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.

MSP-MVS		90.38 5	91.87 1	85.88 116	92.83 86	64.03 242	93.06 136	94.33 67	82.19 45	93.65 4	96.15 51	85.89 1	97.19 99	91.02 51	97.75 1	96.43 32
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
MM		90.87 2	91.52 2	88.92 16	92.12 107	71.10 29	97.02 3	96.04 6	88.70 2	91.57 20	96.19 49	70.12 50	98.91 22	96.83 2	95.06 17	96.76 16

MCST-MVS		91.08 1	91.46 3	89.94 5	97.66 2	73.37 12	97.13 2	95.58 12	89.33 1	85.77 72	96.26 47	72.84 33	99.38 2	92.64 33	95.93 9	97.08 12

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 174	93.00 82	58.16 385	96.72 9	94.41 61	86.50 9	90.25 35	97.83 2	75.46 17	98.67 30	92.78 32	95.49 13	97.32 7

DVP-MVS++		90.53 4	91.09 5	88.87 17	97.31 4	69.91 45	93.96 91	94.37 65	72.48 244	92.07 12	96.85 28	83.82 2	99.15 3	91.53 47	97.42 4	97.55 5

patch_mono-2		89.71 11	90.99 6	85.85 119	96.04 26	63.70 259	95.04 43	95.19 23	86.74 8	91.53 21	95.15 85	73.86 25	97.58 70	93.38 27	92.00 76	96.28 39

MGCNet		90.32 6	90.90 7	88.55 24	94.05 50	70.23 39	97.00 5	93.73 86	87.30 4	92.15 9	96.15 51	66.38 76	98.94 21	96.71 3	94.67 33	96.47 29

CNVR-MVS		90.32 6	90.89 8	88.61 23	96.76 9	70.65 32	96.47 14	94.83 37	84.83 17	89.07 44	96.80 31	70.86 46	99.06 16	92.64 33	95.71 11	96.12 42

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 171	76.68 2	97.29 1	95.35 18	82.87 37	91.58 19	97.22 9	79.93 6	99.10 10	83.12 129	97.64 2	97.94 1

SED-MVS		89.94 9	90.36 10	88.70 19	96.45 13	69.38 62	96.89 6	94.44 56	71.65 274	92.11 10	97.21 10	76.79 10	99.11 7	92.34 36	95.36 14	97.62 3

DELS-MVS		90.05 8	90.09 11	89.94 5	93.14 77	73.88 9	97.01 4	94.40 63	88.32 3	85.71 73	94.91 92	74.11 24	98.91 22	87.26 79	95.94 8	97.03 13
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
CANet		89.61 12	89.99 12	88.46 25	94.39 44	69.71 54	96.53 13	93.78 79	86.89 7	89.68 41	95.78 58	65.94 81	99.10 10	92.99 30	93.91 46	96.58 22

HPM-MVS++		89.37 14	89.95 13	87.64 37	95.10 32	68.23 106	95.24 34	94.49 54	82.43 42	88.90 46	96.35 42	71.89 43	98.63 31	88.76 65	96.40 6	96.06 43

DVP-MVS		89.41 13	89.73 14	88.45 26	96.40 16	69.99 41	96.64 10	94.52 52	71.92 260	90.55 31	96.93 22	73.77 26	99.08 12	91.91 42	94.90 22	96.29 37
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
NCCC		89.07 16	89.46 15	87.91 30	96.60 11	69.05 78	96.38 15	94.64 47	84.42 21	86.74 62	96.20 48	66.56 75	98.76 28	89.03 64	94.56 34	95.92 52

MED-MVS		88.94 17	89.45 16	87.42 47	94.76 35	67.28 132	94.47 64	94.87 33	70.09 306	91.27 24	96.95 18	76.77 12	98.98 17	91.55 44	94.28 37	95.99 48

fmvsm_l_conf0.5_n_9		88.24 21	89.36 17	84.85 163	88.15 232	61.94 310	95.65 25	89.70 306	85.54 12	92.07 12	97.33 6	67.51 67	97.27 94	96.23 5	92.07 75	95.35 78

fmvsm_s_conf0.5_n_11		87.99 26	89.25 18	84.23 200	89.07 190	61.60 320	94.87 51	89.06 334	85.65 11	91.09 27	97.41 5	68.26 59	97.43 81	95.07 13	92.74 65	93.66 189

fmvsm_s_conf0.5_n_9		88.14 22	89.21 19	84.92 158	89.29 182	61.41 327	92.97 141	88.36 361	86.96 6	91.49 22	97.49 4	69.48 55	97.46 77	97.00 1	89.88 113	95.89 54

DPE-MVS		88.77 18	89.21 19	87.45 46	96.26 22	67.56 125	94.17 77	94.15 72	68.77 328	90.74 29	97.27 7	76.09 15	98.49 34	90.58 55	94.91 21	96.30 36
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 35	94.76 35	68.73 88	94.47 64	94.87 33	73.09 231	91.27 24	96.95 18	76.77 12	98.98 17	84.41 112	94.28 37	95.37 74

fmvsm_s_conf0.5_n_8		87.96 27	88.93 22	85.07 153	88.43 219	61.78 313	94.73 59	91.74 183	85.87 10	91.66 18	97.50 3	64.03 107	98.33 39	96.28 4	90.08 109	95.10 95

balanced_conf03		89.08 15	88.84 23	89.81 7	93.66 59	75.15 5	90.61 280	93.43 101	84.06 24	86.20 67	90.17 227	72.42 38	96.98 116	93.09 29	95.92 10	97.29 8

fmvsm_s_conf0.5_n_6		87.50 37	88.72 24	83.84 212	86.89 281	60.04 361	95.05 41	92.17 162	84.80 18	92.27 7	96.37 40	64.62 99	96.54 142	94.43 19	91.86 78	94.94 104

fmvsm_s_conf0.5_n_10		87.93 30	88.67 25	85.71 126	88.69 201	63.71 257	94.56 62	90.22 282	85.04 15	92.27 7	97.05 13	63.67 115	98.15 43	95.09 12	91.39 88	95.27 86

TSAR-MVS + MP.		88.11 25	88.64 26	86.54 93	91.73 125	68.04 111	90.36 287	93.55 93	82.89 35	91.29 23	92.89 147	72.27 40	96.03 170	87.99 69	94.77 26	95.54 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ME-MVS		88.25 20	88.55 27	87.33 52	96.33 19	67.28 132	93.93 93	94.81 38	70.09 306	88.91 45	96.95 18	70.12 50	98.73 29	91.55 44	94.28 37	95.99 48

test_fmvsm_n_1920		87.69 34	88.50 28	85.27 146	87.05 268	63.55 266	93.69 109	91.08 228	84.18 23	90.17 37	97.04 15	67.58 66	97.99 47	95.72 8	90.03 110	94.26 157

EPNet		87.84 32	88.38 29	86.23 106	93.30 71	66.05 175	95.26 33	94.84 36	87.09 5	88.06 49	94.53 101	66.79 72	97.34 87	83.89 119	91.68 82	95.29 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		87.96 27	88.37 30	86.70 75	93.51 67	65.32 196	95.15 37	93.84 78	78.17 130	85.93 71	94.80 95	75.80 16	98.21 41	89.38 58	88.78 125	96.59 20

fmvsm_l_conf0.5_n_3		87.54 35	88.29 31	85.30 143	86.92 279	62.63 293	95.02 45	90.28 277	84.95 16	90.27 34	96.86 26	65.36 88	97.52 75	94.93 15	90.03 110	95.76 58

SMA-MVS		88.14 22	88.29 31	87.67 36	93.21 74	68.72 90	93.85 99	94.03 75	74.18 204	91.74 16	96.67 34	65.61 86	98.42 38	89.24 61	96.08 7	95.88 55
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
fmvsm_l_conf0.5_n		87.49 38	88.19 33	85.39 137	86.95 274	64.37 228	94.30 74	88.45 359	80.51 70	92.70 5	96.86 26	69.98 52	97.15 104	95.83 7	88.08 133	94.65 129

fmvsm_l_conf0.5_n_a		87.44 40	88.15 34	85.30 143	87.10 266	64.19 237	94.41 69	88.14 369	80.24 81	92.54 6	96.97 17	69.52 54	97.17 100	95.89 6	88.51 128	94.56 133

DeepC-MVS_fast	79.48 2	87.95 29	88.00 35	87.79 33	95.86 29	68.32 100	95.74 21	94.11 73	83.82 26	83.49 98	96.19 49	64.53 102	98.44 36	83.42 128	94.88 25	96.61 19
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_3		86.88 46	87.99 36	83.58 226	87.26 259	60.74 341	93.21 133	87.94 376	84.22 22	91.70 17	97.27 7	65.91 83	95.02 234	93.95 24	90.42 104	94.99 101

APDe-MVS		87.54 35	87.84 37	86.65 78	96.07 25	66.30 170	94.84 53	93.78 79	69.35 317	88.39 48	96.34 43	67.74 65	97.66 65	90.62 54	93.44 55	96.01 46
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
lupinMVS		87.74 33	87.77 38	87.63 41	89.24 187	71.18 26	96.57 12	92.90 126	82.70 39	87.13 57	95.27 78	64.99 92	95.80 186	89.34 59	91.80 80	95.93 51

fmvsm_s_conf0.5_n_4		86.79 53	87.63 39	84.27 198	86.15 297	61.48 324	94.69 60	91.16 214	83.79 28	90.51 33	96.28 45	64.24 104	98.22 40	95.00 14	86.88 145	93.11 207

9.14			87.63 39		93.86 53		94.41 69	94.18 70	72.76 239	86.21 66	96.51 37	66.64 73	97.88 53	90.08 56	94.04 43

PS-MVSNAJ		88.14 22	87.61 41	89.71 8	92.06 110	76.72 1	95.75 20	93.26 107	83.86 25	89.55 42	96.06 53	53.55 271	97.89 52	91.10 49	93.31 57	94.54 136

dcpmvs_2		87.37 41	87.55 42	86.85 64	95.04 34	68.20 108	90.36 287	90.66 256	79.37 105	81.20 122	93.67 131	74.73 19	96.55 141	90.88 52	92.00 76	95.82 56

SD-MVS		87.49 38	87.49 43	87.50 45	93.60 61	68.82 85	93.90 96	92.63 141	76.86 159	87.90 51	95.76 59	66.17 78	97.63 67	89.06 63	91.48 86	96.05 44
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
train_agg		87.21 43	87.42 44	86.60 81	94.18 46	67.28 132	94.16 78	93.51 95	71.87 265	85.52 76	95.33 72	68.19 60	97.27 94	89.09 62	94.90 22	95.25 90

xiu_mvs_v2_base		87.92 31	87.38 45	89.55 13	91.41 137	76.43 3	95.74 21	93.12 115	83.53 29	89.55 42	95.95 56	53.45 275	97.68 60	91.07 50	92.62 66	94.54 136

test_fmvsmconf_n		86.58 56	87.17 46	84.82 165	85.28 318	62.55 294	94.26 76	89.78 297	83.81 27	87.78 53	96.33 44	65.33 89	96.98 116	94.40 20	87.55 139	94.95 103

SF-MVS		87.03 45	87.09 47	86.84 65	92.70 92	67.45 130	93.64 112	93.76 82	70.78 298	86.25 65	96.44 39	66.98 70	97.79 56	88.68 66	94.56 34	95.28 85

SPE-MVS-test		86.14 68	87.01 48	83.52 227	92.63 94	59.36 373	95.49 28	91.92 172	80.09 82	85.46 78	95.53 67	61.82 152	95.77 191	86.77 87	93.37 56	95.41 71

alignmvs		87.28 42	86.97 49	88.24 29	91.30 139	71.14 28	95.61 26	93.56 92	79.30 106	87.07 59	95.25 80	68.43 57	96.93 124	87.87 70	84.33 184	96.65 18

fmvsm_s_conf0.5_n_5		86.38 61	86.94 50	84.71 176	84.67 330	63.29 273	94.04 87	89.99 292	82.88 36	87.85 52	96.03 54	62.89 136	96.36 151	94.15 21	89.95 112	94.48 146

fmvsm_s_conf0.5_n		86.39 59	86.91 51	84.82 165	87.36 258	63.54 267	94.74 56	90.02 290	82.52 40	90.14 38	96.92 24	62.93 134	97.84 55	95.28 11	82.26 211	93.07 210

SteuartSystems-ACMMP		86.82 52	86.90 52	86.58 84	90.42 156	66.38 167	96.09 17	93.87 77	77.73 140	84.01 93	95.66 61	63.39 122	97.94 48	87.40 77	93.55 54	95.42 70
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PVSNet_Blended		86.73 54	86.86 53	86.31 105	93.76 55	67.53 127	96.33 16	93.61 90	82.34 44	81.00 128	93.08 141	63.19 127	97.29 90	87.08 83	91.38 89	94.13 166

PHI-MVS		86.83 50	86.85 54	86.78 70	93.47 68	65.55 191	95.39 31	95.10 26	71.77 270	85.69 74	96.52 36	62.07 147	98.77 27	86.06 92	95.60 12	96.03 45

UBG		86.83 50	86.70 55	87.20 54	93.07 80	69.81 49	93.43 125	95.56 14	81.52 52	81.50 117	92.12 168	73.58 29	96.28 154	84.37 113	85.20 171	95.51 68

fmvsm_s_conf0.5_n_7		85.24 86	86.69 56	80.91 315	84.52 335	60.10 359	93.35 128	90.35 270	83.41 31	86.54 64	96.27 46	60.50 167	90.02 400	94.84 16	90.38 105	92.61 224

BP-MVS1		86.54 57	86.68 57	86.13 109	87.80 247	67.18 139	92.97 141	95.62 11	79.92 86	82.84 105	94.14 119	74.95 18	96.46 147	82.91 133	88.96 124	94.74 119

NormalMVS		86.39 59	86.66 58	85.60 131	92.12 107	65.95 180	94.88 49	90.83 244	84.69 19	83.67 96	94.10 120	63.16 129	96.91 128	85.31 96	91.15 93	93.93 177

CS-MVS		85.80 75	86.65 59	83.27 239	92.00 115	58.92 377	95.31 32	91.86 177	79.97 83	84.82 84	95.40 70	62.26 144	95.51 216	86.11 91	92.08 74	95.37 74

testing11		86.71 55	86.44 60	87.55 43	93.54 65	71.35 23	93.65 111	95.58 12	81.36 59	80.69 133	92.21 166	72.30 39	96.46 147	85.18 100	83.43 199	94.82 114

SymmetryMVS		86.32 62	86.39 61	86.12 110	90.52 154	65.95 180	94.88 49	94.58 51	84.69 19	83.67 96	94.10 120	63.16 129	96.91 128	85.31 96	86.59 154	95.51 68

MG-MVS		87.11 44	86.27 62	89.62 9	97.79 1	76.27 4	94.96 48	94.49 54	78.74 121	83.87 94	92.94 145	64.34 103	96.94 122	75.19 210	94.09 42	95.66 62

