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.

fmvsm_s_conf0.5_n_9		88.14 22	89.21 19	84.92 157	89.29 182	61.41 326	92.97 141	88.36 360	86.96 6	91.49 22	97.49 4	69.48 55	97.46 77	97.00 1	89.88 113	95.89 54

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

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

fmvsm_s_conf0.5_n_8		87.96 27	88.93 22	85.07 152	88.43 219	61.78 312	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

fmvsm_l_conf0.5_n_9		88.24 21	89.36 17	84.85 162	88.15 232	61.94 309	95.65 25	89.70 305	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_l_conf0.5_n_a		87.44 40	88.15 34	85.30 142	87.10 265	64.19 236	94.41 69	88.14 368	80.24 81	92.54 6	96.97 17	69.52 54	97.17 100	95.89 6	88.51 128	94.56 133

fmvsm_l_conf0.5_n		87.49 38	88.19 33	85.39 136	86.95 273	64.37 227	94.30 74	88.45 358	80.51 70	92.70 5	96.86 26	69.98 52	97.15 104	95.83 7	88.08 133	94.65 129

test_fmvsm_n_1920		87.69 34	88.50 28	85.27 145	87.05 267	63.55 265	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	94.26 156

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

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

fmvsm_s_conf0.5_n		86.39 59	86.91 51	84.82 164	87.36 258	63.54 266	94.74 56	90.02 289	82.52 40	90.14 38	96.92 24	62.93 134	97.84 55	95.28 11	82.26 210	93.07 209

fmvsm_s_conf0.5_n_10		87.93 30	88.67 25	85.71 125	88.69 201	63.71 256	94.56 62	90.22 281	85.04 15	92.27 7	97.05 13	63.67 115	98.15 43	95.09 12	91.39 88	95.27 86

fmvsm_s_conf0.5_n_11		87.99 26	89.25 18	84.23 199	89.07 190	61.60 319	94.87 51	89.06 333	85.65 11	91.09 27	97.41 5	68.26 59	97.43 81	95.07 13	92.74 65	93.66 188

fmvsm_s_conf0.5_n_4		86.79 53	87.63 39	84.27 197	86.15 296	61.48 323	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	93.11 206

fmvsm_l_conf0.5_n_3		87.54 35	88.29 31	85.30 142	86.92 278	62.63 292	95.02 45	90.28 276	84.95 16	90.27 34	96.86 26	65.36 88	97.52 75	94.93 15	90.03 110	95.76 58

fmvsm_s_conf0.5_n_7		85.24 86	86.69 56	80.91 314	84.52 334	60.10 358	93.35 128	90.35 269	83.41 31	86.54 64	96.27 46	60.50 167	90.02 399	94.84 16	90.38 105	92.61 223

fmvsm_s_conf0.1_n		85.61 80	85.93 72	84.68 177	82.95 361	63.48 268	94.03 89	89.46 310	81.69 50	89.86 39	96.74 32	61.85 151	97.75 58	94.74 17	82.01 217	92.81 219

fmvsm_s_conf0.5_n_a		85.75 76	86.09 69	84.72 173	85.73 309	63.58 263	93.79 105	89.32 316	81.42 57	90.21 36	96.91 25	62.41 141	97.67 62	94.48 18	80.56 238	92.90 215

fmvsm_s_conf0.5_n_6		87.50 37	88.72 24	83.84 211	86.89 280	60.04 360	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

test_fmvsmconf_n		86.58 56	87.17 46	84.82 164	85.28 317	62.55 293	94.26 76	89.78 296	83.81 27	87.78 53	96.33 44	65.33 89	96.98 116	94.40 20	87.55 139	94.95 103

fmvsm_s_conf0.5_n_5		86.38 61	86.94 50	84.71 175	84.67 329	63.29 272	94.04 87	89.99 291	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_2		85.06 90	85.60 79	83.44 232	86.92 278	60.53 347	94.41 69	87.31 383	83.30 32	88.72 47	96.72 33	54.28 262	97.75 58	94.07 22	84.68 181	92.04 246

test_fmvsmconf0.1_n		85.71 77	86.08 70	84.62 183	80.83 380	62.33 298	93.84 102	88.81 345	83.50 30	87.00 60	96.01 55	63.36 123	96.93 124	94.04 23	87.29 142	94.61 131

fmvsm_s_conf0.5_n_3		86.88 46	87.99 36	83.58 225	87.26 259	60.74 340	93.21 133	87.94 375	84.22 22	91.70 17	97.27 7	65.91 83	95.02 233	93.95 24	90.42 104	94.99 101

fmvsm_s_conf0.1_n_a		84.76 100	84.84 93	84.53 185	80.23 393	63.50 267	92.79 151	88.73 348	80.46 71	89.84 40	96.65 35	60.96 160	97.57 72	93.80 25	80.14 240	92.53 228

fmvsm_s_conf0.1_n_2		84.40 108	84.78 95	83.27 238	85.25 318	60.41 350	94.13 81	85.69 408	83.05 34	87.99 50	96.37 40	52.75 279	97.68 60	93.75 26	84.05 191	91.71 254

test_fmvsmvis_n_1920		83.80 129	83.48 117	84.77 168	82.51 364	63.72 255	91.37 240	83.99 426	81.42 57	77.68 181	95.74 60	58.37 203	97.58 70	93.38 27	86.87 146	93.00 212

patch_mono-2		89.71 11	90.99 6	85.85 118	96.04 26	63.70 258	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

balanced_conf03		89.08 15	88.84 23	89.81 7	93.66 59	75.15 5	90.61 279	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

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

test_fmvsmconf0.01_n		83.70 133	83.52 113	84.25 198	75.26 443	61.72 316	92.17 187	87.24 385	82.36 43	84.91 83	95.41 69	55.60 242	96.83 131	92.85 31	85.87 162	94.21 159

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 173	93.00 82	58.16 384	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

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

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

balanced_ft_v1		84.95 95	83.81 107	88.38 27	93.31 70	73.59 11	85.95 372	92.51 145	77.25 152	73.97 238	89.14 248	59.30 188	95.25 228	92.50 35	90.34 107	96.31 35

SED-MVS		89.94 9	90.36 10	88.70 19	96.45 13	69.38 61	96.89 6	94.44 56	71.65 273	92.11 10	97.21 10	76.79 10	99.11 7	92.34 36	95.36 14	97.62 3

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

test_241102_TWO								94.41 61	71.65 273	92.07 12	97.21 10	74.58 21	99.11 7	92.34 36	95.36 14	96.59 20

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

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_vis1_n_1920		81.66 176	82.01 159	80.64 318	82.24 366	55.09 414	94.76 55	86.87 389	81.67 51	84.40 88	94.63 99	38.17 398	94.67 253	91.98 41	83.34 200	92.16 244

DVP-MVS		89.41 13	89.73 14	88.45 26	96.40 16	69.99 41	96.64 10	94.52 52	71.92 259	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
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

MED-MVS test				87.42 47	94.76 35	67.28 131	94.47 64	94.87 33	73.09 230	91.27 24	96.95 18		98.98 17	91.55 44	94.28 37	95.99 48

MED-MVS		88.94 17	89.45 16	87.42 47	94.76 35	67.28 131	94.47 64	94.87 33	70.09 305	91.27 24	96.95 18	76.77 12	98.98 17	91.55 44	94.28 37	95.99 48

ME-MVS		88.25 20	88.55 27	87.33 52	96.33 19	67.28 131	93.93 93	94.81 38	70.09 305	88.91 45	96.95 18	70.12 50	98.73 29	91.55 44	94.28 37	95.99 48

DVP-MVS++		90.53 4	91.09 5	88.87 17	97.31 4	69.91 45	93.96 91	94.37 65	72.48 243	92.07 12	96.85 28	83.82 2	99.15 3	91.53 47	97.42 4	97.55 5

test_0728_THIRD									72.48 243	90.55 31	96.93 22	76.24 14	99.08 12	91.53 47	94.99 18	96.43 32

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	53.55 270	97.89 52	91.10 49	93.31 57	94.54 136

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	53.45 274	97.68 60	91.07 50	92.62 66	94.54 136

MSP-MVS		90.38 5	91.87 1	85.88 115	92.83 86	64.03 241	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
dcpmvs_2		87.37 41	87.55 42	86.85 64	95.04 34	68.20 107	90.36 286	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

test_cas_vis1_n_1920		80.45 206	80.61 183	79.97 337	78.25 420	57.01 401	94.04 87	88.33 362	79.06 115	82.81 107	93.70 130	38.65 393	91.63 372	90.82 53	79.81 242	91.27 267

APDe-MVS		87.54 35	87.84 37	86.65 78	96.07 25	66.30 169	94.84 53	93.78 79	69.35 316	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
DPE-MVS		88.77 18	89.21 19	87.45 46	96.26 22	67.56 124	94.17 77	94.15 72	68.77 327	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
9.14			87.63 39		93.86 53		94.41 69	94.18 70	72.76 238	86.21 66	96.51 37	66.64 73	97.88 53	90.08 56	94.04 43

test9_res														89.41 57	94.96 19	95.29 83

TSAR-MVS + GP.		87.96 27	88.37 30	86.70 75	93.51 67	65.32 195	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

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

ETV-MVS		86.01 70	86.11 68	85.70 126	90.21 161	67.02 145	93.43 125	91.92 172	81.21 61	84.13 92	94.07 124	60.93 161	95.63 202	89.28 60	89.81 114	94.46 147

SMA-MVS		88.14 22	88.29 31	87.67 36	93.21 74	68.72 89	93.85 99	94.03 75	74.18 203	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
train_agg		87.21 43	87.42 44	86.60 81	94.18 46	67.28 131	94.16 78	93.51 95	71.87 264	85.52 76	95.33 72	68.19 60	97.27 94	89.09 62	94.90 22	95.25 90

SD-MVS		87.49 38	87.49 43	87.50 45	93.60 61	68.82 84	93.90 96	92.63 141	76.86 158	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
NCCC		89.07 16	89.46 15	87.91 30	96.60 11	69.05 77	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

HPM-MVS++		89.37 14	89.95 13	87.64 37	95.10 32	68.23 105	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

SF-MVS		87.03 45	87.09 47	86.84 65	92.70 92	67.45 129	93.64 112	93.76 82	70.78 297	86.25 65	96.44 39	66.98 70	97.79 56	88.68 66	94.56 34	95.28 85

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

TSAR-MVS + MP.		88.11 25	88.64 26	86.54 92	91.73 125	68.04 110	90.36 286	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
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

jason		86.40 58	86.17 66	87.11 57	86.16 295	70.54 34	95.71 24	92.19 159	82.00 47	84.58 86	94.34 111	61.86 150	95.53 214	87.76 71	90.89 97	95.27 86
jason: jason.
h-mvs33		83.01 151	82.56 151	84.35 193	89.34 178	62.02 305	92.72 154	93.76 82	81.45 54	82.73 108	92.25 164	60.11 172	97.13 105	87.69 72	62.96 386	93.91 179

hse-mvs2		81.12 191	81.11 173	81.16 302	86.52 287	57.48 393	89.40 316	91.16 214	81.45 54	82.73 108	90.49 213	60.11 172	94.58 254	87.69 72	60.41 413	91.41 260

ZD-MVS					96.63 10	65.50 192		93.50 97	70.74 298	85.26 81	95.19 84	64.92 95	97.29 90	87.51 74	93.01 61

mvsmamba		81.55 178	80.72 179	84.03 206	91.42 134	66.93 152	83.08 399	89.13 327	78.55 125	67.50 330	87.02 288	51.79 287	90.07 398	87.48 75	90.49 103	95.10 95

test_prior2							95.10 39		75.40 184	85.25 82	95.61 63	67.94 63		87.47 76	94.77 26

SteuartSystems-ACMMP		86.82 52	86.90 52	86.58 84	90.42 156	66.38 166	96.09 17	93.87 77	77.73 139	84.01 93	95.66 61	63.39 122	97.94 48	87.40 77	93.55 54	95.42 70
Skip Steuart: Steuart Systems R&D Blog.
diffmvs		84.28 112	83.83 106	85.61 129	87.40 256	68.02 111	90.88 264	89.24 319	80.54 69	81.64 115	92.52 153	59.83 176	94.52 262	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
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
casdiffmvs_mvg		85.66 79	85.18 86	87.09 58	88.22 230	69.35 64	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
diffmvs_AUTHOR		83.97 124	83.49 116	85.39 136	86.09 297	67.83 116	90.76 269	89.05 334	79.94 84	81.43 120	92.23 165	59.53 182	94.42 265	87.18 81	85.22 170	93.92 178

MGCFI-Net		85.59 81	85.73 77	85.17 149	91.41 137	62.44 294	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

PVSNet_BlendedMVS		83.38 143	83.43 120	83.22 240	93.76 55	67.53 126	94.06 83	93.61 90	79.13 111	81.00 128	85.14 313	63.19 127	97.29 90	87.08 83	73.91 298	84.83 384

PVSNet_Blended		86.73 54	86.86 53	86.31 104	93.76 55	67.53 126	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	94.13 165

MP-MVS-pluss		85.24 86	85.13 87	85.56 131	91.42 134	65.59 188	91.54 231	92.51 145	74.56 194	80.62 134	95.64 62	59.15 191	97.00 112	86.94 85	93.80 47	94.07 169
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VDD-MVS		83.06 150	81.81 163	86.81 68	90.86 149	67.70 120	95.40 30	91.50 197	75.46 181	81.78 114	92.34 161	40.09 388	97.13 105	86.85 86	82.04 216	95.60 64

