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	72.48 233	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	85.88 106	92.83 84	64.03 226	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	71.65 263	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	84.72 164	93.00 80	58.16 366	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	85.85 109	96.04 25	63.70 241	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	71.92 249	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 97	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	90.61 264	93.43 97	84.06 24	86.20 66	90.17 222	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 71	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	67.28 122	94.47 61	94.87 32	70.09 295	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	67.56 115	94.17 74	94.15 68	68.77 316	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 80	94.47 61	94.87 32	73.09 220	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	67.28 122	93.93 90	94.81 37	70.09 295	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	84.85 153	88.15 230	61.94 291	95.65 25	89.70 288	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	84.92 148	89.29 180	61.41 308	92.97 138	88.36 343	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 82	93.85 96	94.03 71	74.18 194	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	53.55 259	97.89 51	91.10 48	93.31 57	94.54 131

TSAR-MVS + MP.		88.11 25	88.64 26	86.54 84	91.73 123	68.04 101	90.36 271	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	84.23 190	89.07 188	61.60 301	94.87 49	89.06 316	85.65 11	91.09 26	97.41 4	68.26 58	97.43 80	95.07 13	92.74 64	93.66 179

fmvsm_s_conf0.5_n_8		87.96 27	88.93 22	85.07 143	88.43 217	61.78 294	94.73 56	91.74 176	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	65.32 184	95.15 37	93.84 74	78.17 126	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 91	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	85.71 116	88.69 199	63.71 239	94.56 59	90.22 264	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	53.45 263	97.68 59	91.07 49	92.62 65	94.54 131

EPNet		87.84 32	88.38 29	86.23 96	93.30 69	66.05 163	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	92.90 120	82.70 39	87.13 56	95.27 77	64.99 91	95.80 181	89.34 58	91.80 79	95.93 48

test_fmvsm_n_1920		87.69 34	88.50 28	85.27 136	87.05 265	63.55 248	93.69 106	91.08 218	84.18 23	90.17 36	97.04 14	67.58 65	97.99 46	95.72 8	90.03 108	94.26 147

fmvsm_l_conf0.5_n_3		87.54 35	88.29 31	85.30 133	86.92 272	62.63 274	95.02 44	90.28 259	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	66.30 158	94.84 51	93.78 75	69.35 305	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	83.84 202	86.89 274	60.04 342	95.05 40	92.17 155	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	85.39 127	86.95 267	64.37 214	94.30 71	88.45 341	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 78	93.90 93	92.63 135	76.86 151	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	85.30 133	87.10 263	64.19 221	94.41 66	88.14 351	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 98	90.36 271	90.66 239	79.37 101	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	79.30 102	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	67.28 122	94.16 75	93.51 91	71.87 254	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	78.74 117	83.87 93	92.94 144	64.34 102	96.94 121	75.19 202	94.09 42	95.66 57

SF-MVS		87.03 45	87.09 47	86.84 63	92.70 90	67.45 120	93.64 109	93.76 78	70.78 287	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	83.58 216	87.26 257	60.74 322	93.21 130	87.94 358	84.22 22	91.70 16	97.27 6	65.91 82	95.02 224	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	68.48 320	77.63 177	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	91.71 179	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	91.71 179	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	65.55 179	95.39 31	95.10 25	71.77 259	85.69 73	96.52 35	62.07 142	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	66.38 155	96.09 17	93.87 73	77.73 135	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	84.27 188	86.15 290	61.48 305	94.69 57	91.16 206	83.79 28	90.51 32	96.28 44	64.24 103	98.22 39	95.00 14	86.88 143	93.11 197

PVSNet_Blended		86.73 54	86.86 53	86.31 95	93.76 54	67.53 117	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	94.13 156

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	83.43 193	94.82 109

test_fmvsmconf_n		86.58 56	87.17 46	84.82 155	85.28 308	62.55 275	94.26 73	89.78 279	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 99	87.80 245	67.18 129	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	86.16 289	70.54 32	95.71 24	92.19 152	82.00 47	84.58 85	94.34 110	61.86 145	95.53 206	87.76 70	90.89 96	95.27 81
jason: jason.
NormalMVS		86.39 59	86.66 58	85.60 121	92.12 105	65.95 168	94.88 47	90.83 227	84.69 19	83.67 95	94.10 119	63.16 127	96.91 127	85.31 95	91.15 92	93.93 167

fmvsm_s_conf0.5_n		86.39 59	86.91 51	84.82 155	87.36 256	63.54 249	94.74 53	90.02 272	82.52 40	90.14 37	96.92 23	62.93 132	97.84 54	95.28 11	82.26 204	93.07 200

fmvsm_s_conf0.5_n_5		86.38 61	86.94 50	84.71 166	84.67 320	63.29 254	94.04 84	89.99 274	82.88 36	87.85 51	96.03 53	62.89 134	96.36 150	94.15 21	89.95 110	94.48 137

SymmetryMVS		86.32 62	86.39 61	86.12 100	90.52 152	65.95 168	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	79.55 167	82.25 206	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	78.70 167	92.07 169	72.45 36	95.41 208	82.11 141	85.78 162	94.44 139

MSLP-MVS++		86.27 65	85.91 73	87.35 48	92.01 112	68.97 74	95.04 42	92.70 126	79.04 112	81.50 116	96.50 37	58.98 190	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	78.43 122	86.00 69	93.07 141	58.22 200	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	79.08 109	78.88 163	93.99 124	62.25 141	98.15 42	85.93 92	91.15 92	94.15 155

SPE-MVS-test		86.14 68	87.01 48	83.52 217	92.63 92	59.36 354	95.49 28	91.92 165	80.09 82	85.46 77	95.53 66	61.82 147	95.77 185	86.77 86	93.37 56	95.41 66

ACMMP_NAP		86.05 69	85.80 75	86.80 66	91.58 127	67.53 117	91.79 206	93.49 94	74.93 184	84.61 84	95.30 73	59.42 180	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	80.02 164	82.22 207	95.13 88

ETV-MVS		86.01 70	86.11 68	85.70 117	90.21 159	67.02 136	93.43 122	91.92 165	81.21 61	84.13 91	94.07 123	60.93 156	95.63 195	89.28 59	89.81 112	94.46 138

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	80.32 162	82.13 209	95.37 69

APD-MVS		85.93 72	85.99 71	85.76 113	95.98 27	65.21 187	93.59 112	92.58 137	66.54 339	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	92.77 124	82.11 46	80.34 140	93.07 141	68.27 57	95.02 224	78.39 183	93.59 53	94.09 158

CS-MVS		85.80 75	86.65 59	83.27 229	92.00 113	58.92 358	95.31 32	91.86 170	79.97 83	84.82 83	95.40 69	62.26 140	95.51 207	86.11 90	92.08 73	95.37 69

fmvsm_s_conf0.5_n_a		85.75 76	86.09 69	84.72 164	85.73 301	63.58 246	93.79 102	89.32 299	81.42 57	90.21 35	96.91 24	62.41 139	97.67 61	94.48 18	80.56 232	92.90 206

test_fmvsmconf0.1_n		85.71 77	86.08 70	84.62 174	80.83 369	62.33 280	93.84 99	88.81 328	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 86	94.75 38	67.19 127	93.89 94	92.83 122	70.90 283	83.09 102	95.28 75	63.62 116	97.36 84	80.63 158	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	91.89 168	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	84.68 168	82.95 352	63.48 251	94.03 86	89.46 293	81.69 50	89.86 38	96.74 31	61.85 146	97.75 57	94.74 17	82.01 211	92.81 210

MGCFI-Net		85.59 81	85.73 77	85.17 140	91.41 135	62.44 276	92.87 146	91.31 196	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 97	87.64 248	67.95 105	92.91 144	92.36 142	77.81 132	83.69 94	94.31 112	72.84 32	96.41 148	80.39 161	85.95 159	94.19 151

DeepC-MVS	77.85 3	85.52 83	85.24 85	86.37 91	88.80 197	66.64 149	92.15 185	93.68 84	81.07 63	76.91 189	93.64 131	62.59 136	98.44 35	85.50 93	92.84 63	94.03 162
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 68	93.04 135	91.76 175	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	86.06 101	93.09 77	65.65 175	93.89 94	93.41 99	73.75 205	79.94 145	94.68 97	60.61 161	98.03 45	82.63 136	93.72 50	94.52 133

fmvsm_s_conf0.5_n_7		85.24 86	86.69 56	80.91 297	84.52 325	60.10 340	93.35 125	90.35 252	83.41 31	86.54 63	96.27 45	60.50 162	90.02 381	94.84 16	90.38 104	92.61 214

MP-MVS-pluss		85.24 86	85.13 87	85.56 122	91.42 132	65.59 177	91.54 218	92.51 139	74.56 187	80.62 133	95.64 61	59.15 185	97.00 111	86.94 84	93.80 47	94.07 160
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	95.19 221	75.94 196	83.27 195	94.81 111

PAPR		85.15 89	84.47 97	87.18 53	96.02 26	68.29 92	91.85 204	93.00 117	76.59 162	79.03 159	95.00 86	61.59 148	97.61 68	78.16 184	89.00 121	95.63 58

fmvsm_s_conf0.5_n_2		85.06 90	85.60 79	83.44 223	86.92 272	60.53 329	94.41 66	87.31 366	83.30 32	88.72 46	96.72 32	54.28 251	97.75 57	94.07 22	84.68 179	92.04 237

MP-MVS		85.02 91	84.97 90	85.17 140	92.60 93	64.27 219	93.24 127	92.27 145	73.13 216	79.63 151	94.43 103	61.90 143	97.17 99	85.00 101	92.56 66	94.06 161
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 80	90.24 276	91.82 174	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	92.32 143	81.87 48	75.68 198	88.27 256	60.18 166	98.60 31	80.46 160	90.27 106	94.96 97

MVSMamba_PlusPlus		84.97 94	83.65 111	88.93 14	90.17 160	74.04 8	87.84 334	92.69 129	62.18 377	81.47 118	87.64 270	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	91.29 201	80.72 67	81.03 125	92.14 166	61.89 144	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	91.13 212	81.11 62	81.54 115	91.98 173	60.35 163	95.73 187	84.47 109	86.56 153	94.84 105

EIA-MVS		84.84 97	84.88 91	84.69 167	91.30 137	62.36 279	93.85 96	92.04 158	79.45 97	79.33 156	94.28 114	62.42 138	96.35 151	80.05 163	91.25 91	95.38 68

lecture		84.77 98	84.81 94	84.65 170	92.12 105	62.27 283	94.74 53	92.64 134	68.35 321	85.53 74	95.30 73	59.77 173	97.91 49	83.73 122	91.15 92	93.77 176

fmvsm_s_conf0.1_n_a		84.76 99	84.84 93	84.53 176	80.23 382	63.50 250	92.79 148	88.73 331	80.46 71	89.84 39	96.65 34	60.96 155	97.57 71	93.80 25	80.14 234	92.53 219

viewcassd2359sk11		84.74 100	84.11 103	86.64 75	88.57 202	69.20 66	92.61 161	91.23 203	80.58 68	80.85 129	91.96 174	61.39 150	95.89 175	84.28 113	85.49 166	94.82 109

HFP-MVS		84.73 101	84.40 99	85.72 115	93.75 56	65.01 193	93.50 117	93.19 107	72.19 243	79.22 157	94.93 89	59.04 188	97.67 61	81.55 147	92.21 69	94.49 136

MVS		84.66 102	82.86 140	90.06 2	90.93 144	74.56 7	87.91 332	95.54 14	68.55 318	72.35 255	94.71 96	59.78 172	98.90 23	81.29 153	94.69 32	96.74 16

GST-MVS		84.63 103	84.29 101	85.66 118	92.82 86	65.27 185	93.04 135	93.13 110	73.20 214	78.89 160	94.18 117	59.41 181	97.85 53	81.45 149	92.48 68	93.86 173

EC-MVSNet		84.53 104	85.04 89	83.01 235	89.34 176	61.37 309	94.42 65	91.09 216	77.91 130	83.24 98	94.20 116	58.37 198	95.40 209	85.35 94	91.41 86	92.27 231

E2		84.45 105	83.74 107	86.56 82	87.90 238	69.06 69	92.53 169	91.13 212	80.35 75	80.58 135	91.69 184	60.70 157	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 69	92.53 169	91.13 212	80.35 75	80.58 135	91.69 184	60.70 157	95.84 178	83.80 120	84.99 171	94.79 112

fmvsm_s_conf0.1_n_2		84.40 107	84.78 95	83.27 229	85.25 309	60.41 332	94.13 78	85.69 391	83.05 34	87.99 49	96.37 39	52.75 268	97.68 59	93.75 26	84.05 189	91.71 245