CSCG		86.87 47	86.26 63	88.72 18	95.05 33	70.79 31	93.83 104	95.33 19	68.48 332	77.63 183	94.35 110	73.04 31	98.45 35	84.92 104	93.71 51	96.92 15

sasdasda		86.85 48	86.25 64	88.66 21	91.80 123	71.92 18	93.54 117	91.71 186	80.26 78	87.55 54	95.25 80	63.59 119	96.93 124	88.18 67	84.34 182	97.11 10

canonicalmvs		86.85 48	86.25 64	88.66 21	91.80 123	71.92 18	93.54 117	91.71 186	80.26 78	87.55 54	95.25 80	63.59 119	96.93 124	88.18 67	84.34 182	97.11 10

jason		86.40 58	86.17 66	87.11 57	86.16 296	70.54 34	95.71 24	92.19 159	82.00 47	84.58 86	94.34 111	61.86 150	95.53 215	87.76 71	90.89 97	95.27 86
jason: jason.
myMVS_eth3d28		86.31 64	86.15 67	86.78 70	93.56 63	70.49 35	92.94 144	95.28 20	82.47 41	78.70 172	92.07 170	72.45 37	95.41 217	82.11 142	85.78 164	94.44 148

ETV-MVS		86.01 70	86.11 68	85.70 127	90.21 161	67.02 146	93.43 125	91.92 172	81.21 61	84.13 92	94.07 124	60.93 161	95.63 203	89.28 60	89.81 114	94.46 147

fmvsm_s_conf0.5_n_a		85.75 76	86.09 69	84.72 174	85.73 310	63.58 264	93.79 105	89.32 317	81.42 57	90.21 36	96.91 25	62.41 141	97.67 62	94.48 18	80.56 239	92.90 216

test_fmvsmconf0.1_n		85.71 77	86.08 70	84.62 184	80.83 381	62.33 299	93.84 102	88.81 346	83.50 30	87.00 60	96.01 55	63.36 123	96.93 124	94.04 23	87.29 142	94.61 131

APD-MVS		85.93 72	85.99 71	85.76 123	95.98 28	65.21 199	93.59 115	92.58 143	66.54 351	86.17 68	95.88 57	63.83 111	97.00 112	86.39 89	92.94 62	95.06 97
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
fmvsm_s_conf0.1_n		85.61 80	85.93 72	84.68 178	82.95 362	63.48 269	94.03 89	89.46 311	81.69 50	89.86 39	96.74 32	61.85 151	97.75 58	94.74 17	82.01 218	92.81 220

MSLP-MVS++		86.27 65	85.91 73	87.35 50	92.01 114	68.97 81	95.04 43	92.70 132	79.04 116	81.50 117	96.50 38	58.98 195	96.78 132	83.49 127	93.93 45	96.29 37

WTY-MVS		86.32 62	85.81 74	87.85 31	92.82 88	69.37 64	95.20 35	95.25 21	82.71 38	81.91 113	94.73 96	67.93 64	97.63 67	79.55 172	82.25 213	96.54 23

ACMMP_NAP		86.05 69	85.80 75	86.80 69	91.58 129	67.53 127	91.79 212	93.49 98	74.93 192	84.61 85	95.30 74	59.42 185	97.92 49	86.13 90	94.92 20	94.94 104

MVS_111021_HR		86.19 67	85.80 75	87.37 49	93.17 76	69.79 50	93.99 90	93.76 82	79.08 113	78.88 168	93.99 125	62.25 145	98.15 43	85.93 93	91.15 93	94.15 165

MGCFI-Net		85.59 81	85.73 77	85.17 150	91.41 137	62.44 295	92.87 149	91.31 204	79.65 93	86.99 61	95.14 86	62.90 135	96.12 162	87.13 82	84.13 190	96.96 14

VNet		86.20 66	85.65 78	87.84 32	93.92 52	69.99 41	95.73 23	95.94 7	78.43 126	86.00 70	93.07 142	58.22 205	97.00 112	85.22 98	84.33 184	96.52 24

fmvsm_s_conf0.5_n_2		85.06 90	85.60 79	83.44 233	86.92 279	60.53 348	94.41 69	87.31 384	83.30 32	88.72 47	96.72 33	54.28 263	97.75 58	94.07 22	84.68 181	92.04 247

testing99		86.01 70	85.47 80	87.63 41	93.62 60	71.25 25	93.47 123	95.23 22	80.42 73	80.60 135	91.95 177	71.73 44	96.50 145	80.02 169	82.22 214	95.13 93

CDPH-MVS		85.71 77	85.46 81	86.46 96	94.75 39	67.19 137	93.89 97	92.83 128	70.90 294	83.09 103	95.28 76	63.62 117	97.36 85	80.63 163	94.18 41	94.84 110

PAPM		85.89 74	85.46 81	87.18 55	88.20 231	72.42 17	92.41 178	92.77 130	82.11 46	80.34 141	93.07 142	68.27 58	95.02 234	78.39 188	93.59 53	94.09 168

GDP-MVS		85.54 82	85.32 83	86.18 107	87.64 250	67.95 115	92.91 147	92.36 149	77.81 137	83.69 95	94.31 113	72.84 33	96.41 149	80.39 166	85.95 161	94.19 161

testing91		85.93 72	85.31 84	87.78 34	93.59 62	71.47 21	93.50 120	95.08 29	80.26 78	80.53 138	91.93 178	70.43 48	96.51 144	80.32 167	82.13 216	95.37 74

DeepC-MVS	77.85 3	85.52 83	85.24 85	86.37 101	88.80 199	66.64 161	92.15 188	93.68 88	81.07 63	76.91 197	93.64 132	62.59 138	98.44 36	85.50 94	92.84 64	94.03 172
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs_mvg		85.66 79	85.18 86	87.09 58	88.22 230	69.35 65	93.74 108	91.89 175	81.47 53	80.10 144	91.45 190	64.80 97	96.35 152	87.23 80	87.69 137	95.58 65
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MP-MVS-pluss		85.24 86	85.13 87	85.56 132	91.42 134	65.59 189	91.54 232	92.51 145	74.56 195	80.62 134	95.64 62	59.15 191	97.00 112	86.94 85	93.80 47	94.07 170
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZNCC-MVS		85.33 85	85.08 88	86.06 111	93.09 79	65.65 187	93.89 97	93.41 103	73.75 215	79.94 146	94.68 98	60.61 166	98.03 46	82.63 137	93.72 50	94.52 138

EC-MVSNet		84.53 105	85.04 89	83.01 245	89.34 178	61.37 328	94.42 68	91.09 224	77.91 135	83.24 99	94.20 117	58.37 203	95.40 218	85.35 95	91.41 87	92.27 241

MP-MVS		85.02 91	84.97 90	85.17 150	92.60 95	64.27 233	93.24 130	92.27 152	73.13 227	79.63 156	94.43 104	61.90 148	97.17 100	85.00 102	92.56 67	94.06 171
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EIA-MVS		84.84 98	84.88 91	84.69 177	91.30 139	62.36 298	93.85 99	92.04 165	79.45 101	79.33 161	94.28 115	62.42 140	96.35 152	80.05 168	91.25 92	95.38 73

casdiffmvs		85.37 84	84.87 92	86.84 65	88.25 228	69.07 75	93.04 138	91.76 182	81.27 60	80.84 131	92.07 170	64.23 105	96.06 168	84.98 103	87.43 141	95.39 72
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_a		84.76 100	84.84 93	84.53 186	80.23 394	63.50 268	92.79 151	88.73 349	80.46 71	89.84 40	96.65 35	60.96 160	97.57 72	93.80 25	80.14 241	92.53 229

lecture		84.77 99	84.81 94	84.65 180	92.12 107	62.27 302	94.74 56	92.64 140	68.35 333	85.53 75	95.30 74	59.77 178	97.91 50	83.73 123	91.15 93	93.77 186

fmvsm_s_conf0.1_n_2		84.40 108	84.78 95	83.27 239	85.25 319	60.41 351	94.13 81	85.69 409	83.05 34	87.99 50	96.37 40	52.75 280	97.68 60	93.75 26	84.05 191	91.71 255

testing222		85.18 88	84.69 96	86.63 80	92.91 84	69.91 45	92.61 164	95.80 9	80.31 77	80.38 140	92.27 162	68.73 56	95.19 231	75.94 204	83.27 201	94.81 116

PAPR		85.15 89	84.47 97	87.18 55	96.02 27	68.29 101	91.85 210	93.00 121	76.59 170	79.03 164	95.00 87	61.59 153	97.61 69	78.16 189	89.00 123	95.63 63

baseline		85.01 92	84.44 98	86.71 74	88.33 225	68.73 88	90.24 292	91.82 181	81.05 64	81.18 123	92.50 154	63.69 114	96.08 167	84.45 111	86.71 152	95.32 81

HFP-MVS		84.73 102	84.40 99	85.72 125	93.75 57	65.01 205	93.50 120	93.19 111	72.19 254	79.22 162	94.93 90	59.04 194	97.67 62	81.55 152	92.21 70	94.49 145

E3new		84.94 96	84.36 100	86.69 77	89.06 191	69.31 66	92.68 161	91.29 209	80.72 67	81.03 126	92.14 167	61.89 149	95.91 174	84.59 108	85.85 163	94.86 106

GST-MVS		84.63 104	84.29 101	85.66 128	92.82 88	65.27 197	93.04 138	93.13 114	73.20 225	78.89 165	94.18 118	59.41 186	97.85 54	81.45 154	92.48 69	93.86 183

viewmanbaseed2359cas		84.89 97	84.26 102	86.78 70	88.50 210	69.77 52	92.69 160	91.13 220	81.11 62	81.54 116	91.98 174	60.35 168	95.73 193	84.47 110	86.56 155	94.84 110

viewcassd2359sk11		84.74 101	84.11 103	86.64 79	88.57 204	69.20 73	92.61 164	91.23 211	80.58 68	80.85 130	91.96 175	61.39 155	95.89 176	84.28 114	85.49 168	94.82 114

ACMMPR		84.37 109	84.06 104	85.28 145	93.56 63	64.37 228	93.50 120	93.15 113	72.19 254	78.85 170	94.86 93	56.69 229	97.45 78	81.55 152	92.20 71	94.02 173

region2R		84.36 110	84.03 105	85.36 141	93.54 65	64.31 231	93.43 125	92.95 124	72.16 257	78.86 169	94.84 94	56.97 224	97.53 74	81.38 156	92.11 73	94.24 159

diffmvs		84.28 112	83.83 106	85.61 130	87.40 256	68.02 112	90.88 265	89.24 320	80.54 69	81.64 115	92.52 153	59.83 176	94.52 263	87.32 78	85.11 172	94.29 155
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
balanced_ft_v1		84.95 95	83.81 107	88.38 27	93.31 70	73.59 11	85.95 373	92.51 145	77.25 153	73.97 239	89.14 249	59.30 188	95.25 229	92.50 35	90.34 107	96.31 35

E2		84.45 106	83.74 108	86.56 86	87.90 240	69.06 76	92.53 172	91.13 220	80.35 75	80.58 136	91.69 185	60.70 162	95.84 179	83.80 121	84.99 173	94.79 117

E3		84.45 106	83.74 108	86.56 86	87.90 240	69.06 76	92.53 172	91.13 220	80.35 75	80.58 136	91.69 185	60.70 162	95.84 179	83.80 121	84.99 173	94.79 117

ETVMVS		84.22 116	83.71 110	85.76 123	92.58 96	68.25 105	92.45 176	95.53 16	79.54 100	79.46 158	91.64 188	70.29 49	94.18 277	69.16 273	82.76 207	94.84 110

EI-MVSNet-Vis-set		83.77 130	83.67 111	84.06 203	92.79 91	63.56 265	91.76 218	94.81 38	79.65 93	77.87 180	94.09 122	63.35 124	97.90 51	79.35 176	79.36 251	90.74 276

MVSMamba_PlusPlus		84.97 94	83.65 112	88.93 15	90.17 162	74.04 8	87.84 350	92.69 135	62.18 395	81.47 119	87.64 277	71.47 45	96.28 154	84.69 106	94.74 31	96.47 29

test_fmvsmconf0.01_n		83.70 133	83.52 113	84.25 199	75.26 444	61.72 317	92.17 187	87.24 386	82.36 43	84.91 83	95.41 69	55.60 243	96.83 131	92.85 31	85.87 162	94.21 160

CANet_DTU		84.09 119	83.52 113	85.81 120	90.30 159	66.82 155	91.87 208	89.01 337	85.27 13	86.09 69	93.74 129	47.71 339	96.98 116	77.90 191	89.78 116	93.65 190

PVSNet_Blended_VisFu		83.97 124	83.50 115	85.39 137	90.02 164	66.59 164	93.77 106	91.73 184	77.43 149	77.08 196	89.81 238	63.77 113	96.97 119	79.67 171	88.21 131	92.60 225

diffmvs_AUTHOR		83.97 124	83.49 116	85.39 137	86.09 298	67.83 117	90.76 270	89.05 335	79.94 84	81.43 120	92.23 165	59.53 182	94.42 266	87.18 81	85.22 170	93.92 179

test_fmvsmvis_n_1920		83.80 129	83.48 117	84.77 169	82.51 365	63.72 256	91.37 241	83.99 427	81.42 57	77.68 182	95.74 60	58.37 203	97.58 70	93.38 27	86.87 146	93.00 213

XVS		83.87 127	83.47 118	85.05 154	93.22 72	63.78 251	92.92 145	92.66 137	73.99 207	78.18 177	94.31 113	55.25 245	97.41 82	79.16 178	91.58 84	93.95 175

CHOSEN 1792x2688		84.98 93	83.45 119	89.57 12	89.94 166	75.14 6	92.07 194	92.32 150	81.87 48	75.68 207	88.27 263	60.18 171	98.60 32	80.46 165	90.27 108	94.96 102

PVSNet_BlendedMVS		83.38 143	83.43 120	83.22 241	93.76 55	67.53 127	94.06 83	93.61 90	79.13 111	81.00 128	85.14 314	63.19 127	97.29 90	87.08 83	73.91 299	84.83 385

MAR-MVS		84.18 117	83.43 120	86.44 98	96.25 23	65.93 182	94.28 75	94.27 69	74.41 198	79.16 163	95.61 63	53.99 266	98.88 26	69.62 267	93.26 58	94.50 144
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
baseline2		83.68 134	83.42 122	84.48 189	87.37 257	66.00 177	90.06 296	95.93 8	79.71 91	69.08 303	90.39 215	77.92 7	96.28 154	78.91 183	81.38 226	91.16 269

CP-MVS		83.71 132	83.40 123	84.65 180	93.14 77	63.84 249	94.59 61	92.28 151	71.03 292	77.41 187	94.92 91	55.21 248	96.19 159	81.32 157	90.70 99	93.91 180

MTAPA		83.91 126	83.38 124	85.50 133	91.89 121	65.16 201	81.75 412	92.23 153	75.32 187	80.53 138	95.21 83	56.06 238	97.16 103	84.86 105	92.55 68	94.18 162

