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.

LCM-MVSNet		86.90 2	88.67 2	81.57 25	91.50 2	63.30 129	84.80 37	87.77 11	86.18 2	96.26 2	96.06 1	90.32 1	84.49 74	68.08 109	97.05 2	96.93 1

UA-Net		81.56 38	82.28 47	79.40 52	88.91 29	69.16 78	84.67 38	80.01 157	75.34 19	79.80 122	94.91 2	69.79 95	80.25 157	72.63 75	94.46 41	88.78 44

mamv4		90.28 1	88.75 1	94.85 1	93.34 1	96.17 1	82.69 61	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

UniMVSNet_ETH3D		76.74 87	79.02 68	69.92 219	89.27 20	43.81 333	74.47 163	71.70 271	72.33 41	85.50 55	93.65 4	77.98 24	76.88 218	54.60 262	91.64 93	89.08 34

tt0320		71.34 179	73.47 135	64.97 299	79.92 147	40.81 361	65.22 317	69.07 309	66.72 76	76.15 193	93.36 5	70.35 86	66.90 349	49.31 309	91.09 111	87.21 62

OurMVSNet-221017-0		78.57 69	78.53 74	78.67 64	80.48 141	64.16 121	80.24 83	82.06 108	61.89 129	88.77 16	93.32 6	57.15 251	82.60 108	70.08 95	92.80 77	89.25 30

tt0320-xc		71.50 174	73.63 133	65.08 297	79.77 149	40.46 368	64.80 325	68.86 313	67.08 71	76.84 172	93.24 7	70.33 87	66.77 356	49.76 301	92.02 89	88.02 52

K. test v3		73.67 123	73.61 134	73.87 132	79.78 148	55.62 210	74.69 159	62.04 364	66.16 82	84.76 66	93.23 8	49.47 305	80.97 144	65.66 137	86.67 208	85.02 114

LTVRE_ROB	75.46 1	84.22 10	84.98 12	81.94 24	84.82 78	75.40 29	91.60 3	87.80 9	73.52 29	88.90 15	93.06 9	71.39 77	81.53 130	81.53 5	92.15 88	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
DTE-MVSNet		80.35 55	82.89 40	72.74 168	89.84 8	37.34 395	77.16 120	81.81 113	80.45 4	90.92 4	92.95 10	74.57 52	86.12 32	63.65 156	94.68 37	94.76 6

Anonymous20231211		75.54 98	77.19 88	70.59 200	77.67 187	45.70 316	74.73 157	80.19 152	68.80 60	82.95 85	92.91 11	66.26 134	76.76 220	58.41 215	92.77 78	89.30 27

PEN-MVS		80.46 53	82.91 39	73.11 149	89.83 9	39.02 378	77.06 123	82.61 100	80.04 5	90.60 7	92.85 12	74.93 49	85.21 62	63.15 163	95.15 23	95.09 2

pmmvs6		71.82 169	73.66 131	66.31 287	75.94 221	42.01 350	66.99 289	72.53 264	63.45 116	76.43 188	92.78 13	72.95 65	69.69 318	51.41 288	90.46 128	87.22 61

PS-CasMVS		80.41 54	82.86 41	73.07 150	89.93 7	39.21 375	77.15 121	81.28 125	79.74 6	90.87 5	92.73 14	75.03 48	84.93 67	63.83 155	95.19 21	95.07 3

gg-mvs-nofinetune		55.75 363	56.75 361	52.72 395	62.87 407	28.04 445	68.92 252	41.36 460	71.09 48	50.80 446	92.63 15	20.74 459	66.86 353	29.97 438	72.41 399	63.25 430

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 71	74.51 57	96.15 3	92.88 8

v7n		79.37 63	80.41 59	76.28 99	78.67 173	55.81 206	79.22 96	82.51 102	70.72 51	87.54 25	92.44 17	68.00 112	81.34 132	72.84 73	91.72 91	91.69 11

PS-MVSNAJss		77.54 78	77.35 87	78.13 75	84.88 77	66.37 99	78.55 102	79.59 167	53.48 236	86.29 40	92.43 18	62.39 175	80.25 157	67.90 114	90.61 126	87.77 54

test_djsdf		78.88 66	78.27 76	80.70 39	81.42 131	71.24 56	83.98 43	75.72 230	52.27 246	87.37 30	92.25 19	68.04 111	80.56 150	72.28 80	91.15 106	90.32 21

SixPastTwentyTwo		75.77 93	76.34 94	74.06 129	81.69 129	54.84 217	76.47 128	75.49 232	64.10 107	87.73 21	92.24 20	50.45 299	81.30 134	67.41 118	91.46 98	86.04 85

reproduce_model		84.87 6	85.80 6	82.05 23	85.52 67	78.14 13	87.69 6	85.36 39	79.26 7	89.12 12	92.10 21	77.52 26	85.92 40	80.47 9	95.20 20	82.10 216

WR-MVS_H		80.22 57	82.17 48	74.39 123	89.46 15	42.69 346	78.24 107	82.24 105	78.21 13	89.57 10	92.10 21	68.05 110	85.59 52	66.04 133	95.62 10	94.88 5

PMVS	70.70 6	81.70 37	83.15 36	77.36 85	90.35 6	82.82 3	82.15 63	79.22 175	74.08 24	87.16 33	91.97 23	84.80 2	76.97 214	64.98 141	93.61 67	72.28 364
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
lecture		83.41 21	85.02 11	78.58 66	83.87 96	67.26 91	84.47 39	88.27 7	73.64 28	87.35 31	91.96 24	78.55 21	82.92 102	81.59 4	95.50 11	85.56 98

MVSMamba_PlusPlus		76.88 85	78.21 77	72.88 162	80.83 137	48.71 268	83.28 55	82.79 94	72.78 32	79.17 129	91.94 25	56.47 260	83.95 80	70.51 93	86.15 212	85.99 86

ANet_high		67.08 258	69.94 205	58.51 363	57.55 440	27.09 448	58.43 382	76.80 217	63.56 113	82.40 92	91.93 26	59.82 215	64.98 369	50.10 299	88.86 167	83.46 170

reproduce-ours		84.97 4	85.93 4	82.10 21	86.11 58	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 33	79.90 10	95.21 18	82.72 199

our_new_method		84.97 4	85.93 4	82.10 21	86.11 58	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 33	79.90 10	95.21 18	82.72 199

mvs_tets		78.93 65	78.67 72	79.72 47	84.81 79	73.93 39	80.65 75	76.50 219	51.98 253	87.40 27	91.86 29	76.09 38	78.53 184	68.58 104	90.20 131	86.69 72

sc_t1		72.50 159	74.23 119	67.33 270	80.05 145	46.99 300	66.58 297	69.48 304	66.28 80	77.62 155	91.83 30	70.98 82	68.62 329	53.86 273	91.40 99	86.37 77

test_0402		78.17 75	79.48 66	74.24 125	83.50 99	59.15 173	72.52 186	74.60 241	75.34 19	88.69 17	91.81 31	75.06 47	82.37 114	65.10 139	88.68 168	81.20 235

APDe-MVS		82.88 28	84.14 19	79.08 56	84.80 80	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	83.47 169
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VDDNet		71.60 172	73.13 145	67.02 279	86.29 48	41.11 356	69.97 234	66.50 330	68.72 62	74.74 220	91.70 33	59.90 213	75.81 229	48.58 316	91.72 91	84.15 150

CP-MVSNet		79.48 61	81.65 52	72.98 154	89.66 13	39.06 377	76.76 124	80.46 147	78.91 9	90.32 8	91.70 33	68.49 103	84.89 68	63.40 160	95.12 24	95.01 4

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 63	79.45 17	94.91 30	88.15 51

EGC-MVSNET		64.77 286	61.17 324	75.60 109	86.90 43	74.47 34	84.04 42	68.62 318	0.60 473	1.13 475	91.61 36	65.32 147	74.15 260	64.01 149	88.28 173	78.17 294

jajsoiax		78.51 70	78.16 78	79.59 49	84.65 82	73.83 41	80.42 78	76.12 225	51.33 264	87.19 32	91.51 37	73.79 59	78.44 188	68.27 107	90.13 135	86.49 76

SMA-MVS		82.12 33	82.68 43	80.43 40	88.90 30	69.52 71	85.12 34	84.76 52	63.53 114	84.23 72	91.47 38	72.02 70	87.16 8	79.74 14	94.36 50	84.61 130
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
COLMAP_ROB	72.78 3	83.75 15	84.11 20	82.68 13	82.97 111	74.39 36	87.18 11	88.18 8	78.98 8	86.11 44	91.47 38	79.70 14	85.76 47	66.91 128	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
TSAR-MVS + MP.		79.05 64	78.81 69	79.74 46	88.94 28	67.52 89	86.61 22	81.38 123	51.71 255	77.15 162	91.42 40	65.49 144	87.20 7	79.44 18	87.17 200	84.51 138
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS-pluss		82.54 31	83.46 30	79.76 45	88.88 31	68.44 82	81.57 68	86.33 20	63.17 120	85.38 57	91.26 41	76.33 35	84.67 73	83.30 2	94.96 28	86.17 82
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
LPG-MVS_test		83.47 20	84.33 17	80.90 36	87.00 40	70.41 64	82.04 65	86.35 18	69.77 57	87.75 19	91.13 42	81.83 3	86.20 27	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 27	77.13 41	95.96 6	86.08 83

ACMH+	66.64 10	81.20 42	82.48 44	77.35 86	81.16 136	62.39 134	80.51 76	87.80 9	73.02 31	87.57 24	91.08 44	80.28 9	82.44 111	64.82 143	96.10 5	87.21 62

ACMMP_NAP		82.33 32	83.28 33	79.46 51	89.28 19	69.09 80	83.62 49	84.98 47	64.77 102	83.97 75	91.02 45	75.53 44	85.93 39	82.00 3	94.36 50	83.35 175

MP-MVS		83.19 23	83.54 28	82.14 20	90.54 5	79.00 9	86.42 25	83.59 84	71.31 45	81.26 106	90.96 46	74.57 52	84.69 72	78.41 26	94.78 33	82.74 198
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testf1		75.66 96	76.57 91	72.95 155	67.07 375	67.62 87	76.10 138	80.68 140	64.95 98	86.58 37	90.94 47	71.20 79	71.68 295	60.46 188	91.13 108	79.56 272

APD_test2		75.66 96	76.57 91	72.95 155	67.07 375	67.62 87	76.10 138	80.68 140	64.95 98	86.58 37	90.94 47	71.20 79	71.68 295	60.46 188	91.13 108	79.56 272

anonymousdsp		78.60 68	77.80 80	81.00 35	78.01 181	74.34 37	80.09 85	76.12 225	50.51 277	89.19 11	90.88 49	71.45 75	77.78 206	73.38 67	90.60 127	90.90 17

PGM-MVS		83.07 26	83.25 35	82.54 16	89.57 14	77.21 24	82.04 65	85.40 37	67.96 66	84.91 64	90.88 49	75.59 41	86.57 16	78.16 28	94.71 36	83.82 156

mPP-MVS		84.01 14	84.39 16	82.88 7	90.65 4	81.38 4	87.08 13	82.79 94	72.41 40	85.11 60	90.85 51	76.65 32	84.89 68	79.30 21	94.63 38	82.35 209

MTAPA		83.19 23	83.87 23	81.13 34	91.16 3	78.16 12	84.87 35	80.63 143	72.08 42	84.93 61	90.79 52	74.65 51	84.42 77	80.98 6	94.75 34	80.82 247

MIMVSNet1		66.57 266	69.23 219	58.59 362	81.26 135	37.73 392	64.06 337	57.62 376	57.02 175	78.40 141	90.75 53	62.65 169	58.10 399	41.77 367	89.58 148	79.95 267

SR-MVS-dyc-post		84.75 7	85.26 9	83.21 4	86.19 51	79.18 7	87.23 9	86.27 21	77.51 14	87.65 22	90.73 54	79.20 16	85.58 53	78.11 29	94.46 41	84.89 115

RE-MVS-def			85.50 7		86.19 51	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

region2R		83.54 18	83.86 24	82.58 15	89.82 10	77.53 18	87.06 16	84.23 74	70.19 55	83.86 76	90.72 56	75.20 45	86.27 24	79.41 19	94.25 55	83.95 154

ACMMPR		83.62 16	83.93 22	82.69 12	89.78 11	77.51 22	87.01 17	84.19 75	70.23 53	84.49 69	90.67 57	75.15 46	86.37 20	79.58 15	94.26 54	84.18 148

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 46	79.37 20	95.17 22	84.62 129
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
CP-MVS		84.12 12	84.55 15	82.80 11	89.42 18	79.74 6	88.19 5	84.43 65	71.96 44	84.70 67	90.56 59	77.12 29	86.18 29	79.24 22	95.36 15	82.49 206

Baseline_NR-MVSNet		70.62 191	73.19 143	62.92 322	76.97 198	34.44 413	68.84 254	70.88 292	60.25 143	79.50 125	90.53 60	61.82 185	69.11 323	54.67 261	95.27 16	85.22 104

DeepC-MVS	72.44 4	81.00 47	80.83 57	81.50 26	86.70 45	70.03 68	82.06 64	87.00 16	59.89 146	80.91 112	90.53 60	72.19 67	88.56 2	73.67 66	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
APD-MVS_3200maxsize		83.57 17	84.33 17	81.31 32	82.83 114	73.53 44	85.50 32	87.45 14	74.11 23	86.45 39	90.52 62	80.02 10	84.48 75	77.73 33	94.34 52	85.93 87

Anonymous20240529		72.56 155	73.79 129	68.86 243	76.89 206	45.21 320	68.80 259	77.25 211	67.16 70	76.89 168	90.44 63	65.95 138	74.19 259	50.75 293	90.00 136	87.18 65

HFP-MVS		83.39 22	84.03 21	81.48 27	89.25 21	75.69 28	87.01 17	84.27 71	70.23 53	84.47 70	90.43 64	76.79 30	85.94 37	79.58 15	94.23 56	82.82 195