ACMMPR		84.37 108	84.06 104	85.28 135	93.56 62	64.37 214	93.50 117	93.15 109	72.19 243	78.85 165	94.86 92	56.69 220	97.45 77	81.55 147	92.20 70	94.02 163

region2R		84.36 109	84.03 105	85.36 131	93.54 64	64.31 217	93.43 122	92.95 118	72.16 246	78.86 164	94.84 93	56.97 215	97.53 73	81.38 151	92.11 72	94.24 149

LFMVS		84.34 110	82.73 142	89.18 13	94.76 34	73.25 11	94.99 45	91.89 168	71.90 251	82.16 111	93.49 135	47.98 321	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 64	95.78 18	95.09 26	69.19 308	81.09 123	92.88 147	57.00 213	97.44 78	81.11 155	81.76 215	96.23 38

DCV-MVSNet		84.28 111	83.16 131	87.64 35	94.52 41	69.24 64	95.78 18	95.09 26	69.19 308	81.09 123	92.88 147	57.00 213	97.44 78	81.11 155	81.76 215	96.23 38

diffmvs		84.28 111	83.83 106	85.61 120	87.40 254	68.02 102	90.88 249	89.24 302	80.54 69	81.64 114	92.52 152	59.83 171	94.52 253	87.32 77	85.11 170	94.29 146
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	67.74 110	84.65 363	94.50 52	79.15 106	82.23 110	87.93 265	66.88 70	96.94 121	80.53 159	82.20 208	96.39 33

ETVMVS		84.22 115	83.71 109	85.76 113	92.58 94	68.25 96	92.45 173	95.53 15	79.54 96	79.46 153	91.64 187	70.29 48	94.18 267	69.16 265	82.76 201	94.84 105

MAR-MVS		84.18 116	83.43 119	86.44 88	96.25 22	65.93 170	94.28 72	94.27 65	74.41 189	79.16 158	95.61 62	53.99 254	98.88 25	69.62 259	93.26 58	94.50 135
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	89.99 285	92.05 157	77.77 134	82.84 104	86.57 287	63.93 109	96.09 163	74.91 207	89.18 118	95.25 85

CANet_DTU		84.09 118	83.52 112	85.81 110	90.30 157	66.82 143	91.87 202	89.01 319	85.27 13	86.09 68	93.74 128	47.71 327	96.98 115	77.90 186	89.78 114	93.65 180

viewdifsd2359ckpt13		84.08 119	83.21 127	86.70 71	88.49 212	69.55 55	92.25 179	91.14 210	79.71 91	79.73 148	91.72 183	58.83 191	95.89 175	82.06 142	84.99 171	94.66 123

viewmacassd2359aftdt		84.03 120	83.18 130	86.59 79	86.76 275	69.44 56	92.44 174	90.85 226	80.38 74	80.78 131	91.33 195	58.54 195	95.62 197	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	58.19 394	93.64 131	73.64 27	92.35 336	82.66 135	78.66 254	96.50 27

E4		84.00 122	83.19 129	86.46 86	86.99 266	68.85 76	92.39 176	90.99 224	79.94 84	80.17 142	91.36 194	59.73 174	95.79 182	82.87 133	84.22 186	94.74 114

diffmvs_AUTHOR		83.97 123	83.49 115	85.39 127	86.09 291	67.83 107	90.76 254	89.05 317	79.94 84	81.43 119	92.23 164	59.53 177	94.42 256	87.18 80	85.22 168	93.92 169

PVSNet_Blended_VisFu		83.97 123	83.50 114	85.39 127	90.02 162	66.59 152	93.77 103	91.73 177	77.43 144	77.08 188	89.81 232	63.77 112	96.97 118	79.67 166	88.21 129	92.60 215

MTAPA		83.91 125	83.38 123	85.50 123	91.89 119	65.16 189	81.75 394	92.23 146	75.32 179	80.53 137	95.21 82	56.06 229	97.16 102	84.86 104	92.55 67	94.18 152

XVS		83.87 126	83.47 117	85.05 144	93.22 70	63.78 233	92.92 142	92.66 131	73.99 197	78.18 171	94.31 112	55.25 235	97.41 81	79.16 173	91.58 83	93.95 165

Effi-MVS+		83.82 127	82.76 141	86.99 60	89.56 172	69.40 57	91.35 229	86.12 385	72.59 230	83.22 101	92.81 150	59.60 176	96.01 171	81.76 146	87.80 134	95.56 61

test_fmvsmvis_n_1920		83.80 128	83.48 116	84.77 159	82.51 355	63.72 238	91.37 227	83.99 409	81.42 57	77.68 176	95.74 59	58.37 198	97.58 69	93.38 27	86.87 144	93.00 203

EI-MVSNet-Vis-set		83.77 129	83.67 110	84.06 193	92.79 89	63.56 247	91.76 210	94.81 37	79.65 93	77.87 174	94.09 121	63.35 123	97.90 50	79.35 171	79.36 244	90.74 266

MVSFormer		83.75 130	82.88 139	86.37 91	89.24 185	71.18 24	89.07 310	90.69 236	65.80 344	87.13 56	94.34 110	64.99 91	92.67 322	72.83 224	91.80 79	95.27 81

CP-MVS		83.71 131	83.40 122	84.65 170	93.14 75	63.84 231	94.59 58	92.28 144	71.03 281	77.41 181	94.92 90	55.21 238	96.19 158	81.32 152	90.70 98	93.91 170

test_fmvsmconf0.01_n		83.70 132	83.52 112	84.25 189	75.26 428	61.72 298	92.17 184	87.24 368	82.36 43	84.91 82	95.41 68	55.60 233	96.83 130	92.85 31	85.87 160	94.21 150

baseline2		83.68 133	83.42 121	84.48 179	87.37 255	66.00 165	90.06 280	95.93 8	79.71 91	69.08 293	90.39 210	77.92 6	96.28 153	78.91 178	81.38 219	91.16 259

viewdifsd2359ckpt09		83.52 134	82.57 145	86.37 91	88.02 235	68.47 87	91.78 208	89.63 289	79.61 95	78.56 169	92.00 172	59.28 183	95.96 172	81.94 144	82.35 202	94.69 118

reproduce-ours		83.51 135	83.33 125	84.06 193	92.18 103	60.49 330	90.74 256	92.04 158	64.35 354	83.24 98	95.59 64	59.05 186	97.27 93	83.61 123	89.17 119	94.41 144

our_new_method		83.51 135	83.33 125	84.06 193	92.18 103	60.49 330	90.74 256	92.04 158	64.35 354	83.24 98	95.59 64	59.05 186	97.27 93	83.61 123	89.17 119	94.41 144

thisisatest0515		83.41 137	82.49 147	86.16 98	89.46 175	68.26 94	93.54 114	94.70 43	74.31 192	75.75 196	90.92 200	72.62 34	96.52 142	69.64 257	81.50 218	93.71 177

PVSNet_BlendedMVS		83.38 138	83.43 119	83.22 231	93.76 54	67.53 117	94.06 80	93.61 86	79.13 107	81.00 127	85.14 305	63.19 125	97.29 89	87.08 82	73.91 292	84.83 366

test2506		83.29 139	82.92 138	84.37 183	88.39 220	63.18 260	92.01 194	91.35 195	77.66 137	78.49 170	91.42 190	64.58 100	95.09 223	73.19 220	89.23 116	94.85 102

PGM-MVS		83.25 140	82.70 143	84.92 148	92.81 88	64.07 225	90.44 266	92.20 150	71.28 275	77.23 185	94.43 103	55.17 239	97.31 88	79.33 172	91.38 88	93.37 187

HPM-MVS		83.25 140	82.95 137	84.17 191	92.25 99	62.88 269	90.91 246	91.86 170	70.30 292	77.12 186	93.96 125	56.75 218	96.28 153	82.04 143	91.34 90	93.34 188
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 142	82.96 135	83.73 208	92.02 109	59.74 346	90.37 270	92.08 156	63.70 361	82.86 103	95.48 67	58.62 193	97.17 99	83.06 129	88.42 127	94.26 147

EI-MVSNet-UG-set		83.14 143	82.96 135	83.67 213	92.28 98	63.19 259	91.38 226	94.68 44	79.22 104	76.60 191	93.75 127	62.64 135	97.76 56	78.07 185	78.01 257	90.05 275

testing3-2		83.11 144	83.15 133	82.98 236	91.92 116	64.01 227	94.39 69	95.37 16	78.32 123	75.53 203	90.06 228	73.18 29	93.18 301	74.34 212	75.27 281	91.77 244

VDD-MVS		83.06 145	81.81 158	86.81 65	90.86 147	67.70 111	95.40 30	91.50 190	75.46 174	81.78 113	92.34 160	40.09 371	97.13 104	86.85 85	82.04 210	95.60 59

h-mvs33		83.01 146	82.56 146	84.35 184	89.34 176	62.02 287	92.72 151	93.76 78	81.45 54	82.73 107	92.25 163	60.11 167	97.13 104	87.69 71	62.96 375	93.91 170

PAPM_NR		82.97 147	81.84 157	86.37 91	94.10 48	66.76 146	87.66 338	92.84 121	69.96 298	74.07 228	93.57 133	63.10 130	97.50 75	70.66 252	90.58 100	94.85 102

mPP-MVS		82.96 148	82.44 148	84.52 177	92.83 84	62.92 267	92.76 149	91.85 172	71.52 271	75.61 201	94.24 115	53.48 262	96.99 114	78.97 176	90.73 97	93.64 181

viewdifsd2359ckpt07		82.95 149	82.04 152	85.66 118	87.19 260	66.73 147	91.56 217	90.39 251	77.58 140	77.58 180	91.19 197	58.57 194	95.65 194	82.32 138	82.01 211	94.60 127

SR-MVS		82.81 150	82.58 144	83.50 220	93.35 68	61.16 312	92.23 182	91.28 202	64.48 353	81.27 120	95.28 75	53.71 258	95.86 177	82.87 133	88.77 124	93.49 185

DP-MVS Recon		82.73 151	81.65 159	85.98 103	97.31 4	67.06 132	95.15 37	91.99 162	69.08 313	76.50 193	93.89 126	54.48 247	98.20 41	70.76 250	85.66 164	92.69 211

CLD-MVS		82.73 151	82.35 150	83.86 201	87.90 238	67.65 113	95.45 29	92.18 153	85.06 14	72.58 246	92.27 161	52.46 271	95.78 183	84.18 114	79.06 249	88.16 303
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 153	82.38 149	83.73 208	89.25 182	59.58 349	92.24 181	94.89 31	77.96 128	79.86 146	92.38 158	56.70 219	97.05 106	77.26 189	80.86 227	94.55 129

3Dnovator	73.91 6	82.69 154	80.82 172	88.31 26	89.57 171	71.26 22	92.60 163	94.39 60	78.84 114	67.89 314	92.48 156	48.42 316	98.52 32	68.80 270	94.40 36	95.15 87

RRT-MVS		82.61 155	81.16 163	86.96 61	91.10 141	68.75 79	87.70 337	92.20 150	76.97 149	72.68 242	87.10 281	51.30 285	96.41 148	83.56 125	87.84 133	95.74 55

viewmambaseed2359dif		82.60 156	81.91 156	84.67 169	85.83 298	66.09 162	90.50 265	89.01 319	75.46 174	79.64 150	92.01 171	59.51 178	94.38 258	82.99 131	82.26 204	93.54 183

MVSTER		82.47 157	82.05 151	83.74 206	92.68 91	69.01 72	91.90 201	93.21 104	79.83 87	72.14 256	85.71 300	74.72 19	94.72 238	75.72 198	72.49 302	87.50 310

TESTMET0.1,1		82.41 158	81.98 155	83.72 210	88.08 231	63.74 235	92.70 153	93.77 77	79.30 102	77.61 178	87.57 272	58.19 201	94.08 272	73.91 214	86.68 151	93.33 190

CostFormer		82.33 159	81.15 164	85.86 108	89.01 192	68.46 88	82.39 391	93.01 115	75.59 172	80.25 141	81.57 350	72.03 41	94.96 228	79.06 175	77.48 265	94.16 154

API-MVS		82.28 160	80.53 181	87.54 42	96.13 23	70.59 31	93.63 110	91.04 222	65.72 346	75.45 204	92.83 149	56.11 228	98.89 24	64.10 318	89.75 115	93.15 195