HY-MVS	76.49 5	84.28 112	83.36 125	87.02 61	92.22 102	67.74 120	84.65 380	94.50 53	79.15 110	82.23 111	87.93 272	66.88 71	96.94 122	80.53 164	82.20 215	96.39 34

reproduce-ours		83.51 140	83.33 126	84.06 203	92.18 105	60.49 349	90.74 272	92.04 165	64.35 372	83.24 99	95.59 65	59.05 192	97.27 94	83.61 124	89.17 121	94.41 153

our_new_method		83.51 140	83.33 126	84.06 203	92.18 105	60.49 349	90.74 272	92.04 165	64.35 372	83.24 99	95.59 65	59.05 192	97.27 94	83.61 124	89.17 121	94.41 153

viewdifsd2359ckpt13		84.08 120	83.21 128	86.70 75	88.49 214	69.55 58	92.25 182	91.14 218	79.71 91	79.73 153	91.72 184	58.83 196	95.89 176	82.06 143	84.99 173	94.66 128

MVS_Test		84.16 118	83.20 129	87.05 60	91.56 130	69.82 48	89.99 301	92.05 164	77.77 139	82.84 105	86.57 294	63.93 110	96.09 164	74.91 215	89.18 120	95.25 90

E4		84.00 123	83.19 130	86.46 96	86.99 269	68.85 83	92.39 179	90.99 237	79.94 84	80.17 143	91.36 195	59.73 179	95.79 188	82.87 134	84.22 188	94.74 119

viewmacassd2359aftdt		84.03 121	83.18 131	86.59 83	86.76 282	69.44 59	92.44 177	90.85 243	80.38 74	80.78 132	91.33 196	58.54 200	95.62 205	82.15 141	85.41 169	94.72 122

test_yl		84.28 112	83.16 132	87.64 37	94.52 42	69.24 71	95.78 18	95.09 27	69.19 320	81.09 124	92.88 148	57.00 222	97.44 79	81.11 160	81.76 222	96.23 40

DCV-MVSNet		84.28 112	83.16 132	87.64 37	94.52 42	69.24 71	95.78 18	95.09 27	69.19 320	81.09 124	92.88 148	57.00 222	97.44 79	81.11 160	81.76 222	96.23 40

testing3-2		83.11 149	83.15 134	82.98 246	91.92 118	64.01 244	94.39 72	95.37 17	78.32 127	75.53 212	90.06 234	73.18 30	93.18 319	74.34 220	75.27 288	91.77 254

ET-MVSNet_ETH3D		84.01 122	83.15 134	86.58 84	90.78 151	70.89 30	94.74 56	94.62 48	81.44 56	58.19 413	93.64 132	73.64 28	92.35 355	82.66 136	78.66 261	96.50 28

reproduce_model		83.15 147	82.96 136	83.73 218	92.02 111	59.74 365	90.37 286	92.08 163	63.70 379	82.86 104	95.48 68	58.62 198	97.17 100	83.06 130	88.42 129	94.26 157

EI-MVSNet-UG-set		83.14 148	82.96 136	83.67 223	92.28 100	63.19 278	91.38 240	94.68 45	79.22 108	76.60 199	93.75 128	62.64 137	97.76 57	78.07 190	78.01 264	90.05 285

HPM-MVS		83.25 145	82.95 138	84.17 201	92.25 101	62.88 288	90.91 262	91.86 177	70.30 303	77.12 193	93.96 126	56.75 227	96.28 154	82.04 144	91.34 91	93.34 198
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test2506		83.29 144	82.92 139	84.37 193	88.39 222	63.18 279	92.01 197	91.35 203	77.66 142	78.49 176	91.42 191	64.58 101	95.09 233	73.19 227	89.23 118	94.85 107

MVSFormer		83.75 131	82.88 140	86.37 101	89.24 187	71.18 26	89.07 326	90.69 253	65.80 361	87.13 57	94.34 111	64.99 92	92.67 341	72.83 231	91.80 80	95.27 86

MVS		84.66 103	82.86 141	90.06 3	90.93 146	74.56 7	87.91 348	95.54 15	68.55 330	72.35 265	94.71 97	59.78 177	98.90 24	81.29 158	94.69 32	96.74 17

Effi-MVS+		83.82 128	82.76 142	86.99 62	89.56 174	69.40 60	91.35 245	86.12 403	72.59 241	83.22 102	92.81 151	59.60 181	96.01 172	81.76 151	87.80 136	95.56 66

LFMVS		84.34 111	82.73 143	89.18 14	94.76 35	73.25 13	94.99 47	91.89 175	71.90 262	82.16 112	93.49 136	47.98 333	97.05 107	82.55 138	84.82 177	97.25 9

PGM-MVS		83.25 145	82.70 144	84.92 158	92.81 90	64.07 241	90.44 282	92.20 157	71.28 286	77.23 191	94.43 104	55.17 249	97.31 89	79.33 177	91.38 89	93.37 197

E5new		83.62 135	82.65 145	86.55 88	86.98 270	69.28 69	91.69 222	90.96 238	79.61 95	79.80 148	91.25 198	58.04 208	95.84 179	81.83 149	83.66 196	94.52 138

E6new		83.62 135	82.65 145	86.55 88	86.98 270	69.29 67	91.69 222	90.95 240	79.60 98	79.80 148	91.25 198	58.04 208	95.84 179	81.84 147	83.67 194	94.52 138

E6		83.62 135	82.65 145	86.55 88	86.98 270	69.29 67	91.69 222	90.95 240	79.60 98	79.80 148	91.25 198	58.04 208	95.84 179	81.84 147	83.67 194	94.52 138

E5		83.62 135	82.65 145	86.55 88	86.98 270	69.28 69	91.69 222	90.96 238	79.61 95	79.80 148	91.25 198	58.04 208	95.84 179	81.83 149	83.66 196	94.52 138

SR-MVS		82.81 155	82.58 149	83.50 230	93.35 69	61.16 331	92.23 185	91.28 210	64.48 371	81.27 121	95.28 76	53.71 270	95.86 178	82.87 134	88.77 126	93.49 195

viewdifsd2359ckpt09		83.52 139	82.57 150	86.37 101	88.02 237	68.47 96	91.78 215	89.63 307	79.61 95	78.56 174	92.00 173	59.28 189	95.96 173	81.94 145	82.35 208	94.69 123

h-mvs33		83.01 151	82.56 151	84.35 194	89.34 178	62.02 306	92.72 154	93.76 82	81.45 54	82.73 108	92.25 164	60.11 172	97.13 105	87.69 72	62.96 387	93.91 180

thisisatest0515		83.41 142	82.49 152	86.16 108	89.46 177	68.26 103	93.54 117	94.70 44	74.31 201	75.75 205	90.92 205	72.62 35	96.52 143	69.64 265	81.50 225	93.71 187

mPP-MVS		82.96 153	82.44 153	84.52 187	92.83 86	62.92 286	92.76 152	91.85 179	71.52 282	75.61 210	94.24 116	53.48 274	96.99 115	78.97 181	90.73 98	93.64 191

sss		82.71 158	82.38 154	83.73 218	89.25 184	59.58 368	92.24 184	94.89 32	77.96 133	79.86 147	92.38 159	56.70 228	97.05 107	77.26 194	80.86 234	94.55 134

CLD-MVS		82.73 156	82.35 155	83.86 211	87.90 240	67.65 123	95.45 29	92.18 160	85.06 14	72.58 256	92.27 162	52.46 283	95.78 189	84.18 115	79.06 256	88.16 313
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSTER		82.47 162	82.05 156	83.74 216	92.68 93	69.01 79	91.90 207	93.21 108	79.83 87	72.14 266	85.71 307	74.72 20	94.72 248	75.72 206	72.49 309	87.50 320

viewdifsd2359ckpt07		82.95 154	82.04 157	85.66 128	87.19 262	66.73 159	91.56 231	90.39 269	77.58 145	77.58 186	91.19 202	58.57 199	95.65 202	82.32 139	82.01 218	94.60 132

PMMVS		81.98 173	82.04 157	81.78 283	89.76 170	56.17 406	91.13 258	90.69 253	77.96 133	80.09 145	93.57 134	46.33 357	94.99 237	81.41 155	87.46 140	94.17 163

test_vis1_n_1920		81.66 177	82.01 159	80.64 319	82.24 367	55.09 415	94.76 55	86.87 390	81.67 51	84.40 88	94.63 99	38.17 399	94.67 254	91.98 41	83.34 200	92.16 245

TESTMET0.1,1		82.41 163	81.98 160	83.72 220	88.08 233	63.74 253	92.70 156	93.77 81	79.30 106	77.61 184	87.57 279	58.19 206	94.08 282	73.91 222	86.68 153	93.33 200

viewmambaseed2359dif		82.60 161	81.91 161	84.67 179	85.83 305	66.09 174	90.50 281	89.01 337	75.46 182	79.64 155	92.01 172	59.51 183	94.38 268	82.99 132	82.26 211	93.54 193

PAPM_NR		82.97 152	81.84 162	86.37 101	94.10 49	66.76 158	87.66 354	92.84 127	69.96 309	74.07 237	93.57 134	63.10 132	97.50 76	70.66 260	90.58 101	94.85 107

VDD-MVS		83.06 150	81.81 163	86.81 68	90.86 149	67.70 121	95.40 30	91.50 197	75.46 182	81.78 114	92.34 161	40.09 389	97.13 105	86.85 86	82.04 217	95.60 64

DP-MVS Recon		82.73 156	81.65 164	85.98 113	97.31 4	67.06 142	95.15 37	91.99 169	69.08 325	76.50 202	93.89 127	54.48 259	98.20 42	70.76 258	85.66 166	92.69 221

MVS_111021_LR		82.02 172	81.52 165	83.51 229	88.42 220	62.88 288	89.77 304	88.93 342	76.78 162	75.55 211	93.10 139	50.31 307	95.38 220	83.82 120	87.02 144	92.26 242

EPP-MVSNet		81.79 175	81.52 165	82.61 256	88.77 200	60.21 357	93.02 140	93.66 89	68.52 331	72.90 250	90.39 215	72.19 41	94.96 238	74.93 214	79.29 254	92.67 222

APD-MVS_3200maxsize		81.64 178	81.32 167	82.59 258	92.36 98	58.74 379	91.39 238	91.01 236	63.35 383	79.72 154	94.62 100	51.82 286	96.14 161	79.71 170	87.93 134	92.89 217

RRT-MVS		82.61 160	81.16 168	86.96 63	91.10 143	68.75 87	87.70 353	92.20 157	76.97 157	72.68 252	87.10 288	51.30 297	96.41 149	83.56 126	87.84 135	95.74 59

CostFormer		82.33 164	81.15 169	85.86 118	89.01 194	68.46 97	82.39 409	93.01 119	75.59 180	80.25 142	81.57 361	72.03 42	94.96 238	79.06 180	77.48 272	94.16 164

xiu_mvs_v1_base_debu		82.16 168	81.12 170	85.26 147	86.42 289	68.72 90	92.59 168	90.44 266	73.12 228	84.20 89	94.36 106	38.04 402	95.73 193	84.12 116	86.81 147	91.33 262

xiu_mvs_v1_base		82.16 168	81.12 170	85.26 147	86.42 289	68.72 90	92.59 168	90.44 266	73.12 228	84.20 89	94.36 106	38.04 402	95.73 193	84.12 116	86.81 147	91.33 262

xiu_mvs_v1_base_debi		82.16 168	81.12 170	85.26 147	86.42 289	68.72 90	92.59 168	90.44 266	73.12 228	84.20 89	94.36 106	38.04 402	95.73 193	84.12 116	86.81 147	91.33 262

hse-mvs2		81.12 192	81.11 173	81.16 303	86.52 288	57.48 394	89.40 317	91.16 214	81.45 54	82.73 108	90.49 213	60.11 172	94.58 255	87.69 72	60.41 414	91.41 261

baseline1		81.84 174	81.03 174	84.28 197	91.60 128	66.62 162	91.08 259	91.66 191	81.87 48	74.86 223	91.67 187	69.98 52	94.92 241	71.76 247	64.75 371	91.29 267

UWE-MVS		80.81 199	81.01 175	80.20 329	89.33 180	57.05 400	91.91 206	94.71 43	75.67 179	75.01 219	89.37 243	63.13 131	91.44 382	67.19 299	82.80 206	92.12 246

WBMVS		81.67 176	80.98 176	83.72 220	93.07 80	69.40 60	94.33 73	93.05 117	76.84 160	72.05 268	84.14 327	74.49 22	93.88 296	72.76 234	68.09 340	87.88 315

3Dnovator	73.91 6	82.69 159	80.82 177	88.31 28	89.57 173	71.26 24	92.60 166	94.39 64	78.84 118	67.89 326	92.48 157	48.42 328	98.52 33	68.80 278	94.40 36	95.15 92

casdiffseed414692147		82.20 166	80.75 178	86.55 88	87.13 265	69.57 57	91.79 212	90.48 261	78.12 131	78.52 175	90.10 233	55.92 240	95.80 186	72.42 240	82.28 210	94.28 156

CDS-MVSNet		81.43 181	80.74 179	83.52 227	86.26 293	64.45 222	92.09 192	90.65 257	75.83 178	73.95 240	89.81 238	63.97 109	92.91 330	71.27 251	82.82 204	93.20 204
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		81.55 179	80.72 180	84.03 207	91.42 134	66.93 153	83.08 400	89.13 328	78.55 125	67.50 331	87.02 289	51.79 288	90.07 399	87.48 75	90.49 103	95.10 95

SR-MVS-dyc-post		81.06 193	80.70 181	82.15 274	92.02 111	58.56 382	90.90 263	90.45 262	62.76 390	78.89 165	94.46 102	51.26 298	95.61 207	78.77 185	86.77 150	92.28 238

ACMMP		81.49 180	80.67 182	83.93 209	91.71 126	62.90 287	92.13 189	92.22 156	71.79 269	71.68 274	93.49 136	50.32 306	96.96 120	78.47 187	84.22 188	91.93 252
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
HQP-MVS		81.14 190	80.64 183	82.64 255	87.54 252	63.66 262	94.06 83	91.70 189	79.80 88	74.18 230	90.30 218	51.63 291	95.61 207	77.63 192	78.90 257	88.63 304

test_cas_vis1_n_1920		80.45 207	80.61 184	79.97 338	78.25 421	57.01 402	94.04 87	88.33 363	79.06 115	82.81 107	93.70 130	38.65 394	91.63 373	90.82 53	79.81 243	91.27 268

3Dnovator+	73.60 7	82.10 171	80.60 185	86.60 81	90.89 148	66.80 157	95.20 35	93.44 100	74.05 206	67.42 333	92.49 156	49.46 318	97.65 66	70.80 257	91.68 82	95.33 79

guyue		81.23 187	80.57 186	83.21 243	86.64 283	61.85 311	92.52 174	92.78 129	78.69 122	74.92 222	89.42 242	50.07 310	95.35 221	80.79 162	79.31 253	92.42 231

API-MVS		82.28 165	80.53 187	87.54 44	96.13 24	70.59 33	93.63 113	91.04 234	65.72 363	75.45 213	92.83 150	56.11 237	98.89 25	64.10 334	89.75 117	93.15 205

RE-MVS-def			80.48 188		92.02 111	58.56 382	90.90 263	90.45 262	62.76 390	78.89 165	94.46 102	49.30 320		78.77 185	86.77 150	92.28 238

