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 1	88.67 1	81.57 24	91.50 1	63.30 130	84.80 39	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 75	68.08 111	97.05 1	96.93 1

MTAPA		83.19 22	83.87 22	81.13 33	91.16 2	78.16 11	84.87 37	80.63 153	72.08 42	84.93 62	90.79 51	74.65 51	84.42 78	80.98 5	94.75 33	80.82 259

mPP-MVS		84.01 13	84.39 15	82.88 6	90.65 3	81.38 3	87.08 13	82.79 100	72.41 39	85.11 61	90.85 50	76.65 32	84.89 69	79.30 20	94.63 37	82.35 221

MP-MVS		83.19 22	83.54 27	82.14 19	90.54 4	79.00 8	86.42 25	83.59 85	71.31 45	81.26 107	90.96 45	74.57 52	84.69 73	78.41 25	94.78 32	82.74 209
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMVS	70.70 6	81.70 36	83.15 35	77.36 86	90.35 5	82.82 2	82.15 64	79.22 187	74.08 23	87.16 32	91.97 22	84.80 2	76.97 225	64.98 143	93.61 68	72.28 387
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 54	82.86 40	73.07 152	89.93 6	39.21 395	77.15 123	81.28 134	79.74 5	90.87 4	92.73 13	75.03 48	84.93 68	63.83 161	95.19 20	95.07 3

DTE-MVSNet		80.35 55	82.89 39	72.74 170	89.84 7	37.34 418	77.16 122	81.81 122	80.45 3	90.92 3	92.95 9	74.57 52	86.12 33	63.65 163	94.68 36	94.76 6

PEN-MVS		80.46 53	82.91 38	73.11 151	89.83 8	39.02 398	77.06 125	82.61 106	80.04 4	90.60 6	92.85 11	74.93 49	85.21 63	63.15 170	95.15 22	95.09 2

region2R		83.54 17	83.86 23	82.58 14	89.82 9	77.53 17	87.06 16	84.23 75	70.19 56	83.86 77	90.72 55	75.20 45	86.27 25	79.41 18	94.25 54	83.95 164

ACMMPR		83.62 15	83.93 21	82.69 11	89.78 10	77.51 21	87.01 17	84.19 76	70.23 54	84.49 70	90.67 56	75.15 46	86.37 19	79.58 14	94.26 53	84.18 158

MSP-MVS		80.49 52	79.67 65	82.96 5	89.70 11	77.46 22	87.16 12	85.10 43	64.94 101	81.05 110	88.38 120	57.10 264	87.10 8	79.75 11	83.87 272	84.31 155
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
CP-MVSNet		79.48 61	81.65 52	72.98 156	89.66 12	39.06 397	76.76 126	80.46 157	78.91 8	90.32 7	91.70 32	68.49 111	84.89 69	63.40 167	95.12 23	95.01 4

PGM-MVS		83.07 25	83.25 34	82.54 15	89.57 13	77.21 23	82.04 66	85.40 36	67.96 67	84.91 65	90.88 48	75.59 41	86.57 15	78.16 27	94.71 35	83.82 166

WR-MVS_H		80.22 57	82.17 48	74.39 124	89.46 14	42.69 359	78.24 109	82.24 114	78.21 12	89.57 9	92.10 20	68.05 118	85.59 53	66.04 135	95.62 9	94.88 5

XVS		83.51 18	83.73 24	82.85 8	89.43 15	77.61 15	86.80 20	84.66 57	72.71 32	82.87 87	90.39 68	73.86 57	86.31 23	78.84 23	94.03 61	84.64 136

X-MVStestdata		76.81 86	74.79 109	82.85 8	89.43 15	77.61 15	86.80 20	84.66 57	72.71 32	82.87 87	9.95 497	73.86 57	86.31 23	78.84 23	94.03 61	84.64 136

CP-MVS		84.12 11	84.55 14	82.80 10	89.42 17	79.74 5	88.19 5	84.43 66	71.96 44	84.70 68	90.56 58	77.12 29	86.18 30	79.24 21	95.36 14	82.49 218

ACMMP_NAP		82.33 31	83.28 32	79.46 50	89.28 18	69.09 79	83.62 51	84.98 46	64.77 104	83.97 76	91.02 44	75.53 44	85.93 40	82.00 2	94.36 49	83.35 186

UniMVSNet_ETH3D		76.74 87	79.02 68	69.92 229	89.27 19	43.81 346	74.47 165	71.70 285	72.33 41	85.50 56	93.65 3	77.98 24	76.88 229	54.60 273	91.64 94	89.08 33

HFP-MVS		83.39 21	84.03 20	81.48 26	89.25 20	75.69 27	87.01 17	84.27 72	70.23 54	84.47 71	90.43 63	76.79 30	85.94 38	79.58 14	94.23 55	82.82 206

ACMMP		84.22 9	84.84 12	82.35 17	89.23 21	76.66 25	87.65 7	85.89 26	71.03 50	85.85 45	90.58 57	78.77 18	85.78 47	79.37 19	95.17 21	84.62 138
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
CPTT-MVS		81.51 39	81.76 50	80.76 37	89.20 22	78.75 9	86.48 24	82.03 118	68.80 61	80.92 112	88.52 116	72.00 72	82.39 115	74.80 49	93.04 75	81.14 249

FOURS1					89.19 23	77.84 13	91.64 1	89.11 2	84.05 2	91.57 2

ZNCC-MVS		83.12 24	83.68 25	81.45 27	89.14 24	73.28 45	86.32 26	85.97 25	67.39 70	84.02 75	90.39 68	74.73 50	86.46 16	80.73 7	94.43 44	84.60 141

HPM-MVS++		79.89 58	79.80 64	80.18 42	89.02 25	78.44 10	83.49 54	80.18 163	64.71 105	78.11 148	88.39 119	65.46 153	83.14 99	77.64 34	91.20 105	78.94 297

GST-MVS		82.79 28	83.27 33	81.34 30	88.99 26	73.29 44	85.94 32	85.13 41	68.58 65	84.14 74	90.21 78	73.37 61	86.41 17	79.09 22	93.98 64	84.30 157

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 45	88.94 27	67.52 88	86.61 22	81.38 132	51.71 269	77.15 169	91.42 39	65.49 152	87.20 6	79.44 17	87.17 209	84.51 148
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
UA-Net		81.56 37	82.28 47	79.40 51	88.91 28	69.16 77	84.67 40	80.01 167	75.34 18	79.80 123	94.91 2	69.79 101	80.25 160	72.63 76	94.46 40	88.78 43

SMA-MVS		82.12 32	82.68 42	80.43 39	88.90 29	69.52 70	85.12 36	84.76 51	63.53 116	84.23 73	91.47 37	72.02 71	87.16 7	79.74 13	94.36 49	84.61 139
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
MP-MVS-pluss		82.54 30	83.46 29	79.76 44	88.88 30	68.44 81	81.57 69	86.33 19	63.17 122	85.38 58	91.26 40	76.33 35	84.67 74	83.30 1	94.96 27	86.17 88
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 4	88.84 4	90.72 1	78.27 11	87.95 17	92.53 15	79.37 15	84.79 72	74.51 56	96.15 2	92.88 7

HPM-MVS_fast		84.59 7	85.10 9	83.06 4	88.60 32	75.83 26	86.27 27	86.89 16	73.69 26	86.17 40	91.70 32	78.23 22	85.20 64	79.45 16	94.91 29	88.15 50

SR-MVS		84.51 8	85.27 7	82.25 18	88.52 33	77.71 14	86.81 19	85.25 40	77.42 16	86.15 41	90.24 76	81.69 5	85.94 38	77.77 31	93.58 69	83.09 195

新几何1				69.99 226	88.37 34	71.34 54		62.08 384	43.85 379	74.99 225	86.11 189	52.85 292	70.57 327	50.99 303	83.23 285	68.05 430

HPM-MVS		84.12 11	84.63 13	82.60 13	88.21 35	74.40 34	85.24 35	87.21 14	70.69 53	85.14 60	90.42 64	78.99 17	86.62 14	80.83 6	94.93 28	86.79 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMM	69.25 9	82.11 33	83.31 31	78.49 67	88.17 36	73.96 37	83.11 58	84.52 64	66.40 80	87.45 25	89.16 99	81.02 8	80.52 156	74.27 59	95.73 7	80.98 255
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 37	69.15 78	67.85 286	59.59 394	41.06 404	73.05 273	85.72 198	48.03 333			80.65 336	66.92 435