HPM-MVS		84.12 12	84.63 14	82.60 14	88.21 36	74.40 35	85.24 33	87.21 15	70.69 52	85.14 59	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
DVP-MVS		81.15 43	83.12 37	75.24 115	86.16 53	60.78 155	83.77 47	80.58 145	72.48 38	85.83 47	90.41 66	78.57 19	85.69 49	75.86 44	94.39 46	79.24 278
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_THIRD									74.03 25	85.83 47	90.41 66	75.58 42	85.69 49	77.43 36	94.74 35	84.31 145

SteuartSystems-ACMMP		83.07 26	83.64 27	81.35 30	85.14 74	71.00 58	85.53 31	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.
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 74	90.39 69	74.73 50	86.46 17	80.73 8	94.43 45	84.60 132

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 86	90.39 69	73.86 57	86.31 22	78.84 24	94.03 60	84.64 127

DVP-MVS++		81.24 41	82.74 42	76.76 91	83.14 104	60.90 153	91.64 1	85.49 33	74.03 25	84.93 61	90.38 71	66.82 125	85.90 41	77.43 36	90.78 122	83.49 166

test_one_0601					85.84 65	61.45 143		85.63 31	75.27 21	85.62 52	90.38 71	76.72 31

FC-MVSNet-test		73.32 131	74.78 110	68.93 241	79.21 159	36.57 397	71.82 206	79.54 169	57.63 171	82.57 91	90.38 71	59.38 221	78.99 176	57.91 220	94.56 39	91.23 13

GBi-Net		68.30 235	68.79 225	66.81 281	73.14 269	40.68 364	71.96 200	73.03 251	54.81 201	74.72 221	90.36 74	48.63 317	75.20 241	47.12 329	85.37 223	84.54 134

test1		68.30 235	68.79 225	66.81 281	73.14 269	40.68 364	71.96 200	73.03 251	54.81 201	74.72 221	90.36 74	48.63 317	75.20 241	47.12 329	85.37 223	84.54 134

FMVSNet1		71.06 183	72.48 161	66.81 281	77.65 188	40.68 364	71.96 200	73.03 251	61.14 134	79.45 126	90.36 74	60.44 205	75.20 241	50.20 298	88.05 177	84.54 134

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 37	77.77 32	93.58 68	83.09 184

ACMH	63.62 14	77.50 81	80.11 61	69.68 221	79.61 151	56.28 200	78.81 99	83.62 83	63.41 118	87.14 34	90.23 78	76.11 37	73.32 268	67.58 115	94.44 44	79.44 276
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		82.79 29	83.27 34	81.34 31	88.99 27	73.29 45	85.94 30	85.13 42	68.58 64	84.14 73	90.21 79	73.37 61	86.41 18	79.09 23	93.98 63	84.30 147

3Dnovator+	73.19 2	81.08 45	80.48 58	82.87 8	81.41 132	72.03 49	84.38 41	86.23 24	77.28 18	80.65 115	90.18 80	59.80 216	87.58 6	73.06 70	91.34 101	89.01 36

DPE-MVS		82.00 35	83.02 38	78.95 61	85.36 70	67.25 92	82.91 57	84.98 47	73.52 29	85.43 56	90.03 81	76.37 34	86.97 13	74.56 55	94.02 62	82.62 203
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ACMP	69.50 8	82.64 30	83.38 31	80.40 41	86.50 46	69.44 73	82.30 62	86.08 25	66.80 74	86.70 35	89.99 82	81.64 6	85.95 36	74.35 59	96.11 4	85.81 89
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test0726					86.16 53	60.78 155	83.81 46	85.10 44	72.48 38	85.27 58	89.96 83	78.57 19

LS3D		80.99 48	80.85 56	81.41 29	78.37 174	71.37 54	87.45 8	85.87 28	77.48 16	81.98 95	89.95 84	69.14 98	85.26 59	66.15 130	91.24 103	87.61 57

TransMVSNet (Re)		69.62 210	71.63 180	63.57 311	76.51 210	35.93 403	65.75 309	71.29 283	61.05 135	75.02 214	89.90 85	65.88 140	70.41 311	49.79 300	89.48 150	84.38 143

RPSCF		75.76 94	74.37 115	79.93 44	74.81 236	77.53 18	77.53 115	79.30 172	59.44 149	78.88 132	89.80 86	71.26 78	73.09 270	57.45 225	80.89 308	89.17 33

SED-MVS		81.78 36	83.48 29	76.67 92	86.12 55	61.06 149	83.62 49	84.72 54	72.61 36	87.38 28	89.70 87	77.48 27	85.89 43	75.29 48	94.39 46	83.08 185

test_241102_TWO								84.80 50	72.61 36	84.93 61	89.70 87	77.73 25	85.89 43	75.29 48	94.22 57	83.25 177

XVG-ACMP-BASELINE		80.54 51	81.06 55	78.98 60	87.01 39	72.91 47	80.23 84	85.56 32	66.56 78	85.64 49	89.57 89	69.12 99	80.55 152	72.51 77	93.37 70	83.48 168

test_241102_ONE					86.12 55	61.06 149		84.72 54	72.64 35	87.38 28	89.47 90	77.48 27	85.74 48

FIs		72.56 155	73.80 128	68.84 244	78.74 172	37.74 391	71.02 218	79.83 159	56.12 187	80.88 114	89.45 91	58.18 235	78.28 195	56.63 232	93.36 71	90.51 20

pm-mvs1		68.40 233	69.85 207	64.04 307	73.10 272	39.94 372	64.61 331	70.50 295	55.52 194	73.97 243	89.33 92	63.91 161	68.38 331	49.68 303	88.02 178	83.81 157

OPM-MVS		80.99 48	81.63 53	79.07 57	86.86 44	69.39 74	79.41 94	84.00 80	65.64 84	85.54 53	89.28 93	76.32 36	83.47 92	74.03 63	93.57 69	84.35 144
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v8		75.07 106	75.64 102	73.35 141	73.42 263	47.46 294	75.20 147	81.45 120	60.05 144	85.64 49	89.26 94	58.08 241	81.80 127	69.71 100	87.97 180	90.79 18

TranMVSNet+NR-MVSNet		76.13 91	77.66 82	71.56 187	84.61 83	42.57 348	70.98 219	78.29 195	68.67 63	83.04 82	89.26 94	72.99 63	80.75 149	55.58 248	95.47 13	91.35 12

SSC-MVS		61.79 322	66.08 271	48.89 419	76.91 203	10.00 477	53.56 416	47.37 437	68.20 65	76.56 181	89.21 96	54.13 274	57.59 400	54.75 259	74.07 388	79.08 281

nrg030		74.87 113	75.99 99	71.52 188	74.90 234	49.88 261	74.10 170	82.58 101	54.55 211	83.50 80	89.21 96	71.51 73	75.74 232	61.24 180	92.34 85	88.94 39

SF-MVS		80.72 50	81.80 49	77.48 82	82.03 124	64.40 119	83.41 53	88.46 6	65.28 92	84.29 71	89.18 98	73.73 60	83.22 96	76.01 43	93.77 65	84.81 122

v10		75.69 95	76.20 96	74.16 127	74.44 246	48.69 269	75.84 144	82.93 93	59.02 154	85.92 45	89.17 99	58.56 232	82.74 106	70.73 89	89.14 159	91.05 14

ACMM	69.25 9	82.11 34	83.31 32	78.49 68	88.17 37	73.96 38	83.11 56	84.52 63	66.40 79	87.45 26	89.16 100	81.02 8	80.52 153	74.27 60	95.73 8	80.98 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZD-MVS					83.91 93	69.36 75		81.09 131	58.91 156	82.73 90	89.11 101	75.77 40	86.63 14	72.73 74	92.93 76

HQP_MVS		78.77 67	78.78 71	78.72 63	85.18 71	65.18 111	82.74 59	85.49 33	65.45 87	78.23 143	89.11 101	60.83 200	86.15 30	71.09 85	90.94 114	84.82 120

plane_prior4											89.11 101

mvs5depth		66.35 270	67.98 242	61.47 335	62.43 409	51.05 243	69.38 243	69.24 307	56.74 180	73.62 246	89.06 104	46.96 325	58.63 395	55.87 242	88.49 170	74.73 335

lessismore_v0				72.75 167	79.60 152	56.83 199		57.37 379		83.80 77	89.01 105	47.45 323	78.74 181	64.39 146	86.49 211	82.69 201

XVG-OURS		79.51 60	79.82 63	78.58 66	86.11 58	74.96 32	76.33 136	84.95 49	66.89 72	82.75 89	88.99 106	66.82 125	78.37 192	74.80 50	90.76 125	82.40 208

APD-MVS		81.13 44	81.73 51	79.36 53	84.47 85	70.53 63	83.85 45	83.70 82	69.43 59	83.67 78	88.96 107	75.89 39	86.41 18	72.62 76	92.95 75	81.14 237
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Gipuma		69.55 212	72.83 154	59.70 351	63.63 405	53.97 224	80.08 86	75.93 228	64.24 106	73.49 251	88.93 108	57.89 245	62.46 378	59.75 201	91.55 97	62.67 433
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
XVG-OURS-SEG-HR		79.62 59	79.99 62	78.49 68	86.46 47	74.79 33	77.15 121	85.39 38	66.73 75	80.39 118	88.85 109	74.43 55	78.33 194	74.73 52	85.79 217	82.35 209

casdiffmvs_mvg		75.26 102	76.18 97	72.52 173	72.87 279	49.47 262	72.94 183	84.71 56	59.49 148	80.90 113	88.81 110	70.07 91	79.71 165	67.40 119	88.39 172	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
MM		78.15 76	77.68 81	79.55 50	80.10 144	65.47 107	80.94 72	78.74 185	71.22 47	72.40 272	88.70 111	60.51 204	87.70 4	77.40 38	89.13 160	85.48 100

VDD-MVS		70.81 188	71.44 186	68.91 242	79.07 165	46.51 306	67.82 275	70.83 293	61.23 133	74.07 239	88.69 112	59.86 214	75.62 234	51.11 290	90.28 130	84.61 130

test2506		61.23 327	60.85 328	62.38 326	78.80 170	27.88 446	67.33 284	37.42 465	54.23 218	67.55 344	88.68 113	17.87 469	74.39 255	46.33 338	89.41 152	84.86 118

ECVR-MVS		64.82 284	65.22 282	63.60 310	78.80 170	31.14 432	66.97 290	56.47 390	54.23 218	69.94 309	88.68 113	37.23 382	74.81 249	45.28 348	89.41 152	84.86 118

mmtdpeth		68.76 228	70.55 200	63.40 315	67.06 377	56.26 201	68.73 262	71.22 287	55.47 195	70.09 306	88.64 115	65.29 148	56.89 402	58.94 208	89.50 149	77.04 314

APD_test1		75.04 107	75.38 106	74.02 130	69.89 334	70.15 66	76.46 129	79.71 162	65.50 86	82.99 84	88.60 116	66.94 122	72.35 282	59.77 200	88.54 169	79.56 272

CPTT-MVS		81.51 39	81.76 50	80.76 38	89.20 23	78.75 10	86.48 24	82.03 109	68.80 60	80.92 111	88.52 117	72.00 71	82.39 113	74.80 50	93.04 74	81.14 237

test1111		64.62 287	65.19 283	62.93 321	79.01 166	29.91 438	65.45 313	54.41 400	54.09 223	71.47 291	88.48 118	37.02 383	74.29 258	46.83 334	89.94 140	84.58 133

Vis-MVSNet		74.85 114	74.56 112	75.72 106	81.63 130	64.64 117	76.35 134	79.06 177	62.85 123	73.33 254	88.41 119	62.54 173	79.59 168	63.94 154	82.92 272	82.94 189
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HPM-MVS++		79.89 58	79.80 64	80.18 43	89.02 26	78.44 11	83.49 52	80.18 153	64.71 103	78.11 146	88.39 120	65.46 145	83.14 97	77.64 35	91.20 104	78.94 282

TestfortrainingZip a		81.05 46	82.35 46	77.16 89	86.27 49	60.63 158	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	84.47 140

ME-MVS		81.36 40	82.39 45	78.28 72	84.42 88	64.31 120	82.78 58	85.02 46	71.25 46	84.81 65	88.38 121	76.53 33	85.81 45	74.09 61	94.20 58	84.73 124

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	81.05 109	88.38 121	57.10 253	87.10 9	79.75 12	83.87 257	84.31 145
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
VPA-MVSNet		68.71 230	70.37 201	63.72 309	76.13 216	38.06 389	64.10 336	71.48 277	56.60 184	74.10 238	88.31 124	64.78 154	69.72 317	47.69 327	90.15 133	83.37 174

ambc				70.10 214	77.74 185	50.21 252	74.28 168	77.93 202		79.26 127	88.29 125	54.11 275	79.77 164	64.43 145	91.10 110	80.30 262

9.14			80.22 60		80.68 139		80.35 81	87.69 12	59.90 145	83.00 83	88.20 126	74.57 52	81.75 128	73.75 65	93.78 64

AllTest		77.66 77	77.43 83	78.35 70	79.19 160	70.81 59	78.60 101	88.64 4	65.37 90	80.09 120	88.17 127	70.33 87	78.43 189	55.60 245	90.90 118	85.81 89

TestCases				78.35 70	79.19 160	70.81 59		88.64 4	65.37 90	80.09 120	88.17 127	70.33 87	78.43 189	55.60 245	90.90 118	85.81 89

LCM-MVSNet-Re		69.10 222	71.57 184	61.70 331	70.37 324	34.30 415	61.45 355	79.62 164	56.81 178	89.59 9	88.16 129	68.44 104	72.94 271	42.30 361	87.33 192	77.85 301

MG-MVS		70.47 193	71.34 187	67.85 260	79.26 157	40.42 369	74.67 160	75.15 236	58.41 160	68.74 332	88.14 130	56.08 263	83.69 86	59.90 198	81.71 293	79.43 277

IS-MVSNet		75.10 105	75.42 105	74.15 128	79.23 158	48.05 282	79.43 92	78.04 199	70.09 56	79.17 129	88.02 131	53.04 280	83.60 87	58.05 219	93.76 66	90.79 18

Elysia		77.52 79	77.43 83	77.78 78	79.01 166	60.26 162	76.55 126	84.34 67	67.82 67	78.73 134	87.94 132	58.68 230	83.79 83	74.70 53	89.10 162	89.28 28