SPE-MVS-test		86.14 68	87.01 48	83.52 226	92.63 94	59.36 372	95.49 28	91.92 172	80.09 82	85.46 78	95.53 67	61.82 152	95.77 190	86.77 87	93.37 56	95.41 71

agg_prior2														86.41 88	94.75 30	95.33 79

APD-MVS		85.93 72	85.99 71	85.76 122	95.98 28	65.21 198	93.59 115	92.58 143	66.54 350	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
ACMMP_NAP		86.05 69	85.80 75	86.80 69	91.58 129	67.53 126	91.79 212	93.49 98	74.93 191	84.61 85	95.30 74	59.42 185	97.92 49	86.13 90	94.92 20	94.94 104

CS-MVS		85.80 75	86.65 59	83.27 238	92.00 115	58.92 376	95.31 32	91.86 177	79.97 83	84.82 84	95.40 70	62.26 144	95.51 215	86.11 91	92.08 74	95.37 74

PHI-MVS		86.83 50	86.85 54	86.78 70	93.47 68	65.55 190	95.39 31	95.10 26	71.77 269	85.69 74	96.52 36	62.07 147	98.77 27	86.06 92	95.60 12	96.03 45

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	94.15 164

DeepC-MVS	77.85 3	85.52 83	85.24 85	86.37 100	88.80 199	66.64 160	92.15 188	93.68 88	81.07 63	76.91 196	93.64 132	62.59 138	98.44 36	85.50 94	92.84 64	94.03 171
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EC-MVSNet		84.53 105	85.04 89	83.01 244	89.34 178	61.37 327	94.42 68	91.09 224	77.91 134	83.24 99	94.20 117	58.37 203	95.40 217	85.35 95	91.41 87	92.27 240

NormalMVS		86.39 59	86.66 58	85.60 130	92.12 107	65.95 179	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	93.93 176

SymmetryMVS		86.32 62	86.39 61	86.12 109	90.52 154	65.95 179	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

test_fmvs1		74.07 325	73.69 309	75.22 394	78.91 411	47.34 454	89.06 327	74.69 457	63.68 379	79.41 159	91.59 189	24.36 457	87.77 420	85.22 98	76.26 282	90.55 279

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

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

SDMVSNet		80.26 210	78.88 221	84.40 190	89.25 184	67.63 123	85.35 375	93.02 118	76.77 162	70.84 281	87.12 285	47.95 335	96.09 164	85.04 101	74.55 289	89.48 294

MP-MVS		85.02 91	84.97 90	85.17 149	92.60 95	64.27 232	93.24 130	92.27 152	73.13 226	79.63 156	94.43 104	61.90 148	97.17 100	85.00 102	92.56 67	94.06 170
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		85.37 84	84.87 92	86.84 65	88.25 228	69.07 74	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
CSCG		86.87 47	86.26 63	88.72 18	95.05 33	70.79 31	93.83 104	95.33 19	68.48 331	77.63 182	94.35 110	73.04 31	98.45 35	84.92 104	93.71 51	96.92 15

MTAPA		83.91 126	83.38 124	85.50 132	91.89 121	65.16 200	81.75 411	92.23 153	75.32 186	80.53 138	95.21 83	56.06 238	97.16 103	84.86 105	92.55 68	94.18 161

MVSMamba_PlusPlus		84.97 94	83.65 112	88.93 15	90.17 162	74.04 8	87.84 349	92.69 135	62.18 394	81.47 119	87.64 276	71.47 45	96.28 154	84.69 106	94.74 31	96.47 29

test_fmvs1_n		72.69 345	71.92 336	74.99 399	71.15 458	47.08 456	87.34 358	75.67 452	63.48 381	78.08 178	91.17 203	20.16 471	87.87 417	84.65 107	75.57 286	90.01 285

E3new		84.94 96	84.36 100	86.69 77	89.06 191	69.31 65	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

test_vis1_n		71.63 356	70.73 347	74.31 408	69.63 465	47.29 455	86.91 362	72.11 465	63.21 385	75.18 216	90.17 227	20.40 469	85.76 435	84.59 108	74.42 293	89.87 286

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	95.73 192	84.47 110	86.56 155	94.84 110

baseline		85.01 92	84.44 98	86.71 74	88.33 225	68.73 87	90.24 291	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

TestfortrainingZip a		88.66 19	88.99 21	87.70 35	94.76 35	68.73 87	94.47 64	94.87 33	73.09 230	91.27 24	96.95 18	76.77 12	98.98 17	84.41 112	94.28 37	95.37 74

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

viewcassd2359sk11		84.74 101	84.11 103	86.64 79	88.57 204	69.20 72	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

CLD-MVS		82.73 156	82.35 155	83.86 210	87.90 240	67.65 122	95.45 29	92.18 160	85.06 14	72.58 255	92.27 162	52.46 282	95.78 188	84.18 115	79.06 255	88.16 312
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
xiu_mvs_v1_base_debu		82.16 167	81.12 170	85.26 146	86.42 288	68.72 89	92.59 168	90.44 265	73.12 227	84.20 89	94.36 106	38.04 401	95.73 192	84.12 116	86.81 147	91.33 261

xiu_mvs_v1_base		82.16 167	81.12 170	85.26 146	86.42 288	68.72 89	92.59 168	90.44 265	73.12 227	84.20 89	94.36 106	38.04 401	95.73 192	84.12 116	86.81 147	91.33 261

xiu_mvs_v1_base_debi		82.16 167	81.12 170	85.26 146	86.42 288	68.72 89	92.59 168	90.44 265	73.12 227	84.20 89	94.36 106	38.04 401	95.73 192	84.12 116	86.81 147	91.33 261

EPNet		87.84 32	88.38 29	86.23 105	93.30 71	66.05 174	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
MVS_111021_LR		82.02 171	81.52 165	83.51 228	88.42 220	62.88 287	89.77 303	88.93 341	76.78 161	75.55 210	93.10 139	50.31 306	95.38 219	83.82 120	87.02 144	92.26 241

E2		84.45 106	83.74 108	86.56 86	87.90 240	69.06 75	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 75	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

lecture		84.77 99	84.81 94	84.65 179	92.12 107	62.27 301	94.74 56	92.64 140	68.35 332	85.53 75	95.30 74	59.77 178	97.91 50	83.73 123	91.15 93	93.77 185

reproduce-ours		83.51 140	83.33 126	84.06 202	92.18 105	60.49 348	90.74 271	92.04 165	64.35 371	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	84.06 202	92.18 105	60.49 348	90.74 271	92.04 165	64.35 371	83.24 99	95.59 65	59.05 192	97.27 94	83.61 124	89.17 121	94.41 153

RRT-MVS		82.61 160	81.16 168	86.96 63	91.10 143	68.75 86	87.70 352	92.20 157	76.97 156	72.68 251	87.10 287	51.30 296	96.41 149	83.56 126	87.84 135	95.74 59

MSLP-MVS++		86.27 65	85.91 73	87.35 50	92.01 114	68.97 80	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

DeepC-MVS_fast	79.48 2	87.95 29	88.00 35	87.79 33	95.86 29	68.32 99	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
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

reproduce_model		83.15 147	82.96 136	83.73 217	92.02 111	59.74 364	90.37 285	92.08 163	63.70 378	82.86 104	95.48 68	58.62 198	97.17 100	83.06 130	88.42 129	94.26 156

AstraMVS		80.66 201	79.79 199	83.28 237	85.07 324	61.64 318	92.19 186	90.58 259	79.40 103	74.77 224	90.18 221	45.93 360	95.61 206	83.04 131	76.96 277	92.60 224

viewmambaseed2359dif		82.60 161	81.91 161	84.67 178	85.83 304	66.09 173	90.50 280	89.01 336	75.46 181	79.64 155	92.01 172	59.51 183	94.38 267	82.99 132	82.26 210	93.54 192

BP-MVS1		86.54 57	86.68 57	86.13 108	87.80 247	67.18 138	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

E4		84.00 123	83.19 130	86.46 95	86.99 268	68.85 82	92.39 179	90.99 237	79.94 84	80.17 143	91.36 195	59.73 179	95.79 187	82.87 134	84.22 188	94.74 119

SR-MVS		82.81 155	82.58 149	83.50 229	93.35 69	61.16 330	92.23 185	91.28 210	64.48 370	81.27 121	95.28 76	53.71 269	95.86 178	82.87 134	88.77 126	93.49 194

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	58.19 412	93.64 132	73.64 28	92.35 354	82.66 136	78.66 260	96.50 28

ZNCC-MVS		85.33 85	85.08 88	86.06 110	93.09 79	65.65 186	93.89 97	93.41 103	73.75 214	79.94 146	94.68 98	60.61 166	98.03 46	82.63 137	93.72 50	94.52 138

LFMVS		84.34 111	82.73 143	89.18 14	94.76 35	73.25 13	94.99 47	91.89 175	71.90 261	82.16 112	93.49 136	47.98 332	97.05 107	82.55 138	84.82 177	97.25 9

viewdifsd2359ckpt07		82.95 154	82.04 157	85.66 127	87.19 262	66.73 158	91.56 230	90.39 268	77.58 144	77.58 185	91.19 202	58.57 199	95.65 201	82.32 139	82.01 217	94.60 132

VDDNet		80.50 204	78.26 228	87.21 53	86.19 293	69.79 50	94.48 63	91.31 204	60.42 410	79.34 160	90.91 206	38.48 396	96.56 140	82.16 140	81.05 227	95.27 86

viewmacassd2359aftdt		84.03 121	83.18 131	86.59 83	86.76 281	69.44 58	92.44 177	90.85 243	80.38 74	80.78 132	91.33 196	58.54 200	95.62 204	82.15 141	85.41 169	94.72 122

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	95.41 216	82.11 142	85.78 164	94.44 148

viewdifsd2359ckpt13		84.08 120	83.21 128	86.70 75	88.49 214	69.55 57	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

HPM-MVS		83.25 145	82.95 138	84.17 200	92.25 101	62.88 287	90.91 261	91.86 177	70.30 302	77.12 192	93.96 126	56.75 227	96.28 154	82.04 144	91.34 91	93.34 197
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
viewdifsd2359ckpt09		83.52 139	82.57 150	86.37 100	88.02 237	68.47 95	91.78 214	89.63 306	79.61 95	78.56 174	92.00 173	59.28 189	95.96 173	81.94 145	82.35 208	94.69 123

nrg030		80.93 195	79.86 197	84.13 201	83.69 350	68.83 83	93.23 131	91.20 212	75.55 180	75.06 217	88.22 266	63.04 133	94.74 246	81.88 146	66.88 350	88.82 301

E6new		83.62 135	82.65 145	86.55 88	86.98 269	69.29 66	91.69 221	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	86.98 269	69.29 66	91.69 221	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

E5new		83.62 135	82.65 145	86.55 88	86.98 269	69.28 68	91.69 221	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

E5		83.62 135	82.65 145	86.55 88	86.98 269	69.28 68	91.69 221	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

Effi-MVS+		83.82 128	82.76 142	86.99 62	89.56 174	69.40 59	91.35 244	86.12 402	72.59 240	83.22 102	92.81 151	59.60 181	96.01 172	81.76 151	87.80 136	95.56 66

HFP-MVS		84.73 102	84.40 99	85.72 124	93.75 57	65.01 204	93.50 120	93.19 111	72.19 253	79.22 162	94.93 90	59.04 194	97.67 62	81.55 152	92.21 70	94.49 145

ACMMPR		84.37 109	84.06 104	85.28 144	93.56 63	64.37 227	93.50 120	93.15 113	72.19 253	78.85 170	94.86 93	56.69 229	97.45 78	81.55 152	92.20 71	94.02 172

GST-MVS		84.63 104	84.29 101	85.66 127	92.82 88	65.27 196	93.04 138	93.13 114	73.20 224	78.89 165	94.18 118	59.41 186	97.85 54	81.45 154	92.48 69	93.86 182

PMMVS		81.98 172	82.04 157	81.78 282	89.76 170	56.17 405	91.13 257	90.69 253	77.96 132	80.09 145	93.57 134	46.33 356	94.99 236	81.41 155	87.46 140	94.17 162

region2R		84.36 110	84.03 105	85.36 140	93.54 65	64.31 230	93.43 125	92.95 124	72.16 256	78.86 169	94.84 94	56.97 224	97.53 74	81.38 156	92.11 73	94.24 158

CP-MVS		83.71 132	83.40 123	84.65 179	93.14 77	63.84 248	94.59 61	92.28 151	71.03 291	77.41 186	94.92 91	55.21 247	96.19 159	81.32 157	90.70 99	93.91 179

MVS		84.66 103	82.86 141	90.06 3	90.93 146	74.56 7	87.91 347	95.54 15	68.55 329	72.35 264	94.71 97	59.78 177	98.90 24	81.29 158	94.69 32	96.74 17

reproduce_monomvs		79.49 225	79.11 219	80.64 318	92.91 84	61.47 324	91.17 256	93.28 106	83.09 33	64.04 364	82.38 346	66.19 77	94.57 256	81.19 159	57.71 421	85.88 367

test_yl		84.28 112	83.16 132	87.64 37	94.52 42	69.24 70	95.78 18	95.09 27	69.19 319	81.09 124	92.88 148	57.00 222	97.44 79	81.11 160	81.76 221	96.23 40

DCV-MVSNet		84.28 112	83.16 132	87.64 37	94.52 42	69.24 70	95.78 18	95.09 27	69.19 319	81.09 124	92.88 148	57.00 222	97.44 79	81.11 160	81.76 221	96.23 40

guyue		81.23 186	80.57 185	83.21 242	86.64 282	61.85 310	92.52 174	92.78 129	78.69 122	74.92 221	89.42 241	50.07 309	95.35 220	80.79 162	79.31 252	92.42 230

CDPH-MVS		85.71 77	85.46 81	86.46 95	94.75 39	67.19 136	93.89 97	92.83 128	70.90 293	83.09 103	95.28 76	63.62 117	97.36 85	80.63 163	94.18 41	94.84 110

