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.

mamv4		90.28 1	88.75 1	94.85 1	93.34 1	96.17 1	82.69 62	91.63 1	86.34 1	97.97 1	94.77 3	66.57 132	95.38 1	87.74 1	97.72 1	93.00 7

Effi-MVS+-dtu		75.43 100	72.28 167	84.91 3	77.05 195	83.58 2	78.47 104	77.70 204	57.68 168	74.89 219	78.13 341	64.80 153	84.26 80	56.46 237	85.32 228	86.88 69

PMVS	70.70 6	81.70 37	83.15 36	77.36 86	90.35 6	82.82 3	82.15 64	79.22 176	74.08 24	87.16 33	91.97 23	84.80 2	76.97 215	64.98 142	93.61 68	72.28 365
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
mPP-MVS		84.01 14	84.39 16	82.88 7	90.65 4	81.38 4	87.08 13	82.79 95	72.41 40	85.11 61	90.85 51	76.65 32	84.89 69	79.30 21	94.63 38	82.35 210

TDRefinement		86.32 3	86.33 3	86.29 2	88.64 32	81.19 5	88.84 4	90.72 2	78.27 12	87.95 18	92.53 16	79.37 15	84.79 72	74.51 57	96.15 3	92.88 8

CP-MVS		84.12 12	84.55 15	82.80 11	89.42 18	79.74 6	88.19 5	84.43 66	71.96 44	84.70 68	90.56 59	77.12 29	86.18 30	79.24 22	95.36 15	82.49 207

SR-MVS-dyc-post		84.75 7	85.26 9	83.21 4	86.19 52	79.18 7	87.23 9	86.27 21	77.51 14	87.65 22	90.73 54	79.20 16	85.58 54	78.11 29	94.46 41	84.89 115

RE-MVS-def			85.50 7		86.19 52	79.18 7	87.23 9	86.27 21	77.51 14	87.65 22	90.73 54	81.38 7		78.11 29	94.46 41	84.89 115

MP-MVS		83.19 23	83.54 28	82.14 20	90.54 5	79.00 9	86.42 25	83.59 85	71.31 45	81.26 107	90.96 46	74.57 52	84.69 73	78.41 26	94.78 33	82.74 199
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		81.51 39	81.76 50	80.76 38	89.20 23	78.75 10	86.48 24	82.03 110	68.80 60	80.92 112	88.52 117	72.00 71	82.39 114	74.80 50	93.04 75	81.14 238

HPM-MVS++		79.89 58	79.80 64	80.18 43	89.02 26	78.44 11	83.49 53	80.18 154	64.71 104	78.11 147	88.39 120	65.46 145	83.14 98	77.64 35	91.20 105	78.94 283

MTAPA		83.19 23	83.87 23	81.13 34	91.16 3	78.16 12	84.87 36	80.63 144	72.08 42	84.93 62	90.79 52	74.65 51	84.42 78	80.98 6	94.75 34	80.82 248

reproduce_model		84.87 6	85.80 6	82.05 23	85.52 68	78.14 13	87.69 6	85.36 39	79.26 7	89.12 12	92.10 21	77.52 26	85.92 41	80.47 9	95.20 20	82.10 217

FOURS1					89.19 24	77.84 14	91.64 1	89.11 3	84.05 3	91.57 3

SR-MVS		84.51 9	85.27 8	82.25 19	88.52 34	77.71 15	86.81 19	85.25 41	77.42 17	86.15 42	90.24 77	81.69 5	85.94 38	77.77 32	93.58 69	83.09 185

XVS		83.51 19	83.73 25	82.85 9	89.43 16	77.61 16	86.80 20	84.66 58	72.71 33	82.87 87	90.39 69	73.86 57	86.31 23	78.84 24	94.03 61	84.64 128

X-MVStestdata		76.81 86	74.79 109	82.85 9	89.43 16	77.61 16	86.80 20	84.66 58	72.71 33	82.87 87	9.95 472	73.86 57	86.31 23	78.84 24	94.03 61	84.64 128

reproduce-ours		84.97 4	85.93 4	82.10 21	86.11 59	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 34	79.90 10	95.21 18	82.72 200

our_new_method		84.97 4	85.93 4	82.10 21	86.11 59	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 34	79.90 10	95.21 18	82.72 200

region2R		83.54 18	83.86 24	82.58 15	89.82 10	77.53 18	87.06 16	84.23 75	70.19 55	83.86 77	90.72 56	75.20 45	86.27 25	79.41 19	94.25 55	83.95 155

RPSCF		75.76 94	74.37 115	79.93 44	74.81 237	77.53 18	77.53 116	79.30 173	59.44 150	78.88 133	89.80 86	71.26 78	73.09 271	57.45 226	80.89 309	89.17 33

ACMMPR		83.62 16	83.93 22	82.69 12	89.78 11	77.51 22	87.01 17	84.19 76	70.23 53	84.49 70	90.67 57	75.15 46	86.37 20	79.58 15	94.26 54	84.18 149

MSP-MVS		80.49 52	79.67 65	82.96 6	89.70 12	77.46 23	87.16 12	85.10 44	64.94 100	81.05 110	88.38 121	57.10 253	87.10 9	79.75 12	83.87 258	84.31 146
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
PGM-MVS		83.07 26	83.25 35	82.54 16	89.57 14	77.21 24	82.04 66	85.40 37	67.96 66	84.91 65	90.88 49	75.59 41	86.57 16	78.16 28	94.71 36	83.82 157

DeepPCF-MVS	71.07 5	78.48 72	77.14 89	82.52 17	84.39 90	77.04 25	76.35 135	84.05 79	56.66 183	80.27 120	85.31 195	68.56 102	87.03 12	67.39 121	91.26 103	83.50 166

ACMMP		84.22 10	84.84 13	82.35 18	89.23 22	76.66 26	87.65 7	85.89 27	71.03 49	85.85 46	90.58 58	78.77 18	85.78 47	79.37 20	95.17 22	84.62 130
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
HPM-MVS_fast		84.59 8	85.10 10	83.06 5	88.60 33	75.83 27	86.27 27	86.89 17	73.69 27	86.17 41	91.70 33	78.23 22	85.20 64	79.45 17	94.91 30	88.15 51

HFP-MVS		83.39 22	84.03 21	81.48 27	89.25 21	75.69 28	87.01 17	84.27 72	70.23 53	84.47 71	90.43 64	76.79 30	85.94 38	79.58 15	94.23 56	82.82 196

LTVRE_ROB	75.46 1	84.22 10	84.98 12	81.94 24	84.82 79	75.40 29	91.60 3	87.80 9	73.52 29	88.90 15	93.06 9	71.39 77	81.53 131	81.53 5	92.15 89	88.91 40
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
CNLPA		73.44 127	73.03 149	74.66 118	78.27 176	75.29 30	75.99 142	78.49 191	65.39 89	75.67 198	83.22 247	61.23 193	66.77 357	53.70 275	85.33 227	81.92 225

PM-MVS		64.49 290	63.61 302	67.14 276	76.68 209	75.15 31	68.49 268	42.85 453	51.17 268	77.85 150	80.51 292	45.76 327	66.31 361	52.83 282	76.35 366	59.96 443

XVG-OURS		79.51 60	79.82 63	78.58 66	86.11 59	74.96 32	76.33 137	84.95 49	66.89 72	82.75 90	88.99 106	66.82 125	78.37 193	74.80 50	90.76 126	82.40 209

XVG-OURS-SEG-HR		79.62 59	79.99 62	78.49 68	86.46 47	74.79 33	77.15 122	85.39 38	66.73 75	80.39 119	88.85 109	74.43 55	78.33 195	74.73 52	85.79 218	82.35 210

EGC-MVSNET		64.77 286	61.17 324	75.60 110	86.90 43	74.47 34	84.04 43	68.62 319	0.60 474	1.13 476	91.61 36	65.32 147	74.15 261	64.01 150	88.28 174	78.17 295

HPM-MVS		84.12 12	84.63 14	82.60 14	88.21 36	74.40 35	85.24 34	87.21 15	70.69 52	85.14 60	90.42 65	78.99 17	86.62 15	80.83 7	94.93 29	86.79 70
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
COLMAP_ROB	72.78 3	83.75 15	84.11 20	82.68 13	82.97 112	74.39 36	87.18 11	88.18 8	78.98 8	86.11 44	91.47 38	79.70 14	85.76 48	66.91 129	95.46 14	87.89 53
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
anonymousdsp		78.60 68	77.80 80	81.00 35	78.01 182	74.34 37	80.09 86	76.12 226	50.51 278	89.19 11	90.88 49	71.45 75	77.78 207	73.38 68	90.60 128	90.90 17

ACMM	69.25 9	82.11 34	83.31 32	78.49 68	88.17 37	73.96 38	83.11 57	84.52 64	66.40 79	87.45 26	89.16 100	81.02 8	80.52 154	74.27 60	95.73 8	80.98 244
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		78.93 65	78.67 72	79.72 47	84.81 80	73.93 39	80.65 76	76.50 220	51.98 254	87.40 27	91.86 29	76.09 38	78.53 185	68.58 105	90.20 132	86.69 72

MVS_111021_LR		72.10 166	71.82 176	72.95 156	79.53 154	73.90 40	70.45 228	66.64 330	56.87 177	76.81 174	81.76 273	68.78 100	71.76 294	61.81 172	83.74 261	73.18 351

jajsoiax		78.51 70	78.16 78	79.59 49	84.65 83	73.83 41	80.42 79	76.12 226	51.33 265	87.19 32	91.51 37	73.79 59	78.44 189	68.27 108	90.13 136	86.49 76

ITE_SJBPF				80.35 42	76.94 200	73.60 42		80.48 147	66.87 73	83.64 80	86.18 176	70.25 90	79.90 164	61.12 184	88.95 167	87.56 58

PatchMatch-RL		58.68 348	57.72 353	61.57 333	76.21 216	73.59 43	61.83 353	49.00 432	47.30 325	61.08 393	68.97 419	50.16 300	59.01 393	36.06 413	68.84 426	52.10 453

APD-MVS_3200maxsize		83.57 17	84.33 17	81.31 32	82.83 115	73.53 44	85.50 33	87.45 14	74.11 23	86.45 39	90.52 62	80.02 10	84.48 76	77.73 33	94.34 52	85.93 87

GST-MVS		82.79 29	83.27 34	81.34 31	88.99 27	73.29 45	85.94 31	85.13 42	68.58 64	84.14 74	90.21 79	73.37 61	86.41 18	79.09 23	93.98 64	84.30 148

ZNCC-MVS		83.12 25	83.68 26	81.45 28	89.14 25	73.28 46	86.32 26	85.97 26	67.39 69	84.02 75	90.39 69	74.73 50	86.46 17	80.73 8	94.43 45	84.60 133

XVG-ACMP-BASELINE		80.54 51	81.06 55	78.98 60	87.01 39	72.91 47	80.23 85	85.56 32	66.56 78	85.64 49	89.57 89	69.12 99	80.55 153	72.51 78	93.37 71	83.48 169

h-mvs33		73.08 136	71.61 182	77.48 83	83.89 96	72.89 48	70.47 227	71.12 290	54.28 217	77.89 148	83.41 234	49.04 311	80.98 144	63.62 158	90.77 125	78.58 287

3Dnovator+	73.19 2	81.08 45	80.48 58	82.87 8	81.41 133	72.03 49	84.38 42	86.23 24	77.28 18	80.65 116	90.18 80	59.80 216	87.58 6	73.06 71	91.34 102	89.01 36

F-COLMAP		75.29 101	73.99 125	79.18 55	81.73 129	71.90 50	81.86 68	82.98 92	59.86 148	72.27 274	84.00 224	64.56 156	83.07 101	51.48 287	87.19 200	82.56 206

hse-mvs2		72.32 161	70.66 199	77.31 88	83.10 109	71.77 51	69.19 250	71.45 279	54.28 217	77.89 148	78.26 337	49.04 311	79.23 172	63.62 158	89.13 161	80.92 245

AUN-MVS		70.22 198	67.88 245	77.22 89	82.96 113	71.61 52	69.08 251	71.39 280	49.17 298	71.70 281	78.07 342	37.62 381	79.21 173	61.81 172	89.15 159	80.82 248

FPMVS		59.43 342	60.07 333	57.51 371	77.62 190	71.52 53	62.33 352	50.92 420	57.40 173	69.40 316	80.00 303	39.14 371	61.92 383	37.47 398	66.36 435	39.09 466

LS3D		80.99 48	80.85 56	81.41 29	78.37 175	71.37 54	87.45 8	85.87 28	77.48 16	81.98 96	89.95 84	69.14 98	85.26 60	66.15 131	91.24 104	87.61 57

新几何1				69.99 217	88.37 35	71.34 55		62.08 363	43.85 357	74.99 216	86.11 182	52.85 281	70.57 308	50.99 293	83.23 271	68.05 405

test_djsdf		78.88 66	78.27 76	80.70 39	81.42 132	71.24 56	83.98 44	75.72 231	52.27 247	87.37 30	92.25 19	68.04 111	80.56 151	72.28 81	91.15 107	90.32 21

N_pmnet		52.06 393	51.11 402	54.92 383	59.64 432	71.03 57	37.42 463	61.62 367	33.68 433	57.12 414	72.10 389	37.94 377	31.03 468	29.13 447	71.35 409	62.70 433

SteuartSystems-ACMMP		83.07 26	83.64 27	81.35 30	85.14 75	71.00 58	85.53 32	84.78 51	70.91 50	85.64 49	90.41 66	75.55 43	87.69 5	79.75 12	95.08 25	85.36 103
Skip Steuart: Steuart Systems R&D Blog.
AllTest		77.66 77	77.43 83	78.35 71	79.19 161	70.81 59	78.60 102	88.64 4	65.37 90	80.09 121	88.17 128	70.33 87	78.43 190	55.60 246	90.90 119	85.81 89

TestCases				78.35 71	79.19 161	70.81 59		88.64 4	65.37 90	80.09 121	88.17 128	70.33 87	78.43 190	55.60 246	90.90 119	85.81 89

TSAR-MVS + GP.		73.08 136	71.60 183	77.54 82	78.99 170	70.73 61	74.96 151	69.38 306	60.73 141	74.39 233	78.44 335	57.72 246	82.78 106	60.16 194	89.60 147	79.11 281

OMC-MVS		79.41 62	78.79 70	81.28 33	80.62 141	70.71 62	80.91 74	84.76 52	62.54 126	81.77 99	86.65 162	71.46 74	83.53 91	67.95 114	92.44 83	89.60 24