XVG-ACMP-BASELINE		80.54 51	81.06 55	78.98 59	87.01 38	72.91 46	80.23 86	85.56 31	66.56 79	85.64 48	89.57 88	69.12 105	80.55 155	72.51 78	93.37 71	83.48 178

save fliter					87.00 39	67.23 92	79.24 97	77.94 213	56.65 186

LPG-MVS_test		83.47 19	84.33 16	80.90 35	87.00 39	70.41 63	82.04 66	86.35 17	69.77 58	87.75 18	91.13 41	81.83 3	86.20 28	77.13 40	95.96 5	86.08 89

LGP-MVS_train				80.90 35	87.00 39	70.41 63		86.35 17	69.77 58	87.75 18	91.13 41	81.83 3	86.20 28	77.13 40	95.96 5	86.08 89

EGC-MVSNET		64.77 297	61.17 340	75.60 110	86.90 42	74.47 33	84.04 44	68.62 339	0.60 499	1.13 501	91.61 35	65.32 155	74.15 272	64.01 155	88.28 176	78.17 310

OPM-MVS		80.99 48	81.63 53	79.07 56	86.86 43	69.39 73	79.41 96	84.00 81	65.64 85	85.54 52	89.28 92	76.32 36	83.47 94	74.03 64	93.57 70	84.35 154
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepC-MVS	72.44 4	81.00 47	80.83 57	81.50 25	86.70 44	70.03 67	82.06 65	87.00 15	59.89 148	80.91 113	90.53 59	72.19 68	88.56 1	73.67 67	94.52 39	85.92 95
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMP	69.50 8	82.64 29	83.38 30	80.40 40	86.50 45	69.44 72	82.30 63	86.08 24	66.80 75	86.70 34	89.99 81	81.64 6	85.95 37	74.35 58	96.11 3	85.81 96
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 59	79.99 62	78.49 67	86.46 46	74.79 32	77.15 123	85.39 37	66.73 76	80.39 119	88.85 108	74.43 55	78.33 197	74.73 51	85.79 226	82.35 221

VDDNet		71.60 180	73.13 150	67.02 291	86.29 47	41.11 372	69.97 241	66.50 352	68.72 63	74.74 230	91.70 32	59.90 222	75.81 240	48.58 327	91.72 92	84.15 160

MED-MVS test				78.47 69	86.27 48	64.31 119	86.10 28	84.54 61	64.93 102	85.54 52	88.38 120		86.37 19	74.09 60	94.20 57	84.73 131

MED-MVS		81.56 37	82.59 43	78.47 69	86.27 48	64.31 119	86.10 28	84.54 61	71.25 46	85.54 52	88.38 120	72.97 64	86.37 19	74.09 60	94.20 57	84.73 131

TestfortrainingZip a		81.05 46	82.35 46	77.16 90	86.27 48	60.63 159	86.10 28	84.54 61	64.93 102	85.54 52	88.38 120	72.97 64	86.37 19	78.23 26	94.20 57	84.47 150

test_0728_SECOND				76.57 95	86.20 51	60.57 160	83.77 49	85.49 32					85.90 42	75.86 43	94.39 45	83.25 188

SR-MVS-dyc-post		84.75 6	85.26 8	83.21 3	86.19 52	79.18 6	87.23 9	86.27 20	77.51 13	87.65 21	90.73 53	79.20 16	85.58 54	78.11 28	94.46 40	84.89 122

RE-MVS-def			85.50 6		86.19 52	79.18 6	87.23 9	86.27 20	77.51 13	87.65 21	90.73 53	81.38 7		78.11 28	94.46 40	84.89 122

DVP-MVS		81.15 43	83.12 36	75.24 116	86.16 54	60.78 156	83.77 49	80.58 155	72.48 37	85.83 46	90.41 65	78.57 19	85.69 50	75.86 43	94.39 45	79.24 291
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 48	85.10 43	72.48 37	85.27 59	89.96 82	78.57 19

SED-MVS		81.78 35	83.48 28	76.67 93	86.12 56	61.06 150	83.62 51	84.72 53	72.61 35	87.38 27	89.70 86	77.48 27	85.89 44	75.29 47	94.39 45	83.08 196

IU-MVS					86.12 56	60.90 154		80.38 159	45.49 359	81.31 106				75.64 46	94.39 45	84.65 135

test_241102_ONE					86.12 56	61.06 150		84.72 53	72.64 34	87.38 27	89.47 89	77.48 27	85.74 49

reproduce-ours		84.97 3	85.93 3	82.10 20	86.11 59	77.53 17	87.08 13	85.81 28	78.70 9	88.94 12	91.88 26	79.74 12	86.05 34	79.90 9	95.21 17	82.72 210

our_new_method		84.97 3	85.93 3	82.10 20	86.11 59	77.53 17	87.08 13	85.81 28	78.70 9	88.94 12	91.88 26	79.74 12	86.05 34	79.90 9	95.21 17	82.72 210

XVG-OURS		79.51 60	79.82 63	78.58 65	86.11 59	74.96 31	76.33 138	84.95 48	66.89 73	82.75 90	88.99 105	66.82 133	78.37 195	74.80 49	90.76 126	82.40 220

test_part2					85.90 62	66.44 97				84.61 69

原ACMM1				73.90 132	85.90 62	65.15 112		81.67 124	50.97 283	74.25 245	86.16 185	61.60 196	83.54 91	56.75 242	91.08 113	73.00 375

testdata				64.13 318	85.87 64	63.34 129		61.80 387	47.83 334	76.42 196	86.60 171	48.83 326	62.31 405	54.46 275	81.26 321	66.74 439

CNVR-MVS		78.49 71	78.59 73	78.16 74	85.86 65	67.40 89	78.12 112	81.50 127	63.92 110	77.51 159	86.56 172	68.43 113	84.82 71	73.83 65	91.61 96	82.26 225

test_one_0601					85.84 66	61.45 144		85.63 30	75.27 20	85.62 51	90.38 70	76.72 31

NCCC		78.25 74	78.04 79	78.89 61	85.61 67	69.45 71	79.80 93	80.99 145	65.77 84	75.55 207	86.25 182	67.42 125	85.42 55	70.10 95	90.88 121	81.81 238

reproduce_model		84.87 5	85.80 5	82.05 22	85.52 68	78.14 12	87.69 6	85.36 38	79.26 6	89.12 11	92.10 20	77.52 26	85.92 41	80.47 8	95.20 19	82.10 228

TEST9					85.47 69	69.32 75	76.42 133	78.69 198	53.73 240	76.97 171	86.74 161	66.84 132	81.10 140

train_agg		76.38 89	76.55 93	75.86 106	85.47 69	69.32 75	76.42 133	78.69 198	54.00 235	76.97 171	86.74 161	66.60 138	81.10 140	72.50 79	91.56 97	77.15 330

DPE-MVS		82.00 34	83.02 37	78.95 60	85.36 71	67.25 91	82.91 59	84.98 46	73.52 28	85.43 57	90.03 80	76.37 34	86.97 12	74.56 54	94.02 63	82.62 214
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HQP_MVS		78.77 67	78.78 71	78.72 62	85.18 72	65.18 110	82.74 61	85.49 32	65.45 88	78.23 145	89.11 100	60.83 209	86.15 31	71.09 86	90.94 115	84.82 127

plane_prior7					85.18 72	66.21 100

SymmetryMVS		74.00 119	72.85 157	77.43 85	85.17 74	70.01 68	79.92 91	68.48 340	58.60 160	75.21 220	84.02 231	52.85 292	81.82 126	61.45 185	89.99 140	80.47 270

SteuartSystems-ACMMP		83.07 25	83.64 26	81.35 29	85.14 75	71.00 57	85.53 33	84.78 50	70.91 51	85.64 48	90.41 65	75.55 43	87.69 4	79.75 11	95.08 24	85.36 110
Skip Steuart: Steuart Systems R&D Blog.
test_8					85.09 76	67.89 84	76.26 139	78.66 200	54.00 235	76.89 175	86.72 164	66.60 138	80.89 150

WR-MVS		71.20 189	72.48 168	67.36 281	84.98 77	35.70 431	64.43 346	68.66 338	65.05 98	81.49 104	86.43 177	57.57 258	76.48 234	50.36 308	93.32 73	89.90 21

PS-MVSNAJss		77.54 78	77.35 87	78.13 76	84.88 78	66.37 98	78.55 104	79.59 179	53.48 247	86.29 39	92.43 17	62.39 183	80.25 160	67.90 116	90.61 127	87.77 53