IB-MVS	77.80 4	82.18 167	80.46 189	87.35 50	89.14 189	70.28 38	95.59 27	95.17 25	78.85 117	70.19 291	85.82 305	70.66 47	97.67 62	72.19 244	66.52 354	94.09 168
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
ECVR-MVS		81.29 185	80.38 190	84.01 208	88.39 222	61.96 308	92.56 171	86.79 392	77.66 142	76.63 198	91.42 191	46.34 356	95.24 230	74.36 219	89.23 118	94.85 107

KinetiMVS		81.43 181	80.11 191	85.38 140	86.60 285	65.47 195	92.90 148	93.54 94	75.33 186	77.31 189	90.39 215	46.81 348	96.75 133	71.65 250	86.46 158	93.93 177

thisisatest0530		81.15 189	80.07 192	84.39 192	88.26 227	65.63 188	91.40 236	94.62 48	71.27 287	70.93 281	89.18 247	72.47 36	96.04 169	65.62 319	76.89 279	91.49 258

test1111		80.84 198	80.02 193	83.33 234	87.87 243	60.76 339	92.62 163	86.86 391	77.86 136	75.73 206	91.39 193	46.35 355	94.70 253	72.79 233	88.68 127	94.52 138

Fast-Effi-MVS+		81.14 190	80.01 194	84.51 188	90.24 160	65.86 183	94.12 82	89.15 326	73.81 214	75.37 215	88.26 264	57.26 217	94.53 262	66.97 302	84.92 176	93.15 205

mvs_anonymous		81.36 183	79.99 195	85.46 134	90.39 158	68.40 98	86.88 365	90.61 258	74.41 198	70.31 290	84.67 319	63.79 112	92.32 357	73.13 228	85.70 165	95.67 61

Vis-MVSNet		80.92 197	79.98 196	83.74 216	88.48 216	61.80 312	93.44 124	88.26 368	73.96 210	77.73 181	91.76 181	49.94 312	94.76 245	65.84 314	90.37 106	94.65 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IMVS_0403		81.19 188	79.88 197	85.13 152	88.54 205	64.75 210	88.84 331	90.80 247	76.73 165	75.21 216	90.18 221	54.22 264	96.21 158	73.47 223	80.95 229	94.43 149

nrg030		80.93 196	79.86 198	84.13 202	83.69 351	68.83 84	93.23 131	91.20 212	75.55 181	75.06 218	88.22 267	63.04 133	94.74 247	81.88 146	66.88 351	88.82 302

1112_ss		80.56 204	79.83 199	82.77 250	88.65 202	60.78 337	92.29 181	88.36 361	72.58 242	72.46 262	94.95 88	65.09 91	93.42 314	66.38 308	77.71 266	94.10 167

AstraMVS		80.66 202	79.79 200	83.28 238	85.07 325	61.64 319	92.19 186	90.58 259	79.40 103	74.77 225	90.18 221	45.93 361	95.61 207	83.04 131	76.96 278	92.60 225

HQP_MVS		80.34 210	79.75 201	82.12 276	86.94 275	62.42 296	93.13 134	91.31 204	78.81 119	72.53 257	89.14 249	50.66 303	95.55 213	76.74 195	78.53 262	88.39 310

UA-Net		80.02 217	79.65 202	81.11 306	89.33 180	57.72 389	86.33 371	89.00 341	77.44 148	81.01 127	89.15 248	59.33 187	95.90 175	61.01 355	84.28 186	89.73 291

Vis-MVSNet (Re-imp)		79.24 233	79.57 203	78.24 367	88.46 217	52.29 426	90.41 284	89.12 329	74.24 203	69.13 301	91.91 179	65.77 84	90.09 398	59.00 368	88.09 132	92.33 235

test-LLR		80.10 215	79.56 204	81.72 285	86.93 277	61.17 329	92.70 156	91.54 194	71.51 283	75.62 208	86.94 290	53.83 267	92.38 352	72.21 242	84.76 179	91.60 256

HyFIR lowres test		81.03 194	79.56 204	85.43 135	87.81 246	68.11 110	90.18 293	90.01 291	70.65 300	72.95 249	86.06 301	63.61 118	94.50 264	75.01 213	79.75 245	93.67 188

HPM-MVS_fast		80.25 212	79.55 206	82.33 266	91.55 131	59.95 362	91.32 247	89.16 325	65.23 368	74.71 227	93.07 142	47.81 338	95.74 192	74.87 217	88.23 130	91.31 266

0.3-1-1-0.015		81.31 184	79.49 207	86.77 73	85.74 309	68.70 94	95.01 46	94.42 59	74.29 202	77.09 195	85.61 308	63.31 126	95.69 201	76.63 198	63.30 384	95.91 53

TAMVS		80.37 209	79.45 208	83.13 244	85.14 322	63.37 270	91.23 252	90.76 252	74.81 194	72.65 254	88.49 257	60.63 165	92.95 325	69.41 269	81.95 220	93.08 209

0.4-1-1-0.2		81.28 186	79.42 209	86.84 65	85.80 307	68.82 85	95.10 39	94.43 58	74.45 197	77.18 192	85.54 309	62.27 143	95.70 199	76.72 197	63.30 384	96.01 46

FIs		79.47 227	79.41 210	79.67 346	85.95 301	59.40 370	91.68 226	93.94 76	78.06 132	68.96 308	88.28 262	66.61 74	91.77 369	66.20 311	74.99 289	87.82 316

IS-MVSNet		80.14 214	79.41 210	82.33 266	87.91 239	60.08 360	91.97 201	88.27 366	72.90 237	71.44 278	91.73 183	61.44 154	93.66 305	62.47 348	86.53 156	93.24 201

IMVS_0407		80.80 200	79.39 212	85.00 157	88.54 205	64.75 210	88.40 339	90.80 247	76.73 165	73.95 240	90.18 221	51.55 293	95.81 185	73.47 223	80.95 229	94.43 149

test-mter		79.96 218	79.38 213	81.72 285	86.93 277	61.17 329	92.70 156	91.54 194	73.85 212	75.62 208	86.94 290	49.84 314	92.38 352	72.21 242	84.76 179	91.60 256

BH-w/o		80.49 206	79.30 214	84.05 206	90.83 150	64.36 230	93.60 114	89.42 314	74.35 200	69.09 302	90.15 229	55.23 247	95.61 207	64.61 329	86.43 159	92.17 244

EPNet_dtu		78.80 244	79.26 215	77.43 375	88.06 234	49.71 443	91.96 202	91.95 171	77.67 141	76.56 201	91.28 197	58.51 201	90.20 396	56.37 377	80.95 229	92.39 232
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
icg_test_0407_2		80.38 208	79.22 216	83.88 210	88.54 205	64.75 210	86.79 366	90.80 247	76.73 165	73.95 240	90.18 221	51.55 293	92.45 350	73.47 223	80.95 229	94.43 149

0.4-1-1-0.1		80.99 195	79.16 217	86.51 95	85.55 314	68.21 107	94.77 54	94.42 59	73.75 215	76.57 200	85.41 311	62.35 142	95.62 205	76.30 203	63.28 386	95.71 60

CPTT-MVS		79.59 223	79.16 217	80.89 317	91.54 132	59.80 364	92.10 191	88.54 358	60.42 411	72.96 248	93.28 138	48.27 329	92.80 335	78.89 184	86.50 157	90.06 284

tpmrst		80.57 203	79.14 219	84.84 164	90.10 163	68.28 102	81.70 413	89.72 304	77.63 144	75.96 204	79.54 393	64.94 94	92.71 338	75.43 208	77.28 275	93.55 192

reproduce_monomvs		79.49 226	79.11 220	80.64 319	92.91 84	61.47 325	91.17 257	93.28 106	83.09 33	64.04 365	82.38 347	66.19 77	94.57 257	81.19 159	57.71 422	85.88 368

1314		80.70 201	78.95 221	85.94 115	87.77 249	67.56 125	87.91 348	92.55 144	72.17 256	67.44 332	93.09 140	50.27 308	97.04 110	71.68 249	87.64 138	93.23 202

SDMVSNet		80.26 211	78.88 222	84.40 191	89.25 184	67.63 124	85.35 376	93.02 118	76.77 163	70.84 282	87.12 286	47.95 336	96.09 164	85.04 101	74.55 290	89.48 295

UGNet		79.87 220	78.68 223	83.45 232	89.96 165	61.51 322	92.13 189	90.79 251	76.83 161	78.85 170	86.33 298	38.16 400	96.17 160	67.93 289	87.17 143	92.67 222
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
PVSNet	73.49 8	80.05 216	78.63 224	84.31 195	90.92 147	64.97 206	92.47 175	91.05 233	79.18 109	72.43 263	90.51 212	37.05 414	94.06 284	68.06 286	86.00 160	93.90 182

Test_1112_low_res		79.56 224	78.60 225	82.43 260	88.24 229	60.39 353	92.09 192	87.99 373	72.10 258	71.84 270	87.42 281	64.62 99	93.04 321	65.80 315	77.30 274	93.85 184

tttt0517		79.50 225	78.53 226	82.41 263	87.22 261	61.43 326	89.75 305	94.76 40	69.29 318	67.91 324	88.06 271	72.92 32	95.63 203	62.91 344	73.90 300	90.16 283

thres200		79.66 222	78.33 227	83.66 224	92.54 97	65.82 185	93.06 136	96.31 3	74.90 193	73.30 246	88.66 255	59.67 180	95.61 207	47.84 416	78.67 260	89.56 294

ab-mvs		80.18 213	78.31 228	85.80 121	88.44 218	65.49 194	83.00 403	92.67 136	71.82 268	77.36 188	85.01 315	54.50 256	96.59 137	76.35 202	75.63 286	95.32 81

VDDNet		80.50 205	78.26 229	87.21 53	86.19 294	69.79 50	94.48 63	91.31 204	60.42 411	79.34 160	90.91 206	38.48 397	96.56 140	82.16 140	81.05 228	95.27 86

EI-MVSNet		78.97 239	78.22 230	81.25 300	85.33 315	62.73 291	89.53 314	93.21 108	72.39 249	72.14 266	90.13 230	60.99 158	94.72 248	67.73 291	72.49 309	86.29 353

OPM-MVS		79.00 238	78.09 231	81.73 284	83.52 354	63.83 250	91.64 228	90.30 275	76.36 174	71.97 269	89.93 237	46.30 358	95.17 232	75.10 211	77.70 267	86.19 356
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
FC-MVSNet-test		77.99 261	78.08 232	77.70 370	84.89 328	55.51 412	90.27 290	93.75 85	76.87 158	66.80 343	87.59 278	65.71 85	90.23 395	62.89 345	73.94 298	87.37 324

VPA-MVSNet		79.03 237	78.00 233	82.11 279	85.95 301	64.48 221	93.22 132	94.66 46	75.05 191	74.04 238	84.95 316	52.17 285	93.52 307	74.90 216	67.04 350	88.32 312

miper_enhance_ethall		78.86 242	77.97 234	81.54 291	88.00 238	65.17 200	91.41 234	89.15 326	75.19 189	68.79 311	83.98 330	67.17 69	92.82 333	72.73 235	65.30 361	86.62 343

viewmsd2359difaftdt		79.42 230	77.96 235	83.81 213	83.88 347	63.85 247	89.54 311	87.38 380	77.39 151	74.94 220	89.95 235	51.11 299	94.72 248	79.52 173	67.90 343	92.88 218

viewdifsd2359ckpt11		79.42 230	77.95 236	83.81 213	83.87 348	63.85 247	89.54 311	87.38 380	77.39 151	74.94 220	89.95 235	51.11 299	94.72 248	79.52 173	67.90 343	92.88 218

tpm2		79.80 221	77.95 236	85.34 142	88.28 226	68.26 103	81.56 415	91.42 200	70.11 305	77.59 185	80.50 379	67.40 68	94.26 275	67.34 296	77.35 273	93.51 194

OMC-MVS		78.67 249	77.91 238	80.95 313	85.76 308	57.40 396	88.49 337	88.67 352	73.85 212	72.43 263	92.10 169	49.29 321	94.55 261	72.73 235	77.89 265	90.91 275

114514_t		79.17 234	77.67 239	83.68 222	95.32 31	65.53 192	92.85 150	91.60 193	63.49 381	67.92 323	90.63 210	46.65 352	95.72 198	67.01 301	83.54 198	89.79 289

SSM_0404		79.46 228	77.65 240	84.91 160	88.37 224	67.04 144	89.59 306	87.03 387	67.99 336	75.45 213	89.32 244	47.98 333	95.34 223	71.23 252	81.90 221	92.34 234

BH-RMVSNet		79.46 228	77.65 240	84.89 161	91.68 127	65.66 186	93.55 116	88.09 371	72.93 234	73.37 245	91.12 204	46.20 359	96.12 162	56.28 378	85.61 167	92.91 215

PCF-MVS	73.15 9	79.29 232	77.63 242	84.29 196	86.06 299	65.96 179	87.03 361	91.10 223	69.86 311	69.79 298	90.64 208	57.54 216	96.59 137	64.37 333	82.29 209	90.32 281
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UWE-MVS-28		76.83 285	77.60 243	74.51 405	84.58 334	50.34 439	88.22 342	94.60 50	74.46 196	66.66 344	88.98 254	62.53 139	85.50 440	57.55 374	80.80 237	87.69 318

UniMVSNet_NR-MVSNet		78.15 257	77.55 244	79.98 336	84.46 338	60.26 355	92.25 182	93.20 110	77.50 147	68.88 309	86.61 293	66.10 79	92.13 361	66.38 308	62.55 391	87.54 319

VPNet		78.82 243	77.53 245	82.70 253	84.52 335	66.44 166	93.93 93	92.23 153	80.46 71	72.60 255	88.38 261	49.18 322	93.13 320	72.47 239	63.97 380	88.55 307

GeoE		78.90 241	77.43 246	83.29 237	88.95 195	62.02 306	92.31 180	86.23 399	70.24 304	71.34 279	89.27 246	54.43 260	94.04 287	63.31 340	80.81 236	93.81 185

AUN-MVS		78.37 253	77.43 246	81.17 302	86.60 285	57.45 395	89.46 316	91.16 214	74.11 205	74.40 229	90.49 213	55.52 244	94.57 257	74.73 218	60.43 413	91.48 259

tfpn200view9		78.79 245	77.43 246	82.88 248	92.21 103	64.49 219	92.05 195	96.28 4	73.48 222	71.75 272	88.26 264	60.07 174	95.32 224	45.16 429	77.58 269	88.83 300

thres400		78.68 247	77.43 246	82.43 260	92.21 103	64.49 219	92.05 195	96.28 4	73.48 222	71.75 272	88.26 264	60.07 174	95.32 224	45.16 429	77.58 269	87.48 321

QAPM		79.95 219	77.39 250	87.64 37	89.63 172	71.41 22	93.30 129	93.70 87	65.34 367	67.39 335	91.75 182	47.83 337	98.96 20	57.71 372	89.81 114	92.54 228

TR-MVS		78.77 246	77.37 251	82.95 247	90.49 155	60.88 335	93.67 110	90.07 286	70.08 308	74.51 228	91.37 194	45.69 362	95.70 199	60.12 362	80.32 240	92.29 237

FA-MVS(test-final)		79.12 235	77.23 252	84.81 168	90.54 153	63.98 246	81.35 418	91.71 186	71.09 291	74.85 224	82.94 340	52.85 278	97.05 107	67.97 287	81.73 224	93.41 196