HY-MVS	76.49 5	84.28 112	83.36 125	87.02 61	92.22 102	67.74 119	84.65 379	94.50 53	79.15 110	82.23 111	87.93 271	66.88 71	96.94 122	80.53 164	82.20 214	96.39 34

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	75.68 206	88.27 262	60.18 171	98.60 32	80.46 165	90.27 108	94.96 102

GDP-MVS		85.54 82	85.32 83	86.18 106	87.64 250	67.95 114	92.91 147	92.36 149	77.81 136	83.69 95	94.31 113	72.84 33	96.41 149	80.39 166	85.95 161	94.19 160

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	82.13 215	95.37 74

EIA-MVS		84.84 98	84.88 91	84.69 176	91.30 139	62.36 297	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

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	82.22 213	95.13 93

APD-MVS_3200maxsize		81.64 177	81.32 167	82.59 257	92.36 98	58.74 378	91.39 237	91.01 236	63.35 382	79.72 154	94.62 100	51.82 285	96.14 161	79.71 170	87.93 134	92.89 216

PVSNet_Blended_VisFu		83.97 124	83.50 115	85.39 136	90.02 164	66.59 163	93.77 106	91.73 184	77.43 148	77.08 195	89.81 237	63.77 113	96.97 119	79.67 171	88.21 131	92.60 224

WTY-MVS		86.32 62	85.81 74	87.85 31	92.82 88	69.37 63	95.20 35	95.25 21	82.71 38	81.91 113	94.73 96	67.93 64	97.63 67	79.55 172	82.25 212	96.54 23

viewdifsd2359ckpt11		79.42 229	77.95 235	83.81 212	83.87 347	63.85 246	89.54 310	87.38 379	77.39 150	74.94 219	89.95 234	51.11 298	94.72 247	79.52 173	67.90 342	92.88 217

viewmsd2359difaftdt		79.42 229	77.96 234	83.81 212	83.88 346	63.85 246	89.54 310	87.38 379	77.39 150	74.94 219	89.95 234	51.11 298	94.72 247	79.52 173	67.90 342	92.88 217

MonoMVSNet		76.99 279	75.08 286	82.73 250	83.32 355	63.24 274	86.47 369	86.37 394	79.08 113	66.31 345	79.30 394	49.80 314	91.72 369	79.37 175	65.70 358	93.23 201

EI-MVSNet-Vis-set		83.77 130	83.67 111	84.06 202	92.79 91	63.56 264	91.76 217	94.81 38	79.65 93	77.87 179	94.09 122	63.35 124	97.90 51	79.35 176	79.36 250	90.74 275

PGM-MVS		83.25 145	82.70 144	84.92 157	92.81 90	64.07 240	90.44 281	92.20 157	71.28 285	77.23 190	94.43 104	55.17 248	97.31 89	79.33 177	91.38 89	93.37 196

XVS		83.87 127	83.47 118	85.05 153	93.22 72	63.78 250	92.92 145	92.66 137	73.99 206	78.18 176	94.31 113	55.25 244	97.41 82	79.16 178	91.58 84	93.95 174

X-MVStestdata		76.86 281	74.13 303	85.05 153	93.22 72	63.78 250	92.92 145	92.66 137	73.99 206	78.18 176	10.19 499	55.25 244	97.41 82	79.16 178	91.58 84	93.95 174

CostFormer		82.33 164	81.15 169	85.86 117	89.01 194	68.46 96	82.39 408	93.01 119	75.59 179	80.25 142	81.57 360	72.03 42	94.96 237	79.06 180	77.48 271	94.16 163

mPP-MVS		82.96 153	82.44 153	84.52 186	92.83 86	62.92 285	92.76 152	91.85 179	71.52 281	75.61 209	94.24 116	53.48 273	96.99 115	78.97 181	90.73 98	93.64 190

LuminaMVS		78.14 257	76.66 260	82.60 256	80.82 381	64.64 215	89.33 317	90.45 261	68.25 333	74.73 225	85.51 309	41.15 383	94.14 277	78.96 182	80.69 237	89.04 297

baseline2		83.68 134	83.42 122	84.48 188	87.37 257	66.00 176	90.06 295	95.93 8	79.71 91	69.08 302	90.39 215	77.92 7	96.28 154	78.91 183	81.38 225	91.16 268

CPTT-MVS		79.59 222	79.16 216	80.89 316	91.54 132	59.80 363	92.10 191	88.54 357	60.42 410	72.96 247	93.28 138	48.27 328	92.80 334	78.89 184	86.50 157	90.06 283

SR-MVS-dyc-post		81.06 192	80.70 180	82.15 273	92.02 111	58.56 381	90.90 262	90.45 261	62.76 389	78.89 165	94.46 102	51.26 297	95.61 206	78.77 185	86.77 150	92.28 237

RE-MVS-def			80.48 187		92.02 111	58.56 381	90.90 262	90.45 261	62.76 389	78.89 165	94.46 102	49.30 319		78.77 185	86.77 150	92.28 237

ACMMP		81.49 179	80.67 181	83.93 208	91.71 126	62.90 286	92.13 189	92.22 156	71.79 268	71.68 273	93.49 136	50.32 305	96.96 120	78.47 187	84.22 188	91.93 251
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
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	95.02 233	78.39 188	93.59 53	94.09 167

PAPR		85.15 89	84.47 97	87.18 55	96.02 27	68.29 100	91.85 210	93.00 121	76.59 169	79.03 164	95.00 87	61.59 153	97.61 69	78.16 189	89.00 123	95.63 63

EI-MVSNet-UG-set		83.14 148	82.96 136	83.67 222	92.28 100	63.19 277	91.38 239	94.68 45	79.22 108	76.60 198	93.75 128	62.64 137	97.76 57	78.07 190	78.01 263	90.05 284

CANet_DTU		84.09 119	83.52 113	85.81 119	90.30 159	66.82 154	91.87 208	89.01 336	85.27 13	86.09 69	93.74 129	47.71 338	96.98 116	77.90 191	89.78 116	93.65 189

BP-MVS														77.63 192

HQP-MVS		81.14 189	80.64 182	82.64 254	87.54 252	63.66 261	94.06 83	91.70 189	79.80 88	74.18 229	90.30 218	51.63 290	95.61 206	77.63 192	78.90 256	88.63 303

sss		82.71 158	82.38 154	83.73 217	89.25 184	59.58 367	92.24 184	94.89 32	77.96 132	79.86 147	92.38 159	56.70 228	97.05 107	77.26 194	80.86 233	94.55 134

HQP_MVS		80.34 209	79.75 200	82.12 275	86.94 274	62.42 295	93.13 134	91.31 204	78.81 119	72.53 256	89.14 248	50.66 302	95.55 212	76.74 195	78.53 261	88.39 309

plane_prior5								91.31 204					95.55 212	76.74 195	78.53 261	88.39 309

0.4-1-1-0.2		81.28 185	79.42 208	86.84 65	85.80 306	68.82 84	95.10 39	94.43 58	74.45 196	77.18 191	85.54 308	62.27 143	95.70 198	76.72 197	63.30 383	96.01 46

0.3-1-1-0.015		81.31 183	79.49 206	86.77 73	85.74 308	68.70 93	95.01 46	94.42 59	74.29 201	77.09 194	85.61 307	63.31 126	95.69 200	76.63 198	63.30 383	95.91 53

gm-plane-assit					88.42 220	67.04 143			78.62 123		91.83 180		97.37 84	76.57 199

CHOSEN 280x420		77.35 273	76.95 257	78.55 361	87.07 266	62.68 291	69.71 463	82.95 434	68.80 326	71.48 276	87.27 284	66.03 80	84.00 447	76.47 200	82.81 205	88.95 298

VortexMVS		77.62 268	76.44 263	81.13 303	88.58 203	63.73 254	91.24 250	91.30 208	77.81 136	65.76 347	81.97 352	49.69 315	93.72 299	76.40 201	65.26 363	85.94 365

ab-mvs		80.18 212	78.31 227	85.80 120	88.44 218	65.49 193	83.00 402	92.67 136	71.82 267	77.36 187	85.01 314	54.50 255	96.59 137	76.35 202	75.63 285	95.32 81

0.4-1-1-0.1		80.99 194	79.16 216	86.51 94	85.55 313	68.21 106	94.77 54	94.42 59	73.75 214	76.57 199	85.41 310	62.35 142	95.62 204	76.30 203	63.28 385	95.71 60

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	95.19 230	75.94 204	83.27 201	94.81 116

mmtdpeth		68.33 383	66.37 379	74.21 409	82.81 362	51.73 427	84.34 382	80.42 441	67.01 348	71.56 274	68.58 457	30.52 443	92.35 354	75.89 205	36.21 476	78.56 454

MVSTER		82.47 162	82.05 156	83.74 215	92.68 93	69.01 78	91.90 207	93.21 108	79.83 87	72.14 265	85.71 306	74.72 20	94.72 247	75.72 206	72.49 308	87.50 319

test_fmvs2		65.78 402	64.84 389	68.60 442	66.54 472	41.71 473	83.27 395	69.81 472	54.38 441	67.91 323	84.54 321	15.35 477	81.22 465	75.65 207	66.16 354	82.88 406

tpmrst		80.57 202	79.14 218	84.84 163	90.10 163	68.28 101	81.70 412	89.72 303	77.63 143	75.96 203	79.54 392	64.94 94	92.71 337	75.43 208	77.28 274	93.55 191

旧先验2							92.00 200		59.37 418	87.54 56			93.47 308	75.39 209

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

OPM-MVS		79.00 237	78.09 230	81.73 283	83.52 353	63.83 249	91.64 227	90.30 274	76.36 173	71.97 268	89.93 236	46.30 357	95.17 231	75.10 211	77.70 266	86.19 355
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Effi-MVS+-dtu		76.14 294	75.28 284	78.72 360	83.22 356	55.17 413	89.87 301	87.78 376	75.42 183	67.98 321	81.43 362	45.08 367	92.52 346	75.08 212	71.63 313	88.48 307

HyFIR lowres test		81.03 193	79.56 203	85.43 134	87.81 246	68.11 109	90.18 292	90.01 290	70.65 299	72.95 248	86.06 300	63.61 118	94.50 263	75.01 213	79.75 244	93.67 187

EPP-MVSNet		81.79 174	81.52 165	82.61 255	88.77 200	60.21 356	93.02 140	93.66 89	68.52 330	72.90 249	90.39 215	72.19 41	94.96 237	74.93 214	79.29 253	92.67 221

MVS_Test		84.16 118	83.20 129	87.05 60	91.56 130	69.82 48	89.99 300	92.05 164	77.77 138	82.84 105	86.57 293	63.93 110	96.09 164	74.91 215	89.18 120	95.25 90

VPA-MVSNet		79.03 236	78.00 232	82.11 278	85.95 300	64.48 220	93.22 132	94.66 46	75.05 190	74.04 237	84.95 315	52.17 284	93.52 306	74.90 216	67.04 349	88.32 311

HPM-MVS_fast		80.25 211	79.55 205	82.33 265	91.55 131	59.95 361	91.32 246	89.16 324	65.23 367	74.71 226	93.07 142	47.81 337	95.74 191	74.87 217	88.23 130	91.31 265

AUN-MVS		78.37 252	77.43 245	81.17 301	86.60 284	57.45 394	89.46 315	91.16 214	74.11 204	74.40 228	90.49 213	55.52 243	94.57 256	74.73 218	60.43 412	91.48 258

ECVR-MVS		81.29 184	80.38 189	84.01 207	88.39 222	61.96 307	92.56 171	86.79 391	77.66 141	76.63 197	91.42 191	46.34 355	95.24 229	74.36 219	89.23 118	94.85 107

testing3-2		83.11 149	83.15 134	82.98 245	91.92 118	64.01 243	94.39 72	95.37 17	78.32 127	75.53 211	90.06 233	73.18 30	93.18 318	74.34 220	75.27 287	91.77 253

mvsany_test1		68.77 378	68.56 366	69.39 438	73.57 451	45.88 463	80.93 420	60.88 485	59.65 416	71.56 274	90.26 220	43.22 375	75.05 473	74.26 221	62.70 389	87.25 328

TESTMET0.1,1		82.41 163	81.98 160	83.72 219	88.08 233	63.74 252	92.70 156	93.77 81	79.30 106	77.61 183	87.57 278	58.19 206	94.08 281	73.91 222	86.68 153	93.33 199

icg_test_0407_2		80.38 207	79.22 215	83.88 209	88.54 205	64.75 209	86.79 365	90.80 247	76.73 164	73.95 239	90.18 221	51.55 292	92.45 349	73.47 223	80.95 228	94.43 149

IMVS_0407		80.80 199	79.39 211	85.00 156	88.54 205	64.75 209	88.40 338	90.80 247	76.73 164	73.95 239	90.18 221	51.55 292	95.81 185	73.47 223	80.95 228	94.43 149

IMVS_0404		78.11 258	76.29 269	83.59 224	88.54 205	64.75 209	84.63 380	90.80 247	76.73 164	61.16 388	90.18 221	40.17 387	91.58 374	73.47 223	80.95 228	94.43 149

IMVS_0403		81.19 187	79.88 196	85.13 151	88.54 205	64.75 209	88.84 330	90.80 247	76.73 164	75.21 215	90.18 221	54.22 263	96.21 158	73.47 223	80.95 228	94.43 149

test2506		83.29 144	82.92 139	84.37 192	88.39 222	63.18 278	92.01 197	91.35 203	77.66 141	78.49 175	91.42 191	64.58 101	95.09 232	73.19 227	89.23 118	94.85 107

mvs_anonymous		81.36 182	79.99 194	85.46 133	90.39 158	68.40 97	86.88 364	90.61 258	74.41 197	70.31 289	84.67 318	63.79 112	92.32 356	73.13 228	85.70 165	95.67 61