APD-MVS		81.13 44	81.73 51	79.36 53	84.47 86	70.53 63	83.85 46	83.70 83	69.43 59	83.67 79	88.96 107	75.89 39	86.41 18	72.62 77	92.95 76	81.14 238
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LPG-MVS_test		83.47 20	84.33 17	80.90 36	87.00 40	70.41 64	82.04 66	86.35 18	69.77 57	87.75 19	91.13 42	81.83 3	86.20 28	77.13 41	95.96 6	86.08 83

LGP-MVS_train				80.90 36	87.00 40	70.41 64		86.35 18	69.77 57	87.75 19	91.13 42	81.83 3	86.20 28	77.13 41	95.96 6	86.08 83

APD_test1		75.04 107	75.38 106	74.02 131	69.89 335	70.15 66	76.46 130	79.71 163	65.50 86	82.99 85	88.60 116	66.94 122	72.35 283	59.77 201	88.54 170	79.56 273

test_prior4						70.14 67	77.57 113

DeepC-MVS	72.44 4	81.00 47	80.83 57	81.50 26	86.70 45	70.03 68	82.06 65	87.00 16	59.89 147	80.91 113	90.53 60	72.19 67	88.56 2	73.67 67	94.52 40	85.92 88
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NormalMVS		76.15 90	75.08 107	79.36 53	83.87 97	70.01 69	79.92 90	84.34 68	58.60 159	75.21 211	84.02 222	52.85 281	81.82 125	61.45 177	95.50 11	86.24 78

SymmetryMVS		74.00 119	72.85 152	77.43 85	85.17 74	70.01 69	79.92 90	68.48 320	58.60 159	75.21 211	84.02 222	52.85 281	81.82 125	61.45 177	89.99 139	80.47 259

SMA-MVS		82.12 33	82.68 43	80.43 40	88.90 30	69.52 71	85.12 35	84.76 52	63.53 115	84.23 73	91.47 38	72.02 70	87.16 8	79.74 14	94.36 50	84.61 131
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
NCCC		78.25 74	78.04 79	78.89 62	85.61 67	69.45 72	79.80 92	80.99 136	65.77 83	75.55 200	86.25 175	67.42 117	85.42 55	70.10 95	90.88 121	81.81 227

ACMP	69.50 8	82.64 30	83.38 31	80.40 41	86.50 46	69.44 73	82.30 63	86.08 25	66.80 74	86.70 35	89.99 82	81.64 6	85.95 37	74.35 59	96.11 4	85.81 89
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OPM-MVS		80.99 48	81.63 53	79.07 57	86.86 44	69.39 74	79.41 95	84.00 81	65.64 84	85.54 53	89.28 93	76.32 36	83.47 93	74.03 64	93.57 70	84.35 145
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ZD-MVS					83.91 94	69.36 75		81.09 132	58.91 157	82.73 91	89.11 101	75.77 40	86.63 14	72.73 75	92.93 77

TEST9					85.47 69	69.32 76	76.42 132	78.69 187	53.73 231	76.97 165	86.74 156	66.84 124	81.10 139

train_agg		76.38 89	76.55 93	75.86 106	85.47 69	69.32 76	76.42 132	78.69 187	54.00 226	76.97 165	86.74 156	66.60 130	81.10 139	72.50 79	91.56 97	77.15 310

UA-Net		81.56 38	82.28 47	79.40 52	88.91 29	69.16 78	84.67 39	80.01 158	75.34 19	79.80 123	94.91 2	69.79 95	80.25 158	72.63 76	94.46 41	88.78 44

test222					87.30 38	69.15 79	67.85 275	59.59 373	41.06 382	73.05 263	85.72 191	48.03 320			80.65 317	66.92 410

ACMMP_NAP		82.33 32	83.28 33	79.46 51	89.28 19	69.09 80	83.62 50	84.98 47	64.77 103	83.97 76	91.02 45	75.53 44	85.93 40	82.00 3	94.36 50	83.35 176

PLC	62.01 16	71.79 170	70.28 202	76.33 99	80.31 144	68.63 81	78.18 110	81.24 127	54.57 211	67.09 350	80.63 291	59.44 219	81.74 130	46.91 333	84.17 255	78.63 285
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MP-MVS-pluss		82.54 31	83.46 30	79.76 45	88.88 31	68.44 82	81.57 69	86.33 20	63.17 121	85.38 58	91.26 41	76.33 35	84.67 74	83.30 2	94.96 28	86.17 82
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TAPA-MVS	65.27 12	75.16 104	74.29 118	77.77 81	74.86 236	68.08 83	77.89 112	84.04 80	55.15 199	76.19 193	83.39 235	66.91 123	80.11 162	60.04 198	90.14 135	85.13 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS_fast	69.89 7	77.17 83	76.33 95	79.70 48	83.90 95	67.94 84	80.06 88	83.75 82	56.73 182	74.88 220	85.32 194	65.54 143	87.79 3	65.61 139	91.14 108	83.35 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_8					85.09 76	67.89 85	76.26 138	78.66 189	54.00 226	76.89 169	86.72 158	66.60 130	80.89 149

SD-MVS		80.28 56	81.55 54	76.47 98	83.57 99	67.83 86	83.39 55	85.35 40	64.42 105	86.14 43	87.07 145	74.02 56	80.97 145	77.70 34	92.32 87	80.62 256
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
testf1		75.66 96	76.57 91	72.95 156	67.07 376	67.62 87	76.10 139	80.68 141	64.95 98	86.58 37	90.94 47	71.20 79	71.68 296	60.46 189	91.13 109	79.56 273

APD_test2		75.66 96	76.57 91	72.95 156	67.07 376	67.62 87	76.10 139	80.68 141	64.95 98	86.58 37	90.94 47	71.20 79	71.68 296	60.46 189	91.13 109	79.56 273

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 46	88.94 28	67.52 89	86.61 22	81.38 124	51.71 256	77.15 163	91.42 40	65.49 144	87.20 7	79.44 18	87.17 201	84.51 139
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		78.49 71	78.59 73	78.16 74	85.86 65	67.40 90	78.12 111	81.50 119	63.92 109	77.51 157	86.56 166	68.43 105	84.82 71	73.83 65	91.61 96	82.26 214

lecture		83.41 21	85.02 11	78.58 66	83.87 97	67.26 91	84.47 40	88.27 7	73.64 28	87.35 31	91.96 24	78.55 21	82.92 103	81.59 4	95.50 11	85.56 98

DPE-MVS		82.00 35	83.02 38	78.95 61	85.36 71	67.25 92	82.91 58	84.98 47	73.52 29	85.43 57	90.03 81	76.37 34	86.97 13	74.56 55	94.02 63	82.62 204
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
save fliter					87.00 40	67.23 93	79.24 96	77.94 202	56.65 184

MSC_two_6792asdad				79.02 58	83.14 105	67.03 94		80.75 138					86.24 26	77.27 39	94.85 31	83.78 159

No_MVS				79.02 58	83.14 105	67.03 94		80.75 138					86.24 26	77.27 39	94.85 31	83.78 159

OPU-MVS				78.65 65	83.44 103	66.85 96	83.62 50				86.12 181	66.82 125	86.01 36	61.72 175	89.79 145	83.08 186

APDe-MVS		82.88 28	84.14 19	79.08 56	84.80 81	66.72 97	86.54 23	85.11 43	72.00 43	86.65 36	91.75 32	78.20 23	87.04 11	77.93 31	94.32 53	83.47 170
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_part2					85.90 62	66.44 98				84.61 69

PS-MVSNAJss		77.54 78	77.35 87	78.13 76	84.88 78	66.37 99	78.55 103	79.59 168	53.48 237	86.29 40	92.43 18	62.39 175	80.25 158	67.90 115	90.61 127	87.77 54

test_fmvsmconf0.01_n		73.91 120	73.64 132	74.71 117	69.79 339	66.25 100	75.90 143	79.90 159	46.03 333	76.48 187	85.02 198	67.96 114	73.97 262	74.47 58	87.22 198	83.90 156

plane_prior7					85.18 72	66.21 101

test_fmvsmconf0.1_n		73.26 133	72.82 155	74.56 119	69.10 346	66.18 102	74.65 162	79.34 172	45.58 336	75.54 201	83.91 227	67.19 120	73.88 265	73.26 69	86.86 204	83.63 164

test_fmvsmconf_n		72.91 146	72.40 164	74.46 120	68.62 350	66.12 103	74.21 170	78.80 184	45.64 335	74.62 227	83.25 243	66.80 128	73.86 266	72.97 72	86.66 210	83.39 173

agg_prior					84.44 88	66.02 104		78.62 190		76.95 167			80.34 156

test_fmvsm_n_1920		69.63 209	68.45 232	73.16 147	70.56 317	65.86 105	70.26 230	78.35 193	37.69 409	74.29 235	78.89 331	61.10 197	68.10 336	65.87 136	79.07 338	85.53 99

plane_prior3						65.67 106			63.82 111	78.23 144

MM		78.15 76	77.68 81	79.55 50	80.10 145	65.47 107	80.94 73	78.74 186	71.22 47	72.40 273	88.70 111	60.51 204	87.70 4	77.40 38	89.13 161	85.48 100

MVS_111021_HR		72.98 143	72.97 151	72.99 154	80.82 139	65.47 107	68.81 258	72.77 261	57.67 169	75.76 196	82.38 260	71.01 81	77.17 213	61.38 179	86.15 213	76.32 322

DP-MVS		78.44 73	79.29 67	75.90 105	81.86 128	65.33 109	79.05 98	84.63 60	74.83 22	80.41 118	86.27 173	71.68 72	83.45 94	62.45 169	92.40 84	78.92 284

plane_prior6					84.18 92	65.31 110						60.83 200

HQP_MVS		78.77 67	78.78 71	78.72 63	85.18 72	65.18 111	82.74 60	85.49 33	65.45 87	78.23 144	89.11 101	60.83 200	86.15 31	71.09 86	90.94 115	84.82 120

plane_prior						65.18 111	80.06 88		61.88 131						89.91 142

原ACMM1				73.90 132	85.90 62	65.15 113		81.67 116	50.97 269	74.25 236	86.16 178	61.60 187	83.54 90	56.75 232	91.08 113	73.00 353

MAR-MVS		67.72 245	66.16 270	72.40 177	74.45 245	64.99 114	74.87 152	77.50 207	48.67 308	65.78 356	68.58 426	57.01 255	77.79 206	46.68 336	81.92 286	74.42 342
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
fmvsm_s_conf0.5_n_9		74.56 115	74.30 117	75.34 113	77.17 194	64.87 115	72.62 186	76.17 225	54.54 213	78.32 143	86.14 179	65.14 151	75.72 234	73.10 70	85.55 222	85.42 101

CS-MVS		76.51 88	76.00 98	78.06 77	77.02 197	64.77 116	80.78 75	82.66 100	60.39 143	74.15 237	83.30 241	69.65 96	82.07 121	69.27 102	86.75 208	87.36 60

Vis-MVSNet		74.85 114	74.56 112	75.72 107	81.63 131	64.64 117	76.35 135	79.06 178	62.85 124	73.33 255	88.41 119	62.54 173	79.59 169	63.94 155	82.92 273	82.94 190
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Fast-Effi-MVS+-dtu		70.00 203	68.74 228	73.77 134	73.47 263	64.53 118	71.36 213	78.14 199	55.81 193	68.84 330	74.71 369	65.36 146	75.75 232	52.00 284	79.00 339	81.03 241

SF-MVS		80.72 50	81.80 49	77.48 83	82.03 125	64.40 119	83.41 54	88.46 6	65.28 92	84.29 72	89.18 98	73.73 60	83.22 97	76.01 43	93.77 66	84.81 122

MED-MVS test				78.47 70	86.27 49	64.31 120	86.10 28	84.54 62	64.93 101	85.54 53	88.38 121		86.37 20	74.09 61	94.20 58	84.73 124

ME-MVS		81.36 40	82.39 45	78.28 73	84.42 89	64.31 120	82.78 59	85.02 46	71.25 46	84.81 66	88.38 121	76.53 33	85.81 46	74.09 61	94.20 58	84.73 124

OurMVSNet-221017-0		78.57 69	78.53 74	78.67 64	80.48 142	64.16 122	80.24 84	82.06 109	61.89 130	88.77 16	93.32 6	57.15 251	82.60 109	70.08 96	92.80 78	89.25 30

fmvsm_l_conf0.5_n_3		71.98 168	71.68 178	72.88 163	72.84 281	64.15 123	73.48 176	77.11 215	48.97 304	71.31 293	84.18 214	67.98 113	71.60 298	68.86 103	80.43 321	82.89 192

test_fmvsmvis_n_1920		72.36 160	72.49 160	71.96 184	71.29 305	64.06 124	72.79 185	81.82 113	40.23 392	81.25 108	81.04 283	70.62 85	68.69 327	69.74 100	83.60 266	83.14 182

CDPH-MVS		77.33 82	77.06 90	78.14 75	84.21 91	63.98 125	76.07 141	83.45 86	54.20 221	77.68 155	87.18 141	69.98 92	85.37 56	68.01 112	92.72 81	85.08 112

UGNet		70.20 199	69.05 221	73.65 135	76.24 215	63.64 126	75.87 144	72.53 265	61.48 133	60.93 397	86.14 179	52.37 285	77.12 214	50.67 295	85.21 229	80.17 267
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_Blended_VisFu		70.04 202	68.88 224	73.53 141	82.71 116	63.62 127	74.81 154	81.95 112	48.53 309	67.16 349	79.18 326	51.42 292	78.38 192	54.39 267	79.72 334	78.60 286

DP-MVS Recon		73.57 126	72.69 156	76.23 101	82.85 114	63.39 128	74.32 166	82.96 93	57.75 167	70.35 302	81.98 268	64.34 158	84.41 79	49.69 303	89.95 140	80.89 246

testdata				64.13 305	85.87 64	63.34 129		61.80 366	47.83 319	76.42 190	86.60 165	48.83 314	62.31 381	54.46 265	81.26 302	66.74 414

LCM-MVSNet		86.90 2	88.67 2	81.57 25	91.50 2	63.30 130	84.80 38	87.77 11	86.18 2	96.26 2	96.06 1	90.32 1	84.49 75	68.08 110	97.05 2	96.93 1

3Dnovator	65.95 11	71.50 174	71.22 189	72.34 178	73.16 269	63.09 131	78.37 105	78.32 194	57.67 169	72.22 276	84.61 205	54.77 268	78.47 187	60.82 187	81.07 307	75.45 328