IB-MVS	77.80 4	82.18 161	80.46 183	87.35 48	89.14 187	70.28 36	95.59 27	95.17 24	78.85 113	70.19 281	85.82 298	70.66 46	97.67 61	72.19 236	66.52 345	94.09 158
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		82.16 162	81.12 165	85.26 137	86.42 282	68.72 82	92.59 165	90.44 248	73.12 217	84.20 88	94.36 105	38.04 384	95.73 187	84.12 115	86.81 145	91.33 252

xiu_mvs_v1_base		82.16 162	81.12 165	85.26 137	86.42 282	68.72 82	92.59 165	90.44 248	73.12 217	84.20 88	94.36 105	38.04 384	95.73 187	84.12 115	86.81 145	91.33 252

xiu_mvs_v1_base_debi		82.16 162	81.12 165	85.26 137	86.42 282	68.72 82	92.59 165	90.44 248	73.12 217	84.20 88	94.36 105	38.04 384	95.73 187	84.12 115	86.81 145	91.33 252

3Dnovator+	73.60 7	82.10 165	80.60 179	86.60 77	90.89 146	66.80 145	95.20 35	93.44 96	74.05 196	67.42 321	92.49 155	49.46 306	97.65 65	70.80 249	91.68 81	95.33 74

MVS_111021_LR		82.02 166	81.52 160	83.51 219	88.42 218	62.88 269	89.77 288	88.93 324	76.78 154	75.55 202	93.10 138	50.31 295	95.38 211	83.82 119	87.02 142	92.26 232

PMMVS		81.98 167	82.04 152	81.78 271	89.76 168	56.17 387	91.13 242	90.69 236	77.96 128	80.09 144	93.57 133	46.33 340	94.99 227	81.41 150	87.46 138	94.17 153

baseline1		81.84 168	81.03 169	84.28 187	91.60 126	66.62 150	91.08 243	91.66 184	81.87 48	74.86 214	91.67 186	69.98 51	94.92 231	71.76 239	64.75 362	91.29 257

EPP-MVSNet		81.79 169	81.52 160	82.61 246	88.77 198	60.21 338	93.02 137	93.66 85	68.52 319	72.90 240	90.39 210	72.19 40	94.96 228	74.93 206	79.29 247	92.67 212

WBMVS		81.67 170	80.98 171	83.72 210	93.07 78	69.40 57	94.33 70	93.05 113	76.84 152	72.05 258	84.14 316	74.49 21	93.88 286	72.76 227	68.09 331	87.88 305

test_vis1_n_1920		81.66 171	82.01 154	80.64 300	82.24 357	55.09 396	94.76 52	86.87 373	81.67 51	84.40 87	94.63 98	38.17 381	94.67 244	91.98 40	83.34 194	92.16 235

APD-MVS_3200maxsize		81.64 172	81.32 162	82.59 248	92.36 96	58.74 360	91.39 224	91.01 223	63.35 365	79.72 149	94.62 99	51.82 274	96.14 160	79.71 165	87.93 132	92.89 207

mvsmamba		81.55 173	80.72 174	84.03 197	91.42 132	66.93 141	83.08 382	89.13 310	78.55 121	67.50 319	87.02 282	51.79 276	90.07 380	87.48 74	90.49 102	95.10 90

ACMMP		81.49 174	80.67 176	83.93 199	91.71 124	62.90 268	92.13 186	92.22 149	71.79 258	71.68 264	93.49 135	50.32 294	96.96 119	78.47 182	84.22 186	91.93 242
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		81.43 175	80.11 185	85.38 130	86.60 278	65.47 183	92.90 145	93.54 90	75.33 178	77.31 183	90.39 210	46.81 332	96.75 132	71.65 242	86.46 156	93.93 167

CDS-MVSNet		81.43 175	80.74 173	83.52 217	86.26 286	64.45 208	92.09 189	90.65 240	75.83 170	73.95 230	89.81 232	63.97 108	92.91 312	71.27 243	82.82 198	93.20 194
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 177	79.99 189	85.46 124	90.39 156	68.40 89	86.88 349	90.61 241	74.41 189	70.31 280	84.67 310	63.79 111	92.32 338	73.13 221	85.70 163	95.67 56

ECVR-MVS		81.29 178	80.38 184	84.01 198	88.39 220	61.96 289	92.56 168	86.79 375	77.66 137	76.63 190	91.42 190	46.34 339	95.24 220	74.36 211	89.23 116	94.85 102

guyue		81.23 179	80.57 180	83.21 233	86.64 276	61.85 292	92.52 171	92.78 123	78.69 118	74.92 213	89.42 236	50.07 298	95.35 212	80.79 157	79.31 246	92.42 221

IMVS_0403		81.19 180	79.88 191	85.13 142	88.54 203	64.75 198	88.84 315	90.80 230	76.73 157	75.21 207	90.18 216	54.22 252	96.21 157	73.47 216	80.95 222	94.43 140

thisisatest0530		81.15 181	80.07 186	84.39 182	88.26 225	65.63 176	91.40 222	94.62 47	71.27 276	70.93 271	89.18 241	72.47 35	96.04 168	65.62 307	76.89 272	91.49 248

Fast-Effi-MVS+		81.14 182	80.01 188	84.51 178	90.24 158	65.86 171	94.12 79	89.15 308	73.81 204	75.37 206	88.26 257	57.26 208	94.53 252	66.97 292	84.92 174	93.15 195

HQP-MVS		81.14 182	80.64 177	82.64 245	87.54 250	63.66 244	94.06 80	91.70 182	79.80 88	74.18 221	90.30 213	51.63 279	95.61 198	77.63 187	78.90 250	88.63 294

hse-mvs2		81.12 184	81.11 168	81.16 285	86.52 281	57.48 375	89.40 301	91.16 206	81.45 54	82.73 107	90.49 208	60.11 167	94.58 245	87.69 71	60.41 402	91.41 251

SR-MVS-dyc-post		81.06 185	80.70 175	82.15 262	92.02 109	58.56 363	90.90 247	90.45 244	62.76 372	78.89 160	94.46 101	51.26 286	95.61 198	78.77 180	86.77 148	92.28 228

HyFIR lowres test		81.03 186	79.56 198	85.43 125	87.81 244	68.11 100	90.18 277	90.01 273	70.65 289	72.95 239	86.06 294	63.61 117	94.50 254	75.01 205	79.75 238	93.67 178

nrg030		80.93 187	79.86 192	84.13 192	83.69 341	68.83 77	93.23 128	91.20 204	75.55 173	75.06 209	88.22 260	63.04 131	94.74 237	81.88 145	66.88 342	88.82 292

Vis-MVSNet		80.92 188	79.98 190	83.74 206	88.48 214	61.80 293	93.44 121	88.26 350	73.96 200	77.73 175	91.76 180	49.94 300	94.76 235	65.84 304	90.37 105	94.65 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 189	80.02 187	83.33 224	87.87 241	60.76 320	92.62 160	86.86 374	77.86 131	75.73 197	91.39 192	46.35 338	94.70 243	72.79 226	88.68 125	94.52 133

UWE-MVS		80.81 190	81.01 170	80.20 310	89.33 178	57.05 381	91.91 200	94.71 42	75.67 171	75.01 210	89.37 237	63.13 129	91.44 363	67.19 289	82.80 200	92.12 236

IMVS_0407		80.80 191	79.39 204	85.00 147	88.54 203	64.75 198	88.40 323	90.80 230	76.73 157	73.95 230	90.18 216	51.55 281	95.81 180	73.47 216	80.95 222	94.43 140

1314		80.70 192	78.95 212	85.94 105	87.77 247	67.56 115	87.91 332	92.55 138	72.17 245	67.44 320	93.09 139	50.27 296	97.04 109	71.68 241	87.64 136	93.23 192

AstraMVS		80.66 193	79.79 194	83.28 228	85.07 315	61.64 300	92.19 183	90.58 242	79.40 99	74.77 216	90.18 216	45.93 344	95.61 198	83.04 130	76.96 271	92.60 215

tpmrst		80.57 194	79.14 210	84.84 154	90.10 161	68.28 93	81.70 395	89.72 286	77.63 139	75.96 195	79.54 382	64.94 93	92.71 319	75.43 200	77.28 268	93.55 182

1112_ss		80.56 195	79.83 193	82.77 240	88.65 200	60.78 318	92.29 178	88.36 343	72.58 231	72.46 252	94.95 87	65.09 90	93.42 298	66.38 298	77.71 259	94.10 157

VDDNet		80.50 196	78.26 220	87.21 51	86.19 287	69.79 48	94.48 60	91.31 196	60.42 394	79.34 155	90.91 201	38.48 379	96.56 139	82.16 139	81.05 221	95.27 81

BH-w/o		80.49 197	79.30 206	84.05 196	90.83 148	64.36 216	93.60 111	89.42 296	74.35 191	69.09 292	90.15 224	55.23 237	95.61 198	64.61 315	86.43 157	92.17 234

test_cas_vis1_n_1920		80.45 198	80.61 178	79.97 319	78.25 409	57.01 383	94.04 84	88.33 345	79.06 111	82.81 106	93.70 129	38.65 376	91.63 354	90.82 52	79.81 236	91.27 258

icg_test_0407_2		80.38 199	79.22 208	83.88 200	88.54 203	64.75 198	86.79 350	90.80 230	76.73 157	73.95 230	90.18 216	51.55 281	92.45 331	73.47 216	80.95 222	94.43 140

TAMVS		80.37 200	79.45 201	83.13 234	85.14 312	63.37 252	91.23 236	90.76 235	74.81 186	72.65 244	88.49 250	60.63 160	92.95 307	69.41 261	81.95 213	93.08 199

HQP_MVS		80.34 201	79.75 195	82.12 264	86.94 268	62.42 277	93.13 131	91.31 196	78.81 115	72.53 247	89.14 243	50.66 291	95.55 204	76.74 190	78.53 255	88.39 300

SDMVSNet		80.26 202	78.88 213	84.40 181	89.25 182	67.63 114	85.35 358	93.02 114	76.77 155	70.84 272	87.12 279	47.95 324	96.09 163	85.04 100	74.55 283	89.48 285

HPM-MVS_fast		80.25 203	79.55 200	82.33 254	91.55 129	59.95 343	91.32 231	89.16 307	65.23 350	74.71 218	93.07 141	47.81 326	95.74 186	74.87 209	88.23 128	91.31 256

ab-mvs		80.18 204	78.31 219	85.80 111	88.44 216	65.49 182	83.00 385	92.67 130	71.82 257	77.36 182	85.01 306	54.50 244	96.59 136	76.35 195	75.63 279	95.32 76

IS-MVSNet		80.14 205	79.41 202	82.33 254	87.91 237	60.08 341	91.97 198	88.27 348	72.90 226	71.44 268	91.73 182	61.44 149	93.66 293	62.47 332	86.53 154	93.24 191

test-LLR		80.10 206	79.56 198	81.72 273	86.93 270	61.17 310	92.70 153	91.54 187	71.51 272	75.62 199	86.94 283	53.83 255	92.38 333	72.21 234	84.76 177	91.60 246

PVSNet	73.49 8	80.05 207	78.63 215	84.31 185	90.92 145	64.97 194	92.47 172	91.05 221	79.18 105	72.43 253	90.51 207	37.05 396	94.06 274	68.06 276	86.00 158	93.90 172

UA-Net		80.02 208	79.65 196	81.11 288	89.33 178	57.72 370	86.33 355	89.00 323	77.44 143	81.01 126	89.15 242	59.33 182	95.90 174	61.01 339	84.28 184	89.73 281

test-mter		79.96 209	79.38 205	81.72 273	86.93 270	61.17 310	92.70 153	91.54 187	73.85 202	75.62 199	86.94 283	49.84 302	92.38 333	72.21 234	84.76 177	91.60 246

QAPM		79.95 210	77.39 241	87.64 35	89.63 170	71.41 20	93.30 126	93.70 83	65.34 349	67.39 323	91.75 181	47.83 325	98.96 19	57.71 355	89.81 112	92.54 218

UGNet		79.87 211	78.68 214	83.45 222	89.96 163	61.51 303	92.13 186	90.79 234	76.83 153	78.85 165	86.33 291	38.16 382	96.17 159	67.93 279	87.17 141	92.67 212
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
tpm2		79.80 212	77.95 227	85.34 132	88.28 224	68.26 94	81.56 397	91.42 193	70.11 294	77.59 179	80.50 368	67.40 67	94.26 265	67.34 286	77.35 266	93.51 184

thres200		79.66 213	78.33 218	83.66 214	92.54 95	65.82 173	93.06 133	96.31 3	74.90 185	73.30 236	88.66 248	59.67 175	95.61 198	47.84 400	78.67 253	89.56 284

CPTT-MVS		79.59 214	79.16 209	80.89 298	91.54 130	59.80 345	92.10 188	88.54 340	60.42 394	72.96 238	93.28 137	48.27 317	92.80 316	78.89 179	86.50 155	90.06 274