SSM_0407		79.09 236	77.21 253	84.75 172	88.50 210	66.98 149	89.21 322	87.03 387	67.99 336	74.12 234	89.32 244	47.98 333	95.29 228	71.23 252	79.52 246	91.98 249

BH-untuned		78.68 247	77.08 254	83.48 231	89.84 167	63.74 253	92.70 156	88.59 355	71.57 280	66.83 342	88.65 256	51.75 289	95.39 219	59.03 367	84.77 178	91.32 265

tpm		78.58 250	77.03 255	83.22 241	85.94 303	64.56 217	83.21 399	91.14 218	78.31 128	73.67 243	79.68 391	64.01 108	92.09 363	66.07 312	71.26 319	93.03 211

thres100view900		78.37 253	77.01 256	82.46 259	91.89 121	63.21 277	91.19 256	96.33 1	72.28 252	70.45 287	87.89 273	60.31 169	95.32 224	45.16 429	77.58 269	88.83 300

AdaColmap		78.94 240	77.00 257	84.76 171	96.34 18	65.86 183	92.66 162	87.97 375	62.18 395	70.56 284	92.37 160	43.53 374	97.35 86	64.50 332	82.86 203	91.05 271

CHOSEN 280x420		77.35 274	76.95 258	78.55 362	87.07 267	62.68 292	69.71 464	82.95 435	68.80 327	71.48 277	87.27 285	66.03 80	84.00 448	76.47 200	82.81 205	88.95 299

cl22		77.94 263	76.78 259	81.42 293	87.57 251	64.93 208	90.67 275	88.86 345	72.45 246	67.63 330	82.68 344	64.07 106	92.91 330	71.79 245	65.30 361	86.44 346

UniMVSNet (Re)		77.58 271	76.78 259	79.98 336	84.11 344	60.80 336	91.76 218	93.17 112	76.56 171	69.93 297	84.78 318	63.32 125	92.36 354	64.89 326	62.51 393	86.78 337

LuminaMVS		78.14 258	76.66 261	82.60 257	80.82 382	64.64 216	89.33 318	90.45 262	68.25 334	74.73 226	85.51 310	41.15 384	94.14 278	78.96 182	80.69 238	89.04 298

thres600view7		78.00 260	76.66 261	82.03 281	91.93 117	63.69 260	91.30 248	96.33 1	72.43 247	70.46 286	87.89 273	60.31 169	94.92 241	42.64 441	76.64 280	87.48 321

MS-PatchMatch		77.90 265	76.50 263	82.12 276	85.99 300	69.95 44	91.75 220	92.70 132	73.97 209	62.58 382	84.44 323	41.11 385	95.78 189	63.76 337	92.17 72	80.62 434

VortexMVS		77.62 269	76.44 264	81.13 304	88.58 203	63.73 255	91.24 251	91.30 208	77.81 137	65.76 348	81.97 353	49.69 316	93.72 300	76.40 201	65.26 364	85.94 366

miper_ehance_all_eth		77.60 270	76.44 264	81.09 310	85.70 311	64.41 226	90.65 276	88.64 354	72.31 250	67.37 336	82.52 345	64.77 98	92.64 344	70.67 259	65.30 361	86.24 355

XXY-MVS		77.94 263	76.44 264	82.43 260	82.60 364	64.44 223	92.01 197	91.83 180	73.59 221	70.00 294	85.82 305	54.43 260	94.76 245	69.63 266	68.02 342	88.10 314

usedtu_dtu_shiyan1		77.89 266	76.39 267	82.40 264	81.92 372	67.01 147	91.94 204	93.00 121	77.01 155	68.44 318	84.15 325	54.78 253	93.25 316	65.76 316	70.53 322	86.94 333

FE-MVSNET3		77.89 266	76.39 267	82.40 264	81.92 372	67.01 147	91.94 204	93.00 121	77.01 155	68.44 318	84.15 325	54.78 253	93.25 316	65.76 316	70.53 322	86.94 333

PS-MVSNAJss		77.26 275	76.31 269	80.13 331	80.64 386	59.16 375	90.63 279	91.06 230	72.80 238	68.58 315	84.57 321	53.55 271	93.96 292	72.97 229	71.96 313	87.27 328

IMVS_0404		78.11 259	76.29 270	83.59 225	88.54 205	64.75 210	84.63 381	90.80 247	76.73 165	61.16 389	90.18 221	40.17 388	91.58 375	73.47 223	80.95 229	94.43 149

MVP-Stereo		77.12 278	76.23 271	79.79 343	81.72 374	66.34 169	89.29 319	90.88 242	70.56 301	62.01 385	82.88 341	49.34 319	94.13 279	65.55 321	93.80 47	78.88 450
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
GA-MVS		78.33 255	76.23 271	84.65 180	83.65 352	66.30 170	91.44 233	90.14 284	76.01 176	70.32 289	84.02 329	42.50 378	94.72 248	70.98 255	77.00 277	92.94 214

WB-MVSnew		77.14 277	76.18 273	80.01 335	86.18 295	63.24 275	91.26 249	94.11 73	71.72 272	73.52 244	87.29 284	45.14 367	93.00 323	56.98 375	79.42 249	83.80 394

FMVSNet3		77.73 268	76.04 274	82.80 249	91.20 142	68.99 80	91.87 208	91.99 169	73.35 224	67.04 338	83.19 339	56.62 230	92.14 360	59.80 364	69.34 328	87.28 327

EPMVS		78.49 252	75.98 275	86.02 112	91.21 141	69.68 55	80.23 427	91.20 212	75.25 188	72.48 261	78.11 402	54.65 255	93.69 304	57.66 373	83.04 202	94.69 123

OpenMVS	70.45 11	78.54 251	75.92 276	86.41 100	85.93 304	71.68 20	92.74 153	92.51 145	66.49 352	64.56 359	91.96 175	43.88 373	98.10 45	54.61 383	90.65 100	89.44 297

DU-MVS		76.86 282	75.84 277	79.91 339	82.96 360	60.26 355	91.26 249	91.54 194	76.46 173	68.88 309	86.35 296	56.16 235	92.13 361	66.38 308	62.55 391	87.35 325

cascas		78.18 256	75.77 278	85.41 136	87.14 264	69.11 74	92.96 143	91.15 217	66.71 350	70.47 285	86.07 300	37.49 408	96.48 146	70.15 263	79.80 244	90.65 277

WR-MVS		76.76 287	75.74 279	79.82 342	84.60 332	62.27 302	92.60 166	92.51 145	76.06 175	67.87 327	85.34 312	56.76 226	90.24 394	62.20 349	63.69 382	86.94 333

v2v482		77.42 273	75.65 280	82.73 251	80.38 390	67.13 141	91.85 210	90.23 280	75.09 190	69.37 299	83.39 336	53.79 269	94.44 265	71.77 246	65.00 368	86.63 342

c3_l		76.83 285	75.47 281	80.93 314	85.02 326	64.18 238	90.39 285	88.11 370	71.66 273	66.65 345	81.64 359	63.58 121	92.56 345	69.31 271	62.86 388	86.04 361

sd_testset		77.08 279	75.37 282	82.20 272	89.25 184	62.11 305	82.06 410	89.09 331	76.77 163	70.84 282	87.12 286	41.43 383	95.01 236	67.23 298	74.55 290	89.48 295

dmvs_re		76.93 281	75.36 283	81.61 289	87.78 248	60.71 343	80.00 431	87.99 373	79.42 102	69.02 305	89.47 241	46.77 350	94.32 269	63.38 339	74.45 293	89.81 288

Anonymous202405211		77.96 262	75.33 284	85.87 117	93.73 58	64.52 218	94.85 52	85.36 412	62.52 393	76.11 203	90.18 221	29.43 447	97.29 90	68.51 280	77.24 276	95.81 57

Effi-MVS+-dtu		76.14 295	75.28 285	78.72 361	83.22 357	55.17 414	89.87 302	87.78 377	75.42 184	67.98 322	81.43 363	45.08 368	92.52 347	75.08 212	71.63 314	88.48 308

IterMVS-LS		76.49 289	75.18 286	80.43 323	84.49 337	62.74 290	90.64 277	88.80 347	72.40 248	65.16 354	81.72 357	60.98 159	92.27 358	67.74 290	64.65 373	86.29 353
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MonoMVSNet		76.99 280	75.08 287	82.73 251	83.32 356	63.24 275	86.47 370	86.37 395	79.08 113	66.31 346	79.30 395	49.80 315	91.72 370	79.37 175	65.70 359	93.23 202

v1144		76.73 288	74.88 288	82.27 268	80.23 394	66.60 163	91.68 226	90.21 283	73.69 218	69.06 304	81.89 354	52.73 281	94.40 267	69.21 272	65.23 365	85.80 369

cl____		76.07 296	74.67 289	80.28 326	85.15 321	61.76 315	90.12 294	88.73 349	71.16 288	65.43 351	81.57 361	61.15 156	92.95 325	66.54 305	62.17 395	86.13 359

DIV-MVS_self_test		76.07 296	74.67 289	80.28 326	85.14 322	61.75 316	90.12 294	88.73 349	71.16 288	65.42 352	81.60 360	61.15 156	92.94 329	66.54 305	62.16 397	86.14 357

PatchmatchNet		77.46 272	74.63 291	85.96 114	89.55 175	70.35 37	79.97 432	89.55 309	72.23 253	70.94 280	76.91 416	57.03 220	92.79 336	54.27 385	81.17 227	94.74 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
NR-MVSNet		76.05 299	74.59 292	80.44 322	82.96 360	62.18 304	90.83 267	91.73 184	77.12 154	60.96 391	86.35 296	59.28 189	91.80 368	60.74 357	61.34 406	87.35 325

LPG-MVS_test		75.82 305	74.58 293	79.56 350	84.31 341	59.37 371	90.44 282	89.73 302	69.49 315	64.86 355	88.42 259	38.65 394	94.30 271	72.56 237	72.76 306	85.01 383

V42		76.46 290	74.55 294	82.19 273	79.14 408	67.82 118	90.26 291	89.42 314	73.75 215	68.63 314	81.89 354	51.31 296	94.09 281	71.69 248	64.84 369	84.66 386

TranMVSNet+NR-MVSNet		75.86 304	74.52 295	79.89 340	82.44 366	60.64 346	91.37 241	91.37 202	76.63 169	67.65 329	86.21 299	52.37 284	91.55 376	61.84 351	60.81 409	87.48 321

v148		76.19 294	74.47 296	81.36 296	80.05 396	64.44 223	91.75 220	90.23 280	73.68 219	67.13 337	80.84 374	55.92 240	93.86 299	68.95 276	61.73 402	85.76 372

eth_miper_zixun_eth		75.96 303	74.40 297	80.66 318	84.66 331	63.02 281	89.28 320	88.27 366	71.88 264	65.73 349	81.65 358	59.45 184	92.81 334	68.13 283	60.53 411	86.14 357

gg-mvs-nofinetune		77.18 276	74.31 298	85.80 121	91.42 134	68.36 99	71.78 458	94.72 42	49.61 456	77.12 193	45.92 485	77.41 9	93.98 291	67.62 292	93.16 60	95.05 98

CVMVSNet		74.04 327	74.27 299	73.33 415	85.33 315	43.94 469	89.53 314	88.39 360	54.33 443	70.37 288	90.13 230	49.17 323	84.05 446	61.83 352	79.36 251	91.99 248

ACMP	71.68 10	75.58 310	74.23 300	79.62 348	84.97 327	59.64 366	90.80 268	89.07 333	70.39 302	62.95 378	87.30 283	38.28 398	93.87 297	72.89 230	71.45 317	85.36 379
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Elysia		76.45 291	74.17 301	83.30 235	80.43 388	64.12 239	89.58 307	90.83 244	61.78 403	72.53 257	85.92 303	34.30 425	94.81 243	68.10 284	84.01 192	90.97 272

StellarMVS		76.45 291	74.17 301	83.30 235	80.43 388	64.12 239	89.58 307	90.83 244	61.78 403	72.53 257	85.92 303	34.30 425	94.81 243	68.10 284	84.01 192	90.97 272

Anonymous20240529		76.84 284	74.15 303	84.88 162	91.02 144	64.95 207	93.84 102	91.09 224	53.57 444	73.00 247	87.42 281	35.91 418	97.32 88	69.14 274	72.41 311	92.36 233

X-MVStestdata		76.86 282	74.13 304	85.05 154	93.22 72	63.78 251	92.92 145	92.66 137	73.99 207	78.18 177	10.19 500	55.25 245	97.41 82	79.16 178	91.58 84	93.95 175

v144192		76.05 299	74.03 305	82.12 276	79.50 402	66.55 165	91.39 238	89.71 305	72.30 251	68.17 320	81.33 366	51.75 289	94.03 289	67.94 288	64.19 375	85.77 370

FMVSNet2		76.07 296	74.01 306	82.26 270	88.85 196	67.66 122	91.33 246	91.61 192	70.84 295	65.98 347	82.25 349	48.03 330	92.00 365	58.46 369	68.73 336	87.10 330

v1192		75.98 301	73.92 307	82.15 274	79.73 398	66.24 172	91.22 253	89.75 299	72.67 240	68.49 316	81.42 364	49.86 313	94.27 273	67.08 300	65.02 367	85.95 364

GBi-Net		75.65 307	73.83 308	81.10 307	88.85 196	65.11 202	90.01 298	90.32 271	70.84 295	67.04 338	80.25 384	48.03 330	91.54 377	59.80 364	69.34 328	86.64 339

test1		75.65 307	73.83 308	81.10 307	88.85 196	65.11 202	90.01 298	90.32 271	70.84 295	67.04 338	80.25 384	48.03 330	91.54 377	59.80 364	69.34 328	86.64 339

test_fmvs1		74.07 326	73.69 310	75.22 395	78.91 412	47.34 455	89.06 328	74.69 458	63.68 380	79.41 159	91.59 189	24.36 458	87.77 421	85.22 98	76.26 283	90.55 280

PLC	68.80 14	75.23 313	73.68 311	79.86 341	92.93 83	58.68 380	90.64 277	88.30 364	60.90 408	64.43 363	90.53 211	42.38 379	94.57 257	56.52 376	76.54 281	86.33 352
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	70.22 12	74.94 318	73.53 312	79.17 356	90.40 157	52.07 427	89.19 324	89.61 308	62.69 392	70.07 292	92.67 152	48.89 327	94.32 269	38.26 456	79.97 242	91.12 270
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1921920		75.63 309	73.49 313	82.06 280	79.38 403	66.35 168	91.07 261	89.48 310	71.98 259	67.99 321	81.22 369	49.16 324	93.90 295	66.56 304	64.56 374	85.92 367

SD_0403		73.79 331	73.48 314	74.69 402	85.33 315	45.56 465	83.80 388	85.57 410	76.55 172	62.96 377	88.45 258	50.62 305	87.59 425	48.80 409	79.28 255	90.92 274

mamba_0408		76.22 293	73.37 315	84.77 169	88.50 210	66.98 149	58.80 484	86.18 401	69.12 323	74.12 234	89.01 252	47.50 340	95.35 221	67.57 293	79.52 246	91.98 249