NP-MVS					83.34 104	63.07 132					85.97 186

SPE-MVS-test		74.89 112	74.23 119	76.86 91	77.01 198	62.94 133	78.98 99	84.61 61	58.62 158	70.17 306	80.80 287	66.74 129	81.96 123	61.74 174	89.40 155	85.69 96

MSLP-MVS++		74.48 116	75.78 100	70.59 201	84.66 82	62.40 134	78.65 101	84.24 74	60.55 142	77.71 154	81.98 268	63.12 164	77.64 209	62.95 165	88.14 176	71.73 371

ACMH+	66.64 10	81.20 42	82.48 44	77.35 87	81.16 137	62.39 135	80.51 77	87.80 9	73.02 31	87.57 24	91.08 44	80.28 9	82.44 112	64.82 144	96.10 5	87.21 62

PHI-MVS		74.92 109	74.36 116	76.61 94	76.40 213	62.32 136	80.38 80	83.15 90	54.16 223	73.23 257	80.75 288	62.19 180	83.86 83	68.02 111	90.92 118	83.65 163

fmvsm_l_conf0.5_n		67.48 248	66.88 264	69.28 231	67.41 371	62.04 137	70.69 225	69.85 301	39.46 395	69.59 314	81.09 282	58.15 237	68.73 326	67.51 118	78.16 353	77.07 314

LF4IMVS		67.50 247	67.31 254	68.08 258	58.86 435	61.93 138	71.43 211	75.90 230	44.67 353	72.42 272	80.20 298	57.16 250	70.44 310	58.99 208	86.12 215	71.88 368

xiu_mvs_v1_base_debu		67.87 242	67.07 258	70.26 209	79.13 163	61.90 139	67.34 282	71.25 285	47.98 316	67.70 342	74.19 377	61.31 190	72.62 276	56.51 234	78.26 350	76.27 323

xiu_mvs_v1_base		67.87 242	67.07 258	70.26 209	79.13 163	61.90 139	67.34 282	71.25 285	47.98 316	67.70 342	74.19 377	61.31 190	72.62 276	56.51 234	78.26 350	76.27 323

xiu_mvs_v1_base_debi		67.87 242	67.07 258	70.26 209	79.13 163	61.90 139	67.34 282	71.25 285	47.98 316	67.70 342	74.19 377	61.31 190	72.62 276	56.51 234	78.26 350	76.27 323

CSCG		74.12 118	74.39 114	73.33 143	79.35 156	61.66 142	77.45 117	81.98 111	62.47 128	79.06 132	80.19 299	61.83 184	78.79 181	59.83 200	87.35 191	79.54 276

MGCNet		75.45 99	74.66 111	77.83 78	75.58 227	61.53 143	78.29 106	77.18 214	63.15 123	69.97 309	87.20 140	57.54 248	87.05 10	74.05 63	88.96 166	84.89 115

test_one_0601					85.84 66	61.45 144		85.63 31	75.27 21	85.62 52	90.38 71	76.72 31

fmvsm_l_conf0.5_n_a		66.66 264	65.97 275	68.72 248	67.09 374	61.38 145	70.03 234	69.15 309	38.59 403	68.41 335	80.36 295	56.56 259	68.32 333	66.10 132	77.45 359	76.46 320

CANet		73.00 141	71.84 175	76.48 97	75.82 224	61.28 146	74.81 154	80.37 151	63.17 121	62.43 386	80.50 293	61.10 197	85.16 66	64.00 151	84.34 254	83.01 189

EPNet		69.10 222	67.32 253	74.46 120	68.33 354	61.27 147	77.56 114	63.57 355	60.95 138	56.62 421	82.75 251	51.53 291	81.24 136	54.36 268	90.20 132	80.88 247
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.1_n_a		67.37 252	66.36 268	70.37 206	70.86 307	61.17 148	74.00 172	57.18 384	40.77 387	68.83 331	80.88 285	63.11 166	67.61 342	66.94 128	74.72 380	82.33 213

fmvsm_s_conf0.5_n_a		67.00 262	65.95 276	70.17 212	69.72 340	61.16 149	73.34 179	56.83 387	40.96 384	68.36 336	80.08 302	62.84 167	67.57 343	66.90 130	74.50 384	81.78 228

SED-MVS		81.78 36	83.48 29	76.67 93	86.12 56	61.06 150	83.62 50	84.72 54	72.61 36	87.38 28	89.70 87	77.48 27	85.89 44	75.29 48	94.39 46	83.08 186

test_241102_ONE					86.12 56	61.06 150		84.72 54	72.64 35	87.38 28	89.47 90	77.48 27	85.74 49

AdaColmap		74.22 117	74.56 112	73.20 146	81.95 126	60.97 152	79.43 93	80.90 137	65.57 85	72.54 271	81.76 273	70.98 82	85.26 60	47.88 326	90.00 137	73.37 349

test12				76.51 96	82.28 122	60.94 153		81.64 117		73.60 249		64.88 152	85.19 65		90.42 130	83.38 174

DVP-MVS++		81.24 41	82.74 42	76.76 92	83.14 105	60.90 154	91.64 1	85.49 33	74.03 25	84.93 62	90.38 71	66.82 125	85.90 42	77.43 36	90.78 123	83.49 167

IU-MVS					86.12 56	60.90 154		80.38 150	45.49 339	81.31 106				75.64 47	94.39 46	84.65 127

DVP-MVS		81.15 43	83.12 37	75.24 116	86.16 54	60.78 156	83.77 48	80.58 146	72.48 38	85.83 47	90.41 66	78.57 19	85.69 50	75.86 44	94.39 46	79.24 279
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
test0726					86.16 54	60.78 156	83.81 47	85.10 44	72.48 38	85.27 59	89.96 83	78.57 19

wuyk23d		61.97 319	66.25 269	49.12 418	58.19 440	60.77 158	66.32 302	52.97 411	55.93 192	90.62 6	86.91 149	73.07 62	35.98 466	20.63 468	91.63 95	50.62 455

TestfortrainingZip a		81.05 46	82.35 46	77.16 90	86.27 49	60.63 159	86.10 28	84.54 62	64.93 101	85.54 53	88.38 121	72.97 64	86.37 20	78.23 27	94.20 58	84.47 141

test_0728_SECOND				76.57 95	86.20 51	60.57 160	83.77 48	85.49 33					85.90 42	75.86 44	94.39 46	83.25 178

MVP-Stereo		61.56 325	59.22 339	68.58 250	79.28 157	60.44 161	69.20 249	71.57 275	43.58 363	56.42 422	78.37 336	39.57 368	76.46 225	34.86 418	60.16 451	68.86 400
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
旧先验1					84.55 85	60.36 162		63.69 354			87.05 146	54.65 270			83.34 269	69.66 392

Elysia		77.52 79	77.43 83	77.78 79	79.01 167	60.26 163	76.55 127	84.34 68	67.82 67	78.73 135	87.94 133	58.68 230	83.79 84	74.70 53	89.10 163	89.28 28

StellarMVS		77.52 79	77.43 83	77.78 79	79.01 167	60.26 163	76.55 127	84.34 68	67.82 67	78.73 135	87.94 133	58.68 230	83.79 84	74.70 53	89.10 163	89.28 28

pmmvs-eth3d		64.41 293	63.27 307	67.82 265	75.81 225	60.18 165	69.49 240	62.05 364	38.81 402	74.13 238	82.23 262	43.76 341	68.65 328	42.53 361	80.63 319	74.63 337

PCF-MVS	63.80 13	72.70 152	71.69 177	75.72 107	78.10 179	60.01 166	73.04 182	81.50 119	45.34 342	79.66 124	84.35 212	65.15 149	82.65 108	48.70 315	89.38 156	84.50 140
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n_5		71.46 176	71.62 181	70.99 197	73.89 258	59.95 167	73.02 183	73.08 251	45.15 348	77.30 162	84.06 220	64.73 155	70.08 314	71.20 85	82.10 284	82.92 191

test_prior				75.27 115	82.15 124	59.85 168		84.33 71					83.39 95			82.58 205

TAMVS		65.31 279	63.75 300	69.97 219	82.23 123	59.76 169	66.78 295	63.37 357	45.20 347	69.79 312	79.37 318	47.42 324	72.17 285	34.48 419	85.15 231	77.99 300

jason		64.47 291	62.84 312	69.34 230	76.91 204	59.20 170	67.15 287	65.67 336	35.29 423	65.16 360	76.74 353	44.67 335	70.68 305	54.74 261	79.28 337	78.14 296
jason: jason.
MVSFormer		69.93 205	69.03 222	72.63 173	74.93 233	59.19 171	83.98 44	75.72 231	52.27 247	63.53 380	76.74 353	43.19 344	80.56 151	72.28 81	78.67 344	78.14 296

lupinMVS		63.36 302	61.49 322	68.97 240	74.93 233	59.19 171	65.80 309	64.52 349	34.68 429	63.53 380	74.25 375	43.19 344	70.62 307	53.88 273	78.67 344	77.10 311

MCST-MVS		73.42 128	73.34 141	73.63 137	81.28 135	59.17 173	74.80 156	83.13 91	45.50 337	72.84 264	83.78 231	65.15 149	80.99 143	64.54 145	89.09 165	80.73 252

fmvsm_s_conf0.1_n		66.60 265	65.54 278	69.77 221	68.99 347	59.15 174	72.12 195	56.74 389	40.72 389	68.25 339	80.14 301	61.18 196	66.92 349	67.34 125	74.40 385	83.23 180

test_0402		78.17 75	79.48 66	74.24 126	83.50 100	59.15 174	72.52 187	74.60 242	75.34 19	88.69 17	91.81 31	75.06 47	82.37 115	65.10 140	88.68 169	81.20 236

fmvsm_s_conf0.5_n		66.34 271	65.27 281	69.57 225	68.20 356	59.14 176	71.66 208	56.48 390	40.92 385	67.78 341	79.46 313	61.23 193	66.90 350	67.39 121	74.32 388	82.66 203

fmvsm_s_conf0.5_n_10		72.30 162	72.02 171	73.15 149	70.76 311	59.05 177	73.40 178	79.63 164	48.80 306	75.39 209	84.03 221	59.60 218	75.18 245	72.85 73	83.68 265	85.21 107

EI-MVSNet-Vis-set		72.78 150	71.87 173	75.54 111	74.77 238	59.02 178	72.24 192	71.56 276	63.92 109	78.59 138	71.59 394	66.22 135	78.60 184	67.58 116	80.32 322	89.00 37

fmvsm_s_conf0.5_n_8		72.87 148	72.85 152	72.93 159	72.25 290	59.01 179	72.35 190	80.13 156	56.32 186	75.74 197	84.12 217	60.14 209	75.05 246	71.71 84	82.90 274	84.75 123

fmvsm_s_conf0.5_n_6		70.08 201	69.97 204	70.39 204	72.99 278	58.93 180	68.84 255	76.40 222	49.08 300	68.75 332	81.65 275	57.34 249	71.97 291	70.91 88	83.81 260	80.26 264

DPM-MVS		69.98 204	69.22 220	72.26 180	82.69 117	58.82 181	70.53 226	81.23 128	47.79 320	64.16 367	80.21 297	51.32 293	83.12 99	60.14 196	84.95 236	74.83 334

fmvsm_l_conf0.5_n_9		70.73 189	71.08 190	69.67 223	70.44 323	58.80 182	70.21 231	75.11 238	48.15 314	73.50 251	82.69 255	65.69 141	68.05 338	70.87 89	83.02 272	82.16 215

HQP5-MVS						58.80 182

EG-PatchMatch MVS		70.70 190	70.88 193	70.16 213	82.64 118	58.80 182	71.48 210	73.64 247	54.98 200	76.55 183	81.77 272	61.10 197	78.94 178	54.87 259	80.84 312	72.74 359

HQP-MVS		75.24 103	75.01 108	75.94 104	82.37 119	58.80 182	77.32 118	84.12 77	59.08 151	71.58 285	85.96 187	58.09 239	85.30 58	67.38 123	89.16 157	83.73 162

EI-MVSNet-UG-set		72.63 153	71.68 178	75.47 112	74.67 240	58.64 186	72.02 198	71.50 277	63.53 115	78.58 140	71.39 398	65.98 137	78.53 185	67.30 126	80.18 325	89.23 31

fmvsm_s_conf0.5_n_4		70.18 200	69.83 208	71.24 194	71.65 297	58.59 187	69.29 247	71.66 273	48.69 307	71.62 282	82.11 264	59.94 212	70.03 315	74.52 56	78.96 340	85.10 110

fmvsm_s_conf0.5_n_3		72.97 144	74.13 122	69.47 226	71.40 302	58.36 188	73.07 181	80.64 143	56.86 178	75.49 203	84.67 202	67.86 115	72.33 284	75.68 46	81.54 299	77.73 303

LuminaMVS		71.15 182	70.79 196	72.24 182	77.20 193	58.34 189	72.18 194	76.20 224	54.91 201	77.74 152	81.93 270	49.17 310	76.31 226	62.12 171	85.66 221	82.07 218

CDS-MVSNet		64.33 294	62.66 315	69.35 229	80.44 143	58.28 190	65.26 317	65.66 337	44.36 355	67.30 348	75.54 360	43.27 343	71.77 293	37.68 395	84.44 252	78.01 299
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_s_conf0.5_n_7		67.30 253	66.92 262	68.43 252	72.78 282	58.22 191	60.90 362	72.51 267	49.62 291	63.66 377	80.65 290	58.56 232	68.63 329	62.83 166	80.76 314	78.45 289

IterMVS-SCA-FT		67.68 246	66.07 272	72.49 175	73.34 266	58.20 192	63.80 340	65.55 339	48.10 315	76.91 168	82.64 256	45.20 331	78.84 179	61.20 182	77.89 356	80.44 261

mvsany_test3		43.76 429	41.01 433	52.01 399	48.09 469	57.74 193	42.47 454	23.85 476	23.30 466	64.80 362	62.17 448	27.12 435	40.59 460	29.17 445	48.11 466	57.69 448

pmmvs4		60.78 331	59.04 341	66.00 291	73.06 275	57.67 194	64.53 333	60.22 370	36.91 415	65.96 353	77.27 348	39.66 367	68.54 331	38.87 384	74.89 379	71.80 369

fmvsm_s_conf0.1_n_2		69.14 221	68.42 233	71.28 192	68.30 355	57.60 195	65.06 321	69.91 300	48.24 310	74.56 230	82.84 250	55.55 265	69.73 317	70.66 92	80.69 316	86.52 75

fmvsm_s_conf0.5_n_2		68.93 224	68.23 238	71.02 196	67.78 365	57.58 196	64.74 328	69.56 304	48.16 313	74.38 234	82.32 261	56.00 264	69.68 320	70.65 93	80.52 320	85.80 93

