43.71 467	45.54 459	68.15 454	10.84 479	64.44 485	57.95 364	35.44 474	73.13 462

test_f		46.58 440	43.45 444	55.96 454	45.18 489	32.05 483	61.18 472	49.49 487	33.39 476	42.05 469	62.48 467	7.00 485	65.56 481	47.08 415	43.21 458	70.27 468

WB-MVS		46.23 441	44.94 443	50.11 462	62.13 474	21.23 495	76.48 439	55.49 481	45.89 459	35.78 473	61.44 470	35.54 412	72.83 470	9.96 489	21.75 484	56.27 477

FPMVS		45.64 442	43.10 446	53.23 460	51.42 485	36.46 478	64.97 468	71.91 459	29.13 480	27.53 480	61.55 469	9.83 481	65.01 483	16.00 486	55.58 424	58.22 476

SSC-MVS		44.51 443	43.35 445	47.99 466	61.01 477	18.90 497	74.12 447	54.36 482	43.42 469	34.10 477	60.02 471	34.42 417	70.39 473	9.14 491	19.57 485	54.68 478

EGC-MVSNET		42.35 444	38.09 447	55.11 456	74.57 441	46.62 453	71.63 453	55.77 480	0.04 494	0.24 495	62.70 466	14.24 475	74.91 468	17.59 483	46.06 453	43.80 480

LCM-MVSNet		40.54 445	35.79 450	54.76 458	36.92 495	30.81 485	51.41 483	69.02 467	22.07 482	24.63 482	45.37 479	4.56 490	65.81 480	33.67 459	34.50 475	67.67 469

APD_test1		40.50 446	37.31 449	50.09 463	51.88 483	35.27 480	59.45 476	52.59 484	21.64 483	26.12 481	57.80 473	4.56 490	66.56 479	22.64 478	39.09 465	48.43 479

test_vis3_rt		40.46 447	37.79 448	48.47 465	44.49 490	33.35 482	66.56 467	32.84 496	32.39 477	29.65 478	39.13 486	3.91 493	68.65 475	50.17 394	40.99 463	43.40 481

ANet_high		40.27 448	35.20 451	55.47 455	34.74 496	34.47 481	63.84 470	71.56 461	48.42 451	18.80 485	41.08 484	9.52 482	64.45 484	20.18 480	8.66 492	67.49 470

test_method		38.59 449	35.16 452	48.89 464	54.33 481	21.35 494	45.32 486	53.71 483	7.41 491	28.74 479	51.62 475	8.70 483	52.87 488	33.73 458	32.89 476	72.47 464

PMMVS2		37.93 450	33.61 453	50.92 461	46.31 487	24.76 491	60.55 475	50.05 485	28.94 481	20.93 483	47.59 476	4.41 492	65.13 482	25.14 475	18.55 487	62.87 473

Gipuma		34.91 451	31.44 454	45.30 467	70.99 453	39.64 475	19.85 490	72.56 457	20.10 485	16.16 489	21.47 490	5.08 489	71.16 472	13.07 487	43.70 457	25.08 487
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 452	29.47 455	42.67 469	41.89 492	30.81 485	52.07 481	43.45 490	15.45 486	18.52 486	44.82 480	2.12 494	58.38 486	16.05 484	30.87 478	38.83 482

APD_test2		32.77 452	29.47 455	42.67 469	41.89 492	30.81 485	52.07 481	43.45 490	15.45 486	18.52 486	44.82 480	2.12 494	58.38 486	16.05 484	30.87 478	38.83 482

PMVS	26.43 22	31.84 454	28.16 457	42.89 468	25.87 498	27.58 489	50.92 484	49.78 486	21.37 484	14.17 490	40.81 485	2.01 496	66.62 478	9.61 490	38.88 468	34.49 486
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 455	24.00 459	26.45 473	43.74 491	18.44 498	60.86 473	39.66 492	15.11 488	9.53 492	22.10 489	6.52 487	46.94 491	8.31 492	10.14 489	13.98 489