Test_1112_low_res		79.56 215	78.60 216	82.43 250	88.24 227	60.39 334	92.09 189	87.99 355	72.10 247	71.84 260	87.42 274	64.62 98	93.04 303	65.80 305	77.30 267	93.85 174

tttt0517		79.50 216	78.53 217	82.41 253	87.22 259	61.43 307	89.75 289	94.76 39	69.29 306	67.91 312	88.06 264	72.92 31	95.63 195	62.91 328	73.90 293	90.16 273

reproduce_monomvs		79.49 217	79.11 211	80.64 300	92.91 82	61.47 306	91.17 241	93.28 102	83.09 33	64.04 353	82.38 336	66.19 76	94.57 247	81.19 154	57.71 410	85.88 349

FIs		79.47 218	79.41 202	79.67 327	85.95 294	59.40 351	91.68 214	93.94 72	78.06 127	68.96 298	88.28 255	66.61 73	91.77 350	66.20 301	74.99 282	87.82 306

SSM_0404		79.46 219	77.65 231	84.91 150	88.37 222	67.04 134	89.59 290	87.03 370	67.99 324	75.45 204	89.32 238	47.98 321	95.34 214	71.23 244	81.90 214	92.34 224

BH-RMVSNet		79.46 219	77.65 231	84.89 151	91.68 125	65.66 174	93.55 113	88.09 353	72.93 223	73.37 235	91.12 199	46.20 342	96.12 161	56.28 361	85.61 165	92.91 205

viewdifsd2359ckpt11		79.42 221	77.95 227	83.81 203	83.87 338	63.85 229	89.54 295	87.38 362	77.39 146	74.94 211	89.95 229	51.11 287	94.72 238	79.52 168	67.90 334	92.88 208

viewmsd2359difaftdt		79.42 221	77.96 226	83.81 203	83.88 337	63.85 229	89.54 295	87.38 362	77.39 146	74.94 211	89.95 229	51.11 287	94.72 238	79.52 168	67.90 334	92.88 208

PCF-MVS	73.15 9	79.29 223	77.63 233	84.29 186	86.06 292	65.96 167	87.03 345	91.10 215	69.86 300	69.79 288	90.64 203	57.54 207	96.59 136	64.37 317	82.29 203	90.32 271
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 224	79.57 197	78.24 348	88.46 215	52.29 408	90.41 268	89.12 311	74.24 193	69.13 291	91.91 178	65.77 83	90.09 379	59.00 351	88.09 130	92.33 225

114514_t		79.17 225	77.67 230	83.68 212	95.32 30	65.53 180	92.85 147	91.60 186	63.49 363	67.92 311	90.63 205	46.65 335	95.72 192	67.01 291	83.54 192	89.79 279

FA-MVS(test-final)		79.12 226	77.23 243	84.81 158	90.54 151	63.98 228	81.35 400	91.71 179	71.09 280	74.85 215	82.94 329	52.85 266	97.05 106	67.97 277	81.73 217	93.41 186

SSM_0407		79.09 227	77.21 244	84.75 162	88.50 208	66.98 137	89.21 306	87.03 370	67.99 324	74.12 225	89.32 238	47.98 321	95.29 219	71.23 244	79.52 239	91.98 239

VPA-MVSNet		79.03 228	78.00 224	82.11 267	85.95 294	64.48 207	93.22 129	94.66 45	75.05 183	74.04 229	84.95 307	52.17 273	93.52 295	74.90 208	67.04 341	88.32 302

OPM-MVS		79.00 229	78.09 222	81.73 272	83.52 344	63.83 232	91.64 216	90.30 257	76.36 166	71.97 259	89.93 231	46.30 341	95.17 222	75.10 203	77.70 260	86.19 337
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 230	78.22 221	81.25 282	85.33 305	62.73 272	89.53 298	93.21 104	72.39 238	72.14 256	90.13 225	60.99 153	94.72 238	67.73 281	72.49 302	86.29 334

AdaColmap		78.94 231	77.00 248	84.76 161	96.34 17	65.86 171	92.66 159	87.97 357	62.18 377	70.56 274	92.37 159	43.53 356	97.35 85	64.50 316	82.86 197	91.05 261

GeoE		78.90 232	77.43 237	83.29 227	88.95 193	62.02 287	92.31 177	86.23 381	70.24 293	71.34 269	89.27 240	54.43 248	94.04 277	63.31 324	80.81 229	93.81 175

miper_enhance_ethall		78.86 233	77.97 225	81.54 277	88.00 236	65.17 188	91.41 220	89.15 308	75.19 181	68.79 301	83.98 319	67.17 68	92.82 314	72.73 228	65.30 352	86.62 331

VPNet		78.82 234	77.53 236	82.70 243	84.52 325	66.44 154	93.93 90	92.23 146	80.46 71	72.60 245	88.38 254	49.18 310	93.13 302	72.47 232	63.97 372	88.55 297

EPNet_dtu		78.80 235	79.26 207	77.43 356	88.06 232	49.71 425	91.96 199	91.95 164	77.67 136	76.56 192	91.28 196	58.51 196	90.20 377	56.37 360	80.95 222	92.39 222
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 236	77.43 237	82.88 238	92.21 101	64.49 205	92.05 192	96.28 4	73.48 211	71.75 262	88.26 257	60.07 169	95.32 215	45.16 413	77.58 262	88.83 290

TR-MVS		78.77 237	77.37 242	82.95 237	90.49 153	60.88 316	93.67 107	90.07 268	70.08 297	74.51 219	91.37 193	45.69 345	95.70 193	60.12 345	80.32 233	92.29 227

thres400		78.68 238	77.43 237	82.43 250	92.21 101	64.49 205	92.05 192	96.28 4	73.48 211	71.75 262	88.26 257	60.07 169	95.32 215	45.16 413	77.58 262	87.48 311

BH-untuned		78.68 238	77.08 245	83.48 221	89.84 165	63.74 235	92.70 153	88.59 337	71.57 269	66.83 330	88.65 249	51.75 277	95.39 210	59.03 350	84.77 176	91.32 255

OMC-MVS		78.67 240	77.91 229	80.95 295	85.76 300	57.40 377	88.49 321	88.67 334	73.85 202	72.43 253	92.10 168	49.29 309	94.55 251	72.73 228	77.89 258	90.91 265

tpm		78.58 241	77.03 246	83.22 231	85.94 296	64.56 203	83.21 381	91.14 210	78.31 124	73.67 233	79.68 380	64.01 107	92.09 344	66.07 302	71.26 312	93.03 201

OpenMVS	70.45 11	78.54 242	75.92 265	86.41 90	85.93 297	71.68 18	92.74 150	92.51 139	66.49 340	64.56 347	91.96 174	43.88 355	98.10 44	54.61 366	90.65 99	89.44 287

EPMVS		78.49 243	75.98 264	86.02 102	91.21 139	69.68 53	80.23 409	91.20 204	75.25 180	72.48 251	78.11 391	54.65 243	93.69 292	57.66 356	83.04 196	94.69 118

AUN-MVS		78.37 244	77.43 237	81.17 284	86.60 278	57.45 376	89.46 300	91.16 206	74.11 195	74.40 220	90.49 208	55.52 234	94.57 247	74.73 210	60.43 401	91.48 249

thres100view900		78.37 244	77.01 247	82.46 249	91.89 119	63.21 258	91.19 240	96.33 1	72.28 241	70.45 277	87.89 266	60.31 164	95.32 215	45.16 413	77.58 262	88.83 290

GA-MVS		78.33 246	76.23 260	84.65 170	83.65 342	66.30 158	91.44 219	90.14 266	76.01 168	70.32 279	84.02 318	42.50 360	94.72 238	70.98 247	77.00 270	92.94 204

cascas		78.18 247	75.77 267	85.41 126	87.14 262	69.11 67	92.96 140	91.15 209	66.71 338	70.47 275	86.07 293	37.49 390	96.48 145	70.15 255	79.80 237	90.65 267

UniMVSNet_NR-MVSNet		78.15 248	77.55 235	79.98 317	84.46 328	60.26 336	92.25 179	93.20 106	77.50 142	68.88 299	86.61 286	66.10 78	92.13 342	66.38 298	62.55 379	87.54 309

LuminaMVS		78.14 249	76.66 252	82.60 247	80.82 370	64.64 202	89.33 302	90.45 244	68.25 322	74.73 217	85.51 302	41.15 366	94.14 268	78.96 177	80.69 231	89.04 288

IMVS_0404		78.11 250	76.29 259	83.59 215	88.54 203	64.75 198	84.63 364	90.80 230	76.73 157	61.16 375	90.18 216	40.17 370	91.58 356	73.47 216	80.95 222	94.43 140

thres600view7		78.00 251	76.66 252	82.03 269	91.93 115	63.69 242	91.30 232	96.33 1	72.43 236	70.46 276	87.89 266	60.31 164	94.92 231	42.64 425	76.64 273	87.48 311

FC-MVSNet-test		77.99 252	78.08 223	77.70 351	84.89 318	55.51 393	90.27 274	93.75 81	76.87 150	66.80 331	87.59 271	65.71 84	90.23 376	62.89 329	73.94 291	87.37 314

Anonymous202405211		77.96 253	75.33 273	85.87 107	93.73 57	64.52 204	94.85 50	85.36 394	62.52 375	76.11 194	90.18 216	29.43 429	97.29 89	68.51 272	77.24 269	95.81 53

cl22		77.94 254	76.78 250	81.42 279	87.57 249	64.93 196	90.67 259	88.86 327	72.45 235	67.63 318	82.68 333	64.07 105	92.91 312	71.79 237	65.30 352	86.44 332

XXY-MVS		77.94 254	76.44 255	82.43 250	82.60 354	64.44 209	92.01 194	91.83 173	73.59 210	70.00 284	85.82 298	54.43 248	94.76 235	69.63 258	68.02 333	88.10 304

MS-PatchMatch		77.90 256	76.50 254	82.12 264	85.99 293	69.95 42	91.75 212	92.70 126	73.97 199	62.58 370	84.44 314	41.11 367	95.78 183	63.76 321	92.17 71	80.62 414

FMVSNet3		77.73 257	76.04 263	82.80 239	91.20 140	68.99 73	91.87 202	91.99 162	73.35 213	67.04 326	83.19 328	56.62 221	92.14 341	59.80 347	69.34 319	87.28 317

VortexMVS		77.62 258	76.44 255	81.13 286	88.58 201	63.73 237	91.24 235	91.30 200	77.81 132	65.76 336	81.97 342	49.69 304	93.72 290	76.40 194	65.26 355	85.94 347

miper_ehance_all_eth		77.60 259	76.44 255	81.09 292	85.70 302	64.41 212	90.65 260	88.64 336	72.31 239	67.37 324	82.52 334	64.77 97	92.64 325	70.67 251	65.30 352	86.24 336

UniMVSNet (Re)		77.58 260	76.78 250	79.98 317	84.11 334	60.80 317	91.76 210	93.17 108	76.56 163	69.93 287	84.78 309	63.32 124	92.36 335	64.89 314	62.51 381	86.78 325

PatchmatchNet		77.46 261	74.63 280	85.96 104	89.55 173	70.35 35	79.97 414	89.55 291	72.23 242	70.94 270	76.91 403	57.03 211	92.79 317	54.27 368	81.17 220	94.74 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 262	75.65 269	82.73 241	80.38 378	67.13 131	91.85 204	90.23 262	75.09 182	69.37 289	83.39 325	53.79 257	94.44 255	71.77 238	65.00 359	86.63 330

CHOSEN 280x420		77.35 263	76.95 249	78.55 343	87.07 264	62.68 273	69.71 446	82.95 416	68.80 315	71.48 267	87.27 278	66.03 79	84.00 429	76.47 193	82.81 199	88.95 289

PS-MVSNAJss		77.26 264	76.31 258	80.13 312	80.64 374	59.16 356	90.63 263	91.06 220	72.80 227	68.58 305	84.57 312	53.55 259	93.96 282	72.97 222	71.96 306	87.27 318

gg-mvs-nofinetune		77.18 265	74.31 287	85.80 111	91.42 132	68.36 90	71.78 440	94.72 41	49.61 438	77.12 186	45.92 467	77.41 8	93.98 281	67.62 282	93.16 59	95.05 93

WB-MVSnew		77.14 266	76.18 262	80.01 316	86.18 288	63.24 256	91.26 233	94.11 69	71.72 261	73.52 234	87.29 277	45.14 350	93.00 305	56.98 358	79.42 242	83.80 375

MVP-Stereo		77.12 267	76.23 260	79.79 324	81.72 362	66.34 157	89.29 303	90.88 225	70.56 290	62.01 373	82.88 330	49.34 307	94.13 269	65.55 309	93.80 47	78.88 431
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 268	75.37 271	82.20 260	89.25 182	62.11 286	82.06 392	89.09 313	76.77 155	70.84 272	87.12 279	41.43 365	95.01 226	67.23 288	74.55 283	89.48 285