PS-MVSNAJss		77.26 274	76.31 268	80.13 330	80.64 385	59.16 374	90.63 278	91.06 230	72.80 237	68.58 314	84.57 320	53.55 270	93.96 291	72.97 229	71.96 312	87.27 327

ACMP	71.68 10	75.58 309	74.23 299	79.62 347	84.97 326	59.64 365	90.80 267	89.07 332	70.39 301	62.95 377	87.30 282	38.28 397	93.87 296	72.89 230	71.45 316	85.36 378
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		83.75 131	82.88 140	86.37 100	89.24 187	71.18 26	89.07 325	90.69 253	65.80 360	87.13 57	94.34 111	64.99 92	92.67 340	72.83 231	91.80 80	95.27 86

test_djsdf		73.76 332	72.56 329	77.39 375	77.00 431	53.93 419	89.07 325	90.69 253	65.80 360	63.92 365	82.03 351	43.14 376	92.67 340	72.83 231	68.53 336	85.57 373

test1111		80.84 197	80.02 192	83.33 233	87.87 243	60.76 338	92.62 163	86.86 390	77.86 135	75.73 205	91.39 193	46.35 354	94.70 252	72.79 233	88.68 127	94.52 138

WBMVS		81.67 175	80.98 176	83.72 219	93.07 80	69.40 59	94.33 73	93.05 117	76.84 159	72.05 267	84.14 326	74.49 22	93.88 295	72.76 234	68.09 339	87.88 314

miper_enhance_ethall		78.86 241	77.97 233	81.54 290	88.00 238	65.17 199	91.41 233	89.15 325	75.19 188	68.79 310	83.98 329	67.17 69	92.82 332	72.73 235	65.30 360	86.62 342

OMC-MVS		78.67 248	77.91 237	80.95 312	85.76 307	57.40 395	88.49 336	88.67 351	73.85 211	72.43 262	92.10 169	49.29 320	94.55 260	72.73 235	77.89 264	90.91 274

LPG-MVS_test		75.82 304	74.58 292	79.56 349	84.31 340	59.37 370	90.44 281	89.73 301	69.49 314	64.86 354	88.42 258	38.65 393	94.30 270	72.56 237	72.76 305	85.01 382

LGP-MVS_train				79.56 349	84.31 340	59.37 370		89.73 301	69.49 314	64.86 354	88.42 258	38.65 393	94.30 270	72.56 237	72.76 305	85.01 382

VPNet		78.82 242	77.53 244	82.70 252	84.52 334	66.44 165	93.93 93	92.23 153	80.46 71	72.60 254	88.38 260	49.18 321	93.13 319	72.47 239	63.97 379	88.55 306

GG-mvs-BLEND				86.53 93	91.91 120	69.67 56	75.02 452	94.75 41		78.67 173	90.85 207	77.91 8	94.56 259	72.25 240	93.74 49	95.36 77

test-LLR		80.10 214	79.56 203	81.72 284	86.93 276	61.17 328	92.70 156	91.54 194	71.51 282	75.62 207	86.94 289	53.83 266	92.38 351	72.21 241	84.76 179	91.60 255

test-mter		79.96 217	79.38 212	81.72 284	86.93 276	61.17 328	92.70 156	91.54 194	73.85 211	75.62 207	86.94 289	49.84 313	92.38 351	72.21 241	84.76 179	91.60 255

IB-MVS	77.80 4	82.18 166	80.46 188	87.35 50	89.14 189	70.28 38	95.59 27	95.17 25	78.85 117	70.19 290	85.82 304	70.66 47	97.67 62	72.19 243	66.52 353	94.09 167
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
cl22		77.94 262	76.78 258	81.42 292	87.57 251	64.93 207	90.67 274	88.86 344	72.45 245	67.63 329	82.68 343	64.07 106	92.91 329	71.79 244	65.30 360	86.44 345

v2v482		77.42 272	75.65 279	82.73 250	80.38 389	67.13 140	91.85 210	90.23 279	75.09 189	69.37 298	83.39 335	53.79 268	94.44 264	71.77 245	65.00 367	86.63 341

baseline1		81.84 173	81.03 174	84.28 196	91.60 128	66.62 161	91.08 258	91.66 191	81.87 48	74.86 222	91.67 187	69.98 52	94.92 240	71.76 246	64.75 370	91.29 266

V42		76.46 289	74.55 293	82.19 272	79.14 407	67.82 117	90.26 290	89.42 313	73.75 214	68.63 313	81.89 353	51.31 295	94.09 280	71.69 247	64.84 368	84.66 385

1314		80.70 200	78.95 220	85.94 114	87.77 249	67.56 124	87.91 347	92.55 144	72.17 255	67.44 331	93.09 140	50.27 307	97.04 110	71.68 248	87.64 138	93.23 201

KinetiMVS		81.43 180	80.11 190	85.38 139	86.60 284	65.47 194	92.90 148	93.54 94	75.33 185	77.31 188	90.39 215	46.81 347	96.75 133	71.65 249	86.46 158	93.93 176

CDS-MVSNet		81.43 180	80.74 178	83.52 226	86.26 292	64.45 221	92.09 192	90.65 257	75.83 177	73.95 239	89.81 237	63.97 109	92.91 329	71.27 250	82.82 204	93.20 203
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SSM_0407		79.09 235	77.21 252	84.75 171	88.50 210	66.98 148	89.21 321	87.03 386	67.99 335	74.12 233	89.32 243	47.98 332	95.29 227	71.23 251	79.52 245	91.98 248

SSM_0404		79.46 227	77.65 239	84.91 159	88.37 224	67.04 143	89.59 305	87.03 386	67.99 335	75.45 212	89.32 243	47.98 332	95.34 222	71.23 251	81.90 220	92.34 233

test_vis1_rt		59.09 432	57.31 429	64.43 451	68.44 468	46.02 462	83.05 401	48.63 494	51.96 447	49.57 451	63.86 469	16.30 475	80.20 467	71.21 253	62.79 388	67.07 477

GA-MVS		78.33 254	76.23 270	84.65 179	83.65 351	66.30 169	91.44 232	90.14 283	76.01 175	70.32 288	84.02 328	42.50 377	94.72 247	70.98 254	77.00 276	92.94 213

jajsoiax		73.05 336	71.51 341	77.67 370	77.46 428	54.83 415	88.81 331	90.04 288	69.13 321	62.85 379	83.51 333	31.16 439	92.75 336	70.83 255	69.80 323	85.43 377

3Dnovator+	73.60 7	82.10 170	80.60 184	86.60 81	90.89 148	66.80 156	95.20 35	93.44 100	74.05 205	67.42 332	92.49 156	49.46 317	97.65 66	70.80 256	91.68 82	95.33 79

DP-MVS Recon		82.73 156	81.65 164	85.98 112	97.31 4	67.06 141	95.15 37	91.99 169	69.08 324	76.50 201	93.89 127	54.48 258	98.20 42	70.76 257	85.66 166	92.69 220

miper_ehance_all_eth		77.60 269	76.44 263	81.09 309	85.70 310	64.41 225	90.65 275	88.64 353	72.31 249	67.37 335	82.52 344	64.77 98	92.64 343	70.67 258	65.30 360	86.24 354

PAPM_NR		82.97 152	81.84 162	86.37 100	94.10 49	66.76 157	87.66 353	92.84 127	69.96 308	74.07 236	93.57 134	63.10 132	97.50 76	70.66 259	90.58 101	94.85 107

XVG-OURS-SEG-HR		74.70 321	73.08 319	79.57 348	78.25 420	57.33 396	80.49 422	87.32 381	63.22 384	68.76 311	90.12 232	44.89 368	91.59 373	70.55 260	74.09 296	89.79 288

mvs_tets		72.71 343	71.11 342	77.52 371	77.41 429	54.52 417	88.45 337	89.76 297	68.76 328	62.70 380	83.26 337	29.49 445	92.71 337	70.51 261	69.62 325	85.34 379

cascas		78.18 255	75.77 277	85.41 135	87.14 264	69.11 73	92.96 143	91.15 217	66.71 349	70.47 284	86.07 299	37.49 407	96.48 146	70.15 262	79.80 243	90.65 276

PVSNet_0	68.08 15	71.81 354	68.32 370	82.27 267	84.68 328	62.31 300	88.68 333	90.31 273	75.84 176	57.93 417	80.65 377	37.85 404	94.19 275	69.94 263	29.05 487	90.31 281

thisisatest0515		83.41 142	82.49 152	86.16 107	89.46 177	68.26 102	93.54 117	94.70 44	74.31 200	75.75 204	90.92 205	72.62 35	96.52 143	69.64 264	81.50 224	93.71 186

XXY-MVS		77.94 262	76.44 263	82.43 259	82.60 363	64.44 222	92.01 197	91.83 180	73.59 220	70.00 293	85.82 304	54.43 259	94.76 244	69.63 265	68.02 341	88.10 313

MAR-MVS		84.18 117	83.43 120	86.44 97	96.25 23	65.93 181	94.28 75	94.27 69	74.41 197	79.16 163	95.61 63	53.99 265	98.88 26	69.62 266	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
Patchmatch-RL test		68.17 385	64.49 395	79.19 354	71.22 457	53.93 419	70.07 462	71.54 469	69.22 318	56.79 421	62.89 470	56.58 231	88.61 407	69.53 267	52.61 437	95.03 100

TAMVS		80.37 208	79.45 207	83.13 243	85.14 321	63.37 269	91.23 251	90.76 252	74.81 193	72.65 253	88.49 256	60.63 165	92.95 324	69.41 268	81.95 219	93.08 208

testdata				81.34 296	89.02 193	57.72 388		89.84 295	58.65 422	85.32 80	94.09 122	57.03 220	93.28 314	69.34 269	90.56 102	93.03 210

c3_l		76.83 284	75.47 280	80.93 313	85.02 325	64.18 237	90.39 284	88.11 369	71.66 272	66.65 344	81.64 358	63.58 121	92.56 344	69.31 270	62.86 387	86.04 360

v1144		76.73 287	74.88 287	82.27 267	80.23 393	66.60 162	91.68 225	90.21 282	73.69 217	69.06 303	81.89 353	52.73 280	94.40 266	69.21 271	65.23 364	85.80 368

ETVMVS		84.22 116	83.71 110	85.76 122	92.58 96	68.25 104	92.45 176	95.53 16	79.54 100	79.46 158	91.64 188	70.29 49	94.18 276	69.16 272	82.76 207	94.84 110

Anonymous20240529		76.84 283	74.15 302	84.88 161	91.02 144	64.95 206	93.84 102	91.09 224	53.57 443	73.00 246	87.42 280	35.91 417	97.32 88	69.14 273	72.41 310	92.36 232

XVG-OURS		74.25 324	72.46 331	79.63 346	78.45 418	57.59 392	80.33 424	87.39 378	63.86 376	68.76 311	89.62 239	40.50 386	91.72 369	69.00 274	74.25 294	89.58 291

v148		76.19 293	74.47 295	81.36 295	80.05 395	64.44 222	91.75 219	90.23 279	73.68 218	67.13 336	80.84 373	55.92 240	93.86 298	68.95 275	61.73 401	85.76 371

anonymousdsp		71.14 359	69.37 360	76.45 386	72.95 453	54.71 416	84.19 384	88.88 342	61.92 399	62.15 383	79.77 389	38.14 400	91.44 381	68.90 276	67.45 347	83.21 403

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	67.89 325	92.48 157	48.42 327	98.52 33	68.80 277	94.40 36	95.15 92

test_fmvs3		56.82 434	54.86 437	62.69 456	53.59 488	35.47 485	75.87 448	65.64 479	43.91 472	55.10 425	71.43 450	6.91 492	74.40 476	68.64 278	52.63 436	78.20 456

Anonymous202405211		77.96 261	75.33 283	85.87 116	93.73 58	64.52 217	94.85 52	85.36 411	62.52 392	76.11 202	90.18 221	29.43 446	97.29 90	68.51 279	77.24 275	95.81 57

usedtu_blend_shiyan5		71.06 360	67.54 373	81.62 287	75.39 440	64.75 209	85.67 373	86.47 393	56.48 435	60.64 392	76.85 418	47.20 343	93.71 300	68.18 280	50.98 442	86.40 346

blend_shiyan4		75.18 314	73.00 321	81.69 286	75.62 439	64.75 209	91.78 214	91.06 230	65.89 359	61.35 387	77.39 406	62.16 146	93.71 300	68.18 280	63.60 382	86.61 343

eth_miper_zixun_eth		75.96 302	74.40 296	80.66 317	84.66 330	63.02 280	89.28 319	88.27 365	71.88 263	65.73 348	81.65 357	59.45 184	92.81 333	68.13 282	60.53 410	86.14 356

Elysia		76.45 290	74.17 300	83.30 234	80.43 387	64.12 238	89.58 306	90.83 244	61.78 402	72.53 256	85.92 302	34.30 424	94.81 242	68.10 283	84.01 192	90.97 271

StellarMVS		76.45 290	74.17 300	83.30 234	80.43 387	64.12 238	89.58 306	90.83 244	61.78 402	72.53 256	85.92 302	34.30 424	94.81 242	68.10 283	84.01 192	90.97 271

PVSNet	73.49 8	80.05 215	78.63 223	84.31 194	90.92 147	64.97 205	92.47 175	91.05 233	79.18 109	72.43 262	90.51 212	37.05 413	94.06 283	68.06 285	86.00 160	93.90 181