SSM_04072		74.86 320	73.37 315	79.35 353	88.50 210	66.98 149	58.80 484	86.18 401	69.12 323	74.12 234	89.01 252	47.50 340	79.09 470	67.57 293	79.52 246	91.98 249

Fast-Effi-MVS+-dtu		75.04 316	73.37 315	80.07 332	80.86 380	59.52 369	91.20 255	85.38 411	71.90 262	65.20 353	84.84 317	41.46 382	92.97 324	66.50 307	72.96 305	87.73 317

SSC-MVS3.2		74.92 319	73.32 318	79.74 345	86.53 287	60.31 354	89.03 329	92.70 132	78.61 124	68.98 307	83.34 337	41.93 381	92.23 359	52.77 393	65.97 357	86.69 338

v8		75.35 311	73.26 319	81.61 289	80.67 385	66.82 155	89.54 311	89.27 319	71.65 274	63.30 373	80.30 383	54.99 251	94.06 284	67.33 297	62.33 394	83.94 392

XVG-OURS-SEG-HR		74.70 322	73.08 320	79.57 349	78.25 421	57.33 397	80.49 423	87.32 382	63.22 385	68.76 312	90.12 232	44.89 369	91.59 374	70.55 261	74.09 297	89.79 289

FE-MVS		75.97 302	73.02 321	84.82 165	89.78 168	65.56 190	77.44 443	91.07 229	64.55 370	72.66 253	79.85 389	46.05 360	96.69 135	54.97 382	80.82 235	92.21 243

blend_shiyan4		75.18 315	73.00 322	81.69 287	75.62 440	64.75 210	91.78 215	91.06 230	65.89 360	61.35 388	77.39 407	62.16 146	93.71 301	68.18 281	63.60 383	86.61 344

v1240		75.21 314	72.98 323	81.88 282	79.20 405	66.00 177	90.75 271	89.11 330	71.63 278	67.41 334	81.22 369	47.36 342	93.87 297	65.46 322	64.72 372	85.77 370

Baseline_NR-MVSNet		73.99 328	72.83 324	77.48 374	80.78 383	59.29 374	91.79 212	84.55 420	68.85 326	68.99 306	80.70 375	56.16 235	92.04 364	62.67 346	60.98 408	81.11 428

SCA		75.82 305	72.76 325	85.01 156	86.63 284	70.08 40	81.06 420	89.19 323	71.60 279	70.01 293	77.09 414	45.53 363	90.25 391	60.43 359	73.27 302	94.68 125

myMVS_eth3d		72.58 348	72.74 326	72.10 427	87.87 243	49.45 445	88.07 344	89.01 337	72.91 235	63.11 374	88.10 268	63.63 116	85.54 437	32.73 472	69.23 331	81.32 426

ACMM	69.62 13	74.34 323	72.73 327	79.17 356	84.25 343	57.87 387	90.36 287	89.93 293	63.17 387	65.64 350	86.04 302	37.79 406	94.10 280	65.89 313	71.52 316	85.55 375
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test0.0.03 1		72.76 342	72.71 328	72.88 419	80.25 393	47.99 451	91.22 253	89.45 312	71.51 283	62.51 383	87.66 276	53.83 267	85.06 442	50.16 401	67.84 347	85.58 373

MDTV_nov1_ep13			72.61 329		89.06 191	68.48 95	80.33 425	90.11 285	71.84 267	71.81 271	75.92 429	53.01 277	93.92 294	48.04 413	73.38 301

test_djsdf		73.76 333	72.56 330	77.39 376	77.00 432	53.93 420	89.07 326	90.69 253	65.80 361	63.92 366	82.03 352	43.14 377	92.67 341	72.83 231	68.53 337	85.57 374

v10		74.77 321	72.54 331	81.46 292	80.33 392	66.71 160	89.15 325	89.08 332	70.94 293	63.08 376	79.86 388	52.52 282	94.04 287	65.70 318	62.17 395	83.64 395

XVG-OURS		74.25 325	72.46 332	79.63 347	78.45 419	57.59 393	80.33 425	87.39 379	63.86 377	68.76 312	89.62 240	40.50 387	91.72 370	69.00 275	74.25 295	89.58 292

CNLPA		74.31 324	72.30 333	80.32 324	91.49 133	61.66 318	90.85 266	80.72 441	56.67 435	63.85 368	90.64 208	46.75 351	90.84 385	53.79 388	75.99 285	88.47 309

tpm cat1		75.30 312	72.21 334	84.58 185	88.52 209	67.77 119	78.16 441	88.02 372	61.88 401	68.45 317	76.37 425	60.65 164	94.03 289	53.77 389	74.11 296	91.93 252

dp		75.01 317	72.09 335	83.76 215	89.28 183	66.22 173	79.96 433	89.75 299	71.16 288	67.80 328	77.19 413	51.81 287	92.54 346	50.39 399	71.44 318	92.51 230

D2MVS		73.80 330	72.02 336	79.15 358	79.15 407	62.97 282	88.58 336	90.07 286	72.94 233	59.22 406	78.30 399	42.31 380	92.70 340	65.59 320	72.00 312	81.79 423

test_fmvs1_n		72.69 346	71.92 337	74.99 400	71.15 459	47.08 457	87.34 359	75.67 453	63.48 382	78.08 179	91.17 203	20.16 472	87.87 418	84.65 107	75.57 287	90.01 286

LCM-MVSNet-Re		72.93 339	71.84 338	76.18 390	88.49 214	48.02 450	80.07 430	70.17 472	73.96 210	52.25 439	80.09 387	49.98 311	88.24 415	67.35 295	84.23 187	92.28 238

pmmvs4		73.92 329	71.81 339	80.25 328	79.17 406	65.24 198	87.43 357	87.26 385	67.64 343	63.46 371	83.91 331	48.96 326	91.53 380	62.94 343	65.49 360	83.96 391

miper_lstm_enhance		73.05 337	71.73 340	77.03 381	83.80 349	58.32 384	81.76 411	88.88 343	69.80 312	61.01 390	78.23 401	57.19 218	87.51 427	65.34 323	59.53 416	85.27 382

pmmvs5		73.35 334	71.52 341	78.86 360	78.64 416	60.61 347	91.08 259	86.90 389	67.69 340	63.32 372	83.64 332	44.33 372	90.53 388	62.04 350	66.02 356	85.46 377

jajsoiax		73.05 337	71.51 342	77.67 371	77.46 429	54.83 416	88.81 332	90.04 289	69.13 322	62.85 380	83.51 334	31.16 440	92.75 337	70.83 256	69.80 324	85.43 378

mvs_tets		72.71 344	71.11 343	77.52 372	77.41 430	54.52 418	88.45 338	89.76 298	68.76 329	62.70 381	83.26 338	29.49 446	92.71 338	70.51 262	69.62 326	85.34 380

pm-mvs1		72.89 340	71.09 344	78.26 366	79.10 409	57.62 391	90.80 268	89.30 318	67.66 341	62.91 379	81.78 356	49.11 325	92.95 325	60.29 361	58.89 419	84.22 390

testing3		70.38 366	70.83 345	69.03 441	85.82 306	43.93 470	90.72 274	90.56 260	68.06 335	60.24 400	86.82 292	64.83 96	84.12 444	26.33 480	64.10 377	79.04 448

IterMVS		72.65 347	70.83 345	78.09 368	82.17 368	62.96 283	87.64 355	86.28 397	71.56 281	60.44 397	78.85 397	45.42 365	86.66 431	63.30 341	61.83 399	84.65 387
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		73.79 331	70.82 347	82.70 253	83.15 358	67.96 113	70.25 461	84.00 425	73.67 220	69.97 295	72.41 442	57.82 213	89.48 404	52.99 392	73.13 303	90.64 278

test_vis1_n		71.63 357	70.73 348	74.31 409	69.63 466	47.29 456	86.91 363	72.11 466	63.21 386	75.18 217	90.17 227	20.40 470	85.76 436	84.59 108	74.42 294	89.87 287

tt0805		73.07 336	70.73 348	80.07 332	78.37 420	57.05 400	87.78 351	92.18 160	61.23 407	67.04 338	86.49 295	31.35 439	94.58 255	65.06 325	67.12 349	88.57 306

UniMVSNet_ETH3D		72.74 343	70.53 350	79.36 352	78.62 417	56.64 404	85.01 378	89.20 322	63.77 378	64.84 357	84.44 323	34.05 427	91.86 367	63.94 335	70.89 321	89.57 293

Anonymous20231211		73.08 335	70.39 351	81.13 304	90.62 152	63.33 271	91.40 236	90.06 288	51.84 449	64.46 362	80.67 377	36.49 416	94.07 283	63.83 336	64.17 376	85.98 363

PatchMatch-RL		72.06 353	69.98 352	78.28 365	89.51 176	55.70 411	83.49 392	83.39 433	61.24 406	63.72 369	82.76 342	34.77 422	93.03 322	53.37 391	77.59 268	86.12 360

IterMVS-SCA-FT		71.55 358	69.97 353	76.32 388	81.48 376	60.67 345	87.64 355	85.99 404	66.17 356	59.50 404	78.88 396	45.53 363	83.65 450	62.58 347	61.93 398	84.63 389

WR-MVS_H		70.59 363	69.94 354	72.53 421	81.03 379	51.43 431	87.35 358	92.03 168	67.38 344	60.23 401	80.70 375	55.84 242	83.45 453	46.33 424	58.58 421	82.72 411

CP-MVSNet		70.50 364	69.91 355	72.26 424	80.71 384	51.00 435	87.23 360	90.30 275	67.84 339	59.64 403	82.69 343	50.23 309	82.30 461	51.28 395	59.28 417	83.46 400

FMVSNet1		72.71 344	69.91 355	81.10 307	83.60 353	65.11 202	90.01 298	90.32 271	63.92 376	63.56 370	80.25 384	36.35 417	91.54 377	54.46 384	66.75 352	86.64 339

tpmvs		72.88 341	69.76 357	82.22 271	90.98 145	67.05 143	78.22 440	88.30 364	63.10 388	64.35 364	74.98 432	55.09 250	94.27 273	43.25 435	69.57 327	85.34 380

Syy-MVS		69.65 372	69.52 358	70.03 436	87.87 243	43.21 471	88.07 344	89.01 337	72.91 235	63.11 374	88.10 268	45.28 366	85.54 437	22.07 485	69.23 331	81.32 426

wanda-best-256-512		72.42 349	69.43 359	81.37 294	75.39 441	64.24 235	91.58 229	91.09 224	66.36 353	60.64 393	76.86 417	47.20 344	93.47 309	64.80 327	50.98 443	86.40 347

FE-blended-shiyan7		72.42 349	69.43 359	81.37 294	75.39 441	64.24 235	91.58 229	91.09 224	66.36 353	60.64 393	76.86 417	47.20 344	93.47 309	64.80 327	50.98 443	86.40 347

anonymousdsp		71.14 360	69.37 361	76.45 387	72.95 454	54.71 417	84.19 385	88.88 343	61.92 400	62.15 384	79.77 390	38.14 401	91.44 382	68.90 277	67.45 348	83.21 404

blended_shiyan6		72.26 351	69.26 362	81.27 299	75.24 445	64.00 245	91.37 241	91.06 230	66.12 357	60.34 399	76.75 420	46.82 347	93.45 312	64.61 329	50.98 443	86.37 350

blended_shiyan8		72.26 351	69.25 363	81.29 298	75.23 446	64.03 242	91.36 244	91.04 234	66.11 358	60.42 398	76.73 421	46.79 349	93.45 312	64.58 331	51.00 442	86.37 350

PS-CasMVS		69.86 371	69.13 364	72.07 428	80.35 391	50.57 438	87.02 362	89.75 299	67.27 345	59.19 407	82.28 348	46.58 353	82.24 462	50.69 398	59.02 418	83.39 402

gbinet_0.2-2-1-0.02		71.92 354	68.92 365	80.91 315	75.87 439	63.30 272	91.95 203	91.40 201	65.62 364	61.57 387	77.27 411	44.71 370	92.88 332	61.00 356	50.87 447	86.54 345

v7n		71.31 359	68.65 366	79.28 354	76.40 434	60.77 338	86.71 367	89.45 312	64.17 375	58.77 411	78.24 400	44.59 371	93.54 306	57.76 371	61.75 401	83.52 398

mvsany_test1		68.77 379	68.56 367	69.39 439	73.57 452	45.88 464	80.93 421	60.88 486	59.65 417	71.56 275	90.26 220	43.22 376	75.05 474	74.26 221	62.70 390	87.25 329

PEN-MVS		69.46 374	68.56 367	72.17 426	79.27 404	49.71 443	86.90 364	89.24 320	67.24 348	59.08 408	82.51 346	47.23 343	83.54 452	48.42 411	57.12 423	83.25 403

MIMVSNet		71.64 356	68.44 369	81.23 301	81.97 371	64.44 223	73.05 455	88.80 347	69.67 314	64.59 358	74.79 434	32.79 431	87.82 419	53.99 386	76.35 282	91.42 260

F-COLMAP		70.66 362	68.44 369	77.32 377	86.37 292	55.91 409	88.00 346	86.32 396	56.94 433	57.28 421	88.07 270	33.58 429	92.49 348	51.02 396	68.37 338	83.55 396

PVSNet_0	68.08 15	71.81 355	68.32 371	82.27 268	84.68 329	62.31 301	88.68 334	90.31 274	75.84 177	57.93 418	80.65 378	37.85 405	94.19 276	69.94 264	29.05 488	90.31 282

CL-MVSNet_self_test		69.92 369	68.09 372	75.41 393	73.25 453	55.90 410	90.05 297	89.90 294	69.96 309	61.96 386	76.54 422	51.05 301	87.64 422	49.51 405	50.59 449	82.70 413

TransMVSNet (Re)		70.07 368	67.66 373	77.31 378	80.62 387	59.13 376	91.78 215	84.94 416	65.97 359	60.08 402	80.44 380	50.78 302	91.87 366	48.84 408	45.46 461	80.94 430

usedtu_blend_shiyan5		71.06 361	67.54 374	81.62 288	75.39 441	64.75 210	85.67 374	86.47 394	56.48 436	60.64 393	76.85 419	47.20 344	93.71 301	68.18 281	50.98 443	86.40 347

tfpnnormal		70.10 367	67.36 375	78.32 364	83.45 355	60.97 334	88.85 330	92.77 130	64.85 369	60.83 392	78.53 398	43.52 375	93.48 308	31.73 475	61.70 403	80.52 435

DTE-MVSNet		68.46 383	67.33 376	71.87 430	77.94 425	49.00 448	86.16 372	88.58 356	66.36 353	58.19 413	82.21 350	46.36 354	83.87 449	44.97 432	55.17 430	82.73 410

DP-MVS		69.90 370	66.48 377	80.14 330	95.36 30	62.93 284	89.56 309	76.11 451	50.27 455	57.69 419	85.23 313	39.68 390	95.73 193	33.35 466	71.05 320	81.78 424

dmvs_testset		65.55 404	66.45 378	62.86 455	79.87 397	22.35 500	76.55 445	71.74 468	77.42 150	55.85 424	87.77 275	51.39 295	80.69 467	31.51 478	65.92 358	85.55 375