LTVRE_ROB	75.46 1	84.22 9	84.98 11	81.94 23	84.82 79	75.40 28	91.60 3	87.80 8	73.52 28	88.90 14	93.06 8	71.39 82	81.53 132	81.53 4	92.15 89	88.91 39
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
mvs_tets		78.93 65	78.67 72	79.72 46	84.81 80	73.93 38	80.65 77	76.50 231	51.98 267	87.40 26	91.86 28	76.09 38	78.53 187	68.58 106	90.20 133	86.69 72

APDe-MVS		82.88 27	84.14 18	79.08 55	84.80 81	66.72 96	86.54 23	85.11 42	72.00 43	86.65 35	91.75 31	78.20 23	87.04 10	77.93 30	94.32 52	83.47 179
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSLP-MVS++		74.48 116	75.78 100	70.59 203	84.66 82	62.40 134	78.65 102	84.24 74	60.55 143	77.71 155	81.98 280	63.12 172	77.64 211	62.95 171	88.14 179	71.73 393

jajsoiax		78.51 70	78.16 78	79.59 48	84.65 83	73.83 40	80.42 80	76.12 237	51.33 278	87.19 31	91.51 36	73.79 59	78.44 191	68.27 109	90.13 137	86.49 80

TranMVSNet+NR-MVSNet		76.13 91	77.66 82	71.56 189	84.61 84	42.57 361	70.98 226	78.29 207	68.67 64	83.04 83	89.26 93	72.99 63	80.75 151	55.58 259	95.47 12	91.35 11

旧先验1					84.55 85	60.36 162		63.69 375			87.05 147	54.65 281			83.34 283	69.66 414

APD-MVS		81.13 44	81.73 51	79.36 52	84.47 86	70.53 62	83.85 47	83.70 83	69.43 60	83.67 79	88.96 106	75.89 39	86.41 17	72.62 77	92.95 76	81.14 249
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
plane_prior1					84.46 87

agg_prior					84.44 88	66.02 103		78.62 201		76.95 173			80.34 158

ME-MVS		81.36 40	82.39 45	78.28 73	84.42 89	64.31 119	82.78 60	85.02 45	71.25 46	84.81 66	88.38 120	76.53 33	85.81 46	74.09 60	94.20 57	84.73 131

DeepPCF-MVS	71.07 5	78.48 72	77.14 89	82.52 16	84.39 90	77.04 24	76.35 136	84.05 79	56.66 185	80.27 120	85.31 204	68.56 108	87.03 11	67.39 122	91.26 103	83.50 175

CDPH-MVS		77.33 82	77.06 90	78.14 75	84.21 91	63.98 125	76.07 142	83.45 86	54.20 230	77.68 156	87.18 142	69.98 97	85.37 56	68.01 113	92.72 81	85.08 119

plane_prior6					84.18 92	65.31 109						60.83 209

114514_t		73.40 133	73.33 147	73.64 136	84.15 93	57.11 199	78.20 110	80.02 166	43.76 382	72.55 280	86.07 192	64.00 167	83.35 97	60.14 205	91.03 114	80.45 271

ZD-MVS					83.91 94	69.36 74		81.09 141	58.91 158	82.73 91	89.11 100	75.77 40	86.63 13	72.73 75	92.93 77

DeepC-MVS_fast	69.89 7	77.17 83	76.33 95	79.70 47	83.90 95	67.94 83	80.06 89	83.75 82	56.73 184	74.88 229	85.32 203	65.54 151	87.79 2	65.61 140	91.14 108	83.35 186
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
h-mvs33		73.08 140	71.61 190	77.48 83	83.89 96	72.89 47	70.47 233	71.12 303	54.28 226	77.89 149	83.41 244	49.04 323	80.98 145	63.62 164	90.77 125	78.58 302

NormalMVS		76.15 90	75.08 107	79.36 52	83.87 97	70.01 68	79.92 91	84.34 68	58.60 160	75.21 220	84.02 231	52.85 292	81.82 126	61.45 185	95.50 10	86.24 84

lecture		83.41 20	85.02 10	78.58 65	83.87 97	67.26 90	84.47 41	88.27 6	73.64 27	87.35 30	91.96 23	78.55 21	82.92 104	81.59 3	95.50 10	85.56 105

SD-MVS		80.28 56	81.55 54	76.47 98	83.57 99	67.83 85	83.39 56	85.35 39	64.42 106	86.14 42	87.07 146	74.02 56	80.97 146	77.70 33	92.32 87	80.62 267
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
DU-MVS		74.91 110	75.57 103	72.93 160	83.50 100	45.79 325	69.47 249	80.14 164	65.22 94	81.74 101	87.08 144	61.82 193	81.07 142	56.21 249	94.98 25	91.93 8

NR-MVSNet		73.62 124	74.05 128	72.33 180	83.50 100	43.71 347	65.65 323	77.32 221	64.32 107	75.59 206	87.08 144	62.45 182	81.34 134	54.90 268	95.63 8	91.93 8

test_0402		78.17 75	79.48 66	74.24 126	83.50 100	59.15 175	72.52 189	74.60 253	75.34 18	88.69 16	91.81 30	75.06 47	82.37 116	65.10 141	88.68 171	81.20 247

OPU-MVS				78.65 64	83.44 103	66.85 95	83.62 51				86.12 188	66.82 133	86.01 36	61.72 183	89.79 146	83.08 196

NP-MVS					83.34 104	63.07 132					85.97 193

DVP-MVS++		81.24 41	82.74 41	76.76 92	83.14 105	60.90 154	91.64 1	85.49 32	74.03 24	84.93 62	90.38 70	66.82 133	85.90 42	77.43 35	90.78 123	83.49 176

MSC_two_6792asdad				79.02 57	83.14 105	67.03 93		80.75 147					86.24 26	77.27 38	94.85 30	83.78 168

No_MVS				79.02 57	83.14 105	67.03 93		80.75 147					86.24 26	77.27 38	94.85 30	83.78 168

UniMVSNet (Re)		75.00 108	75.48 104	73.56 141	83.14 105	47.92 288	70.41 235	81.04 143	63.67 114	79.54 126	86.37 178	62.83 176	81.82 126	57.10 240	95.25 16	90.94 15

hse-mvs2		72.32 166	70.66 209	77.31 88	83.10 109	71.77 50	69.19 257	71.45 292	54.28 226	77.89 149	78.26 350	49.04 323	79.23 174	63.62 164	89.13 163	80.92 256

UniMVSNet_NR-MVSNet		74.90 111	75.65 101	72.64 173	83.04 110	45.79 325	69.26 255	78.81 193	66.66 78	81.74 101	86.88 151	63.26 171	81.07 142	56.21 249	94.98 25	91.05 13

HyFIR lowres test		63.01 319	60.47 351	70.61 202	83.04 110	54.10 226	59.93 388	72.24 284	33.67 459	69.00 330	75.63 374	38.69 394	76.93 227	36.60 425	75.45 396	80.81 261

COLMAP_ROB	72.78 3	83.75 14	84.11 19	82.68 12	82.97 112	74.39 35	87.18 11	88.18 7	78.98 7	86.11 43	91.47 37	79.70 14	85.76 48	66.91 130	95.46 13	87.89 52
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		70.22 208	67.88 256	77.22 89	82.96 113	71.61 51	69.08 260	71.39 293	49.17 313	71.70 292	78.07 355	37.62 402	79.21 175	61.81 180	89.15 161	80.82 259

DP-MVS Recon		73.57 126	72.69 161	76.23 101	82.85 114	63.39 128	74.32 167	82.96 97	57.75 168	70.35 313	81.98 280	64.34 166	84.41 79	49.69 313	89.95 141	80.89 257

APD-MVS_3200maxsize		83.57 16	84.33 16	81.31 31	82.83 115	73.53 43	85.50 34	87.45 13	74.11 22	86.45 38	90.52 61	80.02 10	84.48 76	77.73 32	94.34 51	85.93 94

PVSNet_Blended_VisFu		70.04 212	68.88 235	73.53 142	82.71 116	63.62 127	74.81 155	81.95 120	48.53 324	67.16 361	79.18 339	51.42 303	78.38 194	54.39 277	79.72 355	78.60 301

DPM-MVS		69.98 214	69.22 231	72.26 181	82.69 117	58.82 182	70.53 232	81.23 136	47.79 335	64.16 389	80.21 310	51.32 304	83.12 100	60.14 205	84.95 244	74.83 356