MonoMVSNet		76.99 269	75.08 276	82.73 241	83.32 346	63.24 256	86.47 354	86.37 377	79.08 109	66.31 334	79.30 384	49.80 303	91.72 351	79.37 170	65.70 350	93.23 192

dmvs_re		76.93 270	75.36 272	81.61 275	87.78 246	60.71 324	80.00 413	87.99 355	79.42 98	69.02 295	89.47 235	46.77 333	94.32 259	63.38 323	74.45 286	89.81 278

X-MVStestdata		76.86 271	74.13 293	85.05 144	93.22 70	63.78 233	92.92 142	92.66 131	73.99 197	78.18 171	10.19 482	55.25 235	97.41 81	79.16 173	91.58 83	93.95 165

DU-MVS		76.86 271	75.84 266	79.91 320	82.96 350	60.26 336	91.26 233	91.54 187	76.46 165	68.88 299	86.35 289	56.16 226	92.13 342	66.38 298	62.55 379	87.35 315

Anonymous20240529		76.84 273	74.15 292	84.88 152	91.02 142	64.95 195	93.84 99	91.09 216	53.57 426	73.00 237	87.42 274	35.91 400	97.32 87	69.14 266	72.41 304	92.36 223

UWE-MVS-28		76.83 274	77.60 234	74.51 386	84.58 324	50.34 421	88.22 326	94.60 49	74.46 188	66.66 332	88.98 247	62.53 137	85.50 421	57.55 357	80.80 230	87.69 308

c3_l		76.83 274	75.47 270	80.93 296	85.02 316	64.18 222	90.39 269	88.11 352	71.66 262	66.65 333	81.64 348	63.58 120	92.56 326	69.31 263	62.86 376	86.04 342

WR-MVS		76.76 276	75.74 268	79.82 323	84.60 322	62.27 283	92.60 163	92.51 139	76.06 167	67.87 315	85.34 303	56.76 217	90.24 375	62.20 333	63.69 374	86.94 323

v1144		76.73 277	74.88 277	82.27 256	80.23 382	66.60 151	91.68 214	90.21 265	73.69 207	69.06 294	81.89 343	52.73 269	94.40 257	69.21 264	65.23 356	85.80 350

IterMVS-LS		76.49 278	75.18 275	80.43 304	84.49 327	62.74 271	90.64 261	88.80 329	72.40 237	65.16 342	81.72 346	60.98 154	92.27 339	67.74 280	64.65 364	86.29 334
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 279	74.55 283	82.19 261	79.14 396	67.82 108	90.26 275	89.42 296	73.75 205	68.63 304	81.89 343	51.31 284	94.09 271	71.69 240	64.84 360	84.66 367

Elysia		76.45 280	74.17 290	83.30 225	80.43 376	64.12 223	89.58 291	90.83 227	61.78 385	72.53 247	85.92 296	34.30 407	94.81 233	68.10 274	84.01 190	90.97 262

StellarMVS		76.45 280	74.17 290	83.30 225	80.43 376	64.12 223	89.58 291	90.83 227	61.78 385	72.53 247	85.92 296	34.30 407	94.81 233	68.10 274	84.01 190	90.97 262

mamba_0408		76.22 282	73.37 304	84.77 159	88.50 208	66.98 137	58.80 467	86.18 383	69.12 311	74.12 225	89.01 245	47.50 328	95.35 212	67.57 283	79.52 239	91.98 239

v148		76.19 283	74.47 285	81.36 280	80.05 384	64.44 209	91.75 212	90.23 262	73.68 208	67.13 325	80.84 363	55.92 231	93.86 289	68.95 268	61.73 390	85.76 353

Effi-MVS+-dtu		76.14 284	75.28 274	78.72 342	83.22 347	55.17 395	89.87 286	87.78 359	75.42 176	67.98 310	81.43 352	45.08 351	92.52 328	75.08 204	71.63 307	88.48 298

cl____		76.07 285	74.67 278	80.28 307	85.15 311	61.76 296	90.12 278	88.73 331	71.16 277	65.43 339	81.57 350	61.15 151	92.95 307	66.54 295	62.17 383	86.13 340

DIV-MVS_self_test		76.07 285	74.67 278	80.28 307	85.14 312	61.75 297	90.12 278	88.73 331	71.16 277	65.42 340	81.60 349	61.15 151	92.94 311	66.54 295	62.16 385	86.14 338

FMVSNet2		76.07 285	74.01 295	82.26 258	88.85 194	67.66 112	91.33 230	91.61 185	70.84 284	65.98 335	82.25 338	48.03 318	92.00 346	58.46 352	68.73 327	87.10 320

v144192		76.05 288	74.03 294	82.12 264	79.50 390	66.55 153	91.39 224	89.71 287	72.30 240	68.17 308	81.33 355	51.75 277	94.03 279	67.94 278	64.19 367	85.77 351

NR-MVSNet		76.05 288	74.59 281	80.44 303	82.96 350	62.18 285	90.83 251	91.73 177	77.12 148	60.96 377	86.35 289	59.28 183	91.80 349	60.74 340	61.34 394	87.35 315

v1192		75.98 290	73.92 296	82.15 262	79.73 386	66.24 160	91.22 237	89.75 281	72.67 229	68.49 306	81.42 353	49.86 301	94.27 263	67.08 290	65.02 358	85.95 345

FE-MVS		75.97 291	73.02 310	84.82 155	89.78 166	65.56 178	77.44 425	91.07 219	64.55 352	72.66 243	79.85 378	46.05 343	96.69 134	54.97 365	80.82 228	92.21 233

eth_miper_zixun_eth		75.96 292	74.40 286	80.66 299	84.66 321	63.02 262	89.28 304	88.27 348	71.88 253	65.73 337	81.65 347	59.45 179	92.81 315	68.13 273	60.53 399	86.14 338

TranMVSNet+NR-MVSNet		75.86 293	74.52 284	79.89 321	82.44 356	60.64 327	91.37 227	91.37 194	76.63 161	67.65 317	86.21 292	52.37 272	91.55 357	61.84 335	60.81 397	87.48 311

SCA		75.82 294	72.76 313	85.01 146	86.63 277	70.08 38	81.06 402	89.19 305	71.60 268	70.01 283	77.09 401	45.53 346	90.25 372	60.43 342	73.27 295	94.68 120

LPG-MVS_test		75.82 294	74.58 282	79.56 331	84.31 331	59.37 352	90.44 266	89.73 284	69.49 303	64.86 343	88.42 252	38.65 376	94.30 261	72.56 230	72.76 299	85.01 364

GBi-Net		75.65 296	73.83 297	81.10 289	88.85 194	65.11 190	90.01 282	90.32 253	70.84 284	67.04 326	80.25 373	48.03 318	91.54 358	59.80 347	69.34 319	86.64 327

test1		75.65 296	73.83 297	81.10 289	88.85 194	65.11 190	90.01 282	90.32 253	70.84 284	67.04 326	80.25 373	48.03 318	91.54 358	59.80 347	69.34 319	86.64 327

v1921920		75.63 298	73.49 302	82.06 268	79.38 391	66.35 156	91.07 245	89.48 292	71.98 248	67.99 309	81.22 358	49.16 312	93.90 285	66.56 294	64.56 365	85.92 348

ACMP	71.68 10	75.58 299	74.23 289	79.62 329	84.97 317	59.64 347	90.80 252	89.07 315	70.39 291	62.95 366	87.30 276	38.28 380	93.87 287	72.89 223	71.45 310	85.36 360
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 300	73.26 308	81.61 275	80.67 373	66.82 143	89.54 295	89.27 301	71.65 263	63.30 361	80.30 372	54.99 241	94.06 274	67.33 287	62.33 382	83.94 373

tpm cat1		75.30 301	72.21 322	84.58 175	88.52 207	67.77 109	78.16 423	88.02 354	61.88 383	68.45 307	76.37 407	60.65 159	94.03 279	53.77 372	74.11 289	91.93 242

PLC	68.80 14	75.23 302	73.68 300	79.86 322	92.93 81	58.68 361	90.64 261	88.30 346	60.90 390	64.43 351	90.53 206	42.38 361	94.57 247	56.52 359	76.54 274	86.33 333
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 303	72.98 311	81.88 270	79.20 393	66.00 165	90.75 255	89.11 312	71.63 267	67.41 322	81.22 358	47.36 330	93.87 287	65.46 310	64.72 363	85.77 351

Fast-Effi-MVS+-dtu		75.04 304	73.37 304	80.07 313	80.86 368	59.52 350	91.20 239	85.38 393	71.90 251	65.20 341	84.84 308	41.46 364	92.97 306	66.50 297	72.96 298	87.73 307

dp		75.01 305	72.09 323	83.76 205	89.28 181	66.22 161	79.96 415	89.75 281	71.16 277	67.80 316	77.19 400	51.81 275	92.54 327	50.39 383	71.44 311	92.51 220

TAPA-MVS	70.22 12	74.94 306	73.53 301	79.17 337	90.40 155	52.07 409	89.19 308	89.61 290	62.69 374	70.07 282	92.67 151	48.89 315	94.32 259	38.26 439	79.97 235	91.12 260
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 307	73.32 307	79.74 326	86.53 280	60.31 335	89.03 313	92.70 126	78.61 120	68.98 297	83.34 326	41.93 363	92.23 340	52.77 376	65.97 348	86.69 326

SSM_04072		74.86 308	73.37 304	79.35 334	88.50 208	66.98 137	58.80 467	86.18 383	69.12 311	74.12 225	89.01 245	47.50 328	79.09 451	67.57 283	79.52 239	91.98 239

v10		74.77 309	72.54 319	81.46 278	80.33 380	66.71 148	89.15 309	89.08 314	70.94 282	63.08 364	79.86 377	52.52 270	94.04 277	65.70 306	62.17 383	83.64 376

XVG-OURS-SEG-HR		74.70 310	73.08 309	79.57 330	78.25 409	57.33 378	80.49 405	87.32 364	63.22 367	68.76 302	90.12 227	44.89 352	91.59 355	70.55 253	74.09 290	89.79 279

ACMM	69.62 13	74.34 311	72.73 315	79.17 337	84.25 333	57.87 368	90.36 271	89.93 275	63.17 369	65.64 338	86.04 295	37.79 388	94.10 270	65.89 303	71.52 309	85.55 356
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 312	72.30 321	80.32 305	91.49 131	61.66 299	90.85 250	80.72 422	56.67 418	63.85 356	90.64 203	46.75 334	90.84 366	53.79 371	75.99 278	88.47 299

XVG-OURS		74.25 313	72.46 320	79.63 328	78.45 407	57.59 374	80.33 407	87.39 361	63.86 359	68.76 302	89.62 234	40.50 369	91.72 351	69.00 267	74.25 288	89.58 282

test_fmvs1		74.07 314	73.69 299	75.22 376	78.91 400	47.34 438	89.06 312	74.69 439	63.68 362	79.41 154	91.59 188	24.36 441	87.77 402	85.22 97	76.26 276	90.55 270

CVMVSNet		74.04 315	74.27 288	73.33 397	85.33 305	43.94 452	89.53 298	88.39 342	54.33 425	70.37 278	90.13 225	49.17 311	84.05 427	61.83 336	79.36 244	91.99 238

Baseline_NR-MVSNet		73.99 316	72.83 312	77.48 355	80.78 371	59.29 355	91.79 206	84.55 402	68.85 314	68.99 296	80.70 364	56.16 226	92.04 345	62.67 330	60.98 396	81.11 408

pmmvs4		73.92 317	71.81 327	80.25 309	79.17 394	65.24 186	87.43 341	87.26 367	67.64 331	63.46 359	83.91 320	48.96 314	91.53 361	62.94 327	65.49 351	83.96 372

D2MVS		73.80 318	72.02 324	79.15 339	79.15 395	62.97 263	88.58 320	90.07 268	72.94 222	59.22 387	78.30 388	42.31 362	92.70 321	65.59 308	72.00 305	81.79 403

SD_0403		73.79 319	73.48 303	74.69 383	85.33 305	45.56 448	83.80 371	85.57 392	76.55 164	62.96 365	88.45 251	50.62 293	87.59 406	48.80 393	79.28 248	90.92 264

CR-MVSNet		73.79 319	70.82 335	82.70 243	83.15 348	67.96 103	70.25 443	84.00 407	73.67 209	69.97 285	72.41 424	57.82 204	89.48 385	52.99 375	73.13 296	90.64 268

test_djsdf		73.76 321	72.56 318	77.39 357	77.00 421	53.93 402	89.07 310	90.69 236	65.80 344	63.92 354	82.03 341	43.14 359	92.67 322	72.83 224	68.53 328	85.57 355

pmmvs5		73.35 322	71.52 329	78.86 341	78.64 404	60.61 328	91.08 243	86.90 372	67.69 328	63.32 360	83.64 321	44.33 354	90.53 369	62.04 334	66.02 347	85.46 358