114514_t		73.40 129	73.33 142	73.64 136	84.15 93	57.11 197	78.20 109	80.02 157	43.76 360	72.55 270	86.07 185	64.00 159	83.35 96	60.14 196	91.03 114	80.45 260

BH-untuned		69.39 215	69.46 212	69.18 233	77.96 183	56.88 198	68.47 269	77.53 206	56.77 180	77.79 151	79.63 310	60.30 208	80.20 161	46.04 341	80.65 317	70.47 384

EC-MVSNet		77.08 84	77.39 86	76.14 103	76.86 208	56.87 199	80.32 83	87.52 13	63.45 117	74.66 225	84.52 208	69.87 94	84.94 67	69.76 99	89.59 148	86.60 73

lessismore_v0				72.75 168	79.60 153	56.83 200		57.37 380		83.80 78	89.01 105	47.45 323	78.74 182	64.39 147	86.49 212	82.69 202

ACMH	63.62 14	77.50 81	80.11 61	69.68 222	79.61 152	56.28 201	78.81 100	83.62 84	63.41 119	87.14 34	90.23 78	76.11 37	73.32 269	67.58 116	94.44 44	79.44 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mmtdpeth		68.76 228	70.55 200	63.40 316	67.06 378	56.26 202	68.73 263	71.22 288	55.47 196	70.09 307	88.64 115	65.29 148	56.89 403	58.94 209	89.50 150	77.04 315

ETV-MVS		72.72 151	72.16 169	74.38 125	76.90 206	55.95 203	73.34 179	84.67 57	62.04 129	72.19 277	70.81 399	65.90 139	85.24 62	58.64 211	84.96 235	81.95 224

API-MVS		70.97 186	71.51 185	69.37 227	75.20 230	55.94 204	80.99 72	76.84 217	62.48 127	71.24 294	77.51 347	61.51 189	80.96 148	52.04 283	85.76 220	71.22 377

patch_mono-2		62.73 314	64.08 297	58.68 362	70.36 326	55.87 205	60.84 363	64.11 352	41.23 380	64.04 368	78.22 338	60.00 210	48.80 426	54.17 270	83.71 263	71.37 374

SSM_0404		72.51 158	72.15 170	73.60 138	78.20 177	55.86 206	74.41 165	79.83 160	53.69 232	73.98 243	84.18 214	62.26 178	82.50 110	58.21 217	84.60 247	82.43 208

v7n		79.37 63	80.41 59	76.28 100	78.67 174	55.81 207	79.22 97	82.51 103	70.72 51	87.54 25	92.44 17	68.00 112	81.34 133	72.84 74	91.72 92	91.69 11

ET-MVSNet_ETH3D		63.32 303	60.69 330	71.20 195	70.15 331	55.66 208	65.02 323	64.32 350	43.28 369	68.99 320	72.05 392	25.46 443	78.19 200	54.16 271	82.80 276	79.74 272

GDP-MVS		70.84 187	69.24 218	75.62 109	76.44 212	55.65 209	74.62 163	82.78 97	49.63 289	72.10 278	83.79 230	31.86 410	82.84 105	64.93 143	87.01 203	88.39 49

EIA-MVS		68.59 232	67.16 256	72.90 161	75.18 231	55.64 210	69.39 243	81.29 125	52.44 246	64.53 363	70.69 400	60.33 207	82.30 117	54.27 269	76.31 367	80.75 251

K. test v3		73.67 123	73.61 134	73.87 133	79.78 149	55.62 211	74.69 160	62.04 365	66.16 82	84.76 67	93.23 8	49.47 305	80.97 145	65.66 138	86.67 209	85.02 114

KinetiMVS		72.61 154	72.54 159	72.82 166	71.47 300	55.27 212	68.54 266	76.50 220	61.70 132	74.95 217	86.08 183	59.17 223	76.95 216	69.96 97	84.45 251	86.24 78

mamba_0408		70.32 195	69.35 214	73.24 145	76.92 201	55.22 213	56.61 394	79.27 174	52.14 249	73.08 259	83.14 248	60.53 202	82.50 110	57.51 224	84.91 239	81.99 221

SSM_04072		67.23 255	69.35 214	60.89 344	76.92 201	55.22 213	56.61 394	79.27 174	52.14 249	73.08 259	83.14 248	60.53 202	45.46 440	57.51 224	84.91 239	81.99 221

SSM_0407		72.15 165	71.85 174	73.06 152	76.92 201	55.22 213	73.59 175	79.83 160	53.69 232	73.08 259	84.18 214	62.26 178	81.98 122	58.21 217	84.91 239	81.99 221

BP-MVS1		71.60 172	70.06 203	76.20 102	74.07 254	55.22 213	74.29 168	73.44 249	57.29 174	73.87 246	84.65 203	32.57 402	83.49 92	72.43 80	87.94 182	89.89 23

JIA-IIPM		54.03 377	51.62 397	61.25 340	59.14 434	55.21 217	59.10 375	47.72 435	50.85 271	50.31 451	85.81 190	20.10 461	63.97 373	36.16 410	55.41 462	64.55 428

SixPastTwentyTwo		75.77 93	76.34 94	74.06 130	81.69 130	54.84 218	76.47 129	75.49 233	64.10 108	87.73 21	92.24 20	50.45 299	81.30 135	67.41 119	91.46 99	86.04 85

BH-w/o		64.81 285	64.29 295	66.36 287	76.08 220	54.71 219	65.61 312	75.23 236	50.10 284	71.05 297	71.86 393	54.33 273	79.02 176	38.20 391	76.14 368	65.36 420

MSDG		67.47 250	67.48 251	67.46 269	70.70 313	54.69 220	66.90 293	78.17 197	60.88 139	70.41 301	74.76 367	61.22 195	73.18 270	47.38 329	76.87 363	74.49 340

Patchmatch-RL test		59.95 338	59.12 340	62.44 326	72.46 288	54.61 221	59.63 372	47.51 437	41.05 383	74.58 228	74.30 374	31.06 419	65.31 367	51.61 286	79.85 330	67.39 407

CLD-MVS		72.88 147	72.36 165	74.43 123	77.03 196	54.30 222	68.77 261	83.43 87	52.12 251	76.79 175	74.44 372	69.54 97	83.91 82	55.88 242	93.25 74	85.09 111
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FE-MVS		68.29 237	66.96 261	72.26 180	74.16 252	54.24 223	77.55 115	73.42 250	57.65 171	72.66 268	84.91 199	32.02 409	81.49 132	48.43 319	81.85 288	81.04 240

HyFIR lowres test		63.01 307	60.47 331	70.61 200	83.04 110	54.10 224	59.93 371	72.24 271	33.67 434	69.00 319	75.63 359	38.69 373	76.93 217	36.60 405	75.45 375	80.81 250

Gipuma		69.55 212	72.83 154	59.70 352	63.63 406	53.97 225	80.08 87	75.93 229	64.24 107	73.49 252	88.93 108	57.89 245	62.46 379	59.75 202	91.55 98	62.67 434
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
OpenMVS	62.51 15	68.76 228	68.75 227	68.78 246	70.56 317	53.91 226	78.29 106	77.35 209	48.85 305	70.22 304	83.52 233	52.65 284	76.93 217	55.31 250	81.99 285	75.49 327

BH-RMVSNet		68.69 231	68.20 240	70.14 214	76.40 213	53.90 227	64.62 331	73.48 248	58.01 164	73.91 245	81.78 271	59.09 224	78.22 197	48.59 316	77.96 354	78.31 291

mvsmamba		68.87 225	67.30 255	73.57 139	76.58 210	53.70 228	84.43 41	74.25 244	45.38 341	76.63 178	84.55 207	35.85 388	85.27 59	49.54 306	78.49 346	81.75 230

PAPM_NR		73.91 120	74.16 121	73.16 147	81.90 127	53.50 229	81.28 71	81.40 122	66.17 81	73.30 256	83.31 240	59.96 211	83.10 100	58.45 215	81.66 295	82.87 194

PMMVS		44.69 423	43.95 432	46.92 426	50.05 466	53.47 230	48.08 440	42.40 455	22.36 467	44.01 466	53.05 462	42.60 349	45.49 439	31.69 431	61.36 449	41.79 464

EPP-MVSNet		73.86 122	73.38 138	75.31 114	78.19 178	53.35 231	80.45 78	77.32 210	65.11 96	76.47 188	86.80 151	49.47 305	83.77 86	53.89 272	92.72 81	88.81 43

IterMVS		63.12 306	62.48 316	65.02 299	66.34 382	52.86 232	63.81 339	62.25 360	46.57 329	71.51 290	80.40 294	44.60 336	66.82 356	51.38 290	75.47 374	75.38 330
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tttt0517		69.46 213	67.79 247	74.46 120	75.34 228	52.72 233	75.05 150	63.27 358	54.69 207	78.87 134	84.37 211	26.63 437	81.15 137	63.95 153	87.93 183	89.51 25

GeoE		73.14 134	73.77 130	71.26 193	78.09 180	52.64 234	74.32 166	79.56 169	56.32 186	76.35 191	83.36 239	70.76 84	77.96 203	63.32 162	81.84 289	83.18 181

QAPM		69.18 220	69.26 217	68.94 241	71.61 298	52.58 235	80.37 81	78.79 185	49.63 289	73.51 250	85.14 197	53.66 276	79.12 174	55.11 252	75.54 373	75.11 333

FA-MVS(test-final)		71.27 180	71.06 191	71.92 185	73.96 255	52.32 236	76.45 131	76.12 226	59.07 154	74.04 242	86.18 176	52.18 286	79.43 171	59.75 202	81.76 290	84.03 153

viewdifsd2359ckpt09		72.87 148	72.43 163	74.17 127	74.45 245	51.70 237	76.39 134	84.50 65	49.48 294	75.34 210	83.23 244	63.12 164	82.43 113	56.99 231	88.41 172	88.37 50

CHOSEN 280x420		41.62 431	39.89 436	46.80 427	61.81 413	51.59 238	33.56 467	35.74 468	27.48 453	37.64 471	53.53 460	23.24 451	42.09 455	27.39 449	58.64 455	46.72 459

CMPMVS	48.73 20	61.54 326	60.89 327	63.52 313	61.08 418	51.55 239	68.07 274	68.00 323	33.88 431	65.87 354	81.25 279	37.91 378	67.71 339	49.32 309	82.60 278	71.31 376
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PS-MVSNAJ		64.27 295	63.73 301	65.90 292	77.82 185	51.42 240	63.33 345	72.33 269	45.09 350	61.60 389	68.04 428	62.39 175	73.95 263	49.07 311	73.87 391	72.34 363

AstraMVS		67.11 257	66.84 265	67.92 259	70.75 312	51.36 241	64.77 327	67.06 328	49.03 302	75.40 206	82.05 265	51.26 294	70.65 306	58.89 210	82.32 281	81.77 229

xiu_mvs_v2_base		64.43 292	63.96 298	65.85 293	77.72 187	51.32 242	63.63 342	72.31 270	45.06 351	61.70 388	69.66 413	62.56 171	73.93 264	49.06 312	73.91 390	72.31 364

guyue		66.95 263	66.74 266	67.56 267	70.12 333	51.14 243	65.05 322	68.68 317	49.98 287	74.64 226	80.83 286	50.77 296	70.34 313	57.72 223	82.89 275	81.21 235

mvs5depth		66.35 270	67.98 242	61.47 336	62.43 410	51.05 244	69.38 244	69.24 308	56.74 181	73.62 247	89.06 104	46.96 325	58.63 396	55.87 243	88.49 171	74.73 336

test_vis1_rt		46.70 417	45.24 425	51.06 405	44.58 472	51.04 245	39.91 459	67.56 325	21.84 469	51.94 443	50.79 465	33.83 394	39.77 461	35.25 417	61.50 448	62.38 437

CHOSEN 1792x2688		58.09 351	56.30 364	63.45 314	79.95 147	50.93 246	54.07 415	65.59 338	28.56 450	61.53 390	74.33 373	41.09 357	66.52 360	33.91 422	67.69 433	72.92 354

TR-MVS		64.59 288	63.54 303	67.73 266	75.75 226	50.83 247	63.39 344	70.29 298	49.33 295	71.55 289	74.55 370	50.94 295	78.46 188	40.43 376	75.69 371	73.89 346

thisisatest0530		67.05 261	65.16 284	72.73 170	73.10 273	50.55 248	71.26 217	63.91 353	50.22 282	74.46 232	80.75 288	26.81 436	80.25 158	59.43 204	86.50 211	87.37 59

dcpmvs_2		71.02 185	72.65 157	66.16 289	76.06 221	50.49 249	71.97 200	79.36 171	50.34 279	82.81 89	83.63 232	64.38 157	67.27 346	61.54 176	83.71 263	80.71 254

test_fmvs1_n		52.70 388	52.01 395	54.76 384	53.83 461	50.36 250	55.80 402	65.90 334	24.96 461	65.39 357	60.64 453	27.69 434	48.46 428	45.88 343	67.99 430	65.46 419

Effi-MVS+		72.10 166	72.28 167	71.58 187	74.21 251	50.33 251	74.72 159	82.73 98	62.62 125	70.77 298	76.83 352	69.96 93	80.97 145	60.20 192	78.43 347	83.45 172

IB-MVS	49.67 18	59.69 340	56.96 359	67.90 260	68.19 357	50.30 252	61.42 357	65.18 342	47.57 322	55.83 425	67.15 435	23.77 449	79.60 168	43.56 356	79.97 327	73.79 347
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
ambc				70.10 215	77.74 186	50.21 253	74.28 169	77.93 203		79.26 128	88.29 126	54.11 275	79.77 165	64.43 146	91.10 111	80.30 263

test_vis3_rt		51.94 396	51.04 403	54.65 385	46.32 471	50.13 254	44.34 452	78.17 197	23.62 465	68.95 322	62.81 445	21.41 458	38.52 464	41.49 369	72.22 403	75.30 332

cascas		64.59 288	62.77 314	70.05 216	75.27 229	50.02 255	61.79 354	71.61 274	42.46 372	63.68 376	68.89 422	49.33 307	80.35 155	47.82 327	84.05 257	79.78 271

test_vis1_n		51.27 400	50.41 410	53.83 388	56.99 443	50.01 256	56.75 392	60.53 369	25.68 459	59.74 405	57.86 457	29.40 430	47.41 433	43.10 359	63.66 442	64.08 430

test_fmvs2		54.80 372	54.11 382	56.88 375	51.76 464	49.95 257	56.70 393	65.80 335	26.22 457	69.42 315	65.25 439	31.82 411	49.98 423	49.63 305	70.36 416	70.71 383

mvsany_test1		37.88 433	35.74 438	44.28 437	47.28 470	49.90 258	36.54 465	24.37 475	19.56 470	45.76 459	53.46 461	32.99 399	37.97 465	26.17 451	35.52 468	44.99 463