LS3D		69.17 375	66.40 379	77.50 373	91.92 118	56.12 407	85.12 377	80.37 443	46.96 462	56.50 423	87.51 280	37.25 409	93.71 301	32.52 474	79.40 250	82.68 414

mmtdpeth		68.33 384	66.37 380	74.21 410	82.81 363	51.73 428	84.34 383	80.42 442	67.01 349	71.56 275	68.58 458	30.52 444	92.35 355	75.89 205	36.21 477	78.56 455

KD-MVS_2432*1600		69.03 377	66.37 380	77.01 382	85.56 312	61.06 332	81.44 416	90.25 278	67.27 345	58.00 416	76.53 423	54.49 257	87.63 423	48.04 413	35.77 479	82.34 417

miper_refine_blended		69.03 377	66.37 380	77.01 382	85.56 312	61.06 332	81.44 416	90.25 278	67.27 345	58.00 416	76.53 423	54.49 257	87.63 423	48.04 413	35.77 479	82.34 417

Anonymous20231206		67.53 392	65.78 383	72.79 420	74.95 447	47.59 453	88.23 341	87.32 382	61.75 405	58.07 415	77.29 410	37.79 406	87.29 429	42.91 437	63.71 381	83.48 399

MSDG		69.54 373	65.73 384	80.96 312	85.11 324	63.71 257	84.19 385	83.28 434	56.95 432	54.50 428	84.03 328	31.50 437	96.03 170	42.87 439	69.13 333	83.14 406

RPMNet		70.42 365	65.68 385	84.63 183	83.15 358	67.96 113	70.25 461	90.45 262	46.83 464	69.97 295	65.10 467	56.48 234	95.30 227	35.79 461	73.13 303	90.64 278

FMVSNet5		68.04 387	65.66 386	75.18 397	84.43 339	57.89 386	83.54 390	86.26 398	61.83 402	53.64 434	73.30 437	37.15 412	85.08 441	48.99 407	61.77 400	82.56 416

XVG-ACMP-BASELINE		68.04 387	65.53 387	75.56 392	74.06 451	52.37 425	78.43 437	85.88 405	62.03 398	58.91 410	81.21 371	20.38 471	91.15 384	60.69 358	68.18 339	83.16 405

EG-PatchMatch MVS		68.55 381	65.41 388	77.96 369	78.69 415	62.93 284	89.86 303	89.17 324	60.55 410	50.27 449	77.73 406	22.60 466	94.06 284	47.18 420	72.65 308	76.88 462

PatchT		69.11 376	65.37 389	80.32 324	82.07 370	63.68 261	67.96 470	87.62 378	50.86 453	69.37 299	65.18 466	57.09 219	88.53 411	41.59 445	66.60 353	88.74 303

test_fmvs2		65.78 403	64.84 390	68.60 443	66.54 473	41.71 474	83.27 396	69.81 473	54.38 442	67.91 324	84.54 322	15.35 478	81.22 466	75.65 207	66.16 355	82.88 407

ACMH	63.93 17	68.62 380	64.81 391	80.03 334	85.22 320	63.25 274	87.72 352	84.66 418	60.83 409	51.57 443	79.43 394	27.29 453	94.96 238	41.76 443	64.84 369	81.88 422
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		67.57 391	64.76 392	76.00 391	72.82 456	53.37 422	88.71 333	86.78 393	53.19 445	57.58 420	78.03 403	35.33 421	92.41 351	55.56 380	54.88 432	82.21 419

our_test_3		68.29 385	64.69 393	79.11 359	78.92 410	64.85 209	88.40 339	85.06 414	60.32 413	52.68 437	76.12 427	40.81 386	89.80 403	44.25 434	55.65 428	82.67 415

ACMH+	65.35 16	67.65 390	64.55 394	76.96 384	84.59 333	57.10 399	88.08 343	80.79 440	58.59 424	53.00 436	81.09 373	26.63 455	92.95 325	46.51 422	61.69 404	80.82 431

USDC		67.43 394	64.51 395	76.19 389	77.94 425	55.29 413	78.38 438	85.00 415	73.17 226	48.36 457	80.37 381	21.23 468	92.48 349	52.15 394	64.02 379	80.81 432

Patchmatch-RL test		68.17 386	64.49 396	79.19 355	71.22 458	53.93 420	70.07 463	71.54 470	69.22 319	56.79 422	62.89 471	56.58 231	88.61 408	69.53 268	52.61 438	95.03 100

CMPMVS	48.56 21	66.77 397	64.41 397	73.84 412	70.65 462	50.31 440	77.79 442	85.73 408	45.54 467	44.76 468	82.14 351	35.40 420	90.14 397	63.18 342	74.54 292	81.07 429
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ADS-MVSNet		68.54 382	64.38 398	81.03 311	88.06 234	66.90 154	68.01 468	84.02 424	57.57 426	64.48 360	69.87 454	38.68 392	89.21 406	40.87 447	67.89 345	86.97 331

FE-MVSNET2		66.80 396	64.06 399	75.03 398	69.84 464	57.11 398	86.57 368	88.57 357	67.94 338	50.97 447	72.16 446	33.79 428	87.55 426	53.94 387	52.74 436	80.45 436

Patchmtry		67.53 392	63.93 400	78.34 363	82.12 369	64.38 227	68.72 465	84.00 425	48.23 461	59.24 405	72.41 442	57.82 213	89.27 405	46.10 425	56.68 427	81.36 425

ppachtmachnet_test		67.72 389	63.70 401	79.77 344	78.92 410	66.04 176	88.68 334	82.90 436	60.11 415	55.45 425	75.96 428	39.19 391	90.55 387	39.53 451	52.55 439	82.71 412

LTVRE_ROB	59.60 19	66.27 399	63.54 402	74.45 406	84.00 346	51.55 430	67.08 472	83.53 430	58.78 422	54.94 427	80.31 382	34.54 423	93.23 318	40.64 449	68.03 341	78.58 454
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
ADS-MVSNet2		66.90 395	63.44 403	77.26 379	88.06 234	60.70 344	68.01 468	75.56 455	57.57 426	64.48 360	69.87 454	38.68 392	84.10 445	40.87 447	67.89 345	86.97 331

UnsupCasMVSNet_eth		65.79 402	63.10 404	73.88 411	70.71 461	50.29 441	81.09 419	89.88 295	72.58 242	49.25 454	74.77 435	32.57 433	87.43 428	55.96 379	41.04 469	83.90 393

EU-MVSNet		64.01 411	63.01 405	67.02 449	74.40 450	38.86 483	83.27 396	86.19 400	45.11 469	54.27 429	81.15 372	36.91 415	80.01 469	48.79 410	57.02 424	82.19 420

OpenMVS_ROB	61.12 18	66.39 398	62.92 406	76.80 386	76.51 433	57.77 388	89.22 321	83.41 432	55.48 440	53.86 432	77.84 404	26.28 456	93.95 293	34.90 463	68.76 335	78.68 453

testgi		64.48 409	62.87 407	69.31 440	71.24 457	40.62 477	85.49 375	79.92 444	65.36 366	54.18 430	83.49 335	23.74 461	84.55 443	41.60 444	60.79 410	82.77 409

test20.03		63.83 412	62.65 408	67.38 448	70.58 463	39.94 479	86.57 368	84.17 422	63.29 384	51.86 441	77.30 409	37.09 413	82.47 459	38.87 455	54.13 434	79.73 442

JIA-IIPM		66.06 400	62.45 409	76.88 385	81.42 378	54.45 419	57.49 486	88.67 352	49.36 457	63.86 367	46.86 484	56.06 238	90.25 391	49.53 404	68.83 334	85.95 364

pmmvs-eth3d		65.53 405	62.32 410	75.19 396	69.39 467	59.59 367	82.80 404	83.43 431	62.52 393	51.30 445	72.49 440	32.86 430	87.16 430	55.32 381	50.73 448	78.83 451

OurMVSNet-221017-0		64.68 407	62.17 411	72.21 425	76.08 437	47.35 454	80.67 422	81.02 439	56.19 437	51.60 442	79.66 392	27.05 454	88.56 410	53.60 390	53.63 435	80.71 433

RPSCF		64.24 410	61.98 412	71.01 433	76.10 436	45.00 466	75.83 450	75.94 452	46.94 463	58.96 409	84.59 320	31.40 438	82.00 463	47.76 418	60.33 415	86.04 361

SixPastTwentyTwo		64.92 406	61.78 413	74.34 408	78.74 414	49.76 442	83.42 395	79.51 446	62.86 389	50.27 449	77.35 408	30.92 442	90.49 389	45.89 426	47.06 455	82.78 408

test_0402		64.54 408	61.09 414	74.92 401	84.10 345	60.75 340	87.95 347	79.71 445	52.03 447	52.41 438	77.20 412	32.21 435	91.64 372	23.14 483	61.03 407	72.36 472

Patchmatch-test		65.86 401	60.94 415	80.62 321	83.75 350	58.83 378	58.91 483	75.26 457	44.50 471	50.95 448	77.09 414	58.81 197	87.90 417	35.13 462	64.03 378	95.12 94

kuosan		60.86 426	60.24 416	62.71 456	81.57 375	46.43 461	75.70 451	85.88 405	57.98 425	48.95 455	69.53 456	58.42 202	76.53 472	28.25 479	35.87 478	65.15 479

MDA-MVSNet_test_wron		63.78 414	60.16 417	74.64 403	78.15 423	60.41 351	83.49 392	84.03 423	56.17 439	39.17 479	71.59 449	37.22 410	83.24 456	42.87 439	48.73 451	80.26 439

YYNet1		63.76 415	60.14 418	74.62 404	78.06 424	60.19 358	83.46 394	83.99 427	56.18 438	39.25 478	71.56 450	37.18 411	83.34 454	42.90 438	48.70 452	80.32 438

COLMAP_ROB	57.96 20	62.98 417	59.65 419	72.98 418	81.44 377	53.00 424	83.75 389	75.53 456	48.34 460	48.81 456	81.40 365	24.14 459	90.30 390	32.95 469	60.52 412	75.65 465
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
K. test v3		63.09 416	59.61 420	73.53 414	76.26 435	49.38 447	83.27 396	77.15 449	64.35 372	47.77 459	72.32 444	28.73 448	87.79 420	49.93 403	36.69 476	83.41 401

sc_t1		63.81 413	59.39 421	77.10 380	77.62 427	56.03 408	84.32 384	73.56 462	46.66 465	58.22 412	73.06 438	23.28 464	90.62 386	50.93 397	46.84 456	84.64 388

Anonymous20240521		62.09 418	59.08 422	71.10 432	67.19 471	48.72 449	83.91 387	85.23 413	50.38 454	47.84 458	71.22 452	20.74 469	85.51 439	46.47 423	58.75 420	79.06 447

KD-MVS_self_test		60.87 425	58.60 423	67.68 446	66.13 474	39.93 480	75.63 452	84.70 417	57.32 430	49.57 452	68.45 459	29.55 445	82.87 457	48.09 412	47.94 453	80.25 440

AllTest		61.66 420	58.06 424	72.46 422	79.57 399	51.42 432	80.17 428	68.61 475	51.25 451	45.88 462	81.23 367	19.86 473	86.58 432	38.98 453	57.01 425	79.39 444

UnsupCasMVSNet_bld		61.60 421	57.71 425	73.29 416	68.73 468	51.64 429	78.61 436	89.05 335	57.20 431	46.11 461	61.96 475	28.70 449	88.60 409	50.08 402	38.90 474	79.63 443

MDA-MVSNet-bldmvs		61.54 422	57.70 426	73.05 417	79.53 401	57.00 403	83.08 400	81.23 438	57.57 426	34.91 483	72.45 441	32.79 431	86.26 434	35.81 460	41.95 467	75.89 464

mvs5depth		61.03 424	57.65 427	71.18 431	67.16 472	47.04 459	72.74 456	77.49 447	57.47 429	60.52 396	72.53 439	22.84 465	88.38 413	49.15 406	38.94 473	78.11 458

tt0320		61.85 419	57.45 428	75.03 398	77.49 428	57.60 392	82.74 405	73.65 461	43.65 475	53.65 433	68.18 460	25.47 457	88.66 407	45.56 428	46.68 457	78.81 452

MIMVSNet1		60.16 430	57.33 429	68.67 442	69.71 465	44.13 468	78.92 435	84.21 421	55.05 441	44.63 469	71.85 447	23.91 460	81.54 465	32.63 473	55.03 431	80.35 437

test_vis1_rt		59.09 433	57.31 430	64.43 452	68.44 469	46.02 463	83.05 402	48.63 495	51.96 448	49.57 452	63.86 470	16.30 476	80.20 468	71.21 254	62.79 389	67.07 478

FE-MVSNET		60.52 427	57.18 431	70.53 434	67.53 470	50.68 437	82.62 406	76.28 450	59.33 420	46.71 460	71.10 453	30.54 443	83.61 451	33.15 468	47.37 454	77.29 461

tt0320-xc		61.51 423	56.89 432	75.37 394	78.50 418	58.61 381	82.61 407	71.27 471	44.31 472	53.17 435	68.03 462	23.38 462	88.46 412	47.77 417	43.00 466	79.03 449

PM-MVS		59.40 431	56.59 433	67.84 444	63.63 477	41.86 472	76.76 444	63.22 483	59.01 421	51.07 446	72.27 445	11.72 485	83.25 455	61.34 353	50.28 450	78.39 456

new-patchmatchnet		59.30 432	56.48 434	67.79 445	65.86 475	44.19 467	82.47 408	81.77 437	59.94 416	43.65 473	66.20 465	27.67 452	81.68 464	39.34 452	41.40 468	77.50 460

TinyColmap		60.32 428	56.42 435	72.00 429	78.78 413	53.18 423	78.36 439	75.64 454	52.30 446	41.59 477	75.82 430	14.76 481	88.35 414	35.84 459	54.71 433	74.46 466

MVS-HIRNet		60.25 429	55.55 436	74.35 407	84.37 340	56.57 405	71.64 459	74.11 459	34.44 482	45.54 466	42.24 490	31.11 441	89.81 401	40.36 450	76.10 284	76.67 463

dongtai		55.18 439	55.46 437	54.34 466	76.03 438	36.88 484	76.07 448	84.61 419	51.28 450	43.41 474	64.61 469	56.56 232	67.81 485	18.09 488	28.50 489	58.32 482

test_fmvs3		56.82 435	54.86 438	62.69 457	53.59 489	35.47 486	75.87 449	65.64 480	43.91 473	55.10 426	71.43 451	6.91 493	74.40 477	68.64 279	52.63 437	78.20 457

DSMNet-mixed		56.78 436	54.44 439	63.79 453	63.21 478	29.44 495	64.43 475	64.10 482	42.12 479	51.32 444	71.60 448	31.76 436	75.04 475	36.23 458	65.20 366	86.87 336

usedtu_dtu_shiyan2		57.76 434	53.69 440	69.95 437	57.60 487	41.80 473	83.50 391	83.67 429	45.26 468	43.79 472	62.82 472	17.63 475	85.93 435	42.56 442	46.40 459	82.12 421

LF4IMVS		54.01 440	52.12 441	59.69 458	62.41 480	39.91 481	68.59 466	68.28 477	42.96 477	44.55 470	75.18 431	14.09 483	68.39 484	41.36 446	51.68 440	70.78 473