StellarMVS		77.52 79	77.43 83	77.78 78	79.01 166	60.26 162	76.55 126	84.34 67	67.82 67	78.73 134	87.94 132	58.68 230	83.79 83	74.70 53	89.10 162	89.28 28

tt0805		76.12 92	78.43 75	69.20 231	81.32 133	41.37 354	76.72 125	77.64 204	63.78 111	82.06 94	87.88 134	79.78 11	79.05 174	64.33 147	92.40 83	87.17 66

tfpnnormal		66.48 267	67.93 243	62.16 328	73.40 264	36.65 396	63.45 342	64.99 342	55.97 189	72.82 264	87.80 135	57.06 254	69.10 324	48.31 320	87.54 184	80.72 252

balanced_conf03		73.59 125	74.06 123	72.17 182	77.48 190	47.72 289	81.43 69	82.20 106	54.38 213	79.19 128	87.68 136	54.41 272	83.57 88	63.98 151	85.78 218	85.22 104

WB-MVS		60.04 337	64.19 296	47.59 422	76.09 217	10.22 476	52.44 423	46.74 439	65.17 95	74.07 239	87.48 137	53.48 277	55.28 406	49.36 307	72.84 396	77.28 305

RRT-MVS		70.33 194	70.73 197	69.14 234	71.93 294	45.24 319	75.10 148	75.08 238	60.85 139	78.62 136	87.36 138	49.54 304	78.64 182	60.16 193	77.90 354	83.55 164

MGCNet		75.45 99	74.66 111	77.83 77	75.58 226	61.53 142	78.29 105	77.18 213	63.15 122	69.97 308	87.20 139	57.54 248	87.05 10	74.05 62	88.96 165	84.89 115

CDPH-MVS		77.33 82	77.06 90	78.14 74	84.21 90	63.98 124	76.07 140	83.45 85	54.20 220	77.68 154	87.18 140	69.98 92	85.37 55	68.01 111	92.72 80	85.08 112

casdiffmvs		73.06 138	73.84 127	70.72 198	71.32 302	46.71 303	70.93 220	84.26 72	55.62 193	77.46 159	87.10 141	67.09 121	77.81 204	63.95 152	86.83 205	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
DU-MVS		74.91 110	75.57 103	72.93 158	83.50 99	45.79 313	69.47 241	80.14 154	65.22 93	81.74 100	87.08 142	61.82 185	81.07 140	56.21 238	94.98 26	91.93 9

NR-MVSNet		73.62 124	74.05 124	72.33 178	83.50 99	43.71 334	65.65 310	77.32 209	64.32 105	75.59 198	87.08 142	62.45 174	81.34 132	54.90 257	95.63 9	91.93 9

SD-MVS		80.28 56	81.55 54	76.47 97	83.57 98	67.83 86	83.39 54	85.35 40	64.42 104	86.14 43	87.07 144	74.02 56	80.97 144	77.70 34	92.32 86	80.62 255
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
旧先验1					84.55 84	60.36 161		63.69 353			87.05 145	54.65 270			83.34 268	69.66 391

ttmdpeth		56.40 360	55.45 371	59.25 355	55.63 450	40.69 363	58.94 377	49.72 425	36.22 417	65.39 356	86.97 146	23.16 452	56.69 403	42.30 361	80.74 314	80.36 261

PatchT		53.35 383	56.47 363	43.99 438	64.19 400	17.46 469	59.15 372	43.10 450	52.11 251	54.74 432	86.95 147	29.97 428	49.98 422	43.62 354	74.40 384	64.53 428

wuyk23d		61.97 319	66.25 269	49.12 417	58.19 439	60.77 157	66.32 301	52.97 410	55.93 191	90.62 6	86.91 148	73.07 62	35.98 465	20.63 467	91.63 94	50.62 454

UniMVSNet_NR-MVSNet		74.90 111	75.65 101	72.64 171	83.04 109	45.79 313	69.26 247	78.81 181	66.66 77	81.74 100	86.88 149	63.26 163	81.07 140	56.21 238	94.98 26	91.05 14

EPP-MVSNet		73.86 122	73.38 138	75.31 113	78.19 177	53.35 230	80.45 77	77.32 209	65.11 96	76.47 187	86.80 150	49.47 305	83.77 85	53.89 271	92.72 80	88.81 43

viewmacassd2359aftdt		71.41 177	72.29 166	68.78 245	71.32 302	44.81 323	70.11 231	81.51 117	52.64 243	74.95 216	86.79 151	66.02 136	74.50 253	62.43 169	84.86 241	87.03 68

TinyColmap		67.98 241	69.28 216	64.08 305	67.98 361	46.82 301	70.04 232	75.26 234	53.05 238	77.36 160	86.79 151	59.39 220	72.59 278	45.64 343	88.01 179	72.83 356

test_prior2							75.57 145		58.92 155	76.53 184	86.78 153	67.83 116		69.81 97	92.76 79

RPMNet		65.77 275	65.08 291	67.84 261	66.37 379	48.24 278	70.93 220	86.27 21	54.66 207	61.35 390	86.77 154	33.29 396	85.67 51	55.93 240	70.17 417	69.62 392

TEST9					85.47 68	69.32 76	76.42 131	78.69 186	53.73 230	76.97 164	86.74 155	66.84 124	81.10 138

train_agg		76.38 89	76.55 93	75.86 105	85.47 68	69.32 76	76.42 131	78.69 186	54.00 225	76.97 164	86.74 155	66.60 130	81.10 138	72.50 78	91.56 96	77.15 309

test_8					85.09 75	67.89 85	76.26 137	78.66 188	54.00 225	76.89 168	86.72 157	66.60 130	80.89 148

MVS_Test		69.84 207	70.71 198	67.24 272	67.49 369	43.25 341	69.87 236	81.22 128	52.69 242	71.57 287	86.68 158	62.09 181	74.51 252	66.05 132	78.74 341	83.96 153

CR-MVSNet		58.96 344	58.49 346	60.36 348	66.37 379	48.24 278	70.93 220	56.40 391	32.87 436	61.35 390	86.66 159	33.19 397	63.22 377	48.50 317	70.17 417	69.62 392

Patchmtry		60.91 329	63.01 311	54.62 385	66.10 385	26.27 454	67.47 279	56.40 391	54.05 224	72.04 278	86.66 159	33.19 397	60.17 387	43.69 353	87.45 188	77.42 303

OMC-MVS		79.41 62	78.79 70	81.28 33	80.62 140	70.71 62	80.91 73	84.76 52	62.54 125	81.77 98	86.65 161	71.46 74	83.53 90	67.95 113	92.44 82	89.60 24

VPNet		65.58 277	67.56 248	59.65 352	79.72 150	30.17 437	60.27 367	62.14 360	54.19 221	71.24 293	86.63 162	58.80 228	67.62 340	44.17 352	90.87 121	81.18 236

IterMVS-LS		73.01 140	73.12 146	72.66 170	73.79 258	49.90 257	71.63 208	78.44 191	58.22 161	80.51 116	86.63 162	58.15 237	79.62 166	62.51 166	88.20 174	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
testdata				64.13 304	85.87 63	63.34 128		61.80 365	47.83 318	76.42 189	86.60 164	48.83 314	62.31 380	54.46 264	81.26 301	66.74 413

LFMVS		67.06 260	67.89 244	64.56 301	78.02 180	38.25 386	70.81 223	59.60 371	65.18 94	71.06 295	86.56 165	43.85 340	75.22 239	46.35 337	89.63 145	80.21 265

CNVR-MVS		78.49 71	78.59 73	78.16 73	85.86 64	67.40 90	78.12 110	81.50 118	63.92 108	77.51 156	86.56 165	68.43 105	84.82 70	73.83 64	91.61 95	82.26 213

FMVSNet2		67.48 248	68.21 239	65.29 294	73.14 269	38.94 379	68.81 257	71.21 288	54.81 201	76.73 175	86.48 167	48.63 317	74.60 251	47.98 324	86.11 215	82.35 209

baseline		73.10 135	73.96 126	70.51 202	71.46 300	46.39 310	72.08 195	84.40 66	55.95 190	76.62 178	86.46 168	67.20 119	78.03 201	64.22 148	87.27 196	87.11 67

WR-MVS		71.20 181	72.48 161	67.36 269	84.98 76	35.70 405	64.43 333	68.66 317	65.05 97	81.49 103	86.43 169	57.57 247	76.48 223	50.36 297	93.32 72	89.90 22

UniMVSNet (Re)		75.00 108	75.48 104	73.56 139	83.14 104	47.92 284	70.41 228	81.04 133	63.67 112	79.54 124	86.37 170	62.83 168	81.82 124	57.10 229	95.25 17	90.94 16

PC_three_1452									46.98 326	81.83 97	86.28 171	66.55 133	84.47 76	63.31 162	90.78 122	83.49 166

DP-MVS		78.44 73	79.29 67	75.90 104	81.86 127	65.33 109	79.05 97	84.63 60	74.83 22	80.41 117	86.27 172	71.68 72	83.45 93	62.45 168	92.40 83	78.92 283

ab-mvs		64.11 296	65.13 287	61.05 340	71.99 293	38.03 390	67.59 276	68.79 315	49.08 299	65.32 358	86.26 173	58.02 244	66.85 354	39.33 379	79.79 332	78.27 291

NCCC		78.25 74	78.04 79	78.89 62	85.61 66	69.45 72	79.80 91	80.99 135	65.77 83	75.55 199	86.25 174	67.42 117	85.42 54	70.10 94	90.88 120	81.81 226

FA-MVS(test-final)		71.27 180	71.06 191	71.92 184	73.96 254	52.32 235	76.45 130	76.12 225	59.07 153	74.04 241	86.18 175	52.18 286	79.43 170	59.75 201	81.76 289	84.03 152

ITE_SJBPF				80.35 42	76.94 199	73.60 42		80.48 146	66.87 73	83.64 79	86.18 175	70.25 90	79.90 163	61.12 183	88.95 166	87.56 58

原ACMM1				73.90 131	85.90 61	65.15 113		81.67 115	50.97 268	74.25 235	86.16 177	61.60 187	83.54 89	56.75 231	91.08 112	73.00 352

fmvsm_s_conf0.5_n_9		74.56 115	74.30 117	75.34 112	77.17 193	64.87 115	72.62 185	76.17 224	54.54 212	78.32 142	86.14 178	65.14 151	75.72 233	73.10 69	85.55 221	85.42 101

UGNet		70.20 199	69.05 221	73.65 134	76.24 214	63.64 125	75.87 143	72.53 264	61.48 132	60.93 396	86.14 178	52.37 285	77.12 213	50.67 294	85.21 228	80.17 266
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
OPU-MVS				78.65 65	83.44 102	66.85 96	83.62 49				86.12 180	66.82 125	86.01 35	61.72 174	89.79 144	83.08 185

新几何1				69.99 216	88.37 35	71.34 55		62.08 362	43.85 356	74.99 215	86.11 181	52.85 281	70.57 307	50.99 292	83.23 270	68.05 404

KinetiMVS		72.61 154	72.54 159	72.82 165	71.47 299	55.27 211	68.54 265	76.50 219	61.70 131	74.95 216	86.08 182	59.17 223	76.95 215	69.96 96	84.45 250	86.24 78

mvs_anonymous		65.08 282	65.49 279	63.83 308	63.79 402	37.60 393	66.52 298	69.82 301	43.44 364	73.46 252	86.08 182	58.79 229	71.75 294	51.90 284	75.63 371	82.15 215

114514_t		73.40 129	73.33 142	73.64 135	84.15 92	57.11 196	78.20 108	80.02 156	43.76 359	72.55 269	86.07 184	64.00 159	83.35 95	60.14 195	91.03 113	80.45 259

NP-MVS					83.34 103	63.07 131					85.97 185

HQP-MVS		75.24 103	75.01 108	75.94 103	82.37 118	58.80 181	77.32 117	84.12 76	59.08 150	71.58 284	85.96 186	58.09 239	85.30 57	67.38 122	89.16 156	83.73 161

Anonymous202405211		66.02 272	66.89 263	63.43 314	74.22 249	38.14 387	59.00 375	66.13 332	63.33 119	69.76 312	85.95 187	51.88 287	70.50 308	44.23 351	87.52 185	81.64 231

Anonymous20240521		63.55 300	66.07 272	55.99 378	66.18 384	44.04 331	68.77 260	68.80 314	46.99 325	72.57 268	85.84 188	39.87 365	50.22 421	53.40 279	92.23 87	73.71 347

JIA-IIPM		54.03 377	51.62 397	61.25 339	59.14 433	55.21 216	59.10 374	47.72 434	50.85 270	50.31 450	85.81 189	20.10 461	63.97 372	36.16 409	55.41 461	64.55 427

test222					87.30 38	69.15 79	67.85 274	59.59 372	41.06 381	73.05 262	85.72 190	48.03 320			80.65 316	66.92 409

KD-MVS_self_test		66.38 268	67.51 249	62.97 320	61.76 413	34.39 414	58.11 385	75.30 233	50.84 271	77.12 163	85.42 191	56.84 256	69.44 320	51.07 291	91.16 105	85.08 112

viewdifsd2359ckpt07		70.24 196	71.30 188	67.05 277	70.55 318	43.90 332	67.15 286	77.48 207	53.60 234	75.49 202	85.35 192	71.42 76	72.13 285	59.03 206	81.60 296	85.12 109

DeepC-MVS_fast	69.89 7	77.17 83	76.33 95	79.70 48	83.90 94	67.94 84	80.06 87	83.75 81	56.73 181	74.88 219	85.32 193	65.54 143	87.79 3	65.61 138	91.14 107	83.35 175
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepPCF-MVS	71.07 5	78.48 72	77.14 89	82.52 17	84.39 89	77.04 25	76.35 134	84.05 78	56.66 182	80.27 119	85.31 194	68.56 102	87.03 12	67.39 120	91.26 102	83.50 165

v2v482		72.55 157	72.58 158	72.43 175	72.92 278	46.72 302	71.41 211	79.13 176	55.27 196	81.17 108	85.25 195	55.41 266	81.13 137	67.25 126	85.46 222	89.43 26