FA-MVS(test-final)		79.12 234	77.23 251	84.81 167	90.54 153	63.98 245	81.35 417	91.71 186	71.09 290	74.85 223	82.94 339	52.85 277	97.05 107	67.97 286	81.73 223	93.41 195

v144192		76.05 298	74.03 304	82.12 275	79.50 401	66.55 164	91.39 237	89.71 304	72.30 250	68.17 319	81.33 365	51.75 288	94.03 288	67.94 287	64.19 374	85.77 369

UGNet		79.87 219	78.68 222	83.45 231	89.96 165	61.51 321	92.13 189	90.79 251	76.83 160	78.85 170	86.33 297	38.16 399	96.17 160	67.93 288	87.17 143	92.67 221
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
IterMVS-LS		76.49 288	75.18 285	80.43 322	84.49 336	62.74 289	90.64 276	88.80 346	72.40 247	65.16 353	81.72 356	60.98 159	92.27 357	67.74 289	64.65 372	86.29 352
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		78.97 238	78.22 229	81.25 299	85.33 314	62.73 290	89.53 313	93.21 108	72.39 248	72.14 265	90.13 230	60.99 158	94.72 247	67.73 290	72.49 308	86.29 352

gg-mvs-nofinetune		77.18 275	74.31 297	85.80 120	91.42 134	68.36 98	71.78 457	94.72 42	49.61 455	77.12 192	45.92 484	77.41 9	93.98 290	67.62 291	93.16 60	95.05 98

mamba_0408		76.22 292	73.37 314	84.77 168	88.50 210	66.98 148	58.80 483	86.18 400	69.12 322	74.12 233	89.01 251	47.50 339	95.35 220	67.57 292	79.52 245	91.98 248

SSM_04072		74.86 319	73.37 314	79.35 352	88.50 210	66.98 148	58.80 483	86.18 400	69.12 322	74.12 233	89.01 251	47.50 339	79.09 469	67.57 292	79.52 245	91.98 248

LCM-MVSNet-Re		72.93 338	71.84 337	76.18 389	88.49 214	48.02 449	80.07 429	70.17 471	73.96 209	52.25 438	80.09 386	49.98 310	88.24 414	67.35 294	84.23 187	92.28 237

tpm2		79.80 220	77.95 235	85.34 141	88.28 226	68.26 102	81.56 414	91.42 200	70.11 304	77.59 184	80.50 378	67.40 68	94.26 274	67.34 295	77.35 272	93.51 193

v8		75.35 310	73.26 318	81.61 288	80.67 384	66.82 154	89.54 310	89.27 318	71.65 273	63.30 372	80.30 382	54.99 250	94.06 283	67.33 296	62.33 393	83.94 391

sd_testset		77.08 278	75.37 281	82.20 271	89.25 184	62.11 304	82.06 409	89.09 330	76.77 162	70.84 281	87.12 285	41.43 382	95.01 235	67.23 297	74.55 289	89.48 294

UWE-MVS		80.81 198	81.01 175	80.20 328	89.33 180	57.05 399	91.91 206	94.71 43	75.67 178	75.01 218	89.37 242	63.13 131	91.44 381	67.19 298	82.80 206	92.12 245

v1192		75.98 300	73.92 306	82.15 273	79.73 397	66.24 171	91.22 252	89.75 298	72.67 239	68.49 315	81.42 363	49.86 312	94.27 272	67.08 299	65.02 366	85.95 363

114514_t		79.17 233	77.67 238	83.68 221	95.32 31	65.53 191	92.85 150	91.60 193	63.49 380	67.92 322	90.63 210	46.65 351	95.72 197	67.01 300	83.54 198	89.79 288

Fast-Effi-MVS+		81.14 189	80.01 193	84.51 187	90.24 160	65.86 182	94.12 82	89.15 325	73.81 213	75.37 214	88.26 263	57.26 217	94.53 261	66.97 301	84.92 176	93.15 204

无先验							92.71 155	92.61 142	62.03 397				97.01 111	66.63 302		93.97 173

v1921920		75.63 308	73.49 312	82.06 279	79.38 402	66.35 167	91.07 260	89.48 309	71.98 258	67.99 320	81.22 368	49.16 323	93.90 294	66.56 303	64.56 373	85.92 366

cl____		76.07 295	74.67 288	80.28 325	85.15 320	61.76 314	90.12 293	88.73 348	71.16 287	65.43 350	81.57 360	61.15 156	92.95 324	66.54 304	62.17 394	86.13 358

DIV-MVS_self_test		76.07 295	74.67 288	80.28 325	85.14 321	61.75 315	90.12 293	88.73 348	71.16 287	65.42 351	81.60 359	61.15 156	92.94 328	66.54 304	62.16 396	86.14 356

Fast-Effi-MVS+-dtu		75.04 315	73.37 314	80.07 331	80.86 379	59.52 368	91.20 254	85.38 410	71.90 261	65.20 352	84.84 316	41.46 381	92.97 323	66.50 306	72.96 304	87.73 316

UniMVSNet_NR-MVSNet		78.15 256	77.55 243	79.98 335	84.46 337	60.26 354	92.25 182	93.20 110	77.50 146	68.88 308	86.61 292	66.10 79	92.13 360	66.38 307	62.55 390	87.54 318

DU-MVS		76.86 281	75.84 276	79.91 338	82.96 359	60.26 354	91.26 248	91.54 194	76.46 172	68.88 308	86.35 295	56.16 235	92.13 360	66.38 307	62.55 390	87.35 324

1112_ss		80.56 203	79.83 198	82.77 249	88.65 202	60.78 336	92.29 181	88.36 360	72.58 241	72.46 261	94.95 88	65.09 91	93.42 313	66.38 307	77.71 265	94.10 166

FIs		79.47 226	79.41 209	79.67 345	85.95 300	59.40 369	91.68 225	93.94 76	78.06 131	68.96 307	88.28 261	66.61 74	91.77 368	66.20 310	74.99 288	87.82 315

tpm		78.58 249	77.03 254	83.22 240	85.94 302	64.56 216	83.21 398	91.14 218	78.31 128	73.67 242	79.68 390	64.01 108	92.09 362	66.07 311	71.26 318	93.03 210

ACMM	69.62 13	74.34 322	72.73 326	79.17 355	84.25 342	57.87 386	90.36 286	89.93 292	63.17 386	65.64 349	86.04 301	37.79 405	94.10 279	65.89 312	71.52 315	85.55 374
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet		80.92 196	79.98 195	83.74 215	88.48 216	61.80 311	93.44 124	88.26 367	73.96 209	77.73 180	91.76 181	49.94 311	94.76 244	65.84 313	90.37 106	94.65 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Test_1112_low_res		79.56 223	78.60 224	82.43 259	88.24 229	60.39 352	92.09 192	87.99 372	72.10 257	71.84 269	87.42 280	64.62 99	93.04 320	65.80 314	77.30 273	93.85 183

usedtu_dtu_shiyan1		77.89 265	76.39 266	82.40 263	81.92 371	67.01 146	91.94 204	93.00 121	77.01 154	68.44 317	84.15 324	54.78 252	93.25 315	65.76 315	70.53 321	86.94 332

FE-MVSNET3		77.89 265	76.39 266	82.40 263	81.92 371	67.01 146	91.94 204	93.00 121	77.01 154	68.44 317	84.15 324	54.78 252	93.25 315	65.76 315	70.53 321	86.94 332

v10		74.77 320	72.54 330	81.46 291	80.33 391	66.71 159	89.15 324	89.08 331	70.94 292	63.08 375	79.86 387	52.52 281	94.04 286	65.70 317	62.17 394	83.64 394

thisisatest0530		81.15 188	80.07 191	84.39 191	88.26 227	65.63 187	91.40 235	94.62 48	71.27 286	70.93 280	89.18 246	72.47 36	96.04 169	65.62 318	76.89 278	91.49 257

D2MVS		73.80 329	72.02 335	79.15 357	79.15 406	62.97 281	88.58 335	90.07 285	72.94 232	59.22 405	78.30 398	42.31 379	92.70 339	65.59 319	72.00 311	81.79 422

MVP-Stereo		77.12 277	76.23 270	79.79 342	81.72 373	66.34 168	89.29 318	90.88 242	70.56 300	62.01 384	82.88 340	49.34 318	94.13 278	65.55 320	93.80 47	78.88 449
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v1240		75.21 313	72.98 322	81.88 281	79.20 404	66.00 176	90.75 270	89.11 329	71.63 277	67.41 333	81.22 368	47.36 341	93.87 296	65.46 321	64.72 371	85.77 369

miper_lstm_enhance		73.05 336	71.73 339	77.03 380	83.80 348	58.32 383	81.76 410	88.88 342	69.80 311	61.01 389	78.23 400	57.19 218	87.51 426	65.34 322	59.53 415	85.27 381

原ACMM1				84.42 189	93.21 74	64.27 232		93.40 104	65.39 364	79.51 157	92.50 154	58.11 207	96.69 135	65.27 323	93.96 44	92.32 235

tt0805		73.07 335	70.73 347	80.07 331	78.37 419	57.05 399	87.78 350	92.18 160	61.23 406	67.04 337	86.49 294	31.35 438	94.58 254	65.06 324	67.12 348	88.57 305

UniMVSNet (Re)		77.58 270	76.78 258	79.98 335	84.11 343	60.80 335	91.76 217	93.17 112	76.56 170	69.93 296	84.78 317	63.32 125	92.36 353	64.89 325	62.51 392	86.78 336

wanda-best-256-512		72.42 348	69.43 358	81.37 293	75.39 440	64.24 234	91.58 228	91.09 224	66.36 352	60.64 392	76.86 416	47.20 343	93.47 308	64.80 326	50.98 442	86.40 346

FE-blended-shiyan7		72.42 348	69.43 358	81.37 293	75.39 440	64.24 234	91.58 228	91.09 224	66.36 352	60.64 392	76.86 416	47.20 343	93.47 308	64.80 326	50.98 442	86.40 346

blended_shiyan6		72.26 350	69.26 361	81.27 298	75.24 444	64.00 244	91.37 240	91.06 230	66.12 356	60.34 398	76.75 419	46.82 346	93.45 311	64.61 328	50.98 442	86.37 349

BH-w/o		80.49 205	79.30 213	84.05 205	90.83 150	64.36 229	93.60 114	89.42 313	74.35 199	69.09 301	90.15 229	55.23 246	95.61 206	64.61 328	86.43 159	92.17 243

blended_shiyan8		72.26 350	69.25 362	81.29 297	75.23 445	64.03 241	91.36 243	91.04 234	66.11 357	60.42 397	76.73 420	46.79 348	93.45 311	64.58 330	51.00 441	86.37 349

AdaColmap		78.94 239	77.00 256	84.76 170	96.34 18	65.86 182	92.66 162	87.97 374	62.18 394	70.56 283	92.37 160	43.53 373	97.35 86	64.50 331	82.86 203	91.05 270

PCF-MVS	73.15 9	79.29 231	77.63 241	84.29 195	86.06 298	65.96 178	87.03 360	91.10 223	69.86 310	69.79 297	90.64 208	57.54 216	96.59 137	64.37 332	82.29 209	90.32 280
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
API-MVS		82.28 165	80.53 186	87.54 44	96.13 24	70.59 33	93.63 113	91.04 234	65.72 362	75.45 212	92.83 150	56.11 237	98.89 25	64.10 333	89.75 117	93.15 204

UniMVSNet_ETH3D		72.74 342	70.53 349	79.36 351	78.62 416	56.64 403	85.01 377	89.20 321	63.77 377	64.84 356	84.44 322	34.05 426	91.86 366	63.94 334	70.89 320	89.57 292

Anonymous20231211		73.08 334	70.39 350	81.13 303	90.62 152	63.33 270	91.40 235	90.06 287	51.84 448	64.46 361	80.67 376	36.49 415	94.07 282	63.83 335	64.17 375	85.98 362

MS-PatchMatch		77.90 264	76.50 262	82.12 275	85.99 299	69.95 44	91.75 219	92.70 132	73.97 208	62.58 381	84.44 322	41.11 384	95.78 188	63.76 336	92.17 72	80.62 433

新几何1				84.73 172	92.32 99	64.28 231		91.46 199	59.56 417	79.77 152	92.90 146	56.95 225	96.57 139	63.40 337	92.91 63	93.34 197

dmvs_re		76.93 280	75.36 282	81.61 288	87.78 248	60.71 342	80.00 430	87.99 372	79.42 102	69.02 304	89.47 240	46.77 349	94.32 268	63.38 338	74.45 292	89.81 287

GeoE		78.90 240	77.43 245	83.29 236	88.95 195	62.02 305	92.31 180	86.23 398	70.24 303	71.34 278	89.27 245	54.43 259	94.04 286	63.31 339	80.81 235	93.81 184

IterMVS		72.65 346	70.83 344	78.09 367	82.17 367	62.96 282	87.64 354	86.28 396	71.56 280	60.44 396	78.85 396	45.42 364	86.66 430	63.30 340	61.83 398	84.65 386
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	48.56 21	66.77 396	64.41 396	73.84 411	70.65 461	50.31 439	77.79 441	85.73 407	45.54 466	44.76 467	82.14 350	35.40 419	90.14 396	63.18 341	74.54 291	81.07 428
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		73.92 328	71.81 338	80.25 327	79.17 405	65.24 197	87.43 356	87.26 384	67.64 342	63.46 370	83.91 330	48.96 325	91.53 379	62.94 342	65.49 359	83.96 390

tttt0517		79.50 224	78.53 225	82.41 262	87.22 261	61.43 325	89.75 304	94.76 40	69.29 317	67.91 323	88.06 270	72.92 32	95.63 202	62.91 343	73.90 299	90.16 282