EG-PatchMatch MVS		70.70 200	70.88 203	70.16 220	82.64 118	58.80 183	71.48 216	73.64 258	54.98 207	76.55 189	81.77 284	61.10 206	78.94 180	54.87 269	80.84 331	72.74 381

HQP-NCC					82.37 119		77.32 119		59.08 152	71.58 296

ACMP_Plane					82.37 119		77.32 119		59.08 152	71.58 296

HQP-MVS		75.24 103	75.01 108	75.94 104	82.37 119	58.80 183	77.32 119	84.12 77	59.08 152	71.58 296	85.96 194	58.09 250	85.30 58	67.38 124	89.16 159	83.73 171

test12				76.51 96	82.28 122	60.94 153		81.64 125		73.60 259		64.88 160	85.19 65		90.42 130	83.38 184

TAMVS		65.31 290	63.75 311	69.97 228	82.23 123	59.76 169	66.78 307	63.37 378	45.20 367	69.79 323	79.37 331	47.42 337	72.17 300	34.48 442	85.15 239	77.99 315

test_prior				75.27 115	82.15 124	59.85 168		84.33 71					83.39 96			82.58 215

SF-MVS		80.72 50	81.80 49	77.48 83	82.03 125	64.40 118	83.41 55	88.46 5	65.28 93	84.29 72	89.18 97	73.73 60	83.22 98	76.01 42	93.77 66	84.81 129

AdaColmap		74.22 117	74.56 112	73.20 147	81.95 126	60.97 152	79.43 94	80.90 146	65.57 86	72.54 281	81.76 285	70.98 87	85.26 60	47.88 336	90.00 138	73.37 371

PAPM_NR		73.91 120	74.16 125	73.16 148	81.90 127	53.50 231	81.28 72	81.40 130	66.17 82	73.30 266	83.31 250	59.96 220	83.10 101	58.45 225	81.66 314	82.87 204

DP-MVS		78.44 73	79.29 67	75.90 105	81.86 128	65.33 108	79.05 99	84.63 59	74.83 21	80.41 118	86.27 180	71.68 73	83.45 95	62.45 175	92.40 84	78.92 298

F-COLMAP		75.29 101	73.99 129	79.18 54	81.73 129	71.90 49	81.86 68	82.98 96	59.86 149	72.27 284	84.00 233	64.56 164	83.07 102	51.48 297	87.19 207	82.56 216

SixPastTwentyTwo		75.77 93	76.34 94	74.06 130	81.69 130	54.84 220	76.47 130	75.49 244	64.10 109	87.73 20	92.24 19	50.45 311	81.30 136	67.41 120	91.46 99	86.04 91

Vis-MVSNet		74.85 114	74.56 112	75.72 107	81.63 131	64.64 116	76.35 136	79.06 189	62.85 125	73.33 265	88.41 118	62.54 181	79.59 171	63.94 160	82.92 287	82.94 200
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_djsdf		78.88 66	78.27 76	80.70 38	81.42 132	71.24 55	83.98 45	75.72 242	52.27 260	87.37 29	92.25 18	68.04 119	80.56 153	72.28 81	91.15 107	90.32 20

3Dnovator+	73.19 2	81.08 45	80.48 58	82.87 7	81.41 133	72.03 48	84.38 43	86.23 23	77.28 17	80.65 116	90.18 79	59.80 225	87.58 5	73.06 71	91.34 102	89.01 35

tt0805		76.12 92	78.43 75	69.20 243	81.32 134	41.37 369	76.72 127	77.64 216	63.78 113	82.06 95	87.88 135	79.78 11	79.05 177	64.33 153	92.40 84	87.17 65

MCST-MVS		73.42 128	73.34 146	73.63 137	81.28 135	59.17 174	74.80 157	83.13 91	45.50 357	72.84 274	83.78 240	65.15 157	80.99 144	64.54 150	89.09 167	80.73 263

MIMVSNet1		66.57 277	69.23 230	58.59 387	81.26 136	37.73 414	64.06 350	57.62 401	57.02 178	78.40 143	90.75 52	62.65 177	58.10 424	41.77 384	89.58 150	79.95 279

ACMH+	66.64 10	81.20 42	82.48 44	77.35 87	81.16 137	62.39 135	80.51 78	87.80 8	73.02 30	87.57 23	91.08 43	80.28 9	82.44 113	64.82 145	96.10 4	87.21 61

MVSMamba_PlusPlus		76.88 85	78.21 77	72.88 164	80.83 138	48.71 272	83.28 57	82.79 100	72.78 31	79.17 131	91.94 24	56.47 271	83.95 81	70.51 94	86.15 221	85.99 93

MVS_111021_HR		72.98 147	72.97 156	72.99 155	80.82 139	65.47 106	68.81 267	72.77 274	57.67 170	75.76 203	82.38 272	71.01 86	77.17 219	61.38 187	86.15 221	76.32 343

9.14			80.22 60		80.68 140		80.35 83	87.69 11	59.90 147	83.00 84	88.20 127	74.57 52	81.75 130	73.75 66	93.78 65

OMC-MVS		79.41 62	78.79 70	81.28 32	80.62 141	70.71 61	80.91 75	84.76 51	62.54 127	81.77 99	86.65 168	71.46 79	83.53 92	67.95 115	92.44 83	89.60 23

OurMVSNet-221017-0		78.57 69	78.53 74	78.67 63	80.48 142	64.16 122	80.24 85	82.06 117	61.89 131	88.77 15	93.32 5	57.15 262	82.60 110	70.08 96	92.80 78	89.25 29

CDS-MVSNet		64.33 305	62.66 327	69.35 240	80.44 143	58.28 191	65.26 329	65.66 358	44.36 377	67.30 360	75.54 375	43.27 358	71.77 312	37.68 415	84.44 262	78.01 314
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	62.01 16	71.79 177	70.28 212	76.33 99	80.31 144	68.63 80	78.18 111	81.24 135	54.57 218	67.09 362	80.63 304	59.44 229	81.74 131	46.91 343	84.17 269	78.63 300
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MM		78.15 76	77.68 81	79.55 49	80.10 145	65.47 106	80.94 74	78.74 197	71.22 48	72.40 283	88.70 110	60.51 213	87.70 3	77.40 37	89.13 163	85.48 107

sc_t1		72.50 164	74.23 123	67.33 282	80.05 146	46.99 308	66.58 310	69.48 318	66.28 81	77.62 158	91.83 29	70.98 87	68.62 354	53.86 284	91.40 100	86.37 83

CHOSEN 1792x2688		58.09 371	56.30 384	63.45 331	79.95 147	50.93 249	54.07 436	65.59 359	28.56 475	61.53 412	74.33 388	41.09 378	66.52 384	33.91 445	67.69 454	72.92 376

tt0320		71.34 187	73.47 140	64.97 312	79.92 148	40.81 377	65.22 330	69.07 326	66.72 77	76.15 201	93.36 4	70.35 91	66.90 374	49.31 320	91.09 112	87.21 61

K. test v3		73.67 123	73.61 138	73.87 133	79.78 149	55.62 213	74.69 161	62.04 386	66.16 83	84.76 67	93.23 7	49.47 317	80.97 146	65.66 139	86.67 217	85.02 121

tt0320-xc		71.50 182	73.63 137	65.08 310	79.77 150	40.46 386	64.80 338	68.86 332	67.08 72	76.84 179	93.24 6	70.33 92	66.77 381	49.76 312	92.02 90	88.02 51

VPNet		65.58 288	67.56 259	59.65 375	79.72 151	30.17 463	60.27 384	62.14 382	54.19 231	71.24 305	86.63 169	58.80 239	67.62 365	44.17 365	90.87 122	81.18 248

ACMH	63.62 14	77.50 81	80.11 61	69.68 233	79.61 152	56.28 203	78.81 101	83.62 84	63.41 120	87.14 33	90.23 77	76.11 37	73.32 280	67.58 117	94.44 43	79.44 288
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 169	79.60 153	56.83 202		57.37 404		83.80 78	89.01 104	47.45 336	78.74 184	64.39 152	86.49 220	82.69 212

MVS_111021_LR		72.10 171	71.82 184	72.95 157	79.53 154	73.90 39	70.45 234	66.64 351	56.87 179	76.81 180	81.76 285	68.78 106	71.76 313	61.81 180	83.74 275	73.18 373

Test_1112_low_res		58.78 367	58.69 364	59.04 383	79.41 155	38.13 408	57.62 408	66.98 350	34.74 452	59.62 428	77.56 359	42.92 363	63.65 400	38.66 406	70.73 435	75.35 353