QAPM		69.18 220	69.26 217	68.94 240	71.61 297	52.58 234	80.37 80	78.79 184	49.63 288	73.51 249	85.14 196	53.66 276	79.12 173	55.11 251	75.54 372	75.11 332

test_fmvsmconf0.01_n		73.91 120	73.64 132	74.71 116	69.79 338	66.25 100	75.90 142	79.90 158	46.03 332	76.48 186	85.02 197	67.96 114	73.97 261	74.47 58	87.22 197	83.90 155

FE-MVS		68.29 237	66.96 261	72.26 179	74.16 251	54.24 222	77.55 114	73.42 249	57.65 170	72.66 267	84.91 198	32.02 409	81.49 131	48.43 318	81.85 287	81.04 239

viewmanbaseed2359cas		70.24 196	70.83 194	68.48 250	69.99 333	44.55 327	69.48 240	81.01 134	50.87 269	73.61 247	84.84 199	64.00 159	74.31 257	60.24 190	83.43 267	86.56 74

v1144		73.29 132	73.39 137	73.01 152	74.12 252	48.11 280	72.01 198	81.08 132	53.83 229	81.77 98	84.68 200	58.07 242	81.91 123	68.10 108	86.86 203	88.99 38

fmvsm_s_conf0.5_n_3		72.97 144	74.13 122	69.47 225	71.40 301	58.36 187	73.07 180	80.64 142	56.86 177	75.49 202	84.67 201	67.86 115	72.33 283	75.68 46	81.54 298	77.73 302

BP-MVS1		71.60 172	70.06 203	76.20 101	74.07 253	55.22 212	74.29 167	73.44 248	57.29 173	73.87 245	84.65 202	32.57 402	83.49 91	72.43 79	87.94 181	89.89 23

MVStest1		55.38 368	54.97 375	56.58 375	43.72 472	40.07 371	59.13 373	47.09 438	34.83 424	76.53 184	84.65 202	13.55 476	53.30 413	55.04 256	80.23 323	76.38 320

3Dnovator	65.95 11	71.50 174	71.22 189	72.34 177	73.16 268	63.09 130	78.37 104	78.32 193	57.67 168	72.22 275	84.61 204	54.77 268	78.47 186	60.82 186	81.07 306	75.45 327

v1192		73.40 129	73.42 136	73.32 143	74.65 242	48.67 270	72.21 192	81.73 114	52.76 241	81.85 96	84.56 205	57.12 252	82.24 118	68.58 104	87.33 192	89.06 35

mvsmamba		68.87 225	67.30 255	73.57 138	76.58 209	53.70 227	84.43 40	74.25 243	45.38 340	76.63 177	84.55 206	35.85 388	85.27 58	49.54 305	78.49 345	81.75 229

EC-MVSNet		77.08 84	77.39 86	76.14 102	76.86 207	56.87 198	80.32 82	87.52 13	63.45 116	74.66 224	84.52 207	69.87 94	84.94 66	69.76 98	89.59 147	86.60 73

USDC		62.80 310	63.10 309	61.89 329	65.19 391	43.30 340	67.42 280	74.20 244	35.80 421	72.25 274	84.48 208	45.67 328	71.95 291	37.95 392	84.97 231	70.42 385

viewcassd2359sk11		71.41 177	71.89 172	69.98 217	73.50 260	46.46 307	68.91 253	82.39 104	53.62 233	74.57 228	84.41 209	67.40 118	77.27 211	61.35 179	80.89 308	86.21 81

tttt0517		69.46 213	67.79 247	74.46 119	75.34 227	52.72 232	75.05 149	63.27 357	54.69 206	78.87 133	84.37 210	26.63 437	81.15 136	63.95 152	87.93 182	89.51 25

PCF-MVS	63.80 13	72.70 152	71.69 177	75.72 106	78.10 178	60.01 165	73.04 181	81.50 118	45.34 341	79.66 123	84.35 211	65.15 149	82.65 107	48.70 314	89.38 155	84.50 139
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1240		73.06 138	73.14 144	72.84 164	74.74 238	47.27 298	71.88 205	81.11 129	51.80 254	82.28 93	84.21 212	56.22 262	82.34 115	68.82 103	87.17 200	88.91 40

SSM_0407		72.15 165	71.85 174	73.06 151	76.92 200	55.22 212	73.59 174	79.83 159	53.69 231	73.08 258	84.18 213	62.26 178	81.98 121	58.21 216	84.91 238	81.99 220

SSM_0404		72.51 158	72.15 170	73.60 137	78.20 176	55.86 205	74.41 164	79.83 159	53.69 231	73.98 242	84.18 213	62.26 178	82.50 109	58.21 216	84.60 246	82.43 207

fmvsm_l_conf0.5_n_3		71.98 168	71.68 178	72.88 162	72.84 280	64.15 122	73.48 175	77.11 214	48.97 303	71.31 292	84.18 213	67.98 113	71.60 297	68.86 102	80.43 320	82.89 191

fmvsm_s_conf0.5_n_8		72.87 148	72.85 152	72.93 158	72.25 289	59.01 178	72.35 189	80.13 155	56.32 185	75.74 196	84.12 216	60.14 209	75.05 245	71.71 83	82.90 273	84.75 123

v148		69.38 216	69.39 213	69.36 227	69.14 344	44.56 326	68.83 256	72.70 262	54.79 204	78.59 137	84.12 216	54.69 269	76.74 221	59.40 204	82.20 281	86.79 70

v144192		72.99 142	73.06 148	72.77 166	74.58 243	47.48 293	71.90 204	80.44 148	51.57 257	81.46 104	84.11 218	58.04 243	82.12 119	67.98 112	87.47 187	88.70 45

fmvsm_s_conf0.5_n_5		71.46 176	71.62 181	70.99 196	73.89 257	59.95 166	73.02 182	73.08 250	45.15 347	77.30 161	84.06 219	64.73 155	70.08 313	71.20 84	82.10 283	82.92 190

fmvsm_s_conf0.5_n_10		72.30 162	72.02 171	73.15 148	70.76 310	59.05 176	73.40 177	79.63 163	48.80 305	75.39 208	84.03 220	59.60 218	75.18 244	72.85 72	83.68 264	85.21 107

NormalMVS		76.15 90	75.08 107	79.36 53	83.87 96	70.01 69	79.92 89	84.34 67	58.60 158	75.21 210	84.02 221	52.85 281	81.82 124	61.45 176	95.50 11	86.24 78

SymmetryMVS		74.00 119	72.85 152	77.43 84	85.17 73	70.01 69	79.92 89	68.48 319	58.60 158	75.21 210	84.02 221	52.85 281	81.82 124	61.45 176	89.99 138	80.47 258

F-COLMAP		75.29 101	73.99 125	79.18 55	81.73 128	71.90 50	81.86 67	82.98 91	59.86 147	72.27 273	84.00 223	64.56 156	83.07 100	51.48 286	87.19 199	82.56 205

viewdifsd2359ckpt11		69.22 217	69.68 210	67.83 262	68.17 357	46.57 304	66.42 299	68.93 311	50.60 275	77.47 158	83.95 224	68.16 107	73.84 266	58.49 212	84.92 236	83.10 182

viewmsd2359difaftdt		69.22 217	69.68 210	67.83 262	68.17 357	46.57 304	66.42 299	68.93 311	50.60 275	77.48 157	83.94 225	68.16 107	73.84 266	58.49 212	84.92 236	83.10 182

test_fmvsmconf0.1_n		73.26 133	72.82 155	74.56 118	69.10 345	66.18 102	74.65 161	79.34 171	45.58 335	75.54 200	83.91 226	67.19 120	73.88 264	73.26 68	86.86 203	83.63 163

v1921920		72.96 145	72.98 150	72.89 161	74.67 239	47.58 291	71.92 203	80.69 139	51.70 256	81.69 102	83.89 227	56.58 258	82.25 117	68.34 106	87.36 189	88.82 42

MIMVSNet		54.39 374	56.12 366	49.20 415	72.57 282	30.91 433	59.98 369	48.43 433	41.66 375	55.94 423	83.86 228	41.19 356	50.42 419	26.05 451	75.38 375	66.27 414

GDP-MVS		70.84 187	69.24 218	75.62 108	76.44 211	55.65 208	74.62 162	82.78 96	49.63 288	72.10 277	83.79 229	31.86 410	82.84 104	64.93 142	87.01 202	88.39 49

MCST-MVS		73.42 128	73.34 141	73.63 136	81.28 134	59.17 172	74.80 155	83.13 90	45.50 336	72.84 263	83.78 230	65.15 149	80.99 142	64.54 144	89.09 164	80.73 251

dcpmvs_2		71.02 185	72.65 157	66.16 288	76.06 220	50.49 248	71.97 199	79.36 170	50.34 278	82.81 88	83.63 231	64.38 157	67.27 345	61.54 175	83.71 262	80.71 253

OpenMVS	62.51 15	68.76 228	68.75 227	68.78 245	70.56 316	53.91 225	78.29 105	77.35 208	48.85 304	70.22 303	83.52 232	52.65 284	76.93 216	55.31 249	81.99 284	75.49 326

h-mvs33		73.08 136	71.61 182	77.48 82	83.89 95	72.89 48	70.47 226	71.12 289	54.28 216	77.89 147	83.41 233	49.04 311	80.98 143	63.62 157	90.77 124	78.58 286

TAPA-MVS	65.27 12	75.16 104	74.29 118	77.77 80	74.86 235	68.08 83	77.89 111	84.04 79	55.15 198	76.19 192	83.39 234	66.91 123	80.11 161	60.04 197	90.14 134	85.13 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet5		55.08 371	55.54 370	53.71 388	65.80 386	33.50 420	56.22 397	52.50 412	43.72 361	61.06 393	83.38 235	25.46 443	54.87 407	30.11 437	81.64 295	72.75 357

VNet		64.01 298	65.15 286	60.57 346	73.28 266	35.61 406	57.60 387	67.08 326	54.61 208	66.76 350	83.37 236	56.28 261	66.87 352	42.19 363	85.20 229	79.23 279

Vis-MVSNet (Re-imp)		62.74 313	63.21 308	61.34 338	72.19 291	31.56 429	67.31 285	53.87 402	53.60 234	69.88 310	83.37 236	40.52 361	70.98 303	41.40 369	86.78 206	81.48 233

GeoE		73.14 134	73.77 130	71.26 192	78.09 179	52.64 233	74.32 165	79.56 168	56.32 185	76.35 190	83.36 238	70.76 84	77.96 202	63.32 161	81.84 288	83.18 180

PAPM_NR		73.91 120	74.16 121	73.16 146	81.90 126	53.50 228	81.28 70	81.40 121	66.17 81	73.30 255	83.31 239	59.96 211	83.10 99	58.45 214	81.66 294	82.87 193

CS-MVS		76.51 88	76.00 98	78.06 76	77.02 196	64.77 116	80.78 74	82.66 99	60.39 142	74.15 236	83.30 240	69.65 96	82.07 120	69.27 101	86.75 207	87.36 60

FMVSNet3		65.00 283	65.16 284	64.52 302	69.47 340	37.56 394	66.63 295	70.38 296	51.55 258	74.72 221	83.27 241	37.89 379	74.44 254	47.12 329	85.37 223	81.57 232

test_fmvsmconf_n		72.91 146	72.40 164	74.46 119	68.62 349	66.12 103	74.21 169	78.80 183	45.64 334	74.62 226	83.25 242	66.80 128	73.86 265	72.97 71	86.66 209	83.39 172

viewdifsd2359ckpt09		72.87 148	72.43 163	74.17 126	74.45 244	51.70 236	76.39 133	84.50 64	49.48 293	75.34 209	83.23 243	63.12 164	82.43 112	56.99 230	88.41 171	88.37 50

V42		71.06 183	70.83 194	71.72 185	67.25 371	47.14 299	65.94 304	80.35 151	51.35 263	83.40 81	83.23 243	59.25 222	78.80 179	65.91 134	80.81 312	89.23 31

test20.03		55.74 364	57.51 356	50.42 406	59.89 428	32.09 426	50.63 429	49.01 430	50.11 282	65.07 360	83.23 243	45.61 329	48.11 430	30.22 436	83.82 258	71.07 380

CNLPA		73.44 127	73.03 149	74.66 117	78.27 175	75.29 30	75.99 141	78.49 190	65.39 89	75.67 197	83.22 246	61.23 193	66.77 356	53.70 274	85.33 226	81.92 224

mamba_0408		70.32 195	69.35 214	73.24 144	76.92 200	55.22 212	56.61 393	79.27 173	52.14 248	73.08 258	83.14 247	60.53 202	82.50 109	57.51 223	84.91 238	81.99 220

SSM_04072		67.23 255	69.35 214	60.89 343	76.92 200	55.22 212	56.61 393	79.27 173	52.14 248	73.08 258	83.14 247	60.53 202	45.46 439	57.51 223	84.91 238	81.99 220

fmvsm_s_conf0.1_n_2		69.14 221	68.42 233	71.28 191	68.30 354	57.60 194	65.06 320	69.91 299	48.24 309	74.56 229	82.84 249	55.55 265	69.73 316	70.66 91	80.69 315	86.52 75

EPNet		69.10 222	67.32 253	74.46 119	68.33 353	61.27 146	77.56 113	63.57 354	60.95 137	56.62 420	82.75 250	51.53 291	81.24 135	54.36 267	90.20 131	80.88 246
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt13		69.89 206	69.74 209	70.32 207	70.82 307	48.73 267	72.39 188	81.39 122	48.20 311	72.73 265	82.73 251	62.61 170	76.50 222	55.87 242	80.93 307	85.73 95

SDMVSNet		66.36 269	67.85 246	61.88 330	73.04 275	46.14 312	58.54 380	71.36 280	51.42 260	68.93 323	82.72 252	65.62 142	62.22 381	54.41 265	84.67 242	77.28 305

sd_testset		63.55 300	65.38 280	58.07 365	73.04 275	38.83 381	57.41 388	65.44 339	51.42 260	68.93 323	82.72 252	63.76 162	58.11 398	41.05 371	84.67 242	77.28 305

fmvsm_l_conf0.5_n_9		70.73 189	71.08 190	69.67 222	70.44 322	58.80 181	70.21 230	75.11 237	48.15 313	73.50 250	82.69 254	65.69 141	68.05 337	70.87 88	83.02 271	82.16 214