Anonymous20231211		73.08 323	70.39 339	81.13 286	90.62 150	63.33 253	91.40 222	90.06 270	51.84 431	64.46 350	80.67 366	36.49 398	94.07 273	63.83 320	64.17 368	85.98 344

tt0805		73.07 324	70.73 336	80.07 313	78.37 408	57.05 381	87.78 335	92.18 153	61.23 389	67.04 326	86.49 288	31.35 421	94.58 245	65.06 313	67.12 340	88.57 296

miper_lstm_enhance		73.05 325	71.73 328	77.03 362	83.80 339	58.32 365	81.76 393	88.88 325	69.80 301	61.01 376	78.23 390	57.19 209	87.51 408	65.34 311	59.53 404	85.27 363

jajsoiax		73.05 325	71.51 330	77.67 352	77.46 418	54.83 397	88.81 316	90.04 271	69.13 310	62.85 368	83.51 323	31.16 422	92.75 318	70.83 248	69.80 315	85.43 359

LCM-MVSNet-Re		72.93 327	71.84 326	76.18 371	88.49 212	48.02 433	80.07 412	70.17 454	73.96 200	52.25 421	80.09 376	49.98 299	88.24 396	67.35 285	84.23 185	92.28 228

pm-mvs1		72.89 328	71.09 332	78.26 347	79.10 397	57.62 372	90.80 252	89.30 300	67.66 329	62.91 367	81.78 345	49.11 313	92.95 307	60.29 344	58.89 407	84.22 371

tpmvs		72.88 329	69.76 345	82.22 259	90.98 143	67.05 133	78.22 422	88.30 346	63.10 370	64.35 352	74.98 414	55.09 240	94.27 263	43.25 419	69.57 318	85.34 361

test0.0.03 1		72.76 330	72.71 316	72.88 401	80.25 381	47.99 434	91.22 237	89.45 294	71.51 272	62.51 371	87.66 269	53.83 255	85.06 423	50.16 385	67.84 338	85.58 354

UniMVSNet_ETH3D		72.74 331	70.53 338	79.36 333	78.62 405	56.64 385	85.01 361	89.20 304	63.77 360	64.84 345	84.44 314	34.05 409	91.86 348	63.94 319	70.89 314	89.57 283

mvs_tets		72.71 332	71.11 331	77.52 353	77.41 419	54.52 400	88.45 322	89.76 280	68.76 317	62.70 369	83.26 327	29.49 428	92.71 319	70.51 254	69.62 317	85.34 361

FMVSNet1		72.71 332	69.91 343	81.10 289	83.60 343	65.11 190	90.01 282	90.32 253	63.92 358	63.56 358	80.25 373	36.35 399	91.54 358	54.46 367	66.75 343	86.64 327

test_fmvs1_n		72.69 334	71.92 325	74.99 381	71.15 441	47.08 440	87.34 343	75.67 434	63.48 364	78.08 173	91.17 198	20.16 455	87.87 399	84.65 106	75.57 280	90.01 276

IterMVS		72.65 335	70.83 333	78.09 349	82.17 358	62.96 264	87.64 339	86.28 379	71.56 270	60.44 380	78.85 386	45.42 348	86.66 413	63.30 325	61.83 387	84.65 368
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 336	72.74 314	72.10 409	87.87 241	49.45 427	88.07 328	89.01 319	72.91 224	63.11 362	88.10 261	63.63 115	85.54 418	32.73 455	69.23 322	81.32 406

PatchMatch-RL		72.06 337	69.98 340	78.28 346	89.51 174	55.70 392	83.49 374	83.39 414	61.24 388	63.72 357	82.76 331	34.77 404	93.03 304	53.37 374	77.59 261	86.12 341

PVSNet_0	68.08 15	71.81 338	68.32 354	82.27 256	84.68 319	62.31 282	88.68 318	90.31 256	75.84 169	57.93 399	80.65 367	37.85 387	94.19 266	69.94 256	29.05 470	90.31 272

MIMVSNet		71.64 339	68.44 352	81.23 283	81.97 361	64.44 209	73.05 437	88.80 329	69.67 302	64.59 346	74.79 416	32.79 413	87.82 400	53.99 369	76.35 275	91.42 250

test_vis1_n		71.63 340	70.73 336	74.31 390	69.63 448	47.29 439	86.91 347	72.11 447	63.21 368	75.18 208	90.17 222	20.40 453	85.76 417	84.59 107	74.42 287	89.87 277

IterMVS-SCA-FT		71.55 341	69.97 341	76.32 369	81.48 364	60.67 326	87.64 339	85.99 386	66.17 342	59.50 385	78.88 385	45.53 346	83.65 431	62.58 331	61.93 386	84.63 370

v7n		71.31 342	68.65 349	79.28 335	76.40 423	60.77 319	86.71 351	89.45 294	64.17 357	58.77 392	78.24 389	44.59 353	93.54 294	57.76 354	61.75 389	83.52 379

anonymousdsp		71.14 343	69.37 347	76.45 368	72.95 436	54.71 398	84.19 368	88.88 325	61.92 382	62.15 372	79.77 379	38.14 383	91.44 363	68.90 269	67.45 339	83.21 385

F-COLMAP		70.66 344	68.44 352	77.32 358	86.37 285	55.91 390	88.00 330	86.32 378	56.94 416	57.28 403	88.07 263	33.58 411	92.49 329	51.02 379	68.37 329	83.55 377

WR-MVS_H		70.59 345	69.94 342	72.53 403	81.03 367	51.43 413	87.35 342	92.03 161	67.38 332	60.23 382	80.70 364	55.84 232	83.45 434	46.33 408	58.58 409	82.72 392

CP-MVSNet		70.50 346	69.91 343	72.26 406	80.71 372	51.00 417	87.23 344	90.30 257	67.84 327	59.64 384	82.69 332	50.23 297	82.30 442	51.28 378	59.28 405	83.46 381

RPMNet		70.42 347	65.68 368	84.63 173	83.15 348	67.96 103	70.25 443	90.45 244	46.83 447	69.97 285	65.10 450	56.48 225	95.30 218	35.79 444	73.13 296	90.64 268

testing3		70.38 348	70.83 333	69.03 422	85.82 299	43.93 453	90.72 258	90.56 243	68.06 323	60.24 381	86.82 285	64.83 95	84.12 425	26.33 463	64.10 369	79.04 429

tfpnnormal		70.10 349	67.36 358	78.32 345	83.45 345	60.97 315	88.85 314	92.77 124	64.85 351	60.83 378	78.53 387	43.52 357	93.48 296	31.73 458	61.70 391	80.52 415

TransMVSNet (Re)		70.07 350	67.66 356	77.31 359	80.62 375	59.13 357	91.78 208	84.94 398	65.97 343	60.08 383	80.44 369	50.78 290	91.87 347	48.84 392	45.46 443	80.94 410

CL-MVSNet_self_test		69.92 351	68.09 355	75.41 374	73.25 435	55.90 391	90.05 281	89.90 276	69.96 298	61.96 374	76.54 404	51.05 289	87.64 403	49.51 389	50.59 432	82.70 394

DP-MVS		69.90 352	66.48 360	80.14 311	95.36 29	62.93 265	89.56 293	76.11 432	50.27 437	57.69 401	85.23 304	39.68 372	95.73 187	33.35 449	71.05 313	81.78 404

PS-CasMVS		69.86 353	69.13 348	72.07 410	80.35 379	50.57 420	87.02 346	89.75 281	67.27 333	59.19 388	82.28 337	46.58 336	82.24 443	50.69 382	59.02 406	83.39 383

Syy-MVS		69.65 354	69.52 346	70.03 418	87.87 241	43.21 454	88.07 328	89.01 319	72.91 224	63.11 362	88.10 261	45.28 349	85.54 418	22.07 468	69.23 322	81.32 406

MSDG		69.54 355	65.73 367	80.96 294	85.11 314	63.71 239	84.19 368	83.28 415	56.95 415	54.50 410	84.03 317	31.50 419	96.03 169	42.87 423	69.13 324	83.14 387

PEN-MVS		69.46 356	68.56 350	72.17 408	79.27 392	49.71 425	86.90 348	89.24 302	67.24 336	59.08 389	82.51 335	47.23 331	83.54 433	48.42 395	57.12 411	83.25 384

LS3D		69.17 357	66.40 362	77.50 354	91.92 116	56.12 388	85.12 359	80.37 424	46.96 445	56.50 405	87.51 273	37.25 391	93.71 291	32.52 457	79.40 243	82.68 395

PatchT		69.11 358	65.37 372	80.32 305	82.07 360	63.68 243	67.96 453	87.62 360	50.86 435	69.37 289	65.18 449	57.09 210	88.53 392	41.59 428	66.60 344	88.74 293

KD-MVS_2432*1600		69.03 359	66.37 363	77.01 363	85.56 303	61.06 313	81.44 398	90.25 260	67.27 333	58.00 397	76.53 405	54.49 245	87.63 404	48.04 397	35.77 461	82.34 398

miper_refine_blended		69.03 359	66.37 363	77.01 363	85.56 303	61.06 313	81.44 398	90.25 260	67.27 333	58.00 397	76.53 405	54.49 245	87.63 404	48.04 397	35.77 461	82.34 398

mvsany_test1		68.77 361	68.56 350	69.39 420	73.57 434	45.88 447	80.93 403	60.88 468	59.65 400	71.56 265	90.26 215	43.22 358	75.05 455	74.26 213	62.70 378	87.25 319

ACMH	63.93 17	68.62 362	64.81 374	80.03 315	85.22 310	63.25 255	87.72 336	84.66 400	60.83 391	51.57 425	79.43 383	27.29 435	94.96 228	41.76 426	64.84 360	81.88 402
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 363	65.41 371	77.96 350	78.69 403	62.93 265	89.86 287	89.17 306	60.55 393	50.27 431	77.73 395	22.60 449	94.06 274	47.18 404	72.65 301	76.88 443

ADS-MVSNet		68.54 364	64.38 381	81.03 293	88.06 232	66.90 142	68.01 451	84.02 406	57.57 409	64.48 348	69.87 437	38.68 374	89.21 387	40.87 430	67.89 336	86.97 321

DTE-MVSNet		68.46 365	67.33 359	71.87 412	77.94 413	49.00 431	86.16 356	88.58 338	66.36 341	58.19 394	82.21 339	46.36 337	83.87 430	44.97 416	55.17 418	82.73 391

mmtdpeth		68.33 366	66.37 363	74.21 391	82.81 353	51.73 410	84.34 366	80.42 423	67.01 337	71.56 265	68.58 441	30.52 426	92.35 336	75.89 197	36.21 459	78.56 436

our_test_3		68.29 367	64.69 376	79.11 340	78.92 398	64.85 197	88.40 323	85.06 396	60.32 396	52.68 419	76.12 409	40.81 368	89.80 384	44.25 418	55.65 416	82.67 396

Patchmatch-RL test		68.17 368	64.49 379	79.19 336	71.22 440	53.93 402	70.07 445	71.54 451	69.22 307	56.79 404	62.89 454	56.58 222	88.61 389	69.53 260	52.61 426	95.03 95

XVG-ACMP-BASELINE		68.04 369	65.53 370	75.56 373	74.06 433	52.37 407	78.43 419	85.88 387	62.03 380	58.91 391	81.21 360	20.38 454	91.15 365	60.69 341	68.18 330	83.16 386

FMVSNet5		68.04 369	65.66 369	75.18 378	84.43 329	57.89 367	83.54 373	86.26 380	61.83 384	53.64 416	73.30 419	37.15 394	85.08 422	48.99 391	61.77 388	82.56 397

ppachtmachnet_test		67.72 371	63.70 384	79.77 325	78.92 398	66.04 164	88.68 318	82.90 417	60.11 398	55.45 407	75.96 410	39.19 373	90.55 368	39.53 434	52.55 427	82.71 393

ACMH+	65.35 16	67.65 372	64.55 377	76.96 365	84.59 323	57.10 380	88.08 327	80.79 421	58.59 407	53.00 418	81.09 362	26.63 437	92.95 307	46.51 406	61.69 392	80.82 411

pmmvs6		67.57 373	64.76 375	76.00 372	72.82 438	53.37 404	88.71 317	86.78 376	53.19 427	57.58 402	78.03 392	35.33 403	92.41 332	55.56 363	54.88 420	82.21 400

Anonymous20231206		67.53 374	65.78 366	72.79 402	74.95 429	47.59 436	88.23 325	87.32 364	61.75 387	58.07 396	77.29 398	37.79 388	87.29 410	42.91 421	63.71 373	83.48 380