CSCG		74.12 118	74.39 118	73.33 144	79.35 156	61.66 142	77.45 118	81.98 119	62.47 129	79.06 133	80.19 312	61.83 192	78.79 183	59.83 209	87.35 194	79.54 287

MVP-Stereo		61.56 341	59.22 359	68.58 261	79.28 157	60.44 161	69.20 256	71.57 288	43.58 385	56.42 444	78.37 349	39.57 389	76.46 235	34.86 440	60.16 476	68.86 424
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 203	71.34 195	67.85 272	79.26 158	40.42 387	74.67 162	75.15 248	58.41 162	68.74 344	88.14 131	56.08 274	83.69 88	59.90 208	81.71 311	79.43 289

IS-MVSNet		75.10 105	75.42 105	74.15 129	79.23 159	48.05 286	79.43 94	78.04 211	70.09 57	79.17 131	88.02 132	53.04 291	83.60 89	58.05 230	93.76 67	90.79 17

TestfortrainingZip				73.58 139	79.21 160	57.65 196	86.10 28	81.22 137	72.34 40	72.08 289	83.19 259	58.95 236	83.71 87		84.76 251	79.38 290

FC-MVSNet-test		73.32 135	74.78 110	68.93 253	79.21 160	36.57 421	71.82 213	79.54 181	57.63 173	82.57 92	90.38 70	59.38 231	78.99 179	57.91 231	94.56 38	91.23 12

AllTest		77.66 77	77.43 83	78.35 71	79.19 162	70.81 58	78.60 103	88.64 3	65.37 91	80.09 121	88.17 128	70.33 92	78.43 192	55.60 256	90.90 119	85.81 96

TestCases				78.35 71	79.19 162	70.81 58		88.64 3	65.37 91	80.09 121	88.17 128	70.33 92	78.43 192	55.60 256	90.90 119	85.81 96

xiu_mvs_v1_base_debu		67.87 253	67.07 269	70.26 216	79.13 164	61.90 139	67.34 293	71.25 298	47.98 331	67.70 354	74.19 392	61.31 199	72.62 289	56.51 244	78.26 371	76.27 344

xiu_mvs_v1_base		67.87 253	67.07 269	70.26 216	79.13 164	61.90 139	67.34 293	71.25 298	47.98 331	67.70 354	74.19 392	61.31 199	72.62 289	56.51 244	78.26 371	76.27 344

xiu_mvs_v1_base_debi		67.87 253	67.07 269	70.26 216	79.13 164	61.90 139	67.34 293	71.25 298	47.98 331	67.70 354	74.19 392	61.31 199	72.62 289	56.51 244	78.26 371	76.27 344

VDD-MVS		70.81 198	71.44 194	68.91 254	79.07 167	46.51 317	67.82 287	70.83 307	61.23 135	74.07 249	88.69 111	59.86 223	75.62 245	51.11 301	90.28 132	84.61 139

Elysia		77.52 79	77.43 83	77.78 79	79.01 168	60.26 163	76.55 128	84.34 68	67.82 68	78.73 136	87.94 133	58.68 241	83.79 84	74.70 52	89.10 165	89.28 27

StellarMVS		77.52 79	77.43 83	77.78 79	79.01 168	60.26 163	76.55 128	84.34 68	67.82 68	78.73 136	87.94 133	58.68 241	83.79 84	74.70 52	89.10 165	89.28 27

test1111		64.62 298	65.19 294	62.93 341	79.01 168	29.91 464	65.45 326	54.41 426	54.09 233	71.47 303	88.48 117	37.02 404	74.29 270	46.83 345	89.94 142	84.58 142

TSAR-MVS + GP.		73.08 140	71.60 191	77.54 82	78.99 171	70.73 60	74.96 152	69.38 319	60.73 142	74.39 242	78.44 348	57.72 257	82.78 107	60.16 203	89.60 148	79.11 293

test2506		61.23 343	60.85 346	62.38 346	78.80 172	27.88 472	67.33 296	37.42 491	54.23 228	67.55 357	88.68 112	17.87 494	74.39 267	46.33 350	89.41 154	84.86 125

ECVR-MVS		64.82 295	65.22 293	63.60 326	78.80 172	31.14 458	66.97 303	56.47 415	54.23 228	69.94 321	88.68 112	37.23 403	74.81 260	45.28 361	89.41 154	84.86 125

FIs		72.56 160	73.80 132	68.84 256	78.74 174	37.74 413	71.02 225	79.83 170	56.12 190	80.88 115	89.45 90	58.18 246	78.28 198	56.63 243	93.36 72	90.51 19

v7n		79.37 63	80.41 59	76.28 100	78.67 175	55.81 209	79.22 98	82.51 110	70.72 52	87.54 24	92.44 16	68.00 120	81.34 134	72.84 74	91.72 92	91.69 10

LS3D		80.99 48	80.85 56	81.41 28	78.37 176	71.37 53	87.45 8	85.87 27	77.48 15	81.98 96	89.95 83	69.14 104	85.26 60	66.15 132	91.24 104	87.61 56

CNLPA		73.44 127	73.03 154	74.66 118	78.27 177	75.29 29	75.99 143	78.49 202	65.39 90	75.67 205	83.22 258	61.23 202	66.77 381	53.70 285	85.33 235	81.92 236

SSM_0404		72.51 163	72.15 178	73.60 138	78.20 178	55.86 208	74.41 166	79.83 170	53.69 241	73.98 252	84.18 223	62.26 186	82.50 111	58.21 227	84.60 256	82.43 219

EPP-MVSNet		73.86 122	73.38 143	75.31 114	78.19 179	53.35 233	80.45 79	77.32 221	65.11 97	76.47 194	86.80 156	49.47 317	83.77 86	53.89 282	92.72 81	88.81 42

PCF-MVS	63.80 13	72.70 157	71.69 185	75.72 107	78.10 180	60.01 166	73.04 184	81.50 127	45.34 362	79.66 125	84.35 221	65.15 157	82.65 109	48.70 325	89.38 157	84.50 149
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		73.14 138	73.77 134	71.26 195	78.09 181	52.64 237	74.32 167	79.56 180	56.32 188	76.35 197	83.36 249	70.76 89	77.96 205	63.32 168	81.84 304	83.18 191

LFMVS		67.06 271	67.89 255	64.56 315	78.02 182	38.25 406	70.81 230	59.60 393	65.18 95	71.06 307	86.56 172	43.85 353	75.22 250	46.35 349	89.63 147	80.21 277

anonymousdsp		78.60 68	77.80 80	81.00 34	78.01 183	74.34 36	80.09 87	76.12 237	50.51 292	89.19 10	90.88 48	71.45 80	77.78 209	73.38 68	90.60 128	90.90 16

BH-untuned		69.39 225	69.46 223	69.18 244	77.96 184	56.88 200	68.47 280	77.53 217	56.77 182	77.79 152	79.63 323	60.30 217	80.20 163	46.04 352	80.65 336	70.47 406

1112_ss		59.48 361	58.99 362	60.96 365	77.84 185	42.39 362	61.42 371	68.45 341	37.96 432	59.93 425	67.46 451	45.11 346	65.07 393	40.89 390	71.81 427	75.41 351

PS-MVSNAJ		64.27 306	63.73 312	65.90 303	77.82 186	51.42 243	63.33 357	72.33 282	45.09 370	61.60 411	68.04 445	62.39 183	73.95 274	49.07 321	73.87 412	72.34 385

ambc				70.10 224	77.74 187	50.21 256	74.28 170	77.93 214		79.26 129	88.29 126	54.11 286	79.77 167	64.43 151	91.10 111	80.30 274

xiu_mvs_v2_base		64.43 303	63.96 309	65.85 304	77.72 188	51.32 245	63.63 354	72.31 283	45.06 371	61.70 410	69.66 430	62.56 179	73.93 275	49.06 322	73.91 411	72.31 386

Anonymous20231211		75.54 98	77.19 88	70.59 203	77.67 189	45.70 329	74.73 159	80.19 162	68.80 61	82.95 86	92.91 10	66.26 142	76.76 231	58.41 226	92.77 79	89.30 26

FMVSNet1		71.06 191	72.48 168	66.81 293	77.65 190	40.68 380	71.96 203	73.03 265	61.14 136	79.45 128	90.36 73	60.44 214	75.20 252	50.20 309	88.05 181	84.54 144