IterMVS-SCA-FT		67.68 246	66.07 272	72.49 174	73.34 265	58.20 191	63.80 339	65.55 338	48.10 314	76.91 167	82.64 255	45.20 331	78.84 178	61.20 181	77.89 355	80.44 260

FE-MVSNET		62.77 311	64.36 293	57.97 368	70.52 320	33.96 416	61.66 354	67.88 323	50.67 273	73.18 257	82.58 256	48.03 320	68.22 333	43.21 357	81.55 297	71.74 369

DIV-MVS_self_test		68.27 238	68.26 236	68.29 254	64.98 395	43.67 335	65.89 305	74.67 239	50.04 284	76.86 170	82.43 257	48.74 315	75.38 235	60.94 184	89.81 142	85.81 89

cl____		68.26 240	68.26 236	68.29 254	64.98 395	43.67 335	65.89 305	74.67 239	50.04 284	76.86 170	82.42 258	48.74 315	75.38 235	60.92 185	89.81 142	85.80 93

MVS_111021_HR		72.98 143	72.97 151	72.99 153	80.82 138	65.47 107	68.81 257	72.77 260	57.67 168	75.76 195	82.38 259	71.01 81	77.17 212	61.38 178	86.15 212	76.32 321

fmvsm_s_conf0.5_n_2		68.93 224	68.23 238	71.02 195	67.78 364	57.58 195	64.74 327	69.56 303	48.16 312	74.38 233	82.32 260	56.00 264	69.68 319	70.65 92	80.52 319	85.80 93

pmmvs-eth3d		64.41 293	63.27 307	67.82 264	75.81 224	60.18 164	69.49 239	62.05 363	38.81 401	74.13 237	82.23 261	43.76 341	68.65 327	42.53 360	80.63 318	74.63 336

diffmvs_AUTHOR		68.27 238	68.59 231	67.32 271	63.76 403	45.37 317	65.31 315	77.19 212	49.25 295	72.68 266	82.19 262	59.62 217	71.17 300	65.75 136	81.53 299	85.42 101

fmvsm_s_conf0.5_n_4		70.18 200	69.83 208	71.24 193	71.65 296	58.59 186	69.29 246	71.66 272	48.69 306	71.62 281	82.11 263	59.94 212	70.03 314	74.52 56	78.96 339	85.10 110

AstraMVS		67.11 257	66.84 265	67.92 258	70.75 311	51.36 240	64.77 326	67.06 327	49.03 301	75.40 205	82.05 264	51.26 294	70.65 305	58.89 209	82.32 280	81.77 228

MGCFI-Net		71.70 171	73.10 147	67.49 267	73.23 267	43.08 342	72.06 196	82.43 103	54.58 209	75.97 194	82.00 265	72.42 66	75.22 239	57.84 221	87.34 191	84.18 148

alignmvs		70.54 192	71.00 192	69.15 233	73.50 260	48.04 283	69.85 237	79.62 164	53.94 228	76.54 183	82.00 265	59.00 225	74.68 250	57.32 226	87.21 198	84.72 125

MSLP-MVS++		74.48 116	75.78 100	70.59 200	84.66 81	62.40 133	78.65 100	84.24 73	60.55 141	77.71 153	81.98 267	63.12 164	77.64 208	62.95 164	88.14 175	71.73 370

DP-MVS Recon		73.57 126	72.69 156	76.23 100	82.85 113	63.39 127	74.32 165	82.96 92	57.75 166	70.35 301	81.98 267	64.34 158	84.41 78	49.69 302	89.95 139	80.89 245

LuminaMVS		71.15 182	70.79 196	72.24 181	77.20 192	58.34 188	72.18 193	76.20 223	54.91 200	77.74 151	81.93 269	49.17 310	76.31 225	62.12 170	85.66 220	82.07 217

BH-RMVSNet		68.69 231	68.20 240	70.14 213	76.40 212	53.90 226	64.62 330	73.48 247	58.01 163	73.91 244	81.78 270	59.09 224	78.22 196	48.59 315	77.96 353	78.31 290

EG-PatchMatch MVS		70.70 190	70.88 193	70.16 212	82.64 117	58.80 181	71.48 209	73.64 246	54.98 199	76.55 182	81.77 271	61.10 197	78.94 177	54.87 258	80.84 311	72.74 358

MVS_111021_LR		72.10 166	71.82 176	72.95 155	79.53 153	73.90 40	70.45 227	66.64 329	56.87 176	76.81 173	81.76 272	68.78 100	71.76 293	61.81 171	83.74 260	73.18 350

AdaColmap		74.22 117	74.56 112	73.20 145	81.95 125	60.97 151	79.43 92	80.90 136	65.57 85	72.54 270	81.76 272	70.98 82	85.26 59	47.88 325	90.00 136	73.37 348

fmvsm_s_conf0.5_n_6		70.08 201	69.97 204	70.39 203	72.99 277	58.93 179	68.84 254	76.40 221	49.08 299	68.75 331	81.65 274	57.34 249	71.97 290	70.91 87	83.81 259	80.26 263

sasdasda		72.29 163	73.38 138	69.04 235	74.23 247	47.37 295	73.93 172	83.18 87	54.36 214	76.61 179	81.64 275	72.03 68	75.34 237	57.12 227	87.28 194	84.40 141

canonicalmvs		72.29 163	73.38 138	69.04 235	74.23 247	47.37 295	73.93 172	83.18 87	54.36 214	76.61 179	81.64 275	72.03 68	75.34 237	57.12 227	87.28 194	84.40 141

MVS-HIRNet		45.53 419	47.29 419	40.24 445	62.29 410	26.82 449	56.02 400	37.41 466	29.74 448	43.69 466	81.27 277	33.96 393	55.48 405	24.46 459	56.79 457	38.43 466

CMPMVS	48.73 20	61.54 326	60.89 327	63.52 312	61.08 417	51.55 238	68.07 273	68.00 322	33.88 430	65.87 353	81.25 278	37.91 378	67.71 338	49.32 308	82.60 277	71.31 375
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
viewmambaseed2359dif		65.63 276	65.13 287	67.11 276	64.57 398	44.73 325	64.12 335	72.48 267	43.08 369	71.59 282	81.17 279	58.90 227	72.46 279	52.94 280	77.33 359	84.13 151

testgi		54.00 379	56.86 360	45.45 431	58.20 438	25.81 457	49.05 434	49.50 427	45.43 339	67.84 339	81.17 279	51.81 290	43.20 452	29.30 441	79.41 335	67.34 408

fmvsm_l_conf0.5_n		67.48 248	66.88 264	69.28 230	67.41 370	62.04 136	70.69 224	69.85 300	39.46 394	69.59 313	81.09 281	58.15 237	68.73 325	67.51 117	78.16 352	77.07 313

test_fmvsmvis_n_1920		72.36 160	72.49 160	71.96 183	71.29 304	64.06 123	72.79 184	81.82 112	40.23 391	81.25 107	81.04 282	70.62 85	68.69 326	69.74 99	83.60 265	83.14 181

CL-MVSNet_self_test		62.44 316	63.40 305	59.55 354	72.34 288	32.38 424	56.39 395	64.84 344	51.21 266	67.46 345	81.01 283	50.75 297	63.51 376	38.47 388	88.12 176	82.75 197

fmvsm_s_conf0.1_n_a		67.37 252	66.36 268	70.37 205	70.86 306	61.17 147	74.00 171	57.18 383	40.77 386	68.83 330	80.88 284	63.11 166	67.61 341	66.94 127	74.72 379	82.33 212

guyue		66.95 263	66.74 266	67.56 266	70.12 332	51.14 242	65.05 321	68.68 316	49.98 286	74.64 225	80.83 285	50.77 296	70.34 312	57.72 222	82.89 274	81.21 234

SPE-MVS-test		74.89 112	74.23 119	76.86 90	77.01 197	62.94 132	78.98 98	84.61 61	58.62 157	70.17 305	80.80 286	66.74 129	81.96 122	61.74 173	89.40 154	85.69 96

thisisatest0530		67.05 261	65.16 284	72.73 169	73.10 272	50.55 247	71.26 216	63.91 352	50.22 281	74.46 231	80.75 287	26.81 436	80.25 157	59.43 203	86.50 210	87.37 59

PHI-MVS		74.92 109	74.36 116	76.61 93	76.40 212	62.32 135	80.38 79	83.15 89	54.16 222	73.23 256	80.75 287	62.19 180	83.86 82	68.02 110	90.92 117	83.65 162

fmvsm_s_conf0.5_n_7		67.30 253	66.92 262	68.43 251	72.78 281	58.22 190	60.90 361	72.51 266	49.62 290	63.66 376	80.65 289	58.56 232	68.63 328	62.83 165	80.76 313	78.45 288

PLC	62.01 16	71.79 170	70.28 202	76.33 98	80.31 143	68.63 81	78.18 109	81.24 126	54.57 210	67.09 349	80.63 290	59.44 219	81.74 129	46.91 332	84.17 254	78.63 284
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PM-MVS		64.49 290	63.61 302	67.14 275	76.68 208	75.15 31	68.49 267	42.85 452	51.17 267	77.85 149	80.51 291	45.76 327	66.31 360	52.83 281	76.35 365	59.96 442

CANet		73.00 141	71.84 175	76.48 96	75.82 223	61.28 145	74.81 153	80.37 150	63.17 120	62.43 385	80.50 292	61.10 197	85.16 65	64.00 150	84.34 253	83.01 188

IterMVS		63.12 306	62.48 316	65.02 298	66.34 381	52.86 231	63.81 338	62.25 359	46.57 328	71.51 289	80.40 293	44.60 336	66.82 355	51.38 289	75.47 373	75.38 329
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_l_conf0.5_n_a		66.66 264	65.97 275	68.72 247	67.09 373	61.38 144	70.03 233	69.15 308	38.59 402	68.41 334	80.36 294	56.56 259	68.32 332	66.10 131	77.45 358	76.46 319

eth_miper_zixun_eth		69.42 214	68.73 229	71.50 189	67.99 360	46.42 308	67.58 277	78.81 181	50.72 272	78.13 145	80.34 295	50.15 301	80.34 155	60.18 192	84.65 244	87.74 55

DPM-MVS		69.98 204	69.22 220	72.26 179	82.69 116	58.82 180	70.53 225	81.23 127	47.79 319	64.16 366	80.21 296	51.32 293	83.12 98	60.14 195	84.95 235	74.83 333

LF4IMVS		67.50 247	67.31 254	68.08 257	58.86 434	61.93 137	71.43 210	75.90 229	44.67 352	72.42 271	80.20 297	57.16 250	70.44 309	58.99 207	86.12 214	71.88 367

CSCG		74.12 118	74.39 114	73.33 142	79.35 155	61.66 141	77.45 116	81.98 110	62.47 127	79.06 131	80.19 298	61.83 184	78.79 180	59.83 199	87.35 190	79.54 275

c3_l		69.82 208	69.89 206	69.61 223	66.24 382	43.48 337	68.12 272	79.61 166	51.43 259	77.72 152	80.18 299	54.61 271	78.15 200	63.62 157	87.50 186	87.20 64

fmvsm_s_conf0.1_n		66.60 265	65.54 278	69.77 220	68.99 346	59.15 173	72.12 194	56.74 388	40.72 388	68.25 338	80.14 300	61.18 196	66.92 348	67.34 124	74.40 384	83.23 179

fmvsm_s_conf0.5_n_a		67.00 262	65.95 276	70.17 211	69.72 339	61.16 148	73.34 178	56.83 386	40.96 383	68.36 335	80.08 301	62.84 167	67.57 342	66.90 129	74.50 383	81.78 227

FPMVS		59.43 342	60.07 333	57.51 370	77.62 189	71.52 53	62.33 351	50.92 419	57.40 172	69.40 315	80.00 302	39.14 371	61.92 382	37.47 397	66.36 434	39.09 465

thres100view900		61.17 328	61.09 325	61.39 336	72.14 292	35.01 409	65.42 314	56.99 384	55.23 197	70.71 298	79.90 303	32.07 407	72.09 286	35.61 413	81.73 290	77.08 311

new-patchmatchnet		52.89 387	55.76 369	44.26 437	59.94 427	6.31 478	37.36 463	50.76 421	41.10 380	64.28 365	79.82 304	44.77 334	48.43 429	36.24 408	87.61 183	78.03 297

thres600view7		61.82 321	61.38 323	63.12 317	71.81 295	34.93 410	64.64 329	56.99 384	54.78 205	70.33 302	79.74 305	32.07 407	72.42 281	38.61 386	83.46 266	82.02 218

testing3-2		56.85 357	57.62 354	54.53 386	75.84 222	22.23 466	51.26 428	49.10 429	61.04 136	63.74 374	79.73 306	22.29 456	59.44 390	31.16 433	84.43 252	81.92 224

diffmvs		67.42 251	67.50 250	67.20 273	62.26 411	45.21 320	64.87 323	77.04 215	48.21 310	71.74 279	79.70 307	58.40 234	71.17 300	64.99 140	80.27 322	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
SSC-MVS3.2		57.01 356	59.50 338	49.57 413	67.73 365	25.95 456	46.68 443	51.75 417	51.41 262	63.84 371	79.66 308	53.28 279	50.34 420	37.85 393	83.28 269	72.41 361

BH-untuned		69.39 215	69.46 212	69.18 232	77.96 182	56.88 197	68.47 268	77.53 205	56.77 179	77.79 150	79.63 309	60.30 208	80.20 160	46.04 340	80.65 316	70.47 383

VortexMVS		65.93 273	66.04 274	65.58 293	67.63 368	47.55 292	64.81 324	72.75 261	47.37 323	75.17 212	79.62 310	49.28 308	71.00 302	55.20 250	82.51 278	78.21 293