FC-MVSNet-test		77.99 260	78.08 231	77.70 369	84.89 327	55.51 411	90.27 289	93.75 85	76.87 157	66.80 342	87.59 277	65.71 85	90.23 394	62.89 344	73.94 297	87.37 323

Baseline_NR-MVSNet		73.99 327	72.83 323	77.48 373	80.78 382	59.29 373	91.79 212	84.55 419	68.85 325	68.99 305	80.70 374	56.16 235	92.04 363	62.67 345	60.98 407	81.11 427

IterMVS-SCA-FT		71.55 357	69.97 352	76.32 387	81.48 375	60.67 344	87.64 354	85.99 403	66.17 355	59.50 403	78.88 395	45.53 362	83.65 449	62.58 346	61.93 397	84.63 388

IS-MVSNet		80.14 213	79.41 209	82.33 265	87.91 239	60.08 359	91.97 201	88.27 365	72.90 236	71.44 277	91.73 183	61.44 154	93.66 304	62.47 347	86.53 156	93.24 200

WR-MVS		76.76 286	75.74 278	79.82 341	84.60 331	62.27 301	92.60 166	92.51 145	76.06 174	67.87 326	85.34 311	56.76 226	90.24 393	62.20 348	63.69 381	86.94 332

pmmvs5		73.35 333	71.52 340	78.86 359	78.64 415	60.61 346	91.08 258	86.90 388	67.69 339	63.32 371	83.64 331	44.33 371	90.53 387	62.04 349	66.02 355	85.46 376

TranMVSNet+NR-MVSNet		75.86 303	74.52 294	79.89 339	82.44 365	60.64 345	91.37 240	91.37 202	76.63 168	67.65 328	86.21 298	52.37 283	91.55 375	61.84 350	60.81 408	87.48 320

CVMVSNet		74.04 326	74.27 298	73.33 414	85.33 314	43.94 468	89.53 313	88.39 359	54.33 442	70.37 287	90.13 230	49.17 322	84.05 445	61.83 351	79.36 250	91.99 247

PM-MVS		59.40 430	56.59 432	67.84 443	63.63 476	41.86 471	76.76 443	63.22 482	59.01 420	51.07 445	72.27 444	11.72 484	83.25 454	61.34 352	50.28 449	78.39 455

testdata2													96.09 164	61.26 353

UA-Net		80.02 216	79.65 201	81.11 305	89.33 180	57.72 388	86.33 370	89.00 340	77.44 147	81.01 127	89.15 247	59.33 187	95.90 175	61.01 354	84.28 186	89.73 290

gbinet_0.2-2-1-0.02		71.92 353	68.92 364	80.91 314	75.87 438	63.30 271	91.95 203	91.40 201	65.62 363	61.57 386	77.27 410	44.71 369	92.88 331	61.00 355	50.87 446	86.54 344

NR-MVSNet		76.05 298	74.59 291	80.44 321	82.96 359	62.18 303	90.83 266	91.73 184	77.12 153	60.96 390	86.35 295	59.28 189	91.80 367	60.74 356	61.34 405	87.35 324

XVG-ACMP-BASELINE		68.04 386	65.53 386	75.56 391	74.06 450	52.37 424	78.43 436	85.88 404	62.03 397	58.91 409	81.21 370	20.38 470	91.15 383	60.69 357	68.18 338	83.16 404

test_post1							78.95 433				20.70 497	53.05 275	91.50 380	60.43 358

SCA		75.82 304	72.76 324	85.01 155	86.63 283	70.08 40	81.06 419	89.19 322	71.60 278	70.01 292	77.09 413	45.53 362	90.25 390	60.43 358	73.27 301	94.68 125

pm-mvs1		72.89 339	71.09 343	78.26 365	79.10 408	57.62 390	90.80 267	89.30 317	67.66 340	62.91 378	81.78 355	49.11 324	92.95 324	60.29 360	58.89 418	84.22 389

TR-MVS		78.77 245	77.37 250	82.95 246	90.49 155	60.88 334	93.67 110	90.07 285	70.08 307	74.51 227	91.37 194	45.69 361	95.70 198	60.12 361	80.32 239	92.29 236

MDTV_nov1_ep13_2view						59.90 362	80.13 428		67.65 341	72.79 250		54.33 261		59.83 362		92.58 226

GBi-Net		75.65 306	73.83 307	81.10 306	88.85 196	65.11 201	90.01 297	90.32 270	70.84 294	67.04 337	80.25 383	48.03 329	91.54 376	59.80 363	69.34 327	86.64 338

test1		75.65 306	73.83 307	81.10 306	88.85 196	65.11 201	90.01 297	90.32 270	70.84 294	67.04 337	80.25 383	48.03 329	91.54 376	59.80 363	69.34 327	86.64 338

FMVSNet3		77.73 267	76.04 273	82.80 248	91.20 142	68.99 79	91.87 208	91.99 169	73.35 223	67.04 337	83.19 338	56.62 230	92.14 359	59.80 363	69.34 327	87.28 326

BH-untuned		78.68 246	77.08 253	83.48 230	89.84 167	63.74 252	92.70 156	88.59 354	71.57 279	66.83 341	88.65 255	51.75 288	95.39 218	59.03 366	84.77 178	91.32 264

Vis-MVSNet (Re-imp)		79.24 232	79.57 202	78.24 366	88.46 217	52.29 425	90.41 283	89.12 328	74.24 202	69.13 300	91.91 179	65.77 84	90.09 397	59.00 367	88.09 132	92.33 234

FMVSNet2		76.07 295	74.01 305	82.26 269	88.85 196	67.66 121	91.33 245	91.61 192	70.84 294	65.98 346	82.25 348	48.03 329	92.00 364	58.46 368	68.73 335	87.10 329

mvsany_test3		48.86 444	46.35 447	56.41 459	46.00 494	31.67 490	62.26 476	47.25 495	43.71 473	45.54 465	68.15 460	10.84 485	64.44 492	57.95 369	35.44 480	73.13 468

v7n		71.31 358	68.65 365	79.28 353	76.40 433	60.77 337	86.71 366	89.45 311	64.17 374	58.77 410	78.24 399	44.59 370	93.54 305	57.76 370	61.75 400	83.52 397

QAPM		79.95 218	77.39 249	87.64 37	89.63 172	71.41 22	93.30 129	93.70 87	65.34 366	67.39 334	91.75 182	47.83 336	98.96 20	57.71 371	89.81 114	92.54 227

EPMVS		78.49 251	75.98 274	86.02 111	91.21 141	69.68 55	80.23 426	91.20 212	75.25 187	72.48 260	78.11 401	54.65 254	93.69 303	57.66 372	83.04 202	94.69 123

UWE-MVS-28		76.83 284	77.60 242	74.51 404	84.58 333	50.34 438	88.22 341	94.60 50	74.46 195	66.66 343	88.98 253	62.53 139	85.50 439	57.55 373	80.80 236	87.69 317

WB-MVSnew		77.14 276	76.18 272	80.01 334	86.18 294	63.24 274	91.26 248	94.11 73	71.72 271	73.52 243	87.29 283	45.14 366	93.00 322	56.98 374	79.42 248	83.80 393

PLC	68.80 14	75.23 312	73.68 310	79.86 340	92.93 83	58.68 379	90.64 276	88.30 363	60.90 407	64.43 362	90.53 211	42.38 378	94.57 256	56.52 375	76.54 280	86.33 351
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPNet_dtu		78.80 243	79.26 214	77.43 374	88.06 234	49.71 442	91.96 202	91.95 171	77.67 140	76.56 200	91.28 197	58.51 201	90.20 395	56.37 376	80.95 228	92.39 231
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-RMVSNet		79.46 227	77.65 239	84.89 160	91.68 127	65.66 185	93.55 116	88.09 370	72.93 233	73.37 244	91.12 204	46.20 358	96.12 162	56.28 377	85.61 167	92.91 214

UnsupCasMVSNet_eth		65.79 401	63.10 403	73.88 410	70.71 460	50.29 440	81.09 418	89.88 294	72.58 241	49.25 453	74.77 434	32.57 432	87.43 427	55.96 378	41.04 468	83.90 392

pmmvs6		67.57 390	64.76 391	76.00 390	72.82 455	53.37 421	88.71 332	86.78 392	53.19 444	57.58 419	78.03 402	35.33 420	92.41 350	55.56 379	54.88 431	82.21 418

pmmvs-eth3d		65.53 404	62.32 409	75.19 395	69.39 466	59.59 366	82.80 403	83.43 430	62.52 392	51.30 444	72.49 439	32.86 429	87.16 429	55.32 380	50.73 447	78.83 450

FE-MVS		75.97 301	73.02 320	84.82 164	89.78 168	65.56 189	77.44 442	91.07 229	64.55 369	72.66 252	79.85 388	46.05 359	96.69 135	54.97 381	80.82 234	92.21 242

OpenMVS	70.45 11	78.54 250	75.92 275	86.41 99	85.93 303	71.68 20	92.74 153	92.51 145	66.49 351	64.56 358	91.96 175	43.88 372	98.10 45	54.61 382	90.65 100	89.44 296

FMVSNet1		72.71 343	69.91 354	81.10 306	83.60 352	65.11 201	90.01 297	90.32 270	63.92 375	63.56 369	80.25 383	36.35 416	91.54 376	54.46 383	66.75 351	86.64 338

PatchmatchNet		77.46 271	74.63 290	85.96 113	89.55 175	70.35 37	79.97 431	89.55 308	72.23 252	70.94 279	76.91 415	57.03 220	92.79 335	54.27 384	81.17 226	94.74 119
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		71.64 355	68.44 368	81.23 300	81.97 370	64.44 222	73.05 454	88.80 346	69.67 313	64.59 357	74.79 433	32.79 430	87.82 418	53.99 385	76.35 281	91.42 259

FE-MVSNET2		66.80 395	64.06 398	75.03 397	69.84 463	57.11 397	86.57 367	88.57 356	67.94 337	50.97 446	72.16 445	33.79 427	87.55 425	53.94 386	52.74 435	80.45 435

CNLPA		74.31 323	72.30 332	80.32 323	91.49 133	61.66 317	90.85 265	80.72 440	56.67 434	63.85 367	90.64 208	46.75 350	90.84 384	53.79 387	75.99 284	88.47 308

tpm cat1		75.30 311	72.21 333	84.58 184	88.52 209	67.77 118	78.16 440	88.02 371	61.88 400	68.45 316	76.37 424	60.65 164	94.03 288	53.77 388	74.11 295	91.93 251

OurMVSNet-221017-0		64.68 406	62.17 410	72.21 424	76.08 436	47.35 453	80.67 421	81.02 438	56.19 436	51.60 441	79.66 391	27.05 453	88.56 409	53.60 389	53.63 434	80.71 432

PatchMatch-RL		72.06 352	69.98 351	78.28 364	89.51 176	55.70 410	83.49 391	83.39 432	61.24 405	63.72 368	82.76 341	34.77 421	93.03 321	53.37 390	77.59 267	86.12 359

CR-MVSNet		73.79 330	70.82 346	82.70 252	83.15 357	67.96 112	70.25 460	84.00 424	73.67 219	69.97 294	72.41 441	57.82 213	89.48 403	52.99 391	73.13 302	90.64 277

SSC-MVS3.2		74.92 318	73.32 317	79.74 344	86.53 286	60.31 353	89.03 328	92.70 132	78.61 124	68.98 306	83.34 336	41.93 380	92.23 358	52.77 392	65.97 356	86.69 337

USDC		67.43 393	64.51 394	76.19 388	77.94 424	55.29 412	78.38 437	85.00 414	73.17 225	48.36 456	80.37 380	21.23 467	92.48 348	52.15 393	64.02 378	80.81 431

CP-MVSNet		70.50 363	69.91 354	72.26 423	80.71 383	51.00 434	87.23 359	90.30 274	67.84 338	59.64 402	82.69 342	50.23 308	82.30 460	51.28 394	59.28 416	83.46 399

F-COLMAP		70.66 361	68.44 368	77.32 376	86.37 291	55.91 408	88.00 345	86.32 395	56.94 432	57.28 420	88.07 269	33.58 428	92.49 347	51.02 395	68.37 337	83.55 395

sc_t1		63.81 412	59.39 420	77.10 379	77.62 426	56.03 407	84.32 383	73.56 461	46.66 464	58.22 411	73.06 437	23.28 463	90.62 385	50.93 396	46.84 455	84.64 387

PS-CasMVS		69.86 370	69.13 363	72.07 427	80.35 390	50.57 437	87.02 361	89.75 298	67.27 344	59.19 406	82.28 347	46.58 352	82.24 461	50.69 397	59.02 417	83.39 401

dp		75.01 316	72.09 334	83.76 214	89.28 183	66.22 172	79.96 432	89.75 298	71.16 287	67.80 327	77.19 412	51.81 286	92.54 345	50.39 398	71.44 317	92.51 229

test_vis3_rt		40.46 452	37.79 453	48.47 471	44.49 496	33.35 488	66.56 472	32.84 502	32.39 483	29.65 484	39.13 492	3.91 499	68.65 482	50.17 399	40.99 469	43.40 487

test0.0.03 1		72.76 341	72.71 327	72.88 418	80.25 392	47.99 450	91.22 252	89.45 311	71.51 282	62.51 382	87.66 275	53.83 266	85.06 441	50.16 400	67.84 346	85.58 372

UnsupCasMVSNet_bld		61.60 420	57.71 424	73.29 415	68.73 467	51.64 428	78.61 435	89.05 334	57.20 430	46.11 460	61.96 474	28.70 448	88.60 408	50.08 401	38.90 473	79.63 442

K. test v3		63.09 415	59.61 419	73.53 413	76.26 434	49.38 446	83.27 395	77.15 448	64.35 371	47.77 458	72.32 443	28.73 447	87.79 419	49.93 402	36.69 475	83.41 400