FPMVS		59.43 362	60.07 353	57.51 396	77.62 191	71.52 52	62.33 365	50.92 445	57.40 174	69.40 327	80.00 316	39.14 392	61.92 407	37.47 418	66.36 459	39.09 491

balanced_conf03		73.59 125	74.06 127	72.17 184	77.48 192	47.72 293	81.43 71	82.20 115	54.38 223	79.19 130	87.68 138	54.41 283	83.57 90	63.98 157	85.78 227	85.22 111

testing3		58.28 370	58.38 368	58.00 393	77.45 193	26.12 481	60.78 378	43.00 477	56.02 195	70.18 316	75.76 370	13.27 502	67.24 371	48.02 334	80.89 328	80.65 266

LuminaMVS		71.15 190	70.79 206	72.24 183	77.20 194	58.34 190	72.18 196	76.20 235	54.91 208	77.74 153	81.93 282	49.17 322	76.31 236	62.12 179	85.66 229	82.07 229

fmvsm_s_conf0.5_n_9		74.56 115	74.30 121	75.34 113	77.17 195	64.87 114	72.62 188	76.17 236	54.54 220	78.32 144	86.14 186	65.14 159	75.72 244	73.10 70	85.55 230	85.42 108

usedtu_dtu_shiyan2		62.25 331	62.27 329	62.18 348	77.08 196	52.84 235	62.56 363	56.33 418	52.43 259	64.22 387	83.26 253	48.47 332	58.06 425	25.75 479	90.34 131	75.64 347

Effi-MVS+-dtu		75.43 100	72.28 174	84.91 2	77.05 197	83.58 1	78.47 105	77.70 215	57.68 169	74.89 228	78.13 354	64.80 161	84.26 80	56.46 247	85.32 236	86.88 68

CLD-MVS		72.88 151	72.36 172	74.43 123	77.03 198	54.30 224	68.77 270	83.43 87	52.12 264	76.79 181	74.44 387	69.54 103	83.91 82	55.88 252	93.25 74	85.09 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CS-MVS		76.51 88	76.00 98	78.06 77	77.02 199	64.77 115	80.78 76	82.66 105	60.39 144	74.15 246	83.30 251	69.65 102	82.07 122	69.27 103	86.75 216	87.36 59

SPE-MVS-test		74.89 112	74.23 123	76.86 91	77.01 200	62.94 133	78.98 100	84.61 60	58.62 159	70.17 317	80.80 300	66.74 137	81.96 124	61.74 182	89.40 156	85.69 103

Baseline_NR-MVSNet		70.62 201	73.19 148	62.92 342	76.97 201	34.44 439	68.84 264	70.88 306	60.25 145	79.50 127	90.53 59	61.82 193	69.11 348	54.67 272	95.27 15	85.22 111

ITE_SJBPF				80.35 41	76.94 202	73.60 41		80.48 156	66.87 74	83.64 80	86.18 183	70.25 95	79.90 166	61.12 192	88.95 169	87.56 57

mamba_0408		70.32 205	69.35 225	73.24 146	76.92 203	55.22 215	56.61 415	79.27 185	52.14 262	73.08 269	83.14 260	60.53 211	82.50 111	57.51 234	84.91 247	81.99 232

SSM_04072		67.23 266	69.35 225	60.89 366	76.92 203	55.22 215	56.61 415	79.27 185	52.14 262	73.08 269	83.14 260	60.53 211	45.46 465	57.51 234	84.91 247	81.99 232

SSM_0407		72.15 170	71.85 182	73.06 153	76.92 203	55.22 215	73.59 176	79.83 170	53.69 241	73.08 269	84.18 223	62.26 186	81.98 123	58.21 227	84.91 247	81.99 232

SSC-MVS		61.79 338	66.08 282	48.89 445	76.91 206	10.00 503	53.56 438	47.37 463	68.20 66	76.56 188	89.21 95	54.13 285	57.59 426	54.75 270	74.07 410	79.08 294

jason		64.47 302	62.84 324	69.34 241	76.91 206	59.20 171	67.15 299	65.67 357	35.29 448	65.16 375	76.74 366	44.67 348	70.68 324	54.74 271	79.28 358	78.14 311
jason: jason.
ETV-MVS		72.72 156	72.16 177	74.38 125	76.90 208	55.95 205	73.34 181	84.67 56	62.04 130	72.19 287	70.81 414	65.90 147	85.24 62	58.64 221	84.96 243	81.95 235

Anonymous20240529		72.56 160	73.79 133	68.86 255	76.89 209	45.21 333	68.80 269	77.25 223	67.16 71	76.89 175	90.44 62	65.95 146	74.19 271	50.75 304	90.00 138	87.18 64

EC-MVSNet		77.08 84	77.39 86	76.14 103	76.86 210	56.87 201	80.32 84	87.52 12	63.45 118	74.66 234	84.52 217	69.87 99	84.94 67	69.76 99	89.59 149	86.60 73

PM-MVS		64.49 301	63.61 313	67.14 287	76.68 211	75.15 30	68.49 279	42.85 478	51.17 281	77.85 151	80.51 305	45.76 340	66.31 385	52.83 292	76.35 387	59.96 468

mvsmamba		68.87 235	67.30 266	73.57 140	76.58 212	53.70 230	84.43 42	74.25 255	45.38 361	76.63 184	84.55 216	35.85 409	85.27 59	49.54 316	78.49 367	81.75 241

TransMVSNet (Re)		69.62 220	71.63 188	63.57 327	76.51 213	35.93 429	65.75 322	71.29 297	61.05 137	75.02 224	89.90 84	65.88 148	70.41 331	49.79 311	89.48 152	84.38 153

GDP-MVS		70.84 197	69.24 229	75.62 109	76.44 214	55.65 211	74.62 164	82.78 102	49.63 303	72.10 288	83.79 239	31.86 431	82.84 106	64.93 144	87.01 211	88.39 48

BH-RMVSNet		68.69 242	68.20 251	70.14 221	76.40 215	53.90 229	64.62 343	73.48 260	58.01 165	73.91 255	81.78 283	59.09 234	78.22 199	48.59 326	77.96 375	78.31 306

PHI-MVS		74.92 109	74.36 120	76.61 94	76.40 215	62.32 136	80.38 81	83.15 90	54.16 232	73.23 267	80.75 301	62.19 188	83.86 83	68.02 112	90.92 118	83.65 172

UGNet		70.20 209	69.05 232	73.65 135	76.24 217	63.64 126	75.87 145	72.53 278	61.48 134	60.93 419	86.14 186	52.37 296	77.12 224	50.67 305	85.21 237	80.17 278
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
PatchMatch-RL		58.68 368	57.72 373	61.57 355	76.21 218	73.59 42	61.83 366	49.00 457	47.30 341	61.08 415	68.97 436	50.16 312	59.01 417	36.06 433	68.84 447	52.10 478

VPA-MVSNet		68.71 240	70.37 211	63.72 325	76.13 219	38.06 409	64.10 349	71.48 291	56.60 187	74.10 248	88.31 125	64.78 162	69.72 340	47.69 338	90.15 135	83.37 185

WB-MVS		60.04 357	64.19 307	47.59 448	76.09 220	10.22 502	52.44 446	46.74 465	65.17 96	74.07 249	87.48 139	53.48 288	55.28 432	49.36 318	72.84 418	77.28 323

PAPM		61.79 338	60.37 352	66.05 301	76.09 220	41.87 364	69.30 253	76.79 230	40.64 412	53.80 459	79.62 324	44.38 350	82.92 104	29.64 464	73.11 417	73.36 372

BH-w/o		64.81 296	64.29 306	66.36 298	76.08 222	54.71 221	65.61 324	75.23 247	50.10 298	71.05 308	71.86 408	54.33 284	79.02 178	38.20 411	76.14 389	65.36 445

dcpmvs_2		71.02 194	72.65 162	66.16 300	76.06 223	50.49 252	71.97 202	79.36 182	50.34 293	82.81 89	83.63 241	64.38 165	67.27 370	61.54 184	83.71 277	80.71 265

pmmvs6		71.82 176	73.66 135	66.31 299	75.94 224	42.01 363	66.99 302	72.53 278	63.45 118	76.43 195	92.78 12	72.95 66	69.69 341	51.41 299	90.46 129	87.22 60

testing3-2		56.85 378	57.62 374	54.53 412	75.84 225	22.23 492	51.26 452	49.10 455	61.04 138	63.74 397	79.73 320	22.29 477	59.44 415	31.16 457	84.43 263	81.92 236