PAPM		61.79 322	60.37 332	66.05 289	76.09 217	41.87 351	69.30 245	76.79 218	40.64 389	53.80 436	79.62 310	44.38 337	82.92 102	29.64 440	73.11 395	73.36 349

fmvsm_s_conf0.5_n		66.34 271	65.27 281	69.57 224	68.20 355	59.14 175	71.66 207	56.48 389	40.92 384	67.78 340	79.46 312	61.23 193	66.90 349	67.39 120	74.32 387	82.66 202

XXY-MVS		55.19 369	57.40 357	48.56 421	64.45 399	34.84 412	51.54 426	53.59 404	38.99 400	63.79 373	79.43 313	56.59 257	45.57 437	36.92 403	71.29 409	65.25 420

SD_0403		61.63 324	62.83 313	58.03 366	72.21 290	32.43 423	69.33 244	69.00 310	44.54 353	62.01 386	79.42 314	55.27 267	66.88 351	36.07 411	77.63 357	74.78 334

MonoMVSNet		62.75 312	63.42 304	60.73 345	65.60 388	40.77 362	72.49 187	70.56 294	52.49 244	75.07 213	79.42 314	39.52 369	69.97 315	46.59 336	69.06 423	71.44 372

MDA-MVSNet-bldmvs		62.34 317	61.73 317	64.16 303	61.64 414	49.90 257	48.11 438	57.24 382	53.31 237	80.95 110	79.39 316	49.00 313	61.55 383	45.92 341	80.05 325	81.03 240

icg_test_0407_2		63.88 299	65.59 277	58.75 360	72.47 283	48.64 271	53.19 417	72.98 254	45.33 342	68.91 325	79.37 317	61.91 182	51.11 417	55.06 252	81.11 302	76.49 315

IMVS_0407		67.26 254	67.35 252	66.97 280	72.47 283	48.64 271	69.03 251	72.98 254	45.33 342	68.91 325	79.37 317	61.91 182	75.77 230	55.06 252	81.11 302	76.49 315

IMVS_0404		62.18 318	63.05 310	59.58 353	72.47 283	48.64 271	55.47 403	72.98 254	45.33 342	55.80 426	79.37 317	49.84 302	53.60 412	55.06 252	81.11 302	76.49 315

IMVS_0403		67.07 259	67.08 257	67.03 278	72.47 283	48.64 271	68.44 269	72.98 254	45.33 342	68.63 333	79.37 317	60.38 206	75.97 226	55.06 252	81.11 302	76.49 315

TAMVS		65.31 279	63.75 300	69.97 218	82.23 122	59.76 168	66.78 294	63.37 356	45.20 346	69.79 311	79.37 317	47.42 324	72.17 284	34.48 418	85.15 230	77.99 299

PAPR		69.20 219	68.66 230	70.82 197	75.15 231	47.77 287	75.31 146	81.11 129	49.62 290	66.33 351	79.27 322	61.53 188	82.96 101	48.12 322	81.50 300	81.74 230

Anonymous20231206		54.13 375	55.82 368	49.04 418	70.89 305	35.96 402	51.73 425	50.87 420	34.86 423	62.49 384	79.22 323	42.52 350	44.29 448	27.95 447	81.88 286	66.88 410

OpenMVS_ROB	54.93 17	63.23 305	63.28 306	63.07 318	69.81 335	45.34 318	68.52 266	67.14 325	43.74 360	70.61 299	79.22 323	47.90 322	72.66 274	48.75 313	73.84 391	71.21 377

PVSNet_Blended_VisFu		70.04 202	68.88 224	73.53 140	82.71 115	63.62 126	74.81 153	81.95 111	48.53 308	67.16 348	79.18 325	51.42 292	78.38 191	54.39 266	79.72 333	78.60 285

MVSTER		63.29 304	61.60 321	68.36 252	59.77 429	46.21 311	60.62 364	71.32 281	41.83 374	75.40 205	79.12 326	30.25 425	75.85 227	56.30 237	79.81 330	83.03 187

tpm		50.60 402	52.42 393	45.14 433	65.18 392	26.29 453	60.30 366	43.50 448	37.41 411	57.01 415	79.09 327	30.20 427	42.32 453	32.77 426	66.36 434	66.81 412

test_yl		65.11 280	65.09 289	65.18 295	70.59 314	40.86 359	63.22 347	72.79 258	57.91 164	68.88 327	79.07 328	42.85 347	74.89 247	45.50 345	84.97 231	79.81 268

DCV-MVSNet		65.11 280	65.09 289	65.18 295	70.59 314	40.86 359	63.22 347	72.79 258	57.91 164	68.88 327	79.07 328	42.85 347	74.89 247	45.50 345	84.97 231	79.81 268

test_fmvsm_n_1920		69.63 209	68.45 232	73.16 146	70.56 316	65.86 105	70.26 229	78.35 192	37.69 408	74.29 234	78.89 330	61.10 197	68.10 335	65.87 135	79.07 337	85.53 99

miper_lstm_enhance		61.97 319	61.63 320	62.98 319	60.04 423	45.74 315	47.53 440	70.95 290	44.04 355	73.06 261	78.84 331	39.72 366	60.33 386	55.82 244	84.64 245	82.88 192

PVSNet_BlendedMVS		65.38 278	64.30 294	68.61 248	69.81 335	49.36 263	65.60 312	78.96 178	45.50 336	59.98 399	78.61 332	51.82 288	78.20 197	44.30 349	84.11 255	78.27 291

baseline1		57.82 353	58.36 349	56.19 377	69.17 343	30.76 435	62.94 349	55.21 395	46.04 331	63.83 372	78.47 333	41.20 355	63.68 374	39.44 378	68.99 424	74.13 342

TSAR-MVS + GP.		73.08 136	71.60 183	77.54 81	78.99 169	70.73 61	74.96 150	69.38 305	60.73 140	74.39 232	78.44 334	57.72 246	82.78 105	60.16 193	89.60 146	79.11 280

MVP-Stereo		61.56 325	59.22 339	68.58 249	79.28 156	60.44 160	69.20 248	71.57 274	43.58 362	56.42 421	78.37 335	39.57 368	76.46 224	34.86 417	60.16 450	68.86 399
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
hse-mvs2		72.32 161	70.66 199	77.31 87	83.10 108	71.77 51	69.19 249	71.45 278	54.28 216	77.89 147	78.26 336	49.04 311	79.23 171	63.62 157	89.13 160	80.92 244

patch_mono-2		62.73 314	64.08 297	58.68 361	70.36 325	55.87 204	60.84 362	64.11 351	41.23 379	64.04 367	78.22 337	60.00 210	48.80 425	54.17 269	83.71 262	71.37 373

D2MVS		62.58 315	61.05 326	67.20 273	63.85 401	47.92 284	56.29 396	69.58 302	39.32 395	70.07 307	78.19 338	34.93 391	72.68 273	53.44 277	83.74 260	81.00 242

HY-MVS	49.31 19	57.96 352	57.59 355	59.10 358	66.85 378	36.17 400	65.13 319	65.39 340	39.24 398	54.69 433	78.14 339	44.28 338	67.18 347	33.75 423	70.79 412	73.95 344

Effi-MVS+-dtu		75.43 100	72.28 167	84.91 3	77.05 194	83.58 2	78.47 103	77.70 203	57.68 167	74.89 218	78.13 340	64.80 153	84.26 79	56.46 236	85.32 227	86.88 69

AUN-MVS		70.22 198	67.88 245	77.22 88	82.96 112	71.61 52	69.08 250	71.39 279	49.17 297	71.70 280	78.07 341	37.62 381	79.21 172	61.81 171	89.15 158	80.82 247

cl22		67.14 256	66.51 267	69.03 237	63.20 406	43.46 338	66.88 293	76.25 222	49.22 296	74.48 230	77.88 342	45.49 330	77.40 210	60.64 187	84.59 247	86.24 78

miper_ehance_all_eth		68.36 234	68.16 241	68.98 238	65.14 394	43.34 339	67.07 288	78.92 180	49.11 298	76.21 191	77.72 343	53.48 277	77.92 203	61.16 182	84.59 247	85.68 97

DSMNet-mixed		43.18 430	44.66 429	38.75 447	54.75 454	28.88 443	57.06 390	27.42 472	13.47 470	47.27 457	77.67 344	38.83 372	39.29 462	25.32 457	60.12 451	48.08 456

Test_1112_low_res		58.78 347	58.69 344	59.04 359	79.41 154	38.13 388	57.62 386	66.98 328	34.74 426	59.62 405	77.56 345	42.92 346	63.65 375	38.66 385	70.73 413	75.35 330

API-MVS		70.97 186	71.51 185	69.37 226	75.20 229	55.94 203	80.99 71	76.84 216	62.48 126	71.24 293	77.51 346	61.51 189	80.96 147	52.04 282	85.76 219	71.22 376

pmmvs4		60.78 331	59.04 341	66.00 290	73.06 274	57.67 193	64.53 332	60.22 369	36.91 414	65.96 352	77.27 347	39.66 367	68.54 330	38.87 383	74.89 378	71.80 368

WBMVS		53.38 381	54.14 381	51.11 403	70.16 329	26.66 450	50.52 431	51.64 418	39.32 395	63.08 382	77.16 348	23.53 450	55.56 404	31.99 428	79.88 328	71.11 379

tfpn200view9		60.35 335	59.97 334	61.51 333	70.78 308	35.35 407	63.27 345	57.47 377	53.00 239	68.31 336	77.09 349	32.45 404	72.09 286	35.61 413	81.73 290	77.08 311

thres400		60.77 332	59.97 334	63.15 316	70.78 308	35.35 407	63.27 345	57.47 377	53.00 239	68.31 336	77.09 349	32.45 404	72.09 286	35.61 413	81.73 290	82.02 218

Effi-MVS+		72.10 166	72.28 167	71.58 186	74.21 250	50.33 250	74.72 158	82.73 97	62.62 124	70.77 297	76.83 351	69.96 93	80.97 144	60.20 191	78.43 346	83.45 171

MVSFormer		69.93 205	69.03 222	72.63 172	74.93 232	59.19 170	83.98 43	75.72 230	52.27 246	63.53 379	76.74 352	43.19 344	80.56 150	72.28 80	78.67 343	78.14 295

jason		64.47 291	62.84 312	69.34 229	76.91 203	59.20 169	67.15 286	65.67 335	35.29 422	65.16 359	76.74 352	44.67 335	70.68 304	54.74 260	79.28 336	78.14 295
jason: jason.
CostFormer		57.35 355	56.14 365	60.97 341	63.76 403	38.43 383	67.50 278	60.22 369	37.14 413	59.12 407	76.34 354	32.78 400	71.99 289	39.12 382	69.27 422	72.47 360

MDTV_nov1_ep13			54.05 383		65.54 389	29.30 441	59.00 375	55.22 394	35.96 420	52.44 439	75.98 355	30.77 422	59.62 389	38.21 389	73.33 394

testing3		58.28 350	58.38 348	58.00 367	77.45 191	26.12 455	60.78 363	43.00 451	56.02 188	70.18 304	75.76 356	13.27 477	67.24 346	48.02 323	80.89 308	80.65 254

EU-MVSNet		60.82 330	60.80 329	60.86 344	68.37 351	41.16 355	72.27 190	68.27 321	26.96 453	69.08 317	75.71 357	32.09 406	67.44 343	55.59 247	78.90 340	73.97 343

HyFIR lowres test		63.01 307	60.47 331	70.61 199	83.04 109	54.10 223	59.93 370	72.24 270	33.67 433	69.00 318	75.63 358	38.69 373	76.93 216	36.60 404	75.45 374	80.81 249

Fast-Effi-MVS+		68.81 227	68.30 235	70.35 206	74.66 241	48.61 275	66.06 303	78.32 193	50.62 274	71.48 290	75.54 359	68.75 101	79.59 168	50.55 296	78.73 342	82.86 194

CDS-MVSNet		64.33 294	62.66 315	69.35 228	80.44 142	58.28 189	65.26 316	65.66 336	44.36 354	67.30 347	75.54 359	43.27 343	71.77 292	37.68 394	84.44 251	78.01 298
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		56.12 361	54.64 378	60.55 347	66.24 382	36.01 401	68.14 271	56.77 387	33.60 434	58.25 410	75.52 361	30.25 425	74.33 256	33.27 424	69.76 421	71.32 374

CANet_DTU		64.04 297	63.83 299	64.66 300	68.39 350	42.97 344	73.45 176	74.50 242	52.05 252	54.78 431	75.44 362	43.99 339	70.42 310	53.49 276	78.41 347	80.59 256

reproduce_monomvs		58.94 345	58.14 350	61.35 337	59.70 430	40.98 358	60.24 368	63.51 355	45.85 333	68.95 321	75.31 363	18.27 467	65.82 362	51.47 287	79.97 326	77.26 308

DELS-MVS		68.83 226	68.31 234	70.38 204	70.55 318	48.31 276	63.78 340	82.13 107	54.00 225	68.96 320	75.17 364	58.95 226	80.06 162	58.55 211	82.74 276	82.76 196
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
pmmvs5		52.49 391	52.58 391	52.21 397	54.99 453	32.38 424	55.45 404	53.84 403	32.15 439	55.49 427	74.81 365	38.08 376	57.37 401	34.02 420	74.40 384	66.88 410

MSDG		67.47 250	67.48 251	67.46 268	70.70 312	54.69 219	66.90 292	78.17 196	60.88 138	70.41 300	74.76 366	61.22 195	73.18 269	47.38 328	76.87 362	74.49 339

UnsupCasMVSNet_eth		52.26 392	53.29 387	49.16 416	55.08 452	33.67 419	50.03 432	58.79 374	37.67 409	63.43 381	74.75 367	41.82 352	45.83 435	38.59 387	59.42 452	67.98 405

Fast-Effi-MVS+-dtu		70.00 203	68.74 228	73.77 133	73.47 262	64.53 118	71.36 212	78.14 198	55.81 192	68.84 329	74.71 368	65.36 146	75.75 231	52.00 283	79.00 338	81.03 240

TR-MVS		64.59 288	63.54 303	67.73 265	75.75 225	50.83 246	63.39 343	70.29 297	49.33 294	71.55 288	74.55 369	50.94 295	78.46 187	40.43 375	75.69 370	73.89 345