EI-MVSNet		69.61 211	69.01 223	71.41 191	73.94 256	49.90 258	71.31 215	71.32 282	58.22 162	75.40 206	70.44 401	58.16 236	75.85 228	62.51 167	79.81 331	88.48 46

MDA-MVSNet-bldmvs		62.34 317	61.73 317	64.16 304	61.64 415	49.90 258	48.11 439	57.24 383	53.31 238	80.95 111	79.39 317	49.00 313	61.55 384	45.92 342	80.05 326	81.03 241

IterMVS-LS		73.01 140	73.12 146	72.66 171	73.79 259	49.90 258	71.63 209	78.44 192	58.22 162	80.51 117	86.63 163	58.15 237	79.62 167	62.51 167	88.20 175	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
nrg030		74.87 113	75.99 99	71.52 189	74.90 235	49.88 262	74.10 171	82.58 102	54.55 212	83.50 81	89.21 96	71.51 73	75.74 233	61.24 181	92.34 86	88.94 39

casdiffmvs_mvg		75.26 102	76.18 97	72.52 174	72.87 280	49.47 263	72.94 184	84.71 56	59.49 149	80.90 114	88.81 110	70.07 91	79.71 166	67.40 120	88.39 173	88.40 48
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		65.38 278	64.30 294	68.61 249	69.81 336	49.36 264	65.60 313	78.96 179	45.50 337	59.98 400	78.61 333	51.82 288	78.20 198	44.30 350	84.11 256	78.27 292

PVSNet_Blended		62.90 309	61.64 319	66.69 285	69.81 336	49.36 264	61.23 359	78.96 179	42.04 373	59.98 400	68.86 423	51.82 288	78.20 198	44.30 350	77.77 357	72.52 360

test_fmvs1		51.51 398	50.86 406	53.48 391	49.72 467	49.35 266	54.11 414	64.96 344	24.64 463	63.66 377	59.61 456	28.33 433	48.45 429	45.38 348	67.30 434	62.66 435

MS-PatchMatch		55.59 366	54.89 376	57.68 370	69.18 343	49.05 267	61.00 361	62.93 359	35.98 420	58.36 410	68.93 421	36.71 385	66.59 359	37.62 397	63.30 443	57.39 449

viewdifsd2359ckpt13		69.89 206	69.74 209	70.32 208	70.82 308	48.73 268	72.39 189	81.39 123	48.20 312	72.73 266	82.73 252	62.61 170	76.50 223	55.87 243	80.93 308	85.73 95

MVSMamba_PlusPlus		76.88 85	78.21 77	72.88 163	80.83 138	48.71 269	83.28 56	82.79 95	72.78 32	79.17 130	91.94 25	56.47 260	83.95 81	70.51 94	86.15 213	85.99 86

v10		75.69 95	76.20 96	74.16 128	74.44 247	48.69 270	75.84 145	82.93 94	59.02 155	85.92 45	89.17 99	58.56 232	82.74 107	70.73 90	89.14 160	91.05 14

v1192		73.40 129	73.42 136	73.32 144	74.65 243	48.67 271	72.21 193	81.73 115	52.76 242	81.85 97	84.56 206	57.12 252	82.24 119	68.58 105	87.33 193	89.06 35

icg_test_0407_2		63.88 299	65.59 277	58.75 361	72.47 284	48.64 272	53.19 418	72.98 255	45.33 343	68.91 326	79.37 318	61.91 182	51.11 418	55.06 253	81.11 303	76.49 316

IMVS_0407		67.26 254	67.35 252	66.97 281	72.47 284	48.64 272	69.03 252	72.98 255	45.33 343	68.91 326	79.37 318	61.91 182	75.77 231	55.06 253	81.11 303	76.49 316

IMVS_0404		62.18 318	63.05 310	59.58 354	72.47 284	48.64 272	55.47 404	72.98 255	45.33 343	55.80 427	79.37 318	49.84 302	53.60 413	55.06 253	81.11 303	76.49 316

IMVS_0403		67.07 259	67.08 257	67.03 279	72.47 284	48.64 272	68.44 270	72.98 255	45.33 343	68.63 334	79.37 318	60.38 206	75.97 227	55.06 253	81.11 303	76.49 316

Fast-Effi-MVS+		68.81 227	68.30 235	70.35 207	74.66 242	48.61 276	66.06 304	78.32 194	50.62 275	71.48 291	75.54 360	68.75 101	79.59 169	50.55 297	78.73 343	82.86 195

DELS-MVS		68.83 226	68.31 234	70.38 205	70.55 319	48.31 277	63.78 341	82.13 108	54.00 226	68.96 321	75.17 365	58.95 226	80.06 163	58.55 212	82.74 277	82.76 197
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
pmmvs3		46.71 416	45.09 426	51.55 401	56.76 445	48.25 278	55.78 403	39.53 465	24.13 464	50.35 450	63.40 443	15.90 472	51.08 419	29.29 443	70.69 415	55.33 452

CR-MVSNet		58.96 344	58.49 346	60.36 349	66.37 380	48.24 279	70.93 221	56.40 392	32.87 437	61.35 391	86.66 160	33.19 397	63.22 378	48.50 318	70.17 418	69.62 393

RPMNet		65.77 275	65.08 291	67.84 262	66.37 380	48.24 279	70.93 221	86.27 21	54.66 208	61.35 391	86.77 155	33.29 396	85.67 52	55.93 241	70.17 418	69.62 393

v1144		73.29 132	73.39 137	73.01 153	74.12 253	48.11 281	72.01 199	81.08 133	53.83 230	81.77 99	84.68 201	58.07 242	81.91 124	68.10 109	86.86 204	88.99 38

test_fmvs3		56.78 358	55.99 367	59.12 358	53.96 460	48.09 282	58.76 380	66.22 332	27.54 452	76.66 177	68.69 425	25.32 445	51.31 417	53.42 279	73.38 394	77.97 301

IS-MVSNet		75.10 105	75.42 105	74.15 129	79.23 159	48.05 283	79.43 93	78.04 200	70.09 56	79.17 130	88.02 132	53.04 280	83.60 88	58.05 220	93.76 67	90.79 18

alignmvs		70.54 192	71.00 192	69.15 234	73.50 261	48.04 284	69.85 238	79.62 165	53.94 229	76.54 184	82.00 266	59.00 225	74.68 251	57.32 227	87.21 199	84.72 126

D2MVS		62.58 315	61.05 326	67.20 274	63.85 402	47.92 285	56.29 397	69.58 303	39.32 396	70.07 308	78.19 339	34.93 391	72.68 274	53.44 278	83.74 261	81.00 243

UniMVSNet (Re)		75.00 108	75.48 104	73.56 140	83.14 105	47.92 285	70.41 229	81.04 134	63.67 113	79.54 125	86.37 171	62.83 168	81.82 125	57.10 230	95.25 17	90.94 16

test_cas_vis1_n_1920		50.90 401	50.92 405	50.83 406	54.12 459	47.80 287	51.44 428	54.61 399	26.95 455	63.95 370	60.85 451	37.86 380	44.97 444	45.53 345	62.97 444	59.72 444

PAPR		69.20 219	68.66 230	70.82 198	75.15 232	47.77 288	75.31 147	81.11 130	49.62 291	66.33 352	79.27 323	61.53 188	82.96 102	48.12 323	81.50 301	81.74 231

CVMVSNet		59.21 343	58.44 347	61.51 334	73.94 256	47.76 289	71.31 215	64.56 348	26.91 456	60.34 399	70.44 401	36.24 387	67.65 340	53.57 276	68.66 427	69.12 398

balanced_conf03		73.59 125	74.06 123	72.17 183	77.48 191	47.72 290	81.43 70	82.20 107	54.38 214	79.19 129	87.68 137	54.41 272	83.57 89	63.98 152	85.78 219	85.22 104

EPNet_dtu		58.93 346	58.52 345	60.16 351	67.91 363	47.70 291	69.97 235	58.02 376	49.73 288	47.28 457	73.02 386	38.14 375	62.34 380	36.57 406	85.99 217	70.43 385
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		72.96 145	72.98 150	72.89 162	74.67 240	47.58 292	71.92 204	80.69 140	51.70 257	81.69 103	83.89 228	56.58 258	82.25 118	68.34 107	87.36 190	88.82 42

VortexMVS		65.93 273	66.04 274	65.58 294	67.63 369	47.55 293	64.81 325	72.75 262	47.37 324	75.17 213	79.62 311	49.28 308	71.00 303	55.20 251	82.51 279	78.21 294

v144192		72.99 142	73.06 148	72.77 167	74.58 244	47.48 294	71.90 205	80.44 149	51.57 258	81.46 105	84.11 219	58.04 243	82.12 120	67.98 113	87.47 188	88.70 45

v8		75.07 106	75.64 102	73.35 142	73.42 264	47.46 295	75.20 148	81.45 121	60.05 145	85.64 49	89.26 94	58.08 241	81.80 128	69.71 101	87.97 181	90.79 18

sasdasda		72.29 163	73.38 138	69.04 236	74.23 248	47.37 296	73.93 173	83.18 88	54.36 215	76.61 180	81.64 276	72.03 68	75.34 238	57.12 228	87.28 195	84.40 142

canonicalmvs		72.29 163	73.38 138	69.04 236	74.23 248	47.37 296	73.93 173	83.18 88	54.36 215	76.61 180	81.64 276	72.03 68	75.34 238	57.12 228	87.28 195	84.40 142

MVS		60.62 333	59.97 334	62.58 325	68.13 360	47.28 298	68.59 264	73.96 246	32.19 438	59.94 402	68.86 423	50.48 298	77.64 209	41.85 367	75.74 370	62.83 432

v1240		73.06 138	73.14 144	72.84 165	74.74 239	47.27 299	71.88 206	81.11 130	51.80 255	82.28 94	84.21 213	56.22 262	82.34 116	68.82 104	87.17 201	88.91 40

V42		71.06 183	70.83 194	71.72 186	67.25 372	47.14 300	65.94 305	80.35 152	51.35 264	83.40 82	83.23 244	59.25 222	78.80 180	65.91 135	80.81 313	89.23 31

sc_t1		72.50 159	74.23 119	67.33 271	80.05 146	46.99 301	66.58 298	69.48 305	66.28 80	77.62 156	91.83 30	70.98 82	68.62 330	53.86 274	91.40 100	86.37 77

TinyColmap		67.98 241	69.28 216	64.08 306	67.98 362	46.82 302	70.04 233	75.26 235	53.05 239	77.36 161	86.79 152	59.39 220	72.59 279	45.64 344	88.01 180	72.83 357

v2v482		72.55 157	72.58 158	72.43 176	72.92 279	46.72 303	71.41 212	79.13 177	55.27 197	81.17 109	85.25 196	55.41 266	81.13 138	67.25 127	85.46 223	89.43 26

casdiffmvs		73.06 138	73.84 127	70.72 199	71.32 303	46.71 304	70.93 221	84.26 73	55.62 194	77.46 160	87.10 142	67.09 121	77.81 205	63.95 153	86.83 206	87.64 56
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt11		69.22 217	69.68 210	67.83 263	68.17 358	46.57 305	66.42 300	68.93 312	50.60 276	77.47 159	83.95 225	68.16 107	73.84 267	58.49 213	84.92 237	83.10 183

viewmsd2359difaftdt		69.22 217	69.68 210	67.83 263	68.17 358	46.57 305	66.42 300	68.93 312	50.60 276	77.48 158	83.94 226	68.16 107	73.84 267	58.49 213	84.92 237	83.10 183

VDD-MVS		70.81 188	71.44 186	68.91 243	79.07 166	46.51 307	67.82 276	70.83 294	61.23 134	74.07 240	88.69 112	59.86 214	75.62 235	51.11 291	90.28 131	84.61 131

viewcassd2359sk11		71.41 177	71.89 172	69.98 218	73.50 261	46.46 308	68.91 254	82.39 105	53.62 234	74.57 229	84.41 210	67.40 118	77.27 212	61.35 180	80.89 309	86.21 81

eth_miper_zixun_eth		69.42 214	68.73 229	71.50 190	67.99 361	46.42 309	67.58 278	78.81 182	50.72 273	78.13 146	80.34 296	50.15 301	80.34 156	60.18 193	84.65 245	87.74 55

thisisatest0515		60.48 334	57.86 352	68.34 254	67.25 372	46.42 309	60.58 366	62.14 361	40.82 386	63.58 379	69.12 417	26.28 439	78.34 194	48.83 313	82.13 283	80.26 264

baseline		73.10 135	73.96 126	70.51 203	71.46 301	46.39 311	72.08 196	84.40 67	55.95 191	76.62 179	86.46 169	67.20 119	78.03 202	64.22 149	87.27 197	87.11 67

MVSTER		63.29 304	61.60 321	68.36 253	59.77 430	46.21 312	60.62 365	71.32 282	41.83 375	75.40 206	79.12 327	30.25 425	75.85 228	56.30 238	79.81 331	83.03 188

SDMVSNet		66.36 269	67.85 246	61.88 331	73.04 276	46.14 313	58.54 381	71.36 281	51.42 261	68.93 324	82.72 253	65.62 142	62.22 382	54.41 266	84.67 243	77.28 306

UniMVSNet_NR-MVSNet		74.90 111	75.65 101	72.64 172	83.04 110	45.79 314	69.26 248	78.81 182	66.66 77	81.74 101	86.88 150	63.26 163	81.07 141	56.21 239	94.98 26	91.05 14

DU-MVS		74.91 110	75.57 103	72.93 159	83.50 100	45.79 314	69.47 242	80.14 155	65.22 93	81.74 101	87.08 143	61.82 185	81.07 141	56.21 239	94.98 26	91.93 9

miper_lstm_enhance		61.97 319	61.63 320	62.98 320	60.04 424	45.74 316	47.53 441	70.95 291	44.04 356	73.06 262	78.84 332	39.72 366	60.33 387	55.82 245	84.64 246	82.88 193