MVE	24.84 23	24.35 456	19.77 462	38.09 471	34.56 497	26.92 490	26.57 488	38.87 494	11.73 490	11.37 491	27.44 487	1.37 497	50.42 490	11.41 488	14.60 488	36.93 484
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 457	23.20 461	25.46 474	41.52 494	16.90 499	60.56 474	38.79 495	14.62 489	8.99 493	20.24 492	7.35 484	45.82 492	7.25 493	9.46 490	13.64 490

tmp_tt		22.26 458	23.75 460	17.80 475	5.23 499	12.06 500	35.26 487	39.48 493	2.82 493	18.94 484	44.20 482	22.23 460	24.64 494	36.30 451	9.31 491	16.69 488

cdsmvs_eth3d_5k		19.86 459	26.47 458	0.00 479	0.00 502	0.00 504	0.00 491	93.45 95	0.00 497	0.00 498	95.27 77	49.56 312	0.00 498	0.00 497	0.00 495	0.00 494

wuyk23d		11.30 460	10.95 463	12.33 476	48.05 486	19.89 496	25.89 489	1.92 500	3.58 492	3.12 494	1.37 494	0.64 498	15.77 495	6.23 494	7.77 493	1.35 491

ab-mvs-re		7.91 461	10.55 464	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	94.95 87	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

testmvs		7.23 462	9.62 465	0.06 478	0.04 500	0.02 503	84.98 372	0.02 501	0.03 495	0.18 496	1.21 495	0.01 500	0.02 496	0.14 495	0.01 494	0.13 493

test123		6.92 463	9.21 466	0.08 477	0.03 501	0.05 502	81.65 407	0.01 502	0.02 496	0.14 497	0.85 496	0.03 499	0.02 496	0.12 496	0.00 495	0.16 492

pcd_1.5k_mvsjas		4.46 464	5.95 467	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	53.55 266	0.00 498	0.00 497	0.00 495	0.00 494

mmdepth		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

monomultidepth		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

test_blank		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

uanet_test		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

DCPMVS		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

sosnet-low-res		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

sosnet		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

uncertanet		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

Regformer		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

uanet		0.00 465	0.00 468	0.00 479	0.00 502	0.00 504	0.00 491	0.00 503	0.00 497	0.00 498	0.00 497	0.00 501	0.00 498	0.00 497	0.00 495	0.00 494

MED-MVS test				87.42 45	94.76 34	67.28 126	94.47 61	94.87 32	73.09 226	91.27 23	96.95 17		98.98 16	91.55 43	94.28 37	95.99 45

TestfortrainingZip							94.47 61

WAC-MVS						49.45 437								31.56 471

FOURS1					93.95 50	61.77 306	93.96 88	91.92 167	62.14 389	86.57 62

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 38	94.77 26	96.51 24

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

No_MVS				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 38	94.77 26	96.51 24

test_one_0601					96.32 19	69.74 51		94.18 66	71.42 280	90.67 29	96.85 27	74.45 22

eth-test2					0.00 502

eth-test					0.00 502

ZD-MVS					96.63 9	65.50 187		93.50 93	70.74 294	85.26 80	95.19 83	64.92 94	97.29 89	87.51 73	93.01 60

RE-MVS-def			80.48 186		92.02 109	58.56 374	90.90 257	90.45 254	62.76 382	78.89 164	94.46 101	49.30 315		78.77 184	86.77 148	92.28 232

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 66	95.28 1				92.34 35	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 15	83.82 2	99.15 2	95.72 8	97.63 3	97.62 2

test_241102_TWO								94.41 57	71.65 269	92.07 11	97.21 9	74.58 20	99.11 6	92.34 35	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 59		94.44 55	71.65 269	92.11 9	97.05 12	76.79 9	99.11 6

9.14			87.63 39		93.86 52		94.41 66	94.18 66	72.76 234	86.21 65	96.51 36	66.64 72	97.88 52	90.08 55	94.04 43

save fliter					93.84 53	67.89 110	95.05 40	92.66 133	78.19 129

test_0728_THIRD									72.48 239	90.55 30	96.93 21	76.24 13	99.08 11	91.53 46	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 61					99.15 2	91.91 41	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 63	71.92 255	91.89 14	97.11 11	73.77 25

GSMVS																94.68 120

test_part2					96.29 20	68.16 103				90.78 27

sam_mvs1												57.85 208				94.68 120

sam_mvs												54.91 247

ambc				69.61 430	61.38 476	41.35 468	49.07 485	85.86 399		50.18 444	66.40 457	10.16 480	88.14 408	45.73 421	44.20 455	79.32 438

MTGPA								92.23 148

test_post1							78.95 427				20.70 491	53.05 271	91.50 373	60.43 353

test_post											23.01 488	56.49 229	92.67 333

patchmatchnet-post											67.62 456	57.62 211	90.25 383

GG-mvs-BLEND				86.53 89	91.91 118	69.67 54	75.02 446	94.75 40		78.67 172	90.85 206	77.91 7	94.56 254	72.25 237	93.74 49	95.36 72