Patchmtry		67.53 374	63.93 383	78.34 344	82.12 359	64.38 213	68.72 448	84.00 407	48.23 444	59.24 386	72.41 424	57.82 204	89.27 386	46.10 409	56.68 415	81.36 405

USDC		67.43 376	64.51 378	76.19 370	77.94 413	55.29 394	78.38 420	85.00 397	73.17 215	48.36 440	80.37 370	21.23 451	92.48 330	52.15 377	64.02 371	80.81 412

ADS-MVSNet2		66.90 377	63.44 386	77.26 360	88.06 232	60.70 325	68.01 451	75.56 436	57.57 409	64.48 348	69.87 437	38.68 374	84.10 426	40.87 430	67.89 336	86.97 321

FE-MVSNET2		66.80 378	64.06 382	75.03 379	69.84 446	57.11 379	86.57 352	88.57 339	67.94 326	50.97 429	72.16 428	33.79 410	87.55 407	53.94 370	52.74 424	80.45 416

CMPMVS	48.56 21	66.77 379	64.41 380	73.84 394	70.65 444	50.31 422	77.79 424	85.73 390	45.54 450	44.76 451	82.14 340	35.40 402	90.14 378	63.18 326	74.54 285	81.07 409
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 380	62.92 389	76.80 367	76.51 422	57.77 369	89.22 305	83.41 413	55.48 422	53.86 414	77.84 393	26.28 438	93.95 283	34.90 446	68.76 326	78.68 434

LTVRE_ROB	59.60 19	66.27 381	63.54 385	74.45 387	84.00 336	51.55 412	67.08 455	83.53 411	58.78 405	54.94 409	80.31 371	34.54 405	93.23 300	40.64 432	68.03 332	78.58 435
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		66.06 382	62.45 392	76.88 366	81.42 366	54.45 401	57.49 469	88.67 334	49.36 439	63.86 355	46.86 466	56.06 229	90.25 372	49.53 388	68.83 325	85.95 345

Patchmatch-test		65.86 383	60.94 398	80.62 302	83.75 340	58.83 359	58.91 466	75.26 438	44.50 453	50.95 430	77.09 401	58.81 192	87.90 398	35.13 445	64.03 370	95.12 89

UnsupCasMVSNet_eth		65.79 384	63.10 387	73.88 393	70.71 443	50.29 423	81.09 401	89.88 277	72.58 231	49.25 436	74.77 417	32.57 415	87.43 409	55.96 362	41.04 451	83.90 374

test_fmvs2		65.78 385	64.84 373	68.60 424	66.54 456	41.71 456	83.27 378	69.81 455	54.38 424	67.91 312	84.54 313	15.35 460	81.22 447	75.65 199	66.16 346	82.88 388

dmvs_testset		65.55 386	66.45 361	62.86 436	79.87 385	22.35 482	76.55 427	71.74 449	77.42 145	55.85 406	87.77 268	51.39 283	80.69 448	31.51 461	65.92 349	85.55 356

pmmvs-eth3d		65.53 387	62.32 393	75.19 377	69.39 449	59.59 348	82.80 386	83.43 412	62.52 375	51.30 427	72.49 422	32.86 412	87.16 412	55.32 364	50.73 431	78.83 432

mamv4		65.18 388	67.43 357	58.44 440	77.88 415	49.36 430	69.40 447	70.99 453	48.31 443	57.78 400	85.53 301	59.01 189	51.88 478	73.67 215	64.32 366	74.07 448

SixPastTwentyTwo		64.92 389	61.78 396	74.34 389	78.74 402	49.76 424	83.42 377	79.51 427	62.86 371	50.27 431	77.35 396	30.92 424	90.49 370	45.89 410	47.06 438	82.78 389

OurMVSNet-221017-0		64.68 390	62.17 394	72.21 407	76.08 426	47.35 437	80.67 404	81.02 420	56.19 419	51.60 424	79.66 381	27.05 436	88.56 391	53.60 373	53.63 423	80.71 413

test_0402		64.54 391	61.09 397	74.92 382	84.10 335	60.75 321	87.95 331	79.71 426	52.03 429	52.41 420	77.20 399	32.21 417	91.64 353	23.14 466	61.03 395	72.36 454

testgi		64.48 392	62.87 390	69.31 421	71.24 439	40.62 459	85.49 357	79.92 425	65.36 348	54.18 412	83.49 324	23.74 444	84.55 424	41.60 427	60.79 398	82.77 390

RPSCF		64.24 393	61.98 395	71.01 415	76.10 425	45.00 449	75.83 432	75.94 433	46.94 446	58.96 390	84.59 311	31.40 420	82.00 444	47.76 402	60.33 403	86.04 342

FE-MVSNET1		64.10 394	60.82 399	73.94 392	66.56 455	54.53 399	85.09 360	87.16 369	60.58 392	48.76 439	70.08 436	26.10 439	87.29 410	50.89 381	52.15 428	79.99 422

EU-MVSNet		64.01 395	63.01 388	67.02 430	74.40 432	38.86 465	83.27 378	86.19 382	45.11 451	54.27 411	81.15 361	36.91 397	80.01 450	48.79 394	57.02 412	82.19 401

test20.03		63.83 396	62.65 391	67.38 429	70.58 445	39.94 461	86.57 352	84.17 404	63.29 366	51.86 423	77.30 397	37.09 395	82.47 440	38.87 438	54.13 422	79.73 423

sc_t1		63.81 397	59.39 405	77.10 361	77.62 416	56.03 389	84.32 367	73.56 443	46.66 448	58.22 393	73.06 420	23.28 447	90.62 367	50.93 380	46.84 439	84.64 369

MDA-MVSNet_test_wron		63.78 398	60.16 401	74.64 384	78.15 411	60.41 332	83.49 374	84.03 405	56.17 421	39.17 461	71.59 431	37.22 392	83.24 437	42.87 423	48.73 434	80.26 419

YYNet1		63.76 399	60.14 402	74.62 385	78.06 412	60.19 339	83.46 376	83.99 409	56.18 420	39.25 460	71.56 432	37.18 393	83.34 435	42.90 422	48.70 435	80.32 418

K. test v3		63.09 400	59.61 404	73.53 396	76.26 424	49.38 429	83.27 378	77.15 430	64.35 354	47.77 442	72.32 426	28.73 430	87.79 401	49.93 387	36.69 458	83.41 382

COLMAP_ROB	57.96 20	62.98 401	59.65 403	72.98 400	81.44 365	53.00 406	83.75 372	75.53 437	48.34 442	48.81 438	81.40 354	24.14 442	90.30 371	32.95 452	60.52 400	75.65 446
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 402	59.08 406	71.10 414	67.19 453	48.72 432	83.91 370	85.23 395	50.38 436	47.84 441	71.22 434	20.74 452	85.51 420	46.47 407	58.75 408	79.06 428

tt0320		61.85 403	57.45 412	75.03 379	77.49 417	57.60 373	82.74 387	73.65 442	43.65 457	53.65 415	68.18 443	25.47 440	88.66 388	45.56 412	46.68 440	78.81 433

AllTest		61.66 404	58.06 408	72.46 404	79.57 387	51.42 414	80.17 410	68.61 457	51.25 433	45.88 445	81.23 356	19.86 456	86.58 414	38.98 436	57.01 413	79.39 425

UnsupCasMVSNet_bld		61.60 405	57.71 409	73.29 398	68.73 450	51.64 411	78.61 418	89.05 317	57.20 414	46.11 444	61.96 457	28.70 431	88.60 390	50.08 386	38.90 456	79.63 424

MDA-MVSNet-bldmvs		61.54 406	57.70 410	73.05 399	79.53 389	57.00 384	83.08 382	81.23 419	57.57 409	34.91 465	72.45 423	32.79 413	86.26 416	35.81 443	41.95 449	75.89 445

tt0320-xc		61.51 407	56.89 416	75.37 375	78.50 406	58.61 362	82.61 389	71.27 452	44.31 454	53.17 417	68.03 445	23.38 445	88.46 393	47.77 401	43.00 448	79.03 430

mvs5depth		61.03 408	57.65 411	71.18 413	67.16 454	47.04 442	72.74 438	77.49 428	57.47 412	60.52 379	72.53 421	22.84 448	88.38 394	49.15 390	38.94 455	78.11 439

KD-MVS_self_test		60.87 409	58.60 407	67.68 427	66.13 457	39.93 462	75.63 434	84.70 399	57.32 413	49.57 434	68.45 442	29.55 427	82.87 438	48.09 396	47.94 436	80.25 420

kuosan		60.86 410	60.24 400	62.71 437	81.57 363	46.43 444	75.70 433	85.88 387	57.98 408	48.95 437	69.53 439	58.42 197	76.53 453	28.25 462	35.87 460	65.15 461

FE-MVSNET		60.52 411	57.18 415	70.53 416	67.53 452	50.68 419	82.62 388	76.28 431	59.33 403	46.71 443	71.10 435	30.54 425	83.61 432	33.15 451	47.37 437	77.29 442

TinyColmap		60.32 412	56.42 419	72.00 411	78.78 401	53.18 405	78.36 421	75.64 435	52.30 428	41.59 459	75.82 412	14.76 463	88.35 395	35.84 442	54.71 421	74.46 447

MVS-HIRNet		60.25 413	55.55 420	74.35 388	84.37 330	56.57 386	71.64 441	74.11 440	34.44 464	45.54 449	42.24 472	31.11 423	89.81 382	40.36 433	76.10 277	76.67 444

MIMVSNet1		60.16 414	57.33 413	68.67 423	69.71 447	44.13 451	78.92 417	84.21 403	55.05 423	44.63 452	71.85 429	23.91 443	81.54 446	32.63 456	55.03 419	80.35 417

PM-MVS		59.40 415	56.59 417	67.84 425	63.63 460	41.86 455	76.76 426	63.22 465	59.01 404	51.07 428	72.27 427	11.72 467	83.25 436	61.34 337	50.28 433	78.39 437

new-patchmatchnet		59.30 416	56.48 418	67.79 426	65.86 458	44.19 450	82.47 390	81.77 418	59.94 399	43.65 455	66.20 448	27.67 434	81.68 445	39.34 435	41.40 450	77.50 441

test_vis1_rt		59.09 417	57.31 414	64.43 433	68.44 451	46.02 446	83.05 384	48.63 477	51.96 430	49.57 434	63.86 453	16.30 458	80.20 449	71.21 246	62.79 377	67.07 460

test_fmvs3		56.82 418	54.86 422	62.69 438	53.59 471	35.47 468	75.87 431	65.64 462	43.91 455	55.10 408	71.43 433	6.91 475	74.40 458	68.64 271	52.63 425	78.20 438

DSMNet-mixed		56.78 419	54.44 423	63.79 434	63.21 461	29.44 477	64.43 458	64.10 464	42.12 461	51.32 426	71.60 430	31.76 418	75.04 456	36.23 441	65.20 357	86.87 324

pmmvs3		55.51 420	51.50 426	67.53 428	57.90 469	50.93 418	80.37 406	73.66 441	40.63 462	44.15 454	64.75 451	16.30 458	78.97 452	44.77 417	40.98 453	72.69 452

TDRefinement		55.28 421	51.58 425	66.39 431	59.53 468	46.15 445	76.23 429	72.80 444	44.60 452	42.49 457	76.28 408	15.29 461	82.39 441	33.20 450	43.75 445	70.62 456

dongtai		55.18 422	55.46 421	54.34 448	76.03 427	36.88 466	76.07 430	84.61 401	51.28 432	43.41 456	64.61 452	56.56 223	67.81 466	18.09 471	28.50 471	58.32 464

LF4IMVS		54.01 423	52.12 424	59.69 439	62.41 463	39.91 463	68.59 449	68.28 459	42.96 459	44.55 453	75.18 413	14.09 465	68.39 465	41.36 429	51.68 429	70.78 455

ttmdpeth		53.34 424	49.96 427	63.45 435	62.07 465	40.04 460	72.06 439	65.64 462	42.54 460	51.88 422	77.79 394	13.94 466	76.48 454	32.93 453	30.82 469	73.84 449

MVStest1		51.35 425	46.89 429	64.74 432	65.06 459	51.10 416	67.33 454	72.58 445	30.20 468	35.30 463	74.82 415	27.70 433	69.89 463	24.44 465	24.57 472	73.22 450