TDRefinement		55.28 438	51.58 442	66.39 450	59.53 485	46.15 462	76.23 447	72.80 463	44.60 470	42.49 475	76.28 426	15.29 479	82.39 460	33.20 467	43.75 463	70.62 474

pmmvs3		55.51 437	51.50 443	67.53 447	57.90 486	50.93 436	80.37 424	73.66 460	40.63 480	44.15 471	64.75 468	16.30 476	78.97 471	44.77 433	40.98 471	72.69 470

ttmdpeth		53.34 441	49.96 444	63.45 454	62.07 482	40.04 478	72.06 457	65.64 480	42.54 478	51.88 440	77.79 405	13.94 484	76.48 473	32.93 470	30.82 487	73.84 467

N_pmnet		50.55 443	49.11 445	54.88 464	77.17 431	4.02 508	84.36 382	2.00 506	48.59 458	45.86 464	68.82 457	32.22 434	82.80 458	31.58 476	51.38 441	77.81 459

MVStest1		51.35 442	46.89 446	64.74 451	65.06 476	51.10 434	67.33 471	72.58 464	30.20 486	35.30 481	74.82 433	27.70 451	69.89 482	24.44 482	24.57 490	73.22 468

new_pmnet		49.31 444	46.44 447	57.93 459	62.84 479	40.74 476	68.47 467	62.96 484	36.48 481	35.09 482	57.81 479	14.97 480	72.18 479	32.86 471	46.44 458	60.88 481

mvsany_test3		48.86 445	46.35 448	56.41 460	46.00 495	31.67 491	62.26 477	47.25 496	43.71 474	45.54 466	68.15 461	10.84 486	64.44 493	57.95 370	35.44 481	73.13 469

WB-MVS		46.23 447	44.94 449	50.11 469	62.13 481	21.23 502	76.48 446	55.49 488	45.89 466	35.78 480	61.44 477	35.54 419	72.83 478	9.96 495	21.75 491	56.27 484

test_f		46.58 446	43.45 450	55.96 461	45.18 496	32.05 490	61.18 478	49.49 494	33.39 483	42.05 476	62.48 474	7.00 492	65.56 489	47.08 421	43.21 465	70.27 475

SSC-MVS		44.51 449	43.35 451	47.99 473	61.01 484	18.90 504	74.12 454	54.36 489	43.42 476	34.10 484	60.02 478	34.42 424	70.39 481	9.14 497	19.57 492	54.68 485

FPMVS		45.64 448	43.10 452	53.23 467	51.42 492	36.46 485	64.97 474	71.91 467	29.13 487	27.53 487	61.55 476	9.83 488	65.01 491	16.00 492	55.58 429	58.22 483

EGC-MVSNET		42.35 450	38.09 453	55.11 463	74.57 448	46.62 460	71.63 460	55.77 487	0.04 501	0.24 502	62.70 473	14.24 482	74.91 476	17.59 489	46.06 460	43.80 487

test_vis3_rt		40.46 453	37.79 454	48.47 472	44.49 497	33.35 489	66.56 473	32.84 503	32.39 484	29.65 485	39.13 493	3.91 500	68.65 483	50.17 400	40.99 470	43.40 488

APD_test1		40.50 452	37.31 455	50.09 470	51.88 490	35.27 487	59.45 482	52.59 491	21.64 490	26.12 488	57.80 480	4.56 497	66.56 487	22.64 484	39.09 472	48.43 486

LCM-MVSNet		40.54 451	35.79 456	54.76 465	36.92 502	30.81 492	51.41 489	69.02 474	22.07 489	24.63 489	45.37 486	4.56 497	65.81 488	33.67 465	34.50 482	67.67 476

ANet_high		40.27 454	35.20 457	55.47 462	34.74 503	34.47 488	63.84 476	71.56 469	48.42 459	18.80 492	41.08 491	9.52 489	64.45 492	20.18 486	8.66 499	67.49 477

test_method		38.59 455	35.16 458	48.89 471	54.33 488	21.35 501	45.32 492	53.71 490	7.41 498	28.74 486	51.62 482	8.70 490	52.87 496	33.73 464	32.89 483	72.47 471

PMMVS2		37.93 456	33.61 459	50.92 468	46.31 494	24.76 498	60.55 481	50.05 492	28.94 488	20.93 490	47.59 483	4.41 499	65.13 490	25.14 481	18.55 494	62.87 480

Gipuma		34.91 457	31.44 460	45.30 474	70.99 460	39.64 482	19.85 496	72.56 465	20.10 492	16.16 496	21.47 497	5.08 496	71.16 480	13.07 493	43.70 464	25.08 494
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 458	29.47 461	42.67 476	41.89 499	30.81 492	52.07 487	43.45 497	15.45 493	18.52 493	44.82 487	2.12 501	58.38 494	16.05 490	30.87 485	38.83 489

APD_test2		32.77 458	29.47 461	42.67 476	41.89 499	30.81 492	52.07 487	43.45 497	15.45 493	18.52 493	44.82 487	2.12 501	58.38 494	16.05 490	30.87 485	38.83 489

PMVS	26.43 22	31.84 460	28.16 463	42.89 475	25.87 505	27.58 496	50.92 490	49.78 493	21.37 491	14.17 497	40.81 492	2.01 503	66.62 486	9.61 496	38.88 475	34.49 493
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
cdsmvs_eth3d_5k		19.86 465	26.47 464	0.00 486	0.00 509	0.00 511	0.00 497	93.45 99	0.00 504	0.00 505	95.27 78	49.56 317	0.00 505	0.00 503	0.00 502	0.00 501

E-PMN		24.61 461	24.00 465	26.45 480	43.74 498	18.44 505	60.86 479	39.66 499	15.11 495	9.53 499	22.10 496	6.52 494	46.94 498	8.31 498	10.14 496	13.98 496

tmp_tt		22.26 464	23.75 466	17.80 482	5.23 506	12.06 507	35.26 493	39.48 500	2.82 500	18.94 491	44.20 489	22.23 467	24.64 501	36.30 457	9.31 498	16.69 495

EMVS		23.76 463	23.20 467	25.46 481	41.52 501	16.90 506	60.56 480	38.79 502	14.62 496	8.99 500	20.24 499	7.35 491	45.82 499	7.25 499	9.46 497	13.64 497

MVE	24.84 23	24.35 462	19.77 468	38.09 478	34.56 504	26.92 497	26.57 494	38.87 501	11.73 497	11.37 498	27.44 494	1.37 504	50.42 497	11.41 494	14.60 495	36.93 491
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 466	10.95 469	12.33 483	48.05 493	19.89 503	25.89 495	1.92 507	3.58 499	3.12 501	1.37 501	0.64 505	15.77 502	6.23 500	7.77 500	1.35 498

ab-mvs-re		7.91 467	10.55 470	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	94.95 88	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

testmvs		7.23 468	9.62 471	0.06 485	0.04 507	0.02 510	84.98 379	0.02 508	0.03 502	0.18 503	1.21 502	0.01 507	0.02 503	0.14 501	0.01 501	0.13 500

test123		6.92 469	9.21 472	0.08 484	0.03 508	0.05 509	81.65 414	0.01 509	0.02 503	0.14 504	0.85 503	0.03 506	0.02 503	0.12 502	0.00 502	0.16 499

pcd_1.5k_mvsjas		4.46 470	5.95 473	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	53.55 271	0.00 505	0.00 503	0.00 502	0.00 501

mmdepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

monomultidepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

test_blank		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

uanet_test		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

DCPMVS		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

sosnet-low-res		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

sosnet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

uncertanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

Regformer		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

uanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 502	0.00 501

MED-MVS test				87.42 47	94.76 35	67.28 132	94.47 64	94.87 33	73.09 231	91.27 24	96.95 18		98.98 17	91.55 44	94.28 37	95.99 48

TestfortrainingZip				90.29 2	97.24 8	73.67 10	94.47 64	95.75 10	69.78 313	95.97 1	98.23 1	80.55 5	99.42 1		93.26 58	97.76 2

WAC-MVS						49.45 445								31.56 477

FOURS1					93.95 51	61.77 314	93.96 91	91.92 172	62.14 397	86.57 63

MSC_two_6792asdad				89.60 10	97.31 4	73.22 14		95.05 30					99.07 14	92.01 39	94.77 26	96.51 25

PC_three_1452									80.91 65	94.07 3	96.83 30	83.57 4	99.12 6	95.70 10	97.42 4	97.55 5

No_MVS				89.60 10	97.31 4	73.22 14		95.05 30					99.07 14	92.01 39	94.77 26	96.51 25

test_one_0601					96.32 20	69.74 53		94.18 70	71.42 285	90.67 30	96.85 28	74.45 23

eth-test2					0.00 509

eth-test					0.00 509

ZD-MVS					96.63 10	65.50 193		93.50 97	70.74 299	85.26 81	95.19 84	64.92 95	97.29 90	87.51 74	93.01 61

IU-MVS					96.46 12	69.91 45		95.18 24	80.75 66	95.28 2				92.34 36	95.36 14	96.47 29

OPU-MVS				89.97 4	97.52 3	73.15 16	96.89 6				97.00 16	83.82 2	99.15 3	95.72 8	97.63 3	97.62 3

test_241102_TWO								94.41 61	71.65 274	92.07 12	97.21 10	74.58 21	99.11 7	92.34 36	95.36 14	96.59 20

test_241102_ONE					96.45 13	69.38 62		94.44 56	71.65 274	92.11 10	97.05 13	76.79 10	99.11 7

save fliter					93.84 54	67.89 116	95.05 41	92.66 137	78.19 129

test_0728_THIRD									72.48 244	90.55 31	96.93 22	76.24 14	99.08 12	91.53 47	94.99 18	96.43 32

test_0728_SECOND				88.70 19	96.45 13	70.43 36	96.64 10	94.37 65					99.15 3	91.91 42	94.90 22	96.51 25

test0726					96.40 16	69.99 41	96.76 8	94.33 67	71.92 260	91.89 15	97.11 12	73.77 26

GSMVS																94.68 125

test_part2					96.29 21	68.16 109				90.78 28

sam_mvs1												57.85 212				94.68 125

sam_mvs												54.91 252

ambc				69.61 438	61.38 483	41.35 475	49.07 491	85.86 407		50.18 451	66.40 464	10.16 487	88.14 416	45.73 427	44.20 462	79.32 446

MTGPA								92.23 153

test_post1							78.95 434				20.70 498	53.05 276	91.50 381	60.43 359

test_post											23.01 495	56.49 233	92.67 341

patchmatchnet-post											67.62 463	57.62 215	90.25 391

GG-mvs-BLEND				86.53 94	91.91 120	69.67 56	75.02 453	94.75 41		78.67 173	90.85 207	77.91 8	94.56 260	72.25 241	93.74 49	95.36 77

MTMP							93.77 106	32.52 504

gm-plane-assit					88.42 220	67.04 144			78.62 123		91.83 180		97.37 84	76.57 199

test9_res														89.41 57	94.96 19	95.29 83

TEST9					94.18 46	67.28 132	94.16 78	93.51 95	71.75 271	85.52 76	95.33 72	68.01 62	97.27 94

test_8					94.19 45	67.19 137	94.15 80	93.42 102	71.87 265	85.38 79	95.35 71	68.19 60	96.95 121

agg_prior2														86.41 88	94.75 30	95.33 79

agg_prior					94.16 48	66.97 152		93.31 105		84.49 87			96.75 133

TestCases				72.46 422	79.57 399	51.42 432		68.61 475	51.25 451	45.88 462	81.23 367	19.86 473	86.58 432	38.98 453	57.01 425	79.39 444

test_prior4						67.18 139	93.92 95

test_prior2							95.10 39		75.40 185	85.25 82	95.61 63	67.94 63		87.47 76	94.77 26

test_prior				86.42 99	94.71 40	67.35 131		93.10 116					96.84 130			95.05 98

旧先验2							92.00 200		59.37 419	87.54 56			93.47 309	75.39 209

新几何2							91.41 234

新几何1				84.73 173	92.32 99	64.28 232		91.46 199	59.56 418	79.77 152	92.90 146	56.95 225	96.57 139	63.40 338	92.91 63	93.34 198

旧先验1					91.94 116	60.74 341		91.50 197			94.36 106	65.23 90			91.84 79	94.55 134

无先验							92.71 155	92.61 142	62.03 398				97.01 111	66.63 303		93.97 174

原ACMM2							92.01 197

原ACMM1				84.42 190	93.21 74	64.27 233		93.40 104	65.39 365	79.51 157	92.50 154	58.11 207	96.69 135	65.27 324	93.96 44	92.32 236

test222					89.77 169	61.60 320	89.55 310	89.42 314	56.83 434	77.28 190	92.43 158	52.76 279			91.14 96	93.09 208

testdata2													96.09 164	61.26 354

segment_acmp												65.94 81

testdata				81.34 297	89.02 193	57.72 389		89.84 296	58.65 423	85.32 80	94.09 122	57.03 220	93.28 315	69.34 270	90.56 102	93.03 211

testdata1							89.21 322		77.55 146

test12				87.09 58	94.60 41	68.86 82		92.91 125		82.67 110		65.44 87	97.55 73		93.69 52	94.84 110

plane_prior7					86.94 275	61.51 322

plane_prior6					87.23 260	62.32 300						50.66 303

plane_prior5								91.31 204					95.55 213	76.74 195	78.53 262	88.39 310

plane_prior4											89.14 249

plane_prior3						61.95 309			79.09 112	72.53 257

plane_prior2							93.13 134		78.81 119

plane_prior1					87.15 263

plane_prior						62.42 296	93.85 99		79.38 104						78.80 259

n2								0.00 510

nn								0.00 510

door-mid								66.01 479

lessismore_v0				73.72 413	72.93 455	47.83 452		61.72 485		45.86 464	73.76 436	28.63 450	89.81 401	47.75 419	31.37 484	83.53 397

LGP-MVS_train				79.56 350	84.31 341	59.37 371		89.73 302	69.49 315	64.86 355	88.42 259	38.65 394	94.30 271	72.56 237	72.76 306	85.01 383

test11								93.01 119

door								66.57 478

HQP5-MVS						63.66 262

HQP-NCC					87.54 252		94.06 83		79.80 88	74.18 230

ACMP_Plane					87.54 252		94.06 83		79.80 88	74.18 230

BP-MVS														77.63 192

HQP4-MVS										74.18 230			95.61 207			88.63 304

HQP3-MVS								91.70 189							78.90 257

HQP2-MVS												51.63 291

NP-MVS					87.41 255	63.04 280					90.30 218

MDTV_nov1_ep13_2view						59.90 363	80.13 429		67.65 342	72.79 251		54.33 262		59.83 363		92.58 227

ACMMP++_ref															71.63 314

ACMMP++															69.72 325

Test By Simon												54.21 265

ITE_SJBPF				70.43 435	74.44 449	47.06 458		77.32 448	60.16 414	54.04 431	83.53 333	23.30 463	84.01 447	43.07 436	61.58 405	80.21 441

DeepMVS_CX				34.71 479	51.45 491	24.73 499		28.48 505	31.46 485	17.49 495	52.75 481	5.80 495	42.60 500	18.18 487	19.42 493	36.81 492

High-resolution multi-view benchmark