GA-MVS		62.91 308	61.66 318	66.66 285	67.09 373	44.49 328	61.18 359	69.36 306	51.33 264	69.33 316	74.47 370	36.83 384	74.94 246	50.60 295	74.72 379	80.57 257

CLD-MVS		72.88 147	72.36 165	74.43 122	77.03 195	54.30 221	68.77 260	83.43 86	52.12 250	76.79 174	74.44 371	69.54 97	83.91 81	55.88 241	93.25 73	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
CHOSEN 1792x2688		58.09 351	56.30 364	63.45 313	79.95 146	50.93 245	54.07 414	65.59 337	28.56 449	61.53 389	74.33 372	41.09 357	66.52 359	33.91 421	67.69 432	72.92 353

Patchmatch-RL test		59.95 338	59.12 340	62.44 325	72.46 287	54.61 220	59.63 371	47.51 436	41.05 382	74.58 227	74.30 373	31.06 419	65.31 366	51.61 285	79.85 329	67.39 406

cdsmvs_eth3d_5k		17.71 440	23.62 441	0.00 459	0.00 482	0.00 484	0.00 471	70.17 298	0.00 477	0.00 478	74.25 374	68.16 107	0.00 478	0.00 477	0.00 476	0.00 474

lupinMVS		63.36 302	61.49 322	68.97 239	74.93 232	59.19 170	65.80 308	64.52 348	34.68 428	63.53 379	74.25 374	43.19 344	70.62 306	53.88 272	78.67 343	77.10 310

xiu_mvs_v1_base_debu		67.87 242	67.07 258	70.26 208	79.13 162	61.90 138	67.34 281	71.25 284	47.98 315	67.70 341	74.19 376	61.31 190	72.62 275	56.51 233	78.26 349	76.27 322

xiu_mvs_v1_base		67.87 242	67.07 258	70.26 208	79.13 162	61.90 138	67.34 281	71.25 284	47.98 315	67.70 341	74.19 376	61.31 190	72.62 275	56.51 233	78.26 349	76.27 322

xiu_mvs_v1_base_debi		67.87 242	67.07 258	70.26 208	79.13 162	61.90 138	67.34 281	71.25 284	47.98 315	67.70 341	74.19 376	61.31 190	72.62 275	56.51 233	78.26 349	76.27 322

tpmvs		55.84 362	55.45 371	57.01 372	60.33 421	33.20 421	65.89 305	59.29 373	47.52 322	56.04 422	73.60 379	31.05 420	68.06 336	40.64 374	64.64 438	69.77 390

SCA		58.57 349	58.04 351	60.17 349	70.17 328	41.07 357	65.19 318	53.38 408	43.34 367	61.00 395	73.48 380	45.20 331	69.38 321	40.34 376	70.31 416	70.05 386

Patchmatch-test		47.93 413	49.96 412	41.84 442	57.42 441	24.26 459	48.75 435	41.49 459	39.30 397	56.79 417	73.48 380	30.48 424	33.87 466	29.29 442	72.61 398	67.39 406

MDA-MVSNet_test_wron		52.57 390	53.49 386	49.81 410	54.24 455	36.47 398	40.48 457	46.58 440	38.13 404	75.47 204	73.32 382	41.05 359	43.85 450	40.98 372	71.20 410	69.10 398

YYNet1		52.58 389	53.50 384	49.85 409	54.15 456	36.45 399	40.53 456	46.55 441	38.09 405	75.52 201	73.31 383	41.08 358	43.88 449	41.10 370	71.14 411	69.21 396

PatchmatchNet		54.60 373	54.27 380	55.59 381	65.17 393	39.08 376	66.92 291	51.80 416	39.89 392	58.39 408	73.12 384	31.69 413	58.33 396	43.01 359	58.38 456	69.38 395
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		58.93 346	58.52 345	60.16 350	67.91 362	47.70 290	69.97 234	58.02 375	49.73 287	47.28 456	73.02 385	38.14 375	62.34 379	36.57 405	85.99 216	70.43 384
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_enhance_ethall		65.86 274	65.05 292	68.28 256	61.62 415	42.62 347	64.74 327	77.97 200	42.52 370	73.42 253	72.79 386	49.66 303	77.68 207	58.12 218	84.59 247	84.54 134

ppachtmachnet_test		60.26 336	59.61 337	62.20 327	67.70 366	44.33 329	58.18 384	60.96 367	40.75 387	65.80 354	72.57 387	41.23 354	63.92 373	46.87 333	82.42 279	78.33 289

N_pmnet		52.06 393	51.11 402	54.92 382	59.64 431	71.03 57	37.42 462	61.62 366	33.68 432	57.12 413	72.10 388	37.94 377	31.03 467	29.13 446	71.35 408	62.70 432

ADS-MVSNet2		48.76 411	47.25 420	53.29 393	55.90 448	40.54 367	47.34 441	54.99 397	31.41 444	50.48 447	72.06 389	31.23 416	54.26 409	25.93 452	55.93 458	65.07 422

ADS-MVSNet		44.62 424	45.58 423	41.73 443	55.90 448	20.83 467	47.34 441	39.94 463	31.41 444	50.48 447	72.06 389	31.23 416	39.31 461	25.93 452	55.93 458	65.07 422

ET-MVSNet_ETH3D		63.32 303	60.69 330	71.20 194	70.15 330	55.66 207	65.02 322	64.32 349	43.28 368	68.99 319	72.05 391	25.46 443	78.19 199	54.16 270	82.80 275	79.74 271

BH-w/o		64.81 285	64.29 295	66.36 286	76.08 219	54.71 218	65.61 311	75.23 235	50.10 283	71.05 296	71.86 392	54.33 273	79.02 175	38.20 390	76.14 367	65.36 419

EI-MVSNet-Vis-set		72.78 150	71.87 173	75.54 110	74.77 237	59.02 177	72.24 191	71.56 275	63.92 108	78.59 137	71.59 393	66.22 135	78.60 183	67.58 115	80.32 321	89.00 37

UnsupCasMVSNet_bld		50.01 407	51.03 404	46.95 424	58.61 435	32.64 422	48.31 436	53.27 409	34.27 429	60.47 397	71.53 394	41.40 353	47.07 433	30.68 434	60.78 449	61.13 440

thres200		57.55 354	57.02 358	59.17 356	67.89 363	34.93 410	58.91 378	57.25 381	50.24 280	64.01 368	71.46 395	32.49 403	71.39 298	31.31 431	79.57 334	71.19 378

UWE-MVS		52.94 386	52.70 389	53.65 389	73.56 259	27.49 447	57.30 389	49.57 426	38.56 403	62.79 383	71.42 396	19.49 464	60.41 385	24.33 460	77.33 359	73.06 351

EI-MVSNet-UG-set		72.63 153	71.68 178	75.47 111	74.67 239	58.64 185	72.02 197	71.50 276	63.53 114	78.58 139	71.39 397	65.98 137	78.53 184	67.30 125	80.18 324	89.23 31

ETV-MVS		72.72 151	72.16 169	74.38 124	76.90 205	55.95 202	73.34 178	84.67 57	62.04 128	72.19 276	70.81 398	65.90 139	85.24 61	58.64 210	84.96 234	81.95 223

EIA-MVS		68.59 232	67.16 256	72.90 160	75.18 230	55.64 209	69.39 242	81.29 124	52.44 245	64.53 362	70.69 399	60.33 207	82.30 116	54.27 268	76.31 366	80.75 250

EI-MVSNet		69.61 211	69.01 223	71.41 190	73.94 255	49.90 257	71.31 214	71.32 281	58.22 161	75.40 205	70.44 400	58.16 236	75.85 227	62.51 166	79.81 330	88.48 46

CVMVSNet		59.21 343	58.44 347	61.51 333	73.94 255	47.76 288	71.31 214	64.56 347	26.91 455	60.34 398	70.44 400	36.24 387	67.65 339	53.57 275	68.66 426	69.12 397

tpm cat1		54.02 378	52.63 390	58.19 364	64.85 397	39.86 373	66.26 302	57.28 380	32.16 438	56.90 416	70.39 402	32.75 401	65.30 367	34.29 419	58.79 453	69.41 394

myMVS_eth3d28		51.35 399	51.99 396	49.44 414	69.21 341	22.51 464	49.82 433	49.11 428	49.00 302	55.03 429	70.31 403	22.73 455	52.88 414	24.33 460	78.39 348	72.92 353

PMMVS2		37.74 434	40.87 434	28.36 451	42.41 474	5.35 479	24.61 467	27.75 471	32.15 439	47.85 455	70.27 404	35.85 388	29.51 469	19.08 468	67.85 430	50.22 455

EPMVS		45.74 418	46.53 421	43.39 440	54.14 457	22.33 465	55.02 406	35.00 468	34.69 427	51.09 445	70.20 405	25.92 441	42.04 455	37.19 398	55.50 460	65.78 416

WB-MVSnew		53.94 380	54.76 377	51.49 401	71.53 298	28.05 444	58.22 383	50.36 422	37.94 407	59.16 406	70.17 406	49.21 309	51.94 415	24.49 458	71.80 406	74.47 340

testing99		55.16 370	54.56 379	56.98 373	70.13 331	30.58 436	54.55 412	54.11 401	49.53 292	56.76 418	70.14 407	22.76 454	65.79 363	36.99 401	76.04 368	74.57 337

testing91		55.74 364	55.29 374	57.08 371	70.63 313	30.85 434	54.94 409	56.31 393	50.34 278	57.08 414	70.10 408	24.50 447	65.86 361	36.98 402	76.75 363	74.53 338

KD-MVS_2432*1600		52.05 394	51.58 398	53.44 391	52.11 461	31.20 430	44.88 449	64.83 345	41.53 376	64.37 363	70.03 409	15.61 473	64.20 370	36.25 406	74.61 381	64.93 424

miper_refine_blended		52.05 394	51.58 398	53.44 391	52.11 461	31.20 430	44.88 449	64.83 345	41.53 376	64.37 363	70.03 409	15.61 473	64.20 370	36.25 406	74.61 381	64.93 424

our_test_3		56.46 359	56.51 362	56.30 376	67.70 366	39.66 374	55.36 405	52.34 414	40.57 390	63.85 370	69.91 411	40.04 364	58.22 397	43.49 356	75.29 377	71.03 381

xiu_mvs_v2_base		64.43 292	63.96 298	65.85 292	77.72 186	51.32 241	63.63 341	72.31 269	45.06 350	61.70 387	69.66 412	62.56 171	73.93 263	49.06 311	73.91 389	72.31 363

tpmrst		50.15 406	51.38 400	46.45 428	56.05 446	24.77 458	64.40 334	49.98 423	36.14 418	53.32 438	69.59 413	35.16 390	48.69 426	39.24 380	58.51 455	65.89 415

WTY-MVS		49.39 409	50.31 411	46.62 427	61.22 416	32.00 427	46.61 444	49.77 424	33.87 431	54.12 435	69.55 414	41.96 351	45.40 440	31.28 432	64.42 439	62.47 435

UWE-MVS-28		44.18 426	44.37 431	43.61 439	60.10 422	16.96 470	52.62 422	33.27 469	36.79 415	48.86 453	69.47 415	19.96 463	45.65 436	13.40 470	64.83 437	68.23 400

thisisatest0515		60.48 334	57.86 352	68.34 253	67.25 371	46.42 308	60.58 365	62.14 360	40.82 385	63.58 378	69.12 416	26.28 439	78.34 193	48.83 312	82.13 282	80.26 263

patchmatchnet-post											68.99 417	31.32 415	69.38 321

PatchMatch-RL		58.68 348	57.72 353	61.57 332	76.21 215	73.59 43	61.83 352	49.00 431	47.30 324	61.08 392	68.97 418	50.16 300	59.01 392	36.06 412	68.84 425	52.10 452

testing222		53.37 382	52.50 392	55.98 379	70.51 321	29.68 439	56.20 398	51.85 415	46.19 330	56.76 418	68.94 419	19.18 465	65.39 365	25.87 454	76.98 361	72.87 355

MS-PatchMatch		55.59 366	54.89 376	57.68 369	69.18 342	49.05 266	61.00 360	62.93 358	35.98 419	58.36 409	68.93 420	36.71 385	66.59 358	37.62 396	63.30 442	57.39 448

cascas		64.59 288	62.77 314	70.05 215	75.27 228	50.02 254	61.79 353	71.61 273	42.46 371	63.68 375	68.89 421	49.33 307	80.35 154	47.82 326	84.05 256	79.78 270

MVS		60.62 333	59.97 334	62.58 324	68.13 359	47.28 297	68.59 263	73.96 245	32.19 437	59.94 401	68.86 422	50.48 298	77.64 208	41.85 366	75.74 369	62.83 431

PVSNet_Blended		62.90 309	61.64 319	66.69 284	69.81 335	49.36 263	61.23 358	78.96 178	42.04 372	59.98 399	68.86 422	51.82 288	78.20 197	44.30 349	77.77 356	72.52 359

test_fmvs3		56.78 358	55.99 367	59.12 357	53.96 459	48.09 281	58.76 379	66.22 331	27.54 451	76.66 176	68.69 424	25.32 445	51.31 416	53.42 278	73.38 393	77.97 300

MAR-MVS		67.72 245	66.16 270	72.40 176	74.45 244	64.99 114	74.87 151	77.50 206	48.67 307	65.78 355	68.58 425	57.01 255	77.79 205	46.68 335	81.92 285	74.42 341
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
testing11		53.13 384	52.26 394	55.75 380	70.44 322	31.73 428	54.75 410	52.40 413	44.81 351	52.36 441	68.40 426	21.83 457	65.74 364	32.64 427	72.73 397	69.78 389

PS-MVSNAJ		64.27 295	63.73 301	65.90 291	77.82 184	51.42 239	63.33 344	72.33 268	45.09 349	61.60 388	68.04 427	62.39 175	73.95 262	49.07 310	73.87 390	72.34 362

ETVMVS		50.32 405	49.87 413	51.68 399	70.30 327	26.66 450	52.33 424	43.93 447	43.54 363	54.91 430	67.95 428	20.01 462	60.17 387	22.47 463	73.40 392	68.22 401

test0.0.03 1		47.72 414	48.31 416	45.93 429	55.53 451	29.39 440	46.40 445	41.21 461	43.41 365	55.81 425	67.65 429	29.22 431	43.77 451	25.73 455	69.87 419	64.62 426