MTMP							93.77 103	32.52 497

gm-plane-assit					88.42 218	67.04 138			78.62 123		91.83 179		97.37 83	76.57 196

test9_res														89.41 56	94.96 19	95.29 78

TEST9					94.18 45	67.28 126	94.16 75	93.51 91	71.75 266	85.52 75	95.33 71	68.01 61	97.27 93

test_8					94.19 44	67.19 131	94.15 77	93.42 98	71.87 260	85.38 78	95.35 70	68.19 59	96.95 120

agg_prior2														86.41 87	94.75 30	95.33 74

agg_prior					94.16 47	66.97 146		93.31 101		84.49 86			96.75 132

TestCases				72.46 414	79.57 393	51.42 424		68.61 468	51.25 443	45.88 455	81.23 362	19.86 466	86.58 424	38.98 447	57.01 420	79.39 436

test_prior4						67.18 133	93.92 92

test_prior2							95.10 39		75.40 183	85.25 81	95.61 62	67.94 62		87.47 75	94.77 26

test_prior				86.42 93	94.71 39	67.35 125		93.10 112					96.84 129			95.05 93

旧先验2							92.00 197		59.37 411	87.54 55			93.47 303	75.39 205

新几何2							91.41 228

新几何1				84.73 167	92.32 97	64.28 226		91.46 194	59.56 410	79.77 151	92.90 145	56.95 221	96.57 138	63.40 333	92.91 62	93.34 192

旧先验1					91.94 114	60.74 333		91.50 192			94.36 105	65.23 89			91.84 78	94.55 129

无先验							92.71 152	92.61 138	62.03 390				97.01 110	66.63 299		93.97 168

原ACMM2							92.01 194

原ACMM1				84.42 184	93.21 72	64.27 227		93.40 100	65.39 357	79.51 156	92.50 153	58.11 203	96.69 134	65.27 320	93.96 44	92.32 230

test222					89.77 167	61.60 312	89.55 304	89.42 306	56.83 426	77.28 188	92.43 157	52.76 274			91.14 95	93.09 202

testdata2													96.09 163	61.26 349

segment_acmp												65.94 80

testdata				81.34 290	89.02 191	57.72 381		89.84 288	58.65 415	85.32 79	94.09 121	57.03 216	93.28 308	69.34 266	90.56 101	93.03 205

testdata1							89.21 316		77.55 145

test12				87.09 56	94.60 40	68.86 79		92.91 121		82.67 109		65.44 86	97.55 72		93.69 52	94.84 105

plane_prior7					86.94 272	61.51 314

plane_prior6					87.23 258	62.32 292						50.66 298

plane_prior5								91.31 198					95.55 208	76.74 194	78.53 259	88.39 304

plane_prior4											89.14 247

plane_prior3						61.95 301			79.09 112	72.53 251

plane_prior2							93.13 131		78.81 119

plane_prior1					87.15 261

plane_prior						62.42 288	93.85 96		79.38 104						78.80 256

n2								0.00 503

nn								0.00 503

door-mid								66.01 472

lessismore_v0				73.72 405	72.93 448	47.83 445		61.72 478		45.86 457	73.76 429	28.63 443	89.81 393	47.75 413	31.37 477	83.53 389

LGP-MVS_train				79.56 342	84.31 335	59.37 363		89.73 294	69.49 309	64.86 349	88.42 256	38.65 387	94.30 265	72.56 234	72.76 303	85.01 375

test11								93.01 115

door								66.57 471

HQP5-MVS						63.66 255

HQP-NCC					87.54 250		94.06 80		79.80 88	74.18 225

ACMP_Plane					87.54 250		94.06 80		79.80 88	74.18 225

BP-MVS														77.63 191

HQP4-MVS										74.18 225			95.61 202			88.63 298

HQP3-MVS								91.70 184							78.90 254

HQP2-MVS												51.63 286

NP-MVS					87.41 253	63.04 272					90.30 217

MDTV_nov1_ep13_2view						59.90 355	80.13 422		67.65 336	72.79 245		54.33 257		59.83 357		92.58 221

MDTV_nov1_ep13			72.61 324		89.06 189	68.48 90	80.33 418	90.11 277	71.84 262	71.81 265	75.92 422	53.01 272	93.92 288	48.04 407	73.38 298

ACMMP++_ref															71.63 311

ACMMP++															69.72 322

Test By Simon												54.21 260

ITE_SJBPF				70.43 427	74.44 442	47.06 451		77.32 440	60.16 406	54.04 424	83.53 328	23.30 456	84.01 439	43.07 430	61.58 400	80.21 433

DeepMVS_CX				34.71 472	51.45 484	24.73 492		28.48 498	31.46 478	17.49 488	52.75 474	5.80 488	42.60 493	18.18 481	19.42 486	36.81 485

High-resolution multi-view benchmark