Anonymous20231211		75.54 98	77.19 88	70.59 201	77.67 188	45.70 317	74.73 158	80.19 153	68.80 60	82.95 86	92.91 11	66.26 134	76.76 221	58.41 216	92.77 79	89.30 27

diffmvs_AUTHOR		68.27 238	68.59 231	67.32 272	63.76 404	45.37 318	65.31 316	77.19 213	49.25 296	72.68 267	82.19 263	59.62 217	71.17 301	65.75 137	81.53 300	85.42 101

OpenMVS_ROB	54.93 17	63.23 305	63.28 306	63.07 319	69.81 336	45.34 319	68.52 267	67.14 326	43.74 361	70.61 300	79.22 324	47.90 322	72.66 275	48.75 314	73.84 392	71.21 378

RRT-MVS		70.33 194	70.73 197	69.14 235	71.93 295	45.24 320	75.10 149	75.08 239	60.85 140	78.62 137	87.36 139	49.54 304	78.64 183	60.16 194	77.90 355	83.55 165

Anonymous20240529		72.56 155	73.79 129	68.86 244	76.89 207	45.21 321	68.80 260	77.25 212	67.16 70	76.89 169	90.44 63	65.95 138	74.19 260	50.75 294	90.00 137	87.18 65

diffmvs		67.42 251	67.50 250	67.20 274	62.26 412	45.21 321	64.87 324	77.04 216	48.21 311	71.74 280	79.70 308	58.40 234	71.17 301	64.99 141	80.27 323	85.22 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_vis1_n_1920		52.96 385	53.50 384	51.32 403	59.15 433	44.90 323	56.13 400	64.29 351	30.56 448	59.87 404	60.68 452	40.16 363	47.47 432	48.25 322	62.46 445	61.58 440

viewmacassd2359aftdt		71.41 177	72.29 166	68.78 246	71.32 303	44.81 324	70.11 232	81.51 118	52.64 244	74.95 217	86.79 152	66.02 136	74.50 254	62.43 170	84.86 242	87.03 68

1314		59.83 339	58.86 343	62.74 324	65.71 388	44.78 325	68.59 264	72.63 264	33.54 436	61.05 395	67.29 434	43.62 342	71.26 300	49.49 307	67.84 432	72.19 366

viewmambaseed2359dif		65.63 276	65.13 287	67.11 277	64.57 399	44.73 326	64.12 336	72.48 268	43.08 370	71.59 283	81.17 280	58.90 227	72.46 280	52.94 281	77.33 360	84.13 152

v148		69.38 216	69.39 213	69.36 228	69.14 345	44.56 327	68.83 257	72.70 263	54.79 205	78.59 138	84.12 217	54.69 269	76.74 222	59.40 205	82.20 282	86.79 70

viewmanbaseed2359cas		70.24 196	70.83 194	68.48 251	69.99 334	44.55 328	69.48 241	81.01 135	50.87 270	73.61 248	84.84 200	64.00 159	74.31 258	60.24 191	83.43 268	86.56 74

GA-MVS		62.91 308	61.66 318	66.66 286	67.09 374	44.49 329	61.18 360	69.36 307	51.33 265	69.33 317	74.47 371	36.83 384	74.94 247	50.60 296	74.72 380	80.57 258

ppachtmachnet_test		60.26 336	59.61 337	62.20 328	67.70 367	44.33 330	58.18 385	60.96 368	40.75 388	65.80 355	72.57 388	41.23 354	63.92 374	46.87 334	82.42 280	78.33 290

baseline2		55.57 367	52.74 388	64.05 307	65.26 391	44.11 331	62.38 351	54.43 400	39.03 400	51.21 445	67.35 433	33.66 395	72.45 281	37.14 400	64.22 441	75.60 326

Anonymous20240521		63.55 300	66.07 272	55.99 379	66.18 385	44.04 332	68.77 261	68.80 315	46.99 326	72.57 269	85.84 189	39.87 365	50.22 422	53.40 280	92.23 88	73.71 348

viewdifsd2359ckpt07		70.24 196	71.30 188	67.05 278	70.55 319	43.90 333	67.15 287	77.48 208	53.60 235	75.49 203	85.35 193	71.42 76	72.13 286	59.03 207	81.60 297	85.12 109

UniMVSNet_ETH3D		76.74 87	79.02 68	69.92 220	89.27 20	43.81 334	74.47 164	71.70 272	72.33 41	85.50 56	93.65 4	77.98 24	76.88 219	54.60 263	91.64 94	89.08 34

NR-MVSNet		73.62 124	74.05 124	72.33 179	83.50 100	43.71 335	65.65 311	77.32 210	64.32 106	75.59 199	87.08 143	62.45 174	81.34 133	54.90 258	95.63 9	91.93 9

cl____		68.26 240	68.26 236	68.29 255	64.98 396	43.67 336	65.89 306	74.67 240	50.04 285	76.86 171	82.42 259	48.74 315	75.38 236	60.92 186	89.81 143	85.80 93

DIV-MVS_self_test		68.27 238	68.26 236	68.29 255	64.98 396	43.67 336	65.89 306	74.67 240	50.04 285	76.86 171	82.43 258	48.74 315	75.38 236	60.94 185	89.81 143	85.81 89

c3_l		69.82 208	69.89 206	69.61 224	66.24 383	43.48 338	68.12 273	79.61 167	51.43 260	77.72 153	80.18 300	54.61 271	78.15 201	63.62 158	87.50 187	87.20 64

cl22		67.14 256	66.51 267	69.03 238	63.20 407	43.46 339	66.88 294	76.25 223	49.22 297	74.48 231	77.88 343	45.49 330	77.40 211	60.64 188	84.59 248	86.24 78

miper_ehance_all_eth		68.36 234	68.16 241	68.98 239	65.14 395	43.34 340	67.07 289	78.92 181	49.11 299	76.21 192	77.72 344	53.48 277	77.92 204	61.16 183	84.59 248	85.68 97

USDC		62.80 310	63.10 309	61.89 330	65.19 392	43.30 341	67.42 281	74.20 245	35.80 422	72.25 275	84.48 209	45.67 328	71.95 292	37.95 393	84.97 232	70.42 386

MVS_Test		69.84 207	70.71 198	67.24 273	67.49 370	43.25 342	69.87 237	81.22 129	52.69 243	71.57 288	86.68 159	62.09 181	74.51 253	66.05 133	78.74 342	83.96 154

MGCFI-Net		71.70 171	73.10 147	67.49 268	73.23 268	43.08 343	72.06 197	82.43 104	54.58 210	75.97 195	82.00 266	72.42 66	75.22 240	57.84 222	87.34 192	84.18 149

EMVS		44.61 425	44.45 430	45.10 435	48.91 468	43.00 344	37.92 462	41.10 463	46.75 328	38.00 470	48.43 467	26.42 438	46.27 435	37.11 401	75.38 376	46.03 460

CANet_DTU		64.04 297	63.83 299	64.66 301	68.39 351	42.97 345	73.45 177	74.50 243	52.05 253	54.78 432	75.44 363	43.99 339	70.42 311	53.49 277	78.41 348	80.59 257

E-PMN		45.17 421	45.36 424	44.60 436	50.07 465	42.75 346	38.66 461	42.29 457	46.39 330	39.55 468	51.15 464	26.00 440	45.37 442	37.68 395	76.41 365	45.69 461

WR-MVS_H		80.22 57	82.17 48	74.39 124	89.46 15	42.69 347	78.24 108	82.24 106	78.21 13	89.57 10	92.10 21	68.05 110	85.59 53	66.04 134	95.62 10	94.88 5

miper_enhance_ethall		65.86 274	65.05 292	68.28 257	61.62 416	42.62 348	64.74 328	77.97 201	42.52 371	73.42 254	72.79 387	49.66 303	77.68 208	58.12 219	84.59 248	84.54 135

TranMVSNet+NR-MVSNet		76.13 91	77.66 82	71.56 188	84.61 84	42.57 349	70.98 220	78.29 196	68.67 63	83.04 83	89.26 94	72.99 63	80.75 150	55.58 249	95.47 13	91.35 12

1112_ss		59.48 341	58.99 342	60.96 343	77.84 184	42.39 350	61.42 357	68.45 321	37.96 407	59.93 403	67.46 431	45.11 333	65.07 369	40.89 374	71.81 406	75.41 329

pmmvs6		71.82 169	73.66 131	66.31 288	75.94 222	42.01 351	66.99 290	72.53 265	63.45 117	76.43 189	92.78 13	72.95 65	69.69 319	51.41 289	90.46 129	87.22 61

test-LLR		50.43 403	50.69 408	49.64 412	60.76 419	41.87 352	53.18 419	45.48 444	43.41 366	49.41 452	60.47 454	29.22 431	44.73 446	42.09 365	72.14 404	62.33 438

test-mter		48.56 412	48.20 417	49.64 412	60.76 419	41.87 352	53.18 419	45.48 444	31.91 443	49.41 452	60.47 454	18.34 466	44.73 446	42.09 365	72.14 404	62.33 438

PAPM		61.79 322	60.37 332	66.05 290	76.09 218	41.87 352	69.30 246	76.79 219	40.64 390	53.80 437	79.62 311	44.38 337	82.92 103	29.64 441	73.11 396	73.36 350

tt0805		76.12 92	78.43 75	69.20 232	81.32 134	41.37 355	76.72 126	77.64 205	63.78 112	82.06 95	87.88 135	79.78 11	79.05 175	64.33 148	92.40 84	87.17 66

EU-MVSNet		60.82 330	60.80 329	60.86 345	68.37 352	41.16 356	72.27 191	68.27 322	26.96 454	69.08 318	75.71 358	32.09 406	67.44 344	55.59 248	78.90 341	73.97 344

VDDNet		71.60 172	73.13 145	67.02 280	86.29 48	41.11 357	69.97 235	66.50 331	68.72 62	74.74 221	91.70 33	59.90 213	75.81 230	48.58 317	91.72 92	84.15 151

SCA		58.57 349	58.04 351	60.17 350	70.17 329	41.07 358	65.19 319	53.38 409	43.34 368	61.00 396	73.48 381	45.20 331	69.38 322	40.34 377	70.31 417	70.05 387

reproduce_monomvs		58.94 345	58.14 350	61.35 338	59.70 431	40.98 359	60.24 369	63.51 356	45.85 334	68.95 322	75.31 364	18.27 467	65.82 363	51.47 288	79.97 327	77.26 309

test_yl		65.11 280	65.09 289	65.18 296	70.59 315	40.86 360	63.22 348	72.79 259	57.91 165	68.88 328	79.07 329	42.85 347	74.89 248	45.50 346	84.97 232	79.81 269

DCV-MVSNet		65.11 280	65.09 289	65.18 296	70.59 315	40.86 360	63.22 348	72.79 259	57.91 165	68.88 328	79.07 329	42.85 347	74.89 248	45.50 346	84.97 232	79.81 269

tt0320		71.34 179	73.47 135	64.97 300	79.92 148	40.81 362	65.22 318	69.07 310	66.72 76	76.15 194	93.36 5	70.35 86	66.90 350	49.31 310	91.09 112	87.21 62

MonoMVSNet		62.75 312	63.42 304	60.73 346	65.60 389	40.77 363	72.49 188	70.56 295	52.49 245	75.07 214	79.42 315	39.52 369	69.97 316	46.59 337	69.06 424	71.44 373

ttmdpeth		56.40 360	55.45 371	59.25 356	55.63 451	40.69 364	58.94 378	49.72 426	36.22 418	65.39 357	86.97 147	23.16 452	56.69 404	42.30 362	80.74 315	80.36 262

GBi-Net		68.30 235	68.79 225	66.81 282	73.14 270	40.68 365	71.96 201	73.03 252	54.81 202	74.72 222	90.36 74	48.63 317	75.20 242	47.12 330	85.37 224	84.54 135

test1		68.30 235	68.79 225	66.81 282	73.14 270	40.68 365	71.96 201	73.03 252	54.81 202	74.72 222	90.36 74	48.63 317	75.20 242	47.12 330	85.37 224	84.54 135

FMVSNet1		71.06 183	72.48 161	66.81 282	77.65 189	40.68 365	71.96 201	73.03 252	61.14 135	79.45 127	90.36 74	60.44 205	75.20 242	50.20 299	88.05 178	84.54 135

ADS-MVSNet2		48.76 411	47.25 420	53.29 394	55.90 449	40.54 368	47.34 442	54.99 398	31.41 445	50.48 448	72.06 390	31.23 416	54.26 410	25.93 453	55.93 459	65.07 423

tt0320-xc		71.50 174	73.63 133	65.08 298	79.77 150	40.46 369	64.80 326	68.86 314	67.08 71	76.84 173	93.24 7	70.33 87	66.77 357	49.76 302	92.02 90	88.02 52

MG-MVS		70.47 193	71.34 187	67.85 261	79.26 158	40.42 370	74.67 161	75.15 237	58.41 161	68.74 333	88.14 131	56.08 263	83.69 87	59.90 199	81.71 294	79.43 278

PVSNet_0	36.71 22	41.12 432	40.78 435	42.14 442	59.97 426	40.13 371	40.97 456	42.24 458	30.81 447	44.86 463	49.41 466	40.70 360	45.12 443	23.15 463	34.96 469	41.16 465

MVStest1		55.38 368	54.97 375	56.58 376	43.72 473	40.07 372	59.13 374	47.09 439	34.83 425	76.53 185	84.65 203	13.55 476	53.30 414	55.04 257	80.23 324	76.38 321

pm-mvs1		68.40 233	69.85 207	64.04 308	73.10 273	39.94 373	64.61 332	70.50 296	55.52 195	73.97 244	89.33 92	63.91 161	68.38 332	49.68 304	88.02 179	83.81 158

tpm cat1		54.02 378	52.63 390	58.19 365	64.85 398	39.86 374	66.26 303	57.28 381	32.16 439	56.90 417	70.39 403	32.75 401	65.30 368	34.29 420	58.79 454	69.41 395

our_test_3		56.46 359	56.51 362	56.30 377	67.70 367	39.66 375	55.36 406	52.34 415	40.57 391	63.85 371	69.91 412	40.04 364	58.22 398	43.49 357	75.29 378	71.03 382