1112_ss		59.48 341	58.99 342	60.96 342	77.84 183	42.39 349	61.42 356	68.45 320	37.96 406	59.93 402	67.46 430	45.11 333	65.07 368	40.89 373	71.81 405	75.41 328

ab-mvs-re		5.62 442	7.50 445	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	67.46 430	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

baseline2		55.57 367	52.74 388	64.05 306	65.26 390	44.11 330	62.38 350	54.43 399	39.03 399	51.21 444	67.35 432	33.66 395	72.45 280	37.14 399	64.22 440	75.60 325

1314		59.83 339	58.86 343	62.74 323	65.71 387	44.78 324	68.59 263	72.63 263	33.54 435	61.05 394	67.29 433	43.62 342	71.26 299	49.49 306	67.84 431	72.19 365

IB-MVS	49.67 18	59.69 340	56.96 359	67.90 259	68.19 356	50.30 251	61.42 356	65.18 341	47.57 321	55.83 424	67.15 434	23.77 449	79.60 167	43.56 355	79.97 326	73.79 346
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
UBG		49.18 410	49.35 414	48.66 420	70.36 325	26.56 452	50.53 430	45.61 442	37.43 410	53.37 437	65.97 435	23.03 453	54.20 410	26.29 449	71.54 407	65.20 421

sss		47.59 415	48.32 415	45.40 432	56.73 445	33.96 416	45.17 447	48.51 432	32.11 441	52.37 440	65.79 436	40.39 362	41.91 456	31.85 429	61.97 446	60.35 441

dp		44.09 427	44.88 428	41.72 444	58.53 437	23.18 461	54.70 411	42.38 455	34.80 425	44.25 464	65.61 437	24.48 448	44.80 444	29.77 439	49.42 464	57.18 449

test_fmvs2		54.80 372	54.11 382	56.88 374	51.76 463	49.95 256	56.70 392	65.80 334	26.22 456	69.42 314	65.25 438	31.82 411	49.98 422	49.63 304	70.36 415	70.71 382

PVSNet	43.83 21	51.56 397	51.17 401	52.73 394	68.34 352	38.27 385	48.22 437	53.56 406	36.41 416	54.29 434	64.94 439	34.60 392	54.20 410	30.34 435	69.87 419	65.71 417

Syy-MVS		54.13 375	55.45 371	50.18 407	68.77 347	23.59 460	55.02 406	44.55 445	43.80 357	58.05 411	64.07 440	46.22 326	58.83 393	46.16 339	72.36 400	68.12 402

myMVS_eth3d		50.36 404	50.52 409	49.88 408	68.77 347	22.69 462	55.02 406	44.55 445	43.80 357	58.05 411	64.07 440	14.16 475	58.83 393	33.90 422	72.36 400	68.12 402

pmmvs3		46.71 416	45.09 426	51.55 400	56.76 444	48.25 277	55.78 402	39.53 464	24.13 463	50.35 449	63.40 442	15.90 472	51.08 418	29.29 442	70.69 414	55.33 451

test_f		43.79 428	45.63 422	38.24 449	42.29 475	38.58 382	34.76 465	47.68 435	22.22 467	67.34 346	63.15 443	31.82 411	30.60 468	39.19 381	62.28 445	45.53 461

test_vis3_rt		51.94 396	51.04 403	54.65 384	46.32 470	50.13 253	44.34 451	78.17 196	23.62 464	68.95 321	62.81 444	21.41 458	38.52 463	41.49 368	72.22 402	75.30 331

gm-plane-assit					62.51 408	33.91 418			37.25 412		62.71 445		72.74 272	38.70 384

MVE	27.91 23	36.69 436	35.64 439	39.84 446	43.37 473	35.85 404	19.49 468	24.61 473	24.68 461	39.05 468	62.63 446	38.67 374	27.10 471	21.04 466	47.25 466	56.56 450
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
mvsany_test3		43.76 429	41.01 433	52.01 398	48.09 468	57.74 192	42.47 453	23.85 475	23.30 465	64.80 361	62.17 447	27.12 435	40.59 459	29.17 444	48.11 465	57.69 447

new_pmnet		37.55 435	39.80 437	30.79 450	56.83 443	16.46 471	39.35 459	30.65 470	25.59 459	45.26 460	61.60 448	24.54 446	28.02 470	21.60 464	52.80 463	47.90 457

dmvs_re		49.91 408	50.77 407	47.34 423	59.98 424	38.86 380	53.18 418	53.58 405	39.75 393	55.06 428	61.58 449	36.42 386	44.40 447	29.15 445	68.23 427	58.75 445

test_cas_vis1_n_1920		50.90 401	50.92 405	50.83 405	54.12 458	47.80 286	51.44 427	54.61 398	26.95 454	63.95 369	60.85 450	37.86 380	44.97 443	45.53 344	62.97 443	59.72 443

test_vis1_n_1920		52.96 385	53.50 384	51.32 402	59.15 432	44.90 322	56.13 399	64.29 350	30.56 447	59.87 403	60.68 451	40.16 363	47.47 431	48.25 321	62.46 444	61.58 439

test_fmvs1_n		52.70 388	52.01 395	54.76 383	53.83 460	50.36 249	55.80 401	65.90 333	24.96 460	65.39 356	60.64 452	27.69 434	48.46 427	45.88 342	67.99 429	65.46 418

test-LLR		50.43 403	50.69 408	49.64 411	60.76 418	41.87 351	53.18 418	45.48 443	43.41 365	49.41 451	60.47 453	29.22 431	44.73 445	42.09 364	72.14 403	62.33 437

test-mter		48.56 412	48.20 417	49.64 411	60.76 418	41.87 351	53.18 418	45.48 443	31.91 442	49.41 451	60.47 453	18.34 466	44.73 445	42.09 364	72.14 403	62.33 437

test_fmvs1		51.51 398	50.86 406	53.48 390	49.72 466	49.35 265	54.11 413	64.96 343	24.64 462	63.66 376	59.61 455	28.33 433	48.45 428	45.38 347	67.30 433	62.66 434

test_vis1_n		51.27 400	50.41 410	53.83 387	56.99 442	50.01 255	56.75 391	60.53 368	25.68 458	59.74 404	57.86 456	29.40 430	47.41 432	43.10 358	63.66 441	64.08 429

dmvs_testset		45.26 420	47.51 418	38.49 448	59.96 426	14.71 472	58.50 381	43.39 449	41.30 378	51.79 443	56.48 457	39.44 370	49.91 424	21.42 465	55.35 462	50.85 453

TESTMET0.1,1		45.17 421	44.93 427	45.89 430	56.02 447	38.31 384	53.18 418	41.94 458	27.85 450	44.86 462	56.47 458	17.93 468	41.50 458	38.08 391	68.06 428	57.85 446

CHOSEN 280x420		41.62 431	39.89 436	46.80 426	61.81 412	51.59 237	33.56 466	35.74 467	27.48 452	37.64 470	53.53 459	23.24 451	42.09 454	27.39 448	58.64 454	46.72 458

mvsany_test1		37.88 433	35.74 438	44.28 436	47.28 469	49.90 257	36.54 464	24.37 474	19.56 469	45.76 458	53.46 460	32.99 399	37.97 464	26.17 450	35.52 467	44.99 462

PMMVS		44.69 423	43.95 432	46.92 425	50.05 465	53.47 229	48.08 439	42.40 454	22.36 466	44.01 465	53.05 461	42.60 349	45.49 438	31.69 430	61.36 448	41.79 463

GG-mvs-BLEND				52.24 396	60.64 420	29.21 442	69.73 238	42.41 453		45.47 459	52.33 462	20.43 460	68.16 334	25.52 456	65.42 436	59.36 444

E-PMN		45.17 421	45.36 424	44.60 435	50.07 464	42.75 345	38.66 460	42.29 456	46.39 329	39.55 467	51.15 463	26.00 440	45.37 441	37.68 394	76.41 364	45.69 460

test_vis1_rt		46.70 417	45.24 425	51.06 404	44.58 471	51.04 244	39.91 458	67.56 324	21.84 468	51.94 442	50.79 464	33.83 394	39.77 460	35.25 416	61.50 447	62.38 436

PVSNet_0	36.71 22	41.12 432	40.78 435	42.14 441	59.97 425	40.13 370	40.97 455	42.24 457	30.81 446	44.86 462	49.41 465	40.70 360	45.12 442	23.15 462	34.96 468	41.16 464

EMVS		44.61 425	44.45 430	45.10 434	48.91 467	43.00 343	37.92 461	41.10 462	46.75 327	38.00 469	48.43 466	26.42 438	46.27 434	37.11 400	75.38 375	46.03 459

dongtai		31.66 437	32.98 440	27.71 452	58.58 436	12.61 474	45.02 448	14.24 478	41.90 373	47.93 454	43.91 467	10.65 478	41.81 457	14.06 469	20.53 471	28.72 468

test_method		19.26 439	19.12 443	19.71 453	9.09 478	1.91 481	7.79 470	53.44 407	1.42 472	10.27 474	35.80 468	17.42 470	25.11 472	12.44 471	24.38 470	32.10 467

kuosan		22.02 438	23.52 442	17.54 454	41.56 476	11.24 475	41.99 454	13.39 479	26.13 457	28.87 471	30.75 469	9.72 479	21.94 473	4.77 474	14.49 472	19.43 469

DeepMVS_CX				11.83 455	15.51 477	13.86 473		11.25 480	5.76 471	20.85 473	26.46 470	17.06 471	9.22 474	9.69 473	13.82 473	12.42 470

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 86	9.95 471	73.86 57	86.31 22	78.84 24	94.03 60	84.64 127

tmp_tt		11.98 441	14.73 444	3.72 456	2.28 479	4.62 480	19.44 469	14.50 477	0.47 474	21.55 472	9.58 472	25.78 442	4.57 475	11.61 472	27.37 469	1.96 471

test_post1							66.63 295				2.08 473	30.66 423	59.33 391	40.34 376

test_post											1.99 474	30.91 421	54.76 408

test123		4.43 444	5.78 447	0.39 458	0.97 480	0.28 482	46.33 446	0.45 481	0.31 475	0.62 476	1.50 475	0.61 481	0.11 477	0.56 475	0.63 474	0.77 473

testmvs		4.06 445	5.28 448	0.41 457	0.64 481	0.16 483	42.54 452	0.31 482	0.26 476	0.50 477	1.40 476	0.77 480	0.17 476	0.56 475	0.55 475	0.90 472

mmdepth		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

monomultidepth		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

test_blank		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uanet_test		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

DCPMVS		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

pcd_1.5k_mvsjas		5.20 443	6.93 446	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	62.39 175	0.00 478	0.00 477	0.00 476	0.00 474

sosnet-low-res		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

sosnet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uncertanet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

Regformer		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

uanet		0.00 446	0.00 449	0.00 459	0.00 482	0.00 484	0.00 471	0.00 483	0.00 477	0.00 478	0.00 477	0.00 482	0.00 478	0.00 477	0.00 476	0.00 474

TestfortrainingZip							86.10 28

WAC-MVS						22.69 462								36.10 410

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

MSC_two_6792asdad				79.02 58	83.14 104	67.03 94		80.75 137					86.24 25	77.27 39	94.85 31	83.78 158

No_MVS				79.02 58	83.14 104	67.03 94		80.75 137					86.24 25	77.27 39	94.85 31	83.78 158

eth-test2					0.00 482

eth-test					0.00 482

IU-MVS					86.12 55	60.90 153		80.38 149	45.49 338	81.31 105				75.64 47	94.39 46	84.65 126

save fliter					87.00 40	67.23 93	79.24 95	77.94 201	56.65 183

test_0728_SECOND				76.57 94	86.20 50	60.57 159	83.77 47	85.49 33					85.90 41	75.86 44	94.39 46	83.25 177

GSMVS																70.05 386

test_part2					85.90 61	66.44 98				84.61 68

sam_mvs1												31.41 414				70.05 386

sam_mvs												31.21 418

MTGPA								80.63 143

MTMP							84.83 36	19.26 476

test9_res														72.12 82	91.37 100	77.40 304

agg_prior2														70.70 90	90.93 116	78.55 287

agg_prior					84.44 87	66.02 104		78.62 189		76.95 166			80.34 155

test_prior4						70.14 67	77.57 112

test_prior				75.27 114	82.15 123	59.85 167		84.33 70					83.39 94			82.58 204

旧先验2							71.17 217		45.11 348	78.54 140			61.28 384	59.19 205

新几何2							71.33 213

无先验							74.82 152	70.94 291	47.75 320				76.85 219	54.47 263		72.09 366

原ACMM2							74.78 156

testdata2													67.30 344	48.34 319

segment_acmp												68.30 106

testdata1							68.34 270		57.24 174

test12				76.51 95	82.28 121	60.94 152		81.64 116		73.60 248		64.88 152	85.19 64		90.42 129	83.38 173

plane_prior7					85.18 71	66.21 101

plane_prior6					84.18 91	65.31 110						60.83 200

plane_prior5								85.49 33					86.15 30	71.09 85	90.94 114	84.82 120

plane_prior3						65.67 106			63.82 110	78.23 143

plane_prior2							82.74 59		65.45 87

plane_prior1					84.46 86

plane_prior						65.18 111	80.06 87		61.88 130						89.91 141

n2								0.00 483

nn								0.00 483

door-mid								55.02 396

test11								82.71 98

door								52.91 411

HQP5-MVS						58.80 181

HQP-NCC					82.37 118		77.32 117		59.08 150	71.58 284

ACMP_Plane					82.37 118		77.32 117		59.08 150	71.58 284

BP-MVS														67.38 122

HQP4-MVS										71.59 282			85.31 56			83.74 160

HQP3-MVS								84.12 76							89.16 156

HQP2-MVS												58.09 239

MDTV_nov1_ep13_2view						18.41 468	53.74 415		31.57 443	44.89 461		29.90 429		32.93 425		71.48 371

ACMMP++_ref															89.47 151

ACMMP++															91.96 90

Test By Simon												62.56 171

High-resolution multi-view benchmark