JIA-IIPM		66.06 399	62.45 408	76.88 384	81.42 377	54.45 418	57.49 485	88.67 351	49.36 456	63.86 366	46.86 483	56.06 238	90.25 390	49.53 403	68.83 333	85.95 363

CL-MVSNet_self_test		69.92 368	68.09 371	75.41 392	73.25 452	55.90 409	90.05 296	89.90 293	69.96 308	61.96 385	76.54 421	51.05 300	87.64 421	49.51 404	50.59 448	82.70 412

mvs5depth		61.03 423	57.65 426	71.18 430	67.16 471	47.04 458	72.74 455	77.49 446	57.47 428	60.52 395	72.53 438	22.84 464	88.38 412	49.15 405	38.94 472	78.11 457

FMVSNet5		68.04 386	65.66 385	75.18 396	84.43 338	57.89 385	83.54 389	86.26 397	61.83 401	53.64 433	73.30 436	37.15 411	85.08 440	48.99 406	61.77 399	82.56 415

TransMVSNet (Re)		70.07 367	67.66 372	77.31 377	80.62 386	59.13 375	91.78 214	84.94 415	65.97 358	60.08 401	80.44 379	50.78 301	91.87 365	48.84 407	45.46 460	80.94 429

SD_0403		73.79 330	73.48 313	74.69 401	85.33 314	45.56 464	83.80 387	85.57 409	76.55 171	62.96 376	88.45 257	50.62 304	87.59 424	48.80 408	79.28 254	90.92 273

EU-MVSNet		64.01 410	63.01 404	67.02 448	74.40 449	38.86 482	83.27 395	86.19 399	45.11 468	54.27 428	81.15 371	36.91 414	80.01 468	48.79 409	57.02 423	82.19 419

PEN-MVS		69.46 373	68.56 366	72.17 425	79.27 403	49.71 442	86.90 363	89.24 319	67.24 347	59.08 407	82.51 345	47.23 342	83.54 451	48.42 410	57.12 422	83.25 402

KD-MVS_self_test		60.87 424	58.60 422	67.68 445	66.13 473	39.93 479	75.63 451	84.70 416	57.32 429	49.57 451	68.45 458	29.55 444	82.87 456	48.09 411	47.94 452	80.25 439

KD-MVS_2432*1600		69.03 376	66.37 379	77.01 381	85.56 311	61.06 331	81.44 415	90.25 277	67.27 344	58.00 415	76.53 422	54.49 256	87.63 422	48.04 412	35.77 478	82.34 416

miper_refine_blended		69.03 376	66.37 379	77.01 381	85.56 311	61.06 331	81.44 415	90.25 277	67.27 344	58.00 415	76.53 422	54.49 256	87.63 422	48.04 412	35.77 478	82.34 416

MDTV_nov1_ep13			72.61 328		89.06 191	68.48 94	80.33 424	90.11 284	71.84 266	71.81 270	75.92 428	53.01 276	93.92 293	48.04 412	73.38 300

thres200		79.66 221	78.33 226	83.66 223	92.54 97	65.82 184	93.06 136	96.31 3	74.90 192	73.30 245	88.66 254	59.67 180	95.61 206	47.84 415	78.67 259	89.56 293

tt0320-xc		61.51 422	56.89 431	75.37 393	78.50 417	58.61 380	82.61 406	71.27 470	44.31 471	53.17 434	68.03 461	23.38 461	88.46 411	47.77 416	43.00 465	79.03 448

RPSCF		64.24 409	61.98 411	71.01 432	76.10 435	45.00 465	75.83 449	75.94 451	46.94 462	58.96 408	84.59 319	31.40 437	82.00 462	47.76 417	60.33 414	86.04 360

lessismore_v0				73.72 412	72.93 454	47.83 451		61.72 484		45.86 463	73.76 435	28.63 449	89.81 400	47.75 418	31.37 483	83.53 396

EG-PatchMatch MVS		68.55 380	65.41 387	77.96 368	78.69 414	62.93 283	89.86 302	89.17 323	60.55 409	50.27 448	77.73 405	22.60 465	94.06 283	47.18 419	72.65 307	76.88 461

test_f		46.58 445	43.45 449	55.96 460	45.18 495	32.05 489	61.18 477	49.49 493	33.39 482	42.05 475	62.48 473	7.00 491	65.56 488	47.08 420	43.21 464	70.27 474

ACMH+	65.35 16	67.65 389	64.55 393	76.96 383	84.59 332	57.10 398	88.08 342	80.79 439	58.59 423	53.00 435	81.09 372	26.63 454	92.95 324	46.51 421	61.69 403	80.82 430

Anonymous20240521		62.09 417	59.08 421	71.10 431	67.19 470	48.72 448	83.91 386	85.23 412	50.38 453	47.84 457	71.22 451	20.74 468	85.51 438	46.47 422	58.75 419	79.06 446

WR-MVS_H		70.59 362	69.94 353	72.53 420	81.03 378	51.43 430	87.35 357	92.03 168	67.38 343	60.23 400	80.70 374	55.84 241	83.45 452	46.33 423	58.58 420	82.72 410

Patchmtry		67.53 391	63.93 399	78.34 362	82.12 368	64.38 226	68.72 464	84.00 424	48.23 460	59.24 404	72.41 441	57.82 213	89.27 404	46.10 424	56.68 426	81.36 424

SixPastTwentyTwo		64.92 405	61.78 412	74.34 407	78.74 413	49.76 441	83.42 394	79.51 445	62.86 388	50.27 448	77.35 407	30.92 441	90.49 388	45.89 425	47.06 454	82.78 407

ambc				69.61 437	61.38 482	41.35 474	49.07 490	85.86 406		50.18 450	66.40 463	10.16 486	88.14 415	45.73 426	44.20 461	79.32 445

tt0320		61.85 418	57.45 427	75.03 397	77.49 427	57.60 391	82.74 404	73.65 460	43.65 474	53.65 432	68.18 459	25.47 456	88.66 406	45.56 427	46.68 456	78.81 451

thres100view900		78.37 252	77.01 255	82.46 258	91.89 121	63.21 276	91.19 255	96.33 1	72.28 251	70.45 286	87.89 272	60.31 169	95.32 223	45.16 428	77.58 268	88.83 299

tfpn200view9		78.79 244	77.43 245	82.88 247	92.21 103	64.49 218	92.05 195	96.28 4	73.48 221	71.75 271	88.26 263	60.07 174	95.32 223	45.16 428	77.58 268	88.83 299

thres400		78.68 246	77.43 245	82.43 259	92.21 103	64.49 218	92.05 195	96.28 4	73.48 221	71.75 271	88.26 263	60.07 174	95.32 223	45.16 428	77.58 268	87.48 320

DTE-MVSNet		68.46 382	67.33 375	71.87 429	77.94 424	49.00 447	86.16 371	88.58 355	66.36 352	58.19 412	82.21 349	46.36 353	83.87 448	44.97 431	55.17 429	82.73 409

pmmvs3		55.51 436	51.50 442	67.53 446	57.90 485	50.93 435	80.37 423	73.66 459	40.63 479	44.15 470	64.75 467	16.30 475	78.97 470	44.77 432	40.98 470	72.69 469

our_test_3		68.29 384	64.69 392	79.11 358	78.92 409	64.85 208	88.40 338	85.06 413	60.32 412	52.68 436	76.12 426	40.81 385	89.80 402	44.25 433	55.65 427	82.67 414

tpmvs		72.88 340	69.76 356	82.22 270	90.98 145	67.05 142	78.22 439	88.30 363	63.10 387	64.35 363	74.98 431	55.09 249	94.27 272	43.25 434	69.57 326	85.34 379

ITE_SJBPF				70.43 434	74.44 448	47.06 457		77.32 447	60.16 413	54.04 430	83.53 332	23.30 462	84.01 446	43.07 435	61.58 404	80.21 440

Anonymous20231206		67.53 391	65.78 382	72.79 419	74.95 446	47.59 452	88.23 340	87.32 381	61.75 404	58.07 414	77.29 409	37.79 405	87.29 428	42.91 436	63.71 380	83.48 398

YYNet1		63.76 414	60.14 417	74.62 403	78.06 423	60.19 357	83.46 393	83.99 426	56.18 437	39.25 477	71.56 449	37.18 410	83.34 453	42.90 437	48.70 451	80.32 437

MDA-MVSNet_test_wron		63.78 413	60.16 416	74.64 402	78.15 422	60.41 350	83.49 391	84.03 422	56.17 438	39.17 478	71.59 448	37.22 409	83.24 455	42.87 438	48.73 450	80.26 438

MSDG		69.54 372	65.73 383	80.96 311	85.11 323	63.71 256	84.19 384	83.28 433	56.95 431	54.50 427	84.03 327	31.50 436	96.03 170	42.87 438	69.13 332	83.14 405

thres600view7		78.00 259	76.66 260	82.03 280	91.93 117	63.69 259	91.30 247	96.33 1	72.43 246	70.46 285	87.89 272	60.31 169	94.92 240	42.64 440	76.64 279	87.48 320

usedtu_dtu_shiyan2		57.76 433	53.69 439	69.95 436	57.60 486	41.80 472	83.50 390	83.67 428	45.26 467	43.79 471	62.82 471	17.63 474	85.93 434	42.56 441	46.40 458	82.12 420

ACMH	63.93 17	68.62 379	64.81 390	80.03 333	85.22 319	63.25 273	87.72 351	84.66 417	60.83 408	51.57 442	79.43 393	27.29 452	94.96 237	41.76 442	64.84 368	81.88 421
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testgi		64.48 408	62.87 406	69.31 439	71.24 456	40.62 476	85.49 374	79.92 443	65.36 365	54.18 429	83.49 334	23.74 460	84.55 442	41.60 443	60.79 409	82.77 408

PatchT		69.11 375	65.37 388	80.32 323	82.07 369	63.68 260	67.96 469	87.62 377	50.86 452	69.37 298	65.18 465	57.09 219	88.53 410	41.59 444	66.60 352	88.74 302

LF4IMVS		54.01 439	52.12 440	59.69 457	62.41 479	39.91 480	68.59 465	68.28 476	42.96 476	44.55 469	75.18 430	14.09 482	68.39 483	41.36 445	51.68 439	70.78 472

ADS-MVSNet2		66.90 394	63.44 402	77.26 378	88.06 234	60.70 343	68.01 467	75.56 454	57.57 425	64.48 359	69.87 453	38.68 391	84.10 444	40.87 446	67.89 344	86.97 330

ADS-MVSNet		68.54 381	64.38 397	81.03 310	88.06 234	66.90 153	68.01 467	84.02 423	57.57 425	64.48 359	69.87 453	38.68 391	89.21 405	40.87 446	67.89 344	86.97 330

LTVRE_ROB	59.60 19	66.27 398	63.54 401	74.45 405	84.00 345	51.55 429	67.08 471	83.53 429	58.78 421	54.94 426	80.31 381	34.54 422	93.23 317	40.64 448	68.03 340	78.58 453
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
MVS-HIRNet		60.25 428	55.55 435	74.35 406	84.37 339	56.57 404	71.64 458	74.11 458	34.44 481	45.54 465	42.24 489	31.11 440	89.81 400	40.36 449	76.10 283	76.67 462

ppachtmachnet_test		67.72 388	63.70 400	79.77 343	78.92 409	66.04 175	88.68 333	82.90 435	60.11 414	55.45 424	75.96 427	39.19 390	90.55 386	39.53 450	52.55 438	82.71 411

new-patchmatchnet		59.30 431	56.48 433	67.79 444	65.86 474	44.19 466	82.47 407	81.77 436	59.94 415	43.65 472	66.20 464	27.67 451	81.68 463	39.34 451	41.40 467	77.50 459

AllTest		61.66 419	58.06 423	72.46 421	79.57 398	51.42 431	80.17 427	68.61 474	51.25 450	45.88 461	81.23 366	19.86 472	86.58 431	38.98 452	57.01 424	79.39 443

TestCases				72.46 421	79.57 398	51.42 431		68.61 474	51.25 450	45.88 461	81.23 366	19.86 472	86.58 431	38.98 452	57.01 424	79.39 443

test20.03		63.83 411	62.65 407	67.38 447	70.58 462	39.94 478	86.57 367	84.17 421	63.29 383	51.86 440	77.30 408	37.09 412	82.47 458	38.87 454	54.13 433	79.73 441

TAPA-MVS	70.22 12	74.94 317	73.53 311	79.17 355	90.40 157	52.07 426	89.19 323	89.61 307	62.69 391	70.07 291	92.67 152	48.89 326	94.32 268	38.26 455	79.97 241	91.12 269
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tmp_tt		22.26 463	23.75 465	17.80 481	5.23 505	12.06 506	35.26 492	39.48 499	2.82 499	18.94 490	44.20 488	22.23 466	24.64 500	36.30 456	9.31 497	16.69 494

DSMNet-mixed		56.78 435	54.44 438	63.79 452	63.21 477	29.44 494	64.43 474	64.10 481	42.12 478	51.32 443	71.60 447	31.76 435	75.04 474	36.23 457	65.20 365	86.87 335

TinyColmap		60.32 427	56.42 434	72.00 428	78.78 412	53.18 422	78.36 438	75.64 453	52.30 445	41.59 476	75.82 429	14.76 480	88.35 413	35.84 458	54.71 432	74.46 465

MDA-MVSNet-bldmvs		61.54 421	57.70 425	73.05 416	79.53 400	57.00 402	83.08 399	81.23 437	57.57 425	34.91 482	72.45 440	32.79 430	86.26 433	35.81 459	41.95 466	75.89 463