N_pmnet		50.55 426	49.11 428	54.88 446	77.17 420	4.02 490	84.36 365	2.00 488	48.59 440	45.86 447	68.82 440	32.22 416	82.80 439	31.58 459	51.38 430	77.81 440

new_pmnet		49.31 427	46.44 430	57.93 441	62.84 462	40.74 458	68.47 450	62.96 466	36.48 463	35.09 464	57.81 461	14.97 462	72.18 460	32.86 454	46.44 441	60.88 463

mvsany_test3		48.86 428	46.35 431	56.41 442	46.00 477	31.67 473	62.26 460	47.25 478	43.71 456	45.54 449	68.15 444	10.84 468	64.44 474	57.95 353	35.44 463	73.13 451

test_f		46.58 429	43.45 433	55.96 443	45.18 478	32.05 472	61.18 461	49.49 476	33.39 465	42.05 458	62.48 456	7.00 474	65.56 470	47.08 405	43.21 447	70.27 457

WB-MVS		46.23 430	44.94 432	50.11 451	62.13 464	21.23 484	76.48 428	55.49 470	45.89 449	35.78 462	61.44 459	35.54 401	72.83 459	9.96 478	21.75 473	56.27 466

FPMVS		45.64 431	43.10 435	53.23 449	51.42 474	36.46 467	64.97 457	71.91 448	29.13 469	27.53 469	61.55 458	9.83 470	65.01 472	16.00 475	55.58 417	58.22 465

SSC-MVS		44.51 432	43.35 434	47.99 455	61.01 467	18.90 486	74.12 436	54.36 471	43.42 458	34.10 466	60.02 460	34.42 406	70.39 462	9.14 480	19.57 474	54.68 467

EGC-MVSNET		42.35 433	38.09 436	55.11 445	74.57 430	46.62 443	71.63 442	55.77 469	0.04 483	0.24 484	62.70 455	14.24 464	74.91 457	17.59 472	46.06 442	43.80 469

LCM-MVSNet		40.54 434	35.79 439	54.76 447	36.92 484	30.81 474	51.41 472	69.02 456	22.07 471	24.63 471	45.37 468	4.56 479	65.81 469	33.67 448	34.50 464	67.67 458

APD_test1		40.50 435	37.31 438	50.09 452	51.88 472	35.27 469	59.45 465	52.59 473	21.64 472	26.12 470	57.80 462	4.56 479	66.56 468	22.64 467	39.09 454	48.43 468

test_vis3_rt		40.46 436	37.79 437	48.47 454	44.49 479	33.35 471	66.56 456	32.84 485	32.39 466	29.65 467	39.13 475	3.91 482	68.65 464	50.17 384	40.99 452	43.40 470

ANet_high		40.27 437	35.20 440	55.47 444	34.74 485	34.47 470	63.84 459	71.56 450	48.42 441	18.80 474	41.08 473	9.52 471	64.45 473	20.18 469	8.66 481	67.49 459

test_method		38.59 438	35.16 441	48.89 453	54.33 470	21.35 483	45.32 475	53.71 472	7.41 480	28.74 468	51.62 464	8.70 472	52.87 477	33.73 447	32.89 465	72.47 453

PMMVS2		37.93 439	33.61 442	50.92 450	46.31 476	24.76 480	60.55 464	50.05 474	28.94 470	20.93 472	47.59 465	4.41 481	65.13 471	25.14 464	18.55 476	62.87 462

Gipuma		34.91 440	31.44 443	45.30 456	70.99 442	39.64 464	19.85 479	72.56 446	20.10 474	16.16 478	21.47 479	5.08 478	71.16 461	13.07 476	43.70 446	25.08 476
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 441	29.47 444	42.67 458	41.89 481	30.81 474	52.07 470	43.45 479	15.45 475	18.52 475	44.82 469	2.12 483	58.38 475	16.05 473	30.87 467	38.83 471

APD_test2		32.77 441	29.47 444	42.67 458	41.89 481	30.81 474	52.07 470	43.45 479	15.45 475	18.52 475	44.82 469	2.12 483	58.38 475	16.05 473	30.87 467	38.83 471

PMVS	26.43 22	31.84 443	28.16 446	42.89 457	25.87 487	27.58 478	50.92 473	49.78 475	21.37 473	14.17 479	40.81 474	2.01 485	66.62 467	9.61 479	38.88 457	34.49 475
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 444	24.00 448	26.45 462	43.74 480	18.44 487	60.86 462	39.66 481	15.11 477	9.53 481	22.10 478	6.52 476	46.94 480	8.31 481	10.14 478	13.98 478

MVE	24.84 23	24.35 445	19.77 451	38.09 460	34.56 486	26.92 479	26.57 477	38.87 483	11.73 479	11.37 480	27.44 476	1.37 486	50.42 479	11.41 477	14.60 477	36.93 473
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 446	23.20 450	25.46 463	41.52 483	16.90 488	60.56 463	38.79 484	14.62 478	8.99 482	20.24 481	7.35 473	45.82 481	7.25 482	9.46 479	13.64 479

tmp_tt		22.26 447	23.75 449	17.80 464	5.23 488	12.06 489	35.26 476	39.48 482	2.82 482	18.94 473	44.20 471	22.23 450	24.64 483	36.30 440	9.31 480	16.69 477

cdsmvs_eth3d_5k		19.86 448	26.47 447	0.00 468	0.00 491	0.00 493	0.00 480	93.45 95	0.00 486	0.00 487	95.27 77	49.56 305	0.00 487	0.00 486	0.00 484	0.00 483

wuyk23d		11.30 449	10.95 452	12.33 465	48.05 475	19.89 485	25.89 478	1.92 489	3.58 481	3.12 483	1.37 483	0.64 487	15.77 484	6.23 483	7.77 482	1.35 480

ab-mvs-re		7.91 450	10.55 453	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	94.95 87	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

testmvs		7.23 451	9.62 454	0.06 467	0.04 489	0.02 492	84.98 362	0.02 490	0.03 484	0.18 485	1.21 484	0.01 489	0.02 485	0.14 484	0.01 483	0.13 482

test123		6.92 452	9.21 455	0.08 466	0.03 490	0.05 491	81.65 396	0.01 491	0.02 485	0.14 486	0.85 485	0.03 488	0.02 485	0.12 485	0.00 484	0.16 481

pcd_1.5k_mvsjas		4.46 453	5.95 456	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	53.55 259	0.00 487	0.00 486	0.00 484	0.00 483

mmdepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

monomultidepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

test_blank		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

uanet_test		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

DCPMVS		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

sosnet-low-res		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

sosnet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

uncertanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

Regformer		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

uanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 484	0.00 483

MED-MVS test				87.42 45	94.76 34	67.28 122	94.47 61	94.87 32	73.09 220	91.27 23	96.95 17		98.98 16	91.55 43	94.28 37	95.99 45

TestfortrainingZip							94.47 61

WAC-MVS						49.45 427								31.56 460

FOURS1					93.95 50	61.77 295	93.96 88	91.92 165	62.14 379	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	71.42 274	90.67 29	96.85 27	74.45 22

eth-test2					0.00 491

eth-test					0.00 491

ZD-MVS					96.63 9	65.50 181		93.50 93	70.74 288	85.26 80	95.19 83	64.92 94	97.29 89	87.51 73	93.01 60

RE-MVS-def			80.48 182		92.02 109	58.56 363	90.90 247	90.45 244	62.76 372	78.89 160	94.46 101	49.30 308		78.77 180	86.77 148	92.28 228

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	71.65 263	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	71.65 263	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	72.76 228	86.21 65	96.51 36	66.64 72	97.88 52	90.08 55	94.04 43

save fliter					93.84 53	67.89 106	95.05 40	92.66 131	78.19 125

test_0728_THIRD									72.48 233	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	71.92 249	91.89 14	97.11 11	73.77 25

GSMVS																94.68 120

test_part2					96.29 20	68.16 99				90.78 27

sam_mvs1												57.85 203				94.68 120

sam_mvs												54.91 242

ambc				69.61 419	61.38 466	41.35 457	49.07 474	85.86 389		50.18 433	66.40 447	10.16 469	88.14 397	45.73 411	44.20 444	79.32 427

MTGPA								92.23 146

test_post1							78.95 416				20.70 480	53.05 264	91.50 362	60.43 342

test_post											23.01 477	56.49 224	92.67 322

patchmatchnet-post											67.62 446	57.62 206	90.25 372

GG-mvs-BLEND				86.53 85	91.91 118	69.67 54	75.02 435	94.75 40		78.67 168	90.85 202	77.91 7	94.56 250	72.25 233	93.74 49	95.36 72

MTMP							93.77 103	32.52 486

gm-plane-assit					88.42 218	67.04 134			78.62 119		91.83 179		97.37 83	76.57 192

test9_res														89.41 56	94.96 19	95.29 78

TEST9					94.18 45	67.28 122	94.16 75	93.51 91	71.75 260	85.52 75	95.33 71	68.01 61	97.27 93

test_8					94.19 44	67.19 127	94.15 77	93.42 98	71.87 254	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	66.97 140		93.31 101		84.49 86			96.75 132

TestCases				72.46 404	79.57 387	51.42 414		68.61 457	51.25 433	45.88 445	81.23 356	19.86 456	86.58 414	38.98 436	57.01 413	79.39 425

test_prior4						67.18 129	93.92 92

test_prior2							95.10 39		75.40 177	85.25 81	95.61 62	67.94 62		87.47 75	94.77 26

test_prior				86.42 89	94.71 39	67.35 121		93.10 112					96.84 129			95.05 93

旧先验2							92.00 197		59.37 402	87.54 55			93.47 297	75.39 201

新几何2							91.41 220

新几何1				84.73 163	92.32 97	64.28 218		91.46 192	59.56 401	79.77 147	92.90 145	56.95 216	96.57 138	63.40 322	92.91 62	93.34 188

旧先验1					91.94 114	60.74 322		91.50 190			94.36 105	65.23 89			91.84 78	94.55 129

无先验							92.71 152	92.61 136	62.03 380				97.01 110	66.63 293		93.97 164

原ACMM2							92.01 194

原ACMM1				84.42 180	93.21 72	64.27 219		93.40 100	65.39 347	79.51 152	92.50 153	58.11 202	96.69 134	65.27 312	93.96 44	92.32 226

test222					89.77 167	61.60 301	89.55 294	89.42 296	56.83 417	77.28 184	92.43 157	52.76 267			91.14 95	93.09 198

testdata2													96.09 163	61.26 338

segment_acmp												65.94 80

testdata				81.34 281	89.02 191	57.72 370		89.84 278	58.65 406	85.32 79	94.09 121	57.03 211	93.28 299	69.34 262	90.56 101	93.03 201

testdata1							89.21 306		77.55 141

test12				87.09 56	94.60 40	68.86 75		92.91 119		82.67 109		65.44 86	97.55 72		93.69 52	94.84 105

plane_prior7					86.94 268	61.51 303

plane_prior6					87.23 258	62.32 281						50.66 291

plane_prior5								91.31 196					95.55 204	76.74 190	78.53 255	88.39 300

plane_prior4											89.14 243

plane_prior3						61.95 290			79.09 108	72.53 247

plane_prior2							93.13 131		78.81 115

plane_prior1					87.15 261

plane_prior						62.42 277	93.85 96		79.38 100						78.80 252

n2								0.00 492

nn								0.00 492

door-mid								66.01 461

lessismore_v0				73.72 395	72.93 437	47.83 435		61.72 467		45.86 447	73.76 418	28.63 432	89.81 382	47.75 403	31.37 466	83.53 378

LGP-MVS_train				79.56 331	84.31 331	59.37 352		89.73 284	69.49 303	64.86 343	88.42 252	38.65 376	94.30 261	72.56 230	72.76 299	85.01 364

test11								93.01 115

door								66.57 460

HQP5-MVS						63.66 244

HQP-NCC					87.54 250		94.06 80		79.80 88	74.18 221

ACMP_Plane					87.54 250		94.06 80		79.80 88	74.18 221

BP-MVS														77.63 187

HQP4-MVS										74.18 221			95.61 198			88.63 294

HQP3-MVS								91.70 182							78.90 250

HQP2-MVS												51.63 279

NP-MVS					87.41 253	63.04 261					90.30 213

MDTV_nov1_ep13_2view						59.90 344	80.13 411		67.65 330	72.79 241		54.33 250		59.83 346		92.58 217

MDTV_nov1_ep13			72.61 317		89.06 189	68.48 86	80.33 407	90.11 267	71.84 256	71.81 261	75.92 411	53.01 265	93.92 284	48.04 397	73.38 294

ACMMP++_ref															71.63 307

ACMMP++															69.72 316

Test By Simon												54.21 253

ITE_SJBPF				70.43 417	74.44 431	47.06 441		77.32 429	60.16 397	54.04 413	83.53 322	23.30 446	84.01 428	43.07 420	61.58 393	80.21 421

DeepMVS_CX				34.71 461	51.45 473	24.73 481		28.48 487	31.46 467	17.49 477	52.75 463	5.80 477	42.60 482	18.18 470	19.42 475	36.81 474

High-resolution multi-view benchmark