PS-CasMVS		80.41 54	82.86 41	73.07 151	89.93 7	39.21 376	77.15 122	81.28 126	79.74 6	90.87 5	92.73 14	75.03 48	84.93 68	63.83 156	95.19 21	95.07 3

PatchmatchNet		54.60 373	54.27 380	55.59 382	65.17 394	39.08 377	66.92 292	51.80 417	39.89 393	58.39 409	73.12 385	31.69 413	58.33 397	43.01 360	58.38 457	69.38 396
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CP-MVSNet		79.48 61	81.65 52	72.98 155	89.66 13	39.06 378	76.76 125	80.46 148	78.91 9	90.32 8	91.70 33	68.49 103	84.89 69	63.40 161	95.12 24	95.01 4

PEN-MVS		80.46 53	82.91 39	73.11 150	89.83 9	39.02 379	77.06 124	82.61 101	80.04 5	90.60 7	92.85 12	74.93 49	85.21 63	63.15 164	95.15 23	95.09 2

FMVSNet2		67.48 248	68.21 239	65.29 295	73.14 270	38.94 380	68.81 258	71.21 289	54.81 202	76.73 176	86.48 168	48.63 317	74.60 252	47.98 325	86.11 216	82.35 210

dmvs_re		49.91 408	50.77 407	47.34 424	59.98 425	38.86 381	53.18 419	53.58 406	39.75 394	55.06 429	61.58 450	36.42 386	44.40 448	29.15 446	68.23 428	58.75 446

sd_testset		63.55 300	65.38 280	58.07 366	73.04 276	38.83 382	57.41 389	65.44 340	51.42 261	68.93 324	82.72 253	63.76 162	58.11 399	41.05 372	84.67 243	77.28 306

test_f		43.79 428	45.63 422	38.24 450	42.29 476	38.58 383	34.76 466	47.68 436	22.22 468	67.34 347	63.15 444	31.82 411	30.60 469	39.19 382	62.28 446	45.53 462

CostFormer		57.35 355	56.14 365	60.97 342	63.76 404	38.43 384	67.50 279	60.22 370	37.14 414	59.12 408	76.34 355	32.78 400	71.99 290	39.12 383	69.27 423	72.47 361

TESTMET0.1,1		45.17 421	44.93 427	45.89 431	56.02 448	38.31 385	53.18 419	41.94 459	27.85 451	44.86 463	56.47 459	17.93 468	41.50 459	38.08 392	68.06 429	57.85 447

PVSNet	43.83 21	51.56 397	51.17 401	52.73 395	68.34 353	38.27 386	48.22 438	53.56 407	36.41 417	54.29 435	64.94 440	34.60 392	54.20 411	30.34 436	69.87 420	65.71 418

LFMVS		67.06 260	67.89 244	64.56 302	78.02 181	38.25 387	70.81 224	59.60 372	65.18 94	71.06 296	86.56 166	43.85 340	75.22 240	46.35 338	89.63 146	80.21 266

Anonymous202405211		66.02 272	66.89 263	63.43 315	74.22 250	38.14 388	59.00 376	66.13 333	63.33 120	69.76 313	85.95 188	51.88 287	70.50 309	44.23 352	87.52 186	81.64 232

Test_1112_low_res		58.78 347	58.69 344	59.04 360	79.41 155	38.13 389	57.62 387	66.98 329	34.74 427	59.62 406	77.56 346	42.92 346	63.65 376	38.66 386	70.73 414	75.35 331

VPA-MVSNet		68.71 230	70.37 201	63.72 310	76.13 217	38.06 390	64.10 337	71.48 278	56.60 185	74.10 239	88.31 125	64.78 154	69.72 318	47.69 328	90.15 134	83.37 175

ab-mvs		64.11 296	65.13 287	61.05 341	71.99 294	38.03 391	67.59 277	68.79 316	49.08 300	65.32 359	86.26 174	58.02 244	66.85 355	39.33 380	79.79 333	78.27 292

FIs		72.56 155	73.80 128	68.84 245	78.74 173	37.74 392	71.02 219	79.83 160	56.12 188	80.88 115	89.45 91	58.18 235	78.28 196	56.63 233	93.36 72	90.51 20

MIMVSNet1		66.57 266	69.23 219	58.59 363	81.26 136	37.73 393	64.06 338	57.62 377	57.02 176	78.40 142	90.75 53	62.65 169	58.10 400	41.77 368	89.58 149	79.95 268

mvs_anonymous		65.08 282	65.49 279	63.83 309	63.79 403	37.60 394	66.52 299	69.82 302	43.44 365	73.46 253	86.08 183	58.79 229	71.75 295	51.90 285	75.63 372	82.15 216

FMVSNet3		65.00 283	65.16 284	64.52 303	69.47 341	37.56 395	66.63 296	70.38 297	51.55 259	74.72 222	83.27 242	37.89 379	74.44 255	47.12 330	85.37 224	81.57 233

DTE-MVSNet		80.35 55	82.89 40	72.74 169	89.84 8	37.34 396	77.16 121	81.81 114	80.45 4	90.92 4	92.95 10	74.57 52	86.12 33	63.65 157	94.68 37	94.76 6

tfpnnormal		66.48 267	67.93 243	62.16 329	73.40 265	36.65 397	63.45 343	64.99 343	55.97 190	72.82 265	87.80 136	57.06 254	69.10 325	48.31 321	87.54 185	80.72 253

FC-MVSNet-test		73.32 131	74.78 110	68.93 242	79.21 160	36.57 398	71.82 207	79.54 170	57.63 172	82.57 92	90.38 71	59.38 221	78.99 177	57.91 221	94.56 39	91.23 13

MDA-MVSNet_test_wron		52.57 390	53.49 386	49.81 411	54.24 456	36.47 399	40.48 458	46.58 441	38.13 405	75.47 205	73.32 383	41.05 359	43.85 451	40.98 373	71.20 411	69.10 399

YYNet1		52.58 389	53.50 384	49.85 410	54.15 457	36.45 400	40.53 457	46.55 442	38.09 406	75.52 202	73.31 384	41.08 358	43.88 450	41.10 371	71.14 412	69.21 397

HY-MVS	49.31 19	57.96 352	57.59 355	59.10 359	66.85 379	36.17 401	65.13 320	65.39 341	39.24 399	54.69 434	78.14 340	44.28 338	67.18 348	33.75 424	70.79 413	73.95 345

tpm2		56.12 361	54.64 378	60.55 348	66.24 383	36.01 402	68.14 272	56.77 388	33.60 435	58.25 411	75.52 362	30.25 425	74.33 257	33.27 425	69.76 422	71.32 375

Anonymous20231206		54.13 375	55.82 368	49.04 419	70.89 306	35.96 403	51.73 426	50.87 421	34.86 424	62.49 385	79.22 324	42.52 350	44.29 449	27.95 448	81.88 287	66.88 411

TransMVSNet (Re)		69.62 210	71.63 180	63.57 312	76.51 211	35.93 404	65.75 310	71.29 284	61.05 136	75.02 215	89.90 85	65.88 140	70.41 312	49.79 301	89.48 151	84.38 144

MVE	27.91 23	36.69 436	35.64 439	39.84 447	43.37 474	35.85 405	19.49 469	24.61 474	24.68 462	39.05 469	62.63 447	38.67 374	27.10 472	21.04 467	47.25 467	56.56 451
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
WR-MVS		71.20 181	72.48 161	67.36 270	84.98 77	35.70 406	64.43 334	68.66 318	65.05 97	81.49 104	86.43 170	57.57 247	76.48 224	50.36 298	93.32 73	89.90 22

VNet		64.01 298	65.15 286	60.57 347	73.28 267	35.61 407	57.60 388	67.08 327	54.61 209	66.76 351	83.37 237	56.28 261	66.87 353	42.19 364	85.20 230	79.23 280

tfpn200view9		60.35 335	59.97 334	61.51 334	70.78 309	35.35 408	63.27 346	57.47 378	53.00 240	68.31 337	77.09 350	32.45 404	72.09 287	35.61 414	81.73 291	77.08 312

thres400		60.77 332	59.97 334	63.15 317	70.78 309	35.35 408	63.27 346	57.47 378	53.00 240	68.31 337	77.09 350	32.45 404	72.09 287	35.61 414	81.73 291	82.02 219

thres100view900		61.17 328	61.09 325	61.39 337	72.14 293	35.01 410	65.42 315	56.99 385	55.23 198	70.71 299	79.90 304	32.07 407	72.09 287	35.61 414	81.73 291	77.08 312

thres600view7		61.82 321	61.38 323	63.12 318	71.81 296	34.93 411	64.64 330	56.99 385	54.78 206	70.33 303	79.74 306	32.07 407	72.42 282	38.61 387	83.46 267	82.02 219

thres200		57.55 354	57.02 358	59.17 357	67.89 364	34.93 411	58.91 379	57.25 382	50.24 281	64.01 369	71.46 396	32.49 403	71.39 299	31.31 432	79.57 335	71.19 379

XXY-MVS		55.19 369	57.40 357	48.56 422	64.45 400	34.84 413	51.54 427	53.59 405	38.99 401	63.79 374	79.43 314	56.59 257	45.57 438	36.92 404	71.29 410	65.25 421

Baseline_NR-MVSNet		70.62 191	73.19 143	62.92 323	76.97 199	34.44 414	68.84 255	70.88 293	60.25 144	79.50 126	90.53 60	61.82 185	69.11 324	54.67 262	95.27 16	85.22 104

KD-MVS_self_test		66.38 268	67.51 249	62.97 321	61.76 414	34.39 415	58.11 386	75.30 234	50.84 272	77.12 164	85.42 192	56.84 256	69.44 321	51.07 292	91.16 106	85.08 112

LCM-MVSNet-Re		69.10 222	71.57 184	61.70 332	70.37 325	34.30 416	61.45 356	79.62 165	56.81 179	89.59 9	88.16 130	68.44 104	72.94 272	42.30 362	87.33 193	77.85 302

FE-MVSNET		62.77 311	64.36 293	57.97 369	70.52 321	33.96 417	61.66 355	67.88 324	50.67 274	73.18 258	82.58 257	48.03 320	68.22 334	43.21 358	81.55 298	71.74 370

sss		47.59 415	48.32 415	45.40 433	56.73 446	33.96 417	45.17 448	48.51 433	32.11 442	52.37 441	65.79 437	40.39 362	41.91 457	31.85 430	61.97 447	60.35 442

gm-plane-assit					62.51 409	33.91 419			37.25 413		62.71 446		72.74 273	38.70 385

UnsupCasMVSNet_eth		52.26 392	53.29 387	49.16 417	55.08 453	33.67 420	50.03 433	58.79 375	37.67 410	63.43 382	74.75 368	41.82 352	45.83 436	38.59 388	59.42 453	67.98 406

FMVSNet5		55.08 371	55.54 370	53.71 389	65.80 387	33.50 421	56.22 398	52.50 413	43.72 362	61.06 394	83.38 236	25.46 443	54.87 408	30.11 438	81.64 296	72.75 358

tpmvs		55.84 362	55.45 371	57.01 373	60.33 422	33.20 422	65.89 306	59.29 374	47.52 323	56.04 423	73.60 380	31.05 420	68.06 337	40.64 375	64.64 439	69.77 391

UnsupCasMVSNet_bld		50.01 407	51.03 404	46.95 425	58.61 436	32.64 423	48.31 437	53.27 410	34.27 430	60.47 398	71.53 395	41.40 353	47.07 434	30.68 435	60.78 450	61.13 441

SD_0403		61.63 324	62.83 313	58.03 367	72.21 291	32.43 424	69.33 245	69.00 311	44.54 354	62.01 387	79.42 315	55.27 267	66.88 352	36.07 412	77.63 358	74.78 335

CL-MVSNet_self_test		62.44 316	63.40 305	59.55 355	72.34 289	32.38 425	56.39 396	64.84 345	51.21 267	67.46 346	81.01 284	50.75 297	63.51 377	38.47 389	88.12 177	82.75 198

pmmvs5		52.49 391	52.58 391	52.21 398	54.99 454	32.38 425	55.45 405	53.84 404	32.15 440	55.49 428	74.81 366	38.08 376	57.37 402	34.02 421	74.40 385	66.88 411

test20.03		55.74 364	57.51 356	50.42 407	59.89 429	32.09 427	50.63 430	49.01 431	50.11 283	65.07 361	83.23 244	45.61 329	48.11 431	30.22 437	83.82 259	71.07 381

WTY-MVS		49.39 409	50.31 411	46.62 428	61.22 417	32.00 428	46.61 445	49.77 425	33.87 432	54.12 436	69.55 415	41.96 351	45.40 441	31.28 433	64.42 440	62.47 436

testing11		53.13 384	52.26 394	55.75 381	70.44 323	31.73 429	54.75 411	52.40 414	44.81 352	52.36 442	68.40 427	21.83 457	65.74 365	32.64 428	72.73 398	69.78 390

Vis-MVSNet (Re-imp)		62.74 313	63.21 308	61.34 339	72.19 292	31.56 430	67.31 286	53.87 403	53.60 235	69.88 311	83.37 237	40.52 361	70.98 304	41.40 370	86.78 207	81.48 234

KD-MVS_2432*1600		52.05 394	51.58 398	53.44 392	52.11 462	31.20 431	44.88 450	64.83 346	41.53 377	64.37 364	70.03 410	15.61 473	64.20 371	36.25 407	74.61 382	64.93 425

miper_refine_blended		52.05 394	51.58 398	53.44 392	52.11 462	31.20 431	44.88 450	64.83 346	41.53 377	64.37 364	70.03 410	15.61 473	64.20 371	36.25 407	74.61 382	64.93 425

ECVR-MVS		64.82 284	65.22 282	63.60 311	78.80 171	31.14 433	66.97 291	56.47 391	54.23 219	69.94 310	88.68 113	37.23 382	74.81 250	45.28 349	89.41 153	84.86 118

MIMVSNet		54.39 374	56.12 366	49.20 416	72.57 283	30.91 434	59.98 370	48.43 434	41.66 376	55.94 424	83.86 229	41.19 356	50.42 420	26.05 452	75.38 376	66.27 415

testing91		55.74 364	55.29 374	57.08 372	70.63 314	30.85 435	54.94 410	56.31 394	50.34 279	57.08 415	70.10 409	24.50 447	65.86 362	36.98 403	76.75 364	74.53 339

baseline1		57.82 353	58.36 349	56.19 378	69.17 344	30.76 436	62.94 350	55.21 396	46.04 332	63.83 373	78.47 334	41.20 355	63.68 375	39.44 379	68.99 425	74.13 343

testing99		55.16 370	54.56 379	56.98 374	70.13 332	30.58 437	54.55 413	54.11 402	49.53 293	56.76 419	70.14 408	22.76 454	65.79 364	36.99 402	76.04 369	74.57 338