RPMNet		70.42 364	65.68 384	84.63 182	83.15 357	67.96 112	70.25 460	90.45 261	46.83 463	69.97 294	65.10 466	56.48 234	95.30 226	35.79 460	73.13 302	90.64 277

Patchmatch-test		65.86 400	60.94 414	80.62 320	83.75 349	58.83 377	58.91 482	75.26 456	44.50 470	50.95 447	77.09 413	58.81 197	87.90 416	35.13 461	64.03 377	95.12 94

OpenMVS_ROB	61.12 18	66.39 397	62.92 405	76.80 385	76.51 432	57.77 387	89.22 320	83.41 431	55.48 439	53.86 431	77.84 403	26.28 455	93.95 292	34.90 462	68.76 334	78.68 452

test_method		38.59 454	35.16 457	48.89 470	54.33 487	21.35 500	45.32 491	53.71 489	7.41 497	28.74 485	51.62 481	8.70 489	52.87 495	33.73 463	32.89 482	72.47 470

LCM-MVSNet		40.54 450	35.79 455	54.76 464	36.92 501	30.81 491	51.41 488	69.02 473	22.07 488	24.63 488	45.37 485	4.56 496	65.81 487	33.67 464	34.50 481	67.67 475

DP-MVS		69.90 369	66.48 376	80.14 329	95.36 30	62.93 283	89.56 308	76.11 450	50.27 454	57.69 418	85.23 312	39.68 389	95.73 192	33.35 465	71.05 319	81.78 423

TDRefinement		55.28 437	51.58 441	66.39 449	59.53 484	46.15 461	76.23 446	72.80 462	44.60 469	42.49 474	76.28 425	15.29 478	82.39 459	33.20 466	43.75 462	70.62 473

FE-MVSNET		60.52 426	57.18 430	70.53 433	67.53 469	50.68 436	82.62 405	76.28 449	59.33 419	46.71 459	71.10 452	30.54 442	83.61 450	33.15 467	47.37 453	77.29 460

COLMAP_ROB	57.96 20	62.98 416	59.65 418	72.98 417	81.44 376	53.00 423	83.75 388	75.53 455	48.34 459	48.81 455	81.40 364	24.14 458	90.30 389	32.95 468	60.52 411	75.65 464
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ttmdpeth		53.34 440	49.96 443	63.45 453	62.07 481	40.04 477	72.06 456	65.64 479	42.54 477	51.88 439	77.79 404	13.94 483	76.48 472	32.93 469	30.82 486	73.84 466

new_pmnet		49.31 443	46.44 446	57.93 458	62.84 478	40.74 475	68.47 466	62.96 483	36.48 480	35.09 481	57.81 478	14.97 479	72.18 478	32.86 470	46.44 457	60.88 480

myMVS_eth3d		72.58 347	72.74 325	72.10 426	87.87 243	49.45 444	88.07 343	89.01 336	72.91 234	63.11 373	88.10 267	63.63 116	85.54 436	32.73 471	69.23 330	81.32 425

MIMVSNet1		60.16 429	57.33 428	68.67 441	69.71 464	44.13 467	78.92 434	84.21 420	55.05 440	44.63 468	71.85 446	23.91 459	81.54 464	32.63 472	55.03 430	80.35 436

LS3D		69.17 374	66.40 378	77.50 372	91.92 118	56.12 406	85.12 376	80.37 442	46.96 461	56.50 422	87.51 279	37.25 408	93.71 300	32.52 473	79.40 249	82.68 413

tfpnnormal		70.10 366	67.36 374	78.32 363	83.45 354	60.97 333	88.85 329	92.77 130	64.85 368	60.83 391	78.53 397	43.52 374	93.48 307	31.73 474	61.70 402	80.52 434

N_pmnet		50.55 442	49.11 444	54.88 463	77.17 430	4.02 507	84.36 381	2.00 505	48.59 457	45.86 463	68.82 456	32.22 433	82.80 457	31.58 475	51.38 440	77.81 458

WAC-MVS						49.45 444								31.56 476

dmvs_testset		65.55 403	66.45 377	62.86 454	79.87 396	22.35 499	76.55 444	71.74 467	77.42 149	55.85 423	87.77 274	51.39 294	80.69 466	31.51 477	65.92 357	85.55 374

kuosan		60.86 425	60.24 415	62.71 455	81.57 374	46.43 460	75.70 450	85.88 404	57.98 424	48.95 454	69.53 455	58.42 202	76.53 471	28.25 478	35.87 477	65.15 478

testing3		70.38 365	70.83 344	69.03 440	85.82 305	43.93 469	90.72 273	90.56 260	68.06 334	60.24 399	86.82 291	64.83 96	84.12 443	26.33 479	64.10 376	79.04 447

PMMVS2		37.93 455	33.61 458	50.92 467	46.31 493	24.76 497	60.55 480	50.05 491	28.94 487	20.93 489	47.59 482	4.41 498	65.13 489	25.14 480	18.55 493	62.87 479

MVStest1		51.35 441	46.89 445	64.74 450	65.06 475	51.10 433	67.33 470	72.58 463	30.20 485	35.30 480	74.82 432	27.70 450	69.89 481	24.44 481	24.57 489	73.22 467

test_0402		64.54 407	61.09 413	74.92 400	84.10 344	60.75 339	87.95 346	79.71 444	52.03 446	52.41 437	77.20 411	32.21 434	91.64 371	23.14 482	61.03 406	72.36 471

APD_test1		40.50 451	37.31 454	50.09 469	51.88 489	35.27 486	59.45 481	52.59 490	21.64 489	26.12 487	57.80 479	4.56 496	66.56 486	22.64 483	39.09 471	48.43 485

Syy-MVS		69.65 371	69.52 357	70.03 435	87.87 243	43.21 470	88.07 343	89.01 336	72.91 234	63.11 373	88.10 267	45.28 365	85.54 436	22.07 484	69.23 330	81.32 425

ANet_high		40.27 453	35.20 456	55.47 461	34.74 502	34.47 487	63.84 475	71.56 468	48.42 458	18.80 491	41.08 490	9.52 488	64.45 491	20.18 485	8.66 498	67.49 476

DeepMVS_CX				34.71 478	51.45 490	24.73 498		28.48 504	31.46 484	17.49 494	52.75 480	5.80 494	42.60 499	18.18 486	19.42 492	36.81 491

dongtai		55.18 438	55.46 436	54.34 465	76.03 437	36.88 483	76.07 447	84.61 418	51.28 449	43.41 473	64.61 468	56.56 232	67.81 484	18.09 487	28.50 488	58.32 481

EGC-MVSNET		42.35 449	38.09 452	55.11 462	74.57 447	46.62 459	71.63 459	55.77 486	0.04 500	0.24 501	62.70 472	14.24 481	74.91 475	17.59 488	46.06 459	43.80 486

testf1		32.77 457	29.47 460	42.67 475	41.89 498	30.81 491	52.07 486	43.45 496	15.45 492	18.52 492	44.82 486	2.12 500	58.38 493	16.05 489	30.87 484	38.83 488

APD_test2		32.77 457	29.47 460	42.67 475	41.89 498	30.81 491	52.07 486	43.45 496	15.45 492	18.52 492	44.82 486	2.12 500	58.38 493	16.05 489	30.87 484	38.83 488

FPMVS		45.64 447	43.10 451	53.23 466	51.42 491	36.46 484	64.97 473	71.91 466	29.13 486	27.53 486	61.55 475	9.83 487	65.01 490	16.00 491	55.58 428	58.22 482

Gipuma		34.91 456	31.44 459	45.30 473	70.99 459	39.64 481	19.85 495	72.56 464	20.10 491	16.16 495	21.47 496	5.08 495	71.16 479	13.07 492	43.70 463	25.08 493
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	24.84 23	24.35 461	19.77 467	38.09 477	34.56 503	26.92 496	26.57 493	38.87 500	11.73 496	11.37 497	27.44 493	1.37 503	50.42 496	11.41 493	14.60 494	36.93 490
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
WB-MVS		46.23 446	44.94 448	50.11 468	62.13 480	21.23 501	76.48 445	55.49 487	45.89 465	35.78 479	61.44 476	35.54 418	72.83 477	9.96 494	21.75 490	56.27 483

PMVS	26.43 22	31.84 459	28.16 462	42.89 474	25.87 504	27.58 495	50.92 489	49.78 492	21.37 490	14.17 496	40.81 491	2.01 502	66.62 485	9.61 495	38.88 474	34.49 492
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SSC-MVS		44.51 448	43.35 450	47.99 472	61.01 483	18.90 503	74.12 453	54.36 488	43.42 475	34.10 483	60.02 477	34.42 423	70.39 480	9.14 496	19.57 491	54.68 484

E-PMN		24.61 460	24.00 464	26.45 479	43.74 497	18.44 504	60.86 478	39.66 498	15.11 494	9.53 498	22.10 495	6.52 493	46.94 497	8.31 497	10.14 495	13.98 495

EMVS		23.76 462	23.20 466	25.46 480	41.52 500	16.90 505	60.56 479	38.79 501	14.62 495	8.99 499	20.24 498	7.35 490	45.82 498	7.25 498	9.46 496	13.64 496

wuyk23d		11.30 465	10.95 468	12.33 482	48.05 492	19.89 502	25.89 494	1.92 506	3.58 498	3.12 500	1.37 500	0.64 504	15.77 501	6.23 499	7.77 499	1.35 497

testmvs		7.23 467	9.62 470	0.06 484	0.04 506	0.02 509	84.98 378	0.02 507	0.03 501	0.18 502	1.21 501	0.01 506	0.02 502	0.14 500	0.01 500	0.13 499

test123		6.92 468	9.21 471	0.08 483	0.03 507	0.05 508	81.65 413	0.01 508	0.02 502	0.14 503	0.85 502	0.03 505	0.02 502	0.12 501	0.00 501	0.16 498

mmdepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

monomultidepth		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

test_blank		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

uanet_test		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

DCPMVS		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

cdsmvs_eth3d_5k		19.86 464	26.47 463	0.00 485	0.00 508	0.00 510	0.00 496	93.45 99	0.00 503	0.00 504	95.27 78	49.56 316	0.00 504	0.00 502	0.00 501	0.00 500

pcd_1.5k_mvsjas		4.46 469	5.95 472	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	53.55 270	0.00 504	0.00 502	0.00 501	0.00 500

sosnet-low-res		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

sosnet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

uncertanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

Regformer		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

ab-mvs-re		7.91 466	10.55 469	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	94.95 88	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

uanet		0.00 470	0.00 473	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	0.00 507	0.00 504	0.00 502	0.00 501	0.00 500

TestfortrainingZip				90.29 2	97.24 8	73.67 10	94.47 64	95.75 10	69.78 312	95.97 1	98.23 1	80.55 5	99.42 1		93.26 58	97.76 2

FOURS1					93.95 51	61.77 313	93.96 91	91.92 172	62.14 396	86.57 63

test_one_0601					96.32 20	69.74 53		94.18 70	71.42 284	90.67 30	96.85 28	74.45 23

eth-test2					0.00 508

eth-test					0.00 508

test_241102_ONE					96.45 13	69.38 61		94.44 56	71.65 273	92.11 10	97.05 13	76.79 10	99.11 7

save fliter					93.84 54	67.89 115	95.05 41	92.66 137	78.19 129

test0726					96.40 16	69.99 41	96.76 8	94.33 67	71.92 259	91.89 15	97.11 12	73.77 26

GSMVS																94.68 125

test_part2					96.29 21	68.16 108				90.78 28

sam_mvs1												57.85 212				94.68 125

sam_mvs												54.91 251

MTGPA								92.23 153

test_post											23.01 494	56.49 233	92.67 340

patchmatchnet-post											67.62 462	57.62 215	90.25 390

MTMP							93.77 106	32.52 503

TEST9					94.18 46	67.28 131	94.16 78	93.51 95	71.75 270	85.52 76	95.33 72	68.01 62	97.27 94

test_8					94.19 45	67.19 136	94.15 80	93.42 102	71.87 264	85.38 79	95.35 71	68.19 60	96.95 121

agg_prior					94.16 48	66.97 151		93.31 105		84.49 87			96.75 133

test_prior4						67.18 138	93.92 95

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

新几何2							91.41 233

旧先验1					91.94 116	60.74 340		91.50 197			94.36 106	65.23 90			91.84 79	94.55 134

原ACMM2							92.01 197

test222					89.77 169	61.60 319	89.55 309	89.42 313	56.83 433	77.28 189	92.43 158	52.76 278			91.14 96	93.09 207

segment_acmp												65.94 81

testdata1							89.21 321		77.55 145

test12				87.09 58	94.60 41	68.86 81		92.91 125		82.67 110		65.44 87	97.55 73		93.69 52	94.84 110

plane_prior7					86.94 274	61.51 321

plane_prior6					87.23 260	62.32 299						50.66 302

plane_prior4											89.14 248

plane_prior3						61.95 308			79.09 112	72.53 256

plane_prior2							93.13 134		78.81 119

plane_prior1					87.15 263

plane_prior						62.42 295	93.85 99		79.38 104						78.80 258

n2								0.00 509

nn								0.00 509

door-mid								66.01 478

test11								93.01 119

door								66.57 477

HQP5-MVS						63.66 261

HQP-NCC					87.54 252		94.06 83		79.80 88	74.18 229

ACMP_Plane					87.54 252		94.06 83		79.80 88	74.18 229

HQP4-MVS										74.18 229			95.61 206			88.63 303

HQP3-MVS								91.70 189							78.90 256

HQP2-MVS												51.63 290

NP-MVS					87.41 255	63.04 279					90.30 218

ACMMP++_ref															71.63 313

ACMMP++															69.72 324

Test By Simon												54.21 264

High-resolution multi-view benchmark