CANet		73.00 145	71.84 183	76.48 97	75.82 226	61.28 146	74.81 155	80.37 160	63.17 122	62.43 408	80.50 306	61.10 206	85.16 66	64.00 156	84.34 268	83.01 199

pmmvs-eth3d		64.41 304	63.27 318	67.82 276	75.81 227	60.18 165	69.49 247	62.05 385	38.81 425	74.13 247	82.23 274	43.76 354	68.65 352	42.53 377	80.63 338	74.63 359

TR-MVS		64.59 299	63.54 314	67.73 277	75.75 228	50.83 250	63.39 356	70.29 311	49.33 309	71.55 300	74.55 385	50.94 307	78.46 190	40.43 396	75.69 392	73.89 368

MGCNet		75.45 99	74.66 111	77.83 78	75.58 229	61.53 143	78.29 107	77.18 225	63.15 124	69.97 320	87.20 141	57.54 259	87.05 9	74.05 63	88.96 168	84.89 122

tttt0517		69.46 223	67.79 258	74.46 120	75.34 230	52.72 236	75.05 151	63.27 379	54.69 214	78.87 135	84.37 220	26.63 458	81.15 138	63.95 158	87.93 186	89.51 24

cascas		64.59 299	62.77 326	70.05 225	75.27 231	50.02 258	61.79 367	71.61 287	42.46 394	63.68 398	68.89 439	49.33 319	80.35 157	47.82 337	84.05 271	79.78 282

API-MVS		70.97 195	71.51 193	69.37 238	75.20 232	55.94 206	80.99 73	76.84 228	62.48 128	71.24 305	77.51 360	61.51 198	80.96 149	52.04 293	85.76 228	71.22 399

EIA-MVS		68.59 243	67.16 267	72.90 162	75.18 233	55.64 212	69.39 250	81.29 133	52.44 258	64.53 380	70.69 415	60.33 216	82.30 118	54.27 279	76.31 388	80.75 262

PAPR		69.20 229	68.66 241	70.82 200	75.15 234	47.77 291	75.31 148	81.11 139	49.62 305	66.33 367	79.27 336	61.53 197	82.96 103	48.12 333	81.50 320	81.74 242

MVSFormer		69.93 215	69.03 233	72.63 174	74.93 235	59.19 172	83.98 45	75.72 242	52.27 260	63.53 402	76.74 366	43.19 359	80.56 153	72.28 81	78.67 365	78.14 311

lupinMVS		63.36 313	61.49 338	68.97 251	74.93 235	59.19 172	65.80 321	64.52 370	34.68 454	63.53 402	74.25 390	43.19 359	70.62 326	53.88 283	78.67 365	77.10 332

nrg030		74.87 113	75.99 99	71.52 190	74.90 237	49.88 265	74.10 172	82.58 107	54.55 219	83.50 81	89.21 95	71.51 78	75.74 243	61.24 189	92.34 86	88.94 38

TAPA-MVS	65.27 12	75.16 104	74.29 122	77.77 81	74.86 238	68.08 82	77.89 113	84.04 80	55.15 206	76.19 200	83.39 245	66.91 131	80.11 164	60.04 207	90.14 136	85.13 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
E4		72.74 155	73.54 139	70.35 209	74.85 239	46.82 309	69.53 246	82.80 99	55.60 201	76.23 198	86.50 174	69.87 99	77.45 213	63.72 162	82.77 291	86.76 71

FE-MVSNET2		68.70 241	69.85 217	65.22 307	74.82 240	37.95 411	67.28 298	73.47 261	53.40 248	77.65 157	87.72 137	59.72 226	73.17 282	46.39 348	88.23 177	84.56 143

RPSCF		75.76 94	74.37 119	79.93 43	74.81 241	77.53 17	77.53 117	79.30 184	59.44 151	78.88 134	89.80 85	71.26 83	73.09 283	57.45 236	80.89 328	89.17 32

EI-MVSNet-Vis-set		72.78 154	71.87 181	75.54 111	74.77 242	59.02 179	72.24 194	71.56 289	63.92 110	78.59 139	71.59 409	66.22 143	78.60 186	67.58 117	80.32 342	89.00 36

v1240		73.06 142	73.14 149	72.84 166	74.74 243	47.27 302	71.88 208	81.11 139	51.80 268	82.28 94	84.21 222	56.22 273	82.34 117	68.82 105	87.17 209	88.91 39

v1921920		72.96 149	72.98 155	72.89 163	74.67 244	47.58 295	71.92 206	80.69 149	51.70 270	81.69 103	83.89 237	56.58 269	82.25 119	68.34 108	87.36 193	88.82 41

EI-MVSNet-UG-set		72.63 158	71.68 186	75.47 112	74.67 244	58.64 187	72.02 200	71.50 290	63.53 116	78.58 141	71.39 413	65.98 145	78.53 187	67.30 127	80.18 345	89.23 30

Fast-Effi-MVS+		68.81 237	68.30 246	70.35 209	74.66 246	48.61 279	66.06 316	78.32 205	50.62 289	71.48 302	75.54 375	68.75 107	79.59 171	50.55 307	78.73 364	82.86 205

v1192		73.40 133	73.42 141	73.32 145	74.65 247	48.67 274	72.21 195	81.73 123	52.76 254	81.85 97	84.56 215	57.12 263	82.24 120	68.58 106	87.33 196	89.06 34

E5new		73.42 128	74.46 114	70.29 212	74.61 248	47.14 303	71.85 211	83.01 92	56.07 191	77.28 165	86.81 152	71.54 76	77.15 220	64.59 146	84.39 264	86.59 74

E5		73.42 128	74.46 114	70.29 212	74.61 248	47.14 303	71.85 211	83.01 92	56.07 191	77.28 165	86.81 152	71.54 76	77.15 220	64.59 146	84.39 264	86.59 74

E6new		73.42 128	74.46 114	70.29 212	74.60 250	47.14 303	71.86 209	82.99 94	56.07 191	77.28 165	86.81 152	71.55 74	77.14 222	64.59 146	84.39 264	86.59 74

E6		73.42 128	74.46 114	70.29 212	74.60 250	47.14 303	71.86 209	82.99 94	56.07 191	77.28 165	86.81 152	71.55 74	77.14 222	64.59 146	84.39 264	86.59 74

v144192		72.99 146	73.06 153	72.77 168	74.58 252	47.48 297	71.90 207	80.44 158	51.57 271	81.46 105	84.11 228	58.04 254	82.12 121	67.98 114	87.47 191	88.70 44

viewdifsd2359ckpt09		72.87 152	72.43 170	74.17 127	74.45 253	51.70 240	76.39 135	84.50 65	49.48 308	75.34 217	83.23 255	63.12 172	82.43 114	56.99 241	88.41 174	88.37 49

MAR-MVS		67.72 256	66.16 281	72.40 178	74.45 253	64.99 113	74.87 153	77.50 218	48.67 323	65.78 371	68.58 443	57.01 266	77.79 208	46.68 346	81.92 301	74.42 364
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
v10		75.69 95	76.20 96	74.16 128	74.44 255	48.69 273	75.84 146	82.93 98	59.02 156	85.92 44	89.17 98	58.56 243	82.74 108	70.73 90	89.14 162	91.05 13

balanced_ft_v1		71.65 179	72.22 176	69.92 229	74.26 256	45.74 327	81.54 70	79.66 174	53.65 243	79.77 124	86.74 161	51.20 306	80.64 152	58.70 220	84.47 260	83.40 182

sasdasda		72.29 168	73.38 143	69.04 247	74.23 257	47.37 299	73.93 174	83.18 88	54.36 224	76.61 186	81.64 288	72.03 69	75.34 248	57.12 238	87.28 198	84.40 151

canonicalmvs		72.29 168	73.38 143	69.04 247	74.23 257	47.37 299	73.93 174	83.18 88	54.36 224	76.61 186	81.64 288	72.03 69	75.34 248	57.12 238	87.28 198	84.40 151

Anonymous202405211		66.02 283	66.89 274	63.43 332	74.22 259	38.14 407	59.00 393	66.13 354	63.33 121	69.76 324	85.95 195	51.88 298	70.50 328	44.23 364	87.52 189	81.64 243

Effi-MVS+		72.10 171	72.28 174	71.58 188	74.21 260	50.33 254	74.72 160	82.73 103	62.62 126	70.77 309	76.83 365	69.96 98	80.97 146	60.20 201	78.43 368	83.45 181