VPNet		65.58 277	67.56 248	59.65 353	79.72 151	30.17 438	60.27 368	62.14 361	54.19 222	71.24 294	86.63 163	58.80 228	67.62 341	44.17 353	90.87 122	81.18 237

test1111		64.62 287	65.19 283	62.93 322	79.01 167	29.91 439	65.45 314	54.41 401	54.09 224	71.47 292	88.48 118	37.02 383	74.29 259	46.83 335	89.94 141	84.58 134

testing222		53.37 382	52.50 392	55.98 380	70.51 322	29.68 440	56.20 399	51.85 416	46.19 331	56.76 419	68.94 420	19.18 465	65.39 366	25.87 455	76.98 362	72.87 356

test0.0.03 1		47.72 414	48.31 416	45.93 430	55.53 452	29.39 441	46.40 446	41.21 462	43.41 366	55.81 426	67.65 430	29.22 431	43.77 452	25.73 456	69.87 420	64.62 427

MDTV_nov1_ep13			54.05 383		65.54 390	29.30 442	59.00 376	55.22 395	35.96 421	52.44 440	75.98 356	30.77 422	59.62 390	38.21 390	73.33 395

GG-mvs-BLEND				52.24 397	60.64 421	29.21 443	69.73 239	42.41 454		45.47 460	52.33 463	20.43 460	68.16 335	25.52 457	65.42 437	59.36 445

DSMNet-mixed		43.18 430	44.66 429	38.75 448	54.75 455	28.88 444	57.06 391	27.42 473	13.47 471	47.27 458	77.67 345	38.83 372	39.29 463	25.32 458	60.12 452	48.08 457

WB-MVSnew		53.94 380	54.76 377	51.49 402	71.53 299	28.05 445	58.22 384	50.36 423	37.94 408	59.16 407	70.17 407	49.21 309	51.94 416	24.49 459	71.80 407	74.47 341

gg-mvs-nofinetune		55.75 363	56.75 361	52.72 396	62.87 408	28.04 446	68.92 253	41.36 461	71.09 48	50.80 447	92.63 15	20.74 459	66.86 354	29.97 439	72.41 400	63.25 431

test2506		61.23 327	60.85 328	62.38 327	78.80 171	27.88 447	67.33 285	37.42 466	54.23 219	67.55 345	88.68 113	17.87 469	74.39 256	46.33 339	89.41 153	84.86 118

UWE-MVS		52.94 386	52.70 389	53.65 390	73.56 260	27.49 448	57.30 390	49.57 427	38.56 404	62.79 384	71.42 397	19.49 464	60.41 386	24.33 461	77.33 360	73.06 352

ANet_high		67.08 258	69.94 205	58.51 364	57.55 441	27.09 449	58.43 383	76.80 218	63.56 114	82.40 93	91.93 26	59.82 215	64.98 370	50.10 300	88.86 168	83.46 171

MVS-HIRNet		45.53 419	47.29 419	40.24 446	62.29 411	26.82 450	56.02 401	37.41 467	29.74 449	43.69 467	81.27 278	33.96 393	55.48 406	24.46 460	56.79 458	38.43 467

WBMVS		53.38 381	54.14 381	51.11 404	70.16 330	26.66 451	50.52 432	51.64 419	39.32 396	63.08 383	77.16 349	23.53 450	55.56 405	31.99 429	79.88 329	71.11 380

ETVMVS		50.32 405	49.87 413	51.68 400	70.30 328	26.66 451	52.33 425	43.93 448	43.54 364	54.91 431	67.95 429	20.01 462	60.17 388	22.47 464	73.40 393	68.22 402

UBG		49.18 410	49.35 414	48.66 421	70.36 326	26.56 453	50.53 431	45.61 443	37.43 411	53.37 438	65.97 436	23.03 453	54.20 411	26.29 450	71.54 408	65.20 422

tpm		50.60 402	52.42 393	45.14 434	65.18 393	26.29 454	60.30 367	43.50 449	37.41 412	57.01 416	79.09 328	30.20 427	42.32 454	32.77 427	66.36 435	66.81 413

Patchmtry		60.91 329	63.01 311	54.62 386	66.10 386	26.27 455	67.47 280	56.40 392	54.05 225	72.04 279	86.66 160	33.19 397	60.17 388	43.69 354	87.45 189	77.42 304

testing3		58.28 350	58.38 348	58.00 368	77.45 192	26.12 456	60.78 364	43.00 452	56.02 189	70.18 305	75.76 357	13.27 477	67.24 347	48.02 324	80.89 309	80.65 255

SSC-MVS3.2		57.01 356	59.50 338	49.57 414	67.73 366	25.95 457	46.68 444	51.75 418	51.41 263	63.84 372	79.66 309	53.28 279	50.34 421	37.85 394	83.28 270	72.41 362

testgi		54.00 379	56.86 360	45.45 432	58.20 439	25.81 458	49.05 435	49.50 428	45.43 340	67.84 340	81.17 280	51.81 290	43.20 453	29.30 442	79.41 336	67.34 409

tpmrst		50.15 406	51.38 400	46.45 429	56.05 447	24.77 459	64.40 335	49.98 424	36.14 419	53.32 439	69.59 414	35.16 390	48.69 427	39.24 381	58.51 456	65.89 416

Patchmatch-test		47.93 413	49.96 412	41.84 443	57.42 442	24.26 460	48.75 436	41.49 460	39.30 398	56.79 418	73.48 381	30.48 424	33.87 467	29.29 443	72.61 399	67.39 407

Syy-MVS		54.13 375	55.45 371	50.18 408	68.77 348	23.59 461	55.02 407	44.55 446	43.80 358	58.05 412	64.07 441	46.22 326	58.83 394	46.16 340	72.36 401	68.12 403

dp		44.09 427	44.88 428	41.72 445	58.53 438	23.18 462	54.70 412	42.38 456	34.80 426	44.25 465	65.61 438	24.48 448	44.80 445	29.77 440	49.42 465	57.18 450

WAC-MVS						22.69 463								36.10 411

myMVS_eth3d		50.36 404	50.52 409	49.88 409	68.77 348	22.69 463	55.02 407	44.55 446	43.80 358	58.05 412	64.07 441	14.16 475	58.83 394	33.90 423	72.36 401	68.12 403

myMVS_eth3d28		51.35 399	51.99 396	49.44 415	69.21 342	22.51 465	49.82 434	49.11 429	49.00 303	55.03 430	70.31 404	22.73 455	52.88 415	24.33 461	78.39 349	72.92 354

EPMVS		45.74 418	46.53 421	43.39 441	54.14 458	22.33 466	55.02 407	35.00 469	34.69 428	51.09 446	70.20 406	25.92 441	42.04 456	37.19 399	55.50 461	65.78 417

testing3-2		56.85 357	57.62 354	54.53 387	75.84 223	22.23 467	51.26 429	49.10 430	61.04 137	63.74 375	79.73 307	22.29 456	59.44 391	31.16 434	84.43 253	81.92 225

ADS-MVSNet		44.62 424	45.58 423	41.73 444	55.90 449	20.83 468	47.34 442	39.94 464	31.41 445	50.48 448	72.06 390	31.23 416	39.31 462	25.93 453	55.93 459	65.07 423

MDTV_nov1_ep13_2view						18.41 469	53.74 416		31.57 444	44.89 462		29.90 429		32.93 426		71.48 372

PatchT		53.35 383	56.47 363	43.99 439	64.19 401	17.46 470	59.15 373	43.10 451	52.11 252	54.74 433	86.95 148	29.97 428	49.98 423	43.62 355	74.40 385	64.53 429

UWE-MVS-28		44.18 426	44.37 431	43.61 440	60.10 423	16.96 471	52.62 423	33.27 470	36.79 416	48.86 454	69.47 416	19.96 463	45.65 437	13.40 471	64.83 438	68.23 401

new_pmnet		37.55 435	39.80 437	30.79 451	56.83 444	16.46 472	39.35 460	30.65 471	25.59 460	45.26 461	61.60 449	24.54 446	28.02 471	21.60 465	52.80 464	47.90 458

dmvs_testset		45.26 420	47.51 418	38.49 449	59.96 427	14.71 473	58.50 382	43.39 450	41.30 379	51.79 444	56.48 458	39.44 370	49.91 425	21.42 466	55.35 463	50.85 454

DeepMVS_CX				11.83 456	15.51 478	13.86 474		11.25 481	5.76 472	20.85 474	26.46 471	17.06 471	9.22 475	9.69 474	13.82 474	12.42 471

dongtai		31.66 437	32.98 440	27.71 453	58.58 437	12.61 475	45.02 449	14.24 479	41.90 374	47.93 455	43.91 468	10.65 478	41.81 458	14.06 470	20.53 472	28.72 469

kuosan		22.02 438	23.52 442	17.54 455	41.56 477	11.24 476	41.99 455	13.39 480	26.13 458	28.87 472	30.75 470	9.72 479	21.94 474	4.77 475	14.49 473	19.43 470

WB-MVS		60.04 337	64.19 296	47.59 423	76.09 218	10.22 477	52.44 424	46.74 440	65.17 95	74.07 240	87.48 138	53.48 277	55.28 407	49.36 308	72.84 397	77.28 306

SSC-MVS		61.79 322	66.08 271	48.89 420	76.91 204	10.00 478	53.56 417	47.37 438	68.20 65	76.56 182	89.21 96	54.13 274	57.59 401	54.75 260	74.07 389	79.08 282

new-patchmatchnet		52.89 387	55.76 369	44.26 438	59.94 428	6.31 479	37.36 464	50.76 422	41.10 381	64.28 366	79.82 305	44.77 334	48.43 430	36.24 409	87.61 184	78.03 298

PMMVS2		37.74 434	40.87 434	28.36 452	42.41 475	5.35 480	24.61 468	27.75 472	32.15 440	47.85 456	70.27 405	35.85 388	29.51 470	19.08 469	67.85 431	50.22 456

tmp_tt		11.98 441	14.73 444	3.72 457	2.28 480	4.62 481	19.44 470	14.50 478	0.47 475	21.55 473	9.58 473	25.78 442	4.57 476	11.61 473	27.37 470	1.96 472

test_method		19.26 439	19.12 443	19.71 454	9.09 479	1.91 482	7.79 471	53.44 408	1.42 473	10.27 475	35.80 469	17.42 470	25.11 473	12.44 472	24.38 471	32.10 468

test123		4.43 444	5.78 447	0.39 459	0.97 481	0.28 483	46.33 447	0.45 482	0.31 476	0.62 477	1.50 476	0.61 481	0.11 478	0.56 476	0.63 475	0.77 474

testmvs		4.06 445	5.28 448	0.41 458	0.64 482	0.16 484	42.54 453	0.31 483	0.26 477	0.50 478	1.40 477	0.77 480	0.17 477	0.56 476	0.55 476	0.90 473

mmdepth		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

monomultidepth		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

test_blank		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

uanet_test		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

DCPMVS		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

cdsmvs_eth3d_5k		17.71 440	23.62 441	0.00 460	0.00 483	0.00 485	0.00 472	70.17 299	0.00 478	0.00 479	74.25 375	68.16 107	0.00 479	0.00 478	0.00 477	0.00 475

pcd_1.5k_mvsjas		5.20 443	6.93 446	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	62.39 175	0.00 479	0.00 478	0.00 477	0.00 475

sosnet-low-res		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

sosnet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

uncertanet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

Regformer		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

ab-mvs-re		5.62 442	7.50 445	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	67.46 431	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

uanet		0.00 446	0.00 449	0.00 460	0.00 483	0.00 485	0.00 472	0.00 484	0.00 478	0.00 479	0.00 478	0.00 482	0.00 479	0.00 478	0.00 477	0.00 475

TestfortrainingZip							86.10 28

PC_three_1452									46.98 327	81.83 98	86.28 172	66.55 133	84.47 77	63.31 163	90.78 123	83.49 167

eth-test2					0.00 483

eth-test					0.00 483

test_241102_TWO								84.80 50	72.61 36	84.93 62	89.70 87	77.73 25	85.89 44	75.29 48	94.22 57	83.25 178

9.14			80.22 60		80.68 140		80.35 82	87.69 12	59.90 146	83.00 84	88.20 127	74.57 52	81.75 129	73.75 66	93.78 65

test_0728_THIRD									74.03 25	85.83 47	90.41 66	75.58 42	85.69 50	77.43 36	94.74 35	84.31 146

GSMVS																70.05 387

sam_mvs1												31.41 414				70.05 387

sam_mvs												31.21 418

MTGPA								80.63 144

test_post1							66.63 296				2.08 474	30.66 423	59.33 392	40.34 377

test_post											1.99 475	30.91 421	54.76 409

patchmatchnet-post											68.99 418	31.32 415	69.38 322

MTMP							84.83 37	19.26 477

test9_res														72.12 83	91.37 101	77.40 305

agg_prior2														70.70 91	90.93 117	78.55 288

test_prior2							75.57 146		58.92 156	76.53 185	86.78 154	67.83 116		69.81 98	92.76 80

旧先验2							71.17 218		45.11 349	78.54 141			61.28 385	59.19 206

新几何2							71.33 214

无先验							74.82 153	70.94 292	47.75 321				76.85 220	54.47 264		72.09 367

原ACMM2							74.78 157

testdata2													67.30 345	48.34 320

segment_acmp												68.30 106

testdata1							68.34 271		57.24 175

plane_prior5								85.49 33					86.15 31	71.09 86	90.94 115	84.82 120

plane_prior4											89.11 101

plane_prior2							82.74 60		65.45 87

plane_prior1					84.46 87

n2								0.00 484

nn								0.00 484

door-mid								55.02 397

test11								82.71 99

door								52.91 412

HQP-NCC					82.37 119		77.32 118		59.08 151	71.58 285

ACMP_Plane					82.37 119		77.32 118		59.08 151	71.58 285

BP-MVS														67.38 123

HQP4-MVS										71.59 283			85.31 57			83.74 161

HQP3-MVS								84.12 77							89.16 157

HQP2-MVS												58.09 239

ACMMP++_ref															89.47 152

ACMMP++															91.96 91

Test By Simon												62.56 171

High-resolution multi-view benchmark