FE-MVS		68.29 248	66.96 272	72.26 181	74.16 261	54.24 225	77.55 116	73.42 263	57.65 172	72.66 278	84.91 208	32.02 430	81.49 133	48.43 329	81.85 303	81.04 251

v1144		73.29 136	73.39 142	73.01 154	74.12 262	48.11 284	72.01 201	81.08 142	53.83 239	81.77 99	84.68 210	58.07 253	81.91 125	68.10 110	86.86 212	88.99 37

E2		71.98 173	72.60 163	70.13 222	74.09 263	46.61 313	69.15 258	82.56 108	54.40 221	75.32 218	85.35 200	68.51 109	77.34 215	62.30 177	81.74 307	86.44 81

E3		71.98 173	72.60 163	70.13 222	74.09 263	46.61 313	69.15 258	82.56 108	54.40 221	75.31 219	85.35 200	68.51 109	77.34 215	62.30 177	81.75 306	86.44 81

BP-MVS1		71.60 180	70.06 213	76.20 102	74.07 265	55.22 215	74.29 169	73.44 262	57.29 175	73.87 256	84.65 212	32.57 423	83.49 93	72.43 80	87.94 185	89.89 22

FA-MVS(test-final)		71.27 188	71.06 200	71.92 186	73.96 266	52.32 239	76.45 132	76.12 237	59.07 155	74.04 251	86.18 183	52.18 297	79.43 173	59.75 211	81.76 305	84.03 162

EI-MVSNet		69.61 221	69.01 234	71.41 193	73.94 267	49.90 261	71.31 221	71.32 295	58.22 163	75.40 213	70.44 418	58.16 247	75.85 238	62.51 173	79.81 352	88.48 45

CVMVSNet		59.21 363	58.44 367	61.51 356	73.94 267	47.76 292	71.31 221	64.56 369	26.91 481	60.34 421	70.44 418	36.24 408	67.65 364	53.57 286	68.66 448	69.12 421

fmvsm_s_conf0.5_n_5		71.46 184	71.62 189	70.99 199	73.89 269	59.95 167	73.02 185	73.08 264	45.15 368	77.30 164	84.06 229	64.73 163	70.08 335	71.20 85	82.10 299	82.92 201

IterMVS-LS		73.01 144	73.12 151	72.66 172	73.79 270	49.90 261	71.63 215	78.44 203	58.22 163	80.51 117	86.63 169	58.15 248	79.62 169	62.51 173	88.20 178	88.48 45
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UWE-MVS		52.94 407	52.70 410	53.65 415	73.56 271	27.49 473	57.30 411	49.57 452	38.56 427	62.79 406	71.42 412	19.49 488	60.41 410	24.33 485	77.33 381	73.06 374

viewcassd2359sk11		71.41 185	71.89 180	69.98 227	73.50 272	46.46 318	68.91 263	82.39 112	53.62 244	74.57 238	84.41 219	67.40 126	77.27 217	61.35 188	80.89 328	86.21 87

alignmvs		70.54 202	71.00 201	69.15 245	73.50 272	48.04 287	69.85 244	79.62 176	53.94 238	76.54 190	82.00 278	59.00 235	74.68 261	57.32 237	87.21 206	84.72 134

Fast-Effi-MVS+-dtu		70.00 213	68.74 239	73.77 134	73.47 274	64.53 117	71.36 219	78.14 210	55.81 199	68.84 341	74.71 384	65.36 154	75.75 242	52.00 294	79.00 360	81.03 252

v8		75.07 106	75.64 102	73.35 143	73.42 275	47.46 298	75.20 149	81.45 129	60.05 146	85.64 48	89.26 93	58.08 252	81.80 129	69.71 101	87.97 184	90.79 17

tfpnnormal		66.48 278	67.93 254	62.16 349	73.40 276	36.65 420	63.45 355	64.99 364	55.97 196	72.82 275	87.80 136	57.06 265	69.10 349	48.31 331	87.54 188	80.72 264

IterMVS-SCA-FT		67.68 257	66.07 283	72.49 176	73.34 277	58.20 193	63.80 352	65.55 360	48.10 330	76.91 174	82.64 268	45.20 344	78.84 181	61.20 190	77.89 377	80.44 272

VNet		64.01 309	65.15 297	60.57 369	73.28 278	35.61 432	57.60 409	67.08 348	54.61 216	66.76 363	83.37 247	56.28 272	66.87 377	42.19 380	85.20 238	79.23 292

MGCFI-Net		71.70 178	73.10 152	67.49 279	73.23 279	43.08 355	72.06 199	82.43 111	54.58 217	75.97 202	82.00 278	72.42 67	75.22 250	57.84 232	87.34 195	84.18 158

3Dnovator	65.95 11	71.50 182	71.22 198	72.34 179	73.16 280	63.09 131	78.37 106	78.32 205	57.67 170	72.22 286	84.61 214	54.77 279	78.47 189	60.82 195	81.07 326	75.45 350

GBi-Net		68.30 246	68.79 236	66.81 293	73.14 281	40.68 380	71.96 203	73.03 265	54.81 209	74.72 231	90.36 73	48.63 329	75.20 252	47.12 340	85.37 232	84.54 144

test1		68.30 246	68.79 236	66.81 293	73.14 281	40.68 380	71.96 203	73.03 265	54.81 209	74.72 231	90.36 73	48.63 329	75.20 252	47.12 340	85.37 232	84.54 144

FMVSNet2		67.48 259	68.21 250	65.29 306	73.14 281	38.94 399	68.81 267	71.21 302	54.81 209	76.73 182	86.48 175	48.63 329	74.60 262	47.98 335	86.11 224	82.35 221

thisisatest0530		67.05 272	65.16 295	72.73 171	73.10 284	50.55 251	71.26 223	63.91 374	50.22 296	74.46 241	80.75 301	26.81 457	80.25 160	59.43 213	86.50 219	87.37 58

pm-mvs1		68.40 244	69.85 217	64.04 321	73.10 284	39.94 390	64.61 344	70.50 309	55.52 202	73.97 253	89.33 91	63.91 169	68.38 356	49.68 314	88.02 182	83.81 167

pmmvs4		60.78 351	59.04 361	66.00 302	73.06 286	57.67 195	64.53 345	60.22 391	36.91 440	65.96 368	77.27 361	39.66 388	68.54 355	38.87 404	74.89 400	71.80 391

SDMVSNet		66.36 280	67.85 257	61.88 353	73.04 287	46.14 324	58.54 402	71.36 294	51.42 274	68.93 335	82.72 265	65.62 150	62.22 406	54.41 276	84.67 252	77.28 323

sd_testset		63.55 311	65.38 291	58.07 391	73.04 287	38.83 401	57.41 410	65.44 361	51.42 274	68.93 335	82.72 265	63.76 170	58.11 423	41.05 388	84.67 252	77.28 323

fmvsm_s_conf0.5_n_6		70.08 211	69.97 214	70.39 206	72.99 289	58.93 181	68.84 264	76.40 233	49.08 315	68.75 343	81.65 287	57.34 260	71.97 306	70.91 88	83.81 274	80.26 275

E3new		70.94 196	71.30 196	69.86 231	72.98 290	46.34 322	68.74 272	82.28 113	53.01 251	73.95 254	83.57 242	66.41 141	77.21 218	60.68 196	80.06 346	86.03 92

v2v482		72.55 162	72.58 165	72.43 177	72.92 291	46.72 311	71.41 218	79.13 188	55.27 204	81.17 109	85.25 205	55.41 277	81.13 139	67.25 128	85.46 231	89.43 25

casdiffmvs_mvg		75.26 102	76.18 97	72.52 175	72.87 292	49.47 266	72.94 186	84.71 55	59.49 150	80.90 114	88.81 109	70.07 96	79.71 168	67.40 121	88.39 175	88.40 47
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_l_conf0.5_n_3		71.98 173	71.68 186	72.88 164	72.84 293	64.15 123	73.48 178	77.11 226	48.97 319	71.31 304	84.18 223	67.98 121	71.60 317	68.86 104	80.43 340	82.89 202

fmvsm_s_conf0.5_n_7		67.30 264	66.92 273	68.43 263	72.78 294	58.22 192	60.90 376	72.51 280	49.62 305	63.66 399	80.65 303	58.56 243	68.63 353	62.83 172	80.76 333	78.45 304

MIMVSNet		54.39 395	56.12 386	49.20 441	72.57 295	30.91 459	59.98 387	48.43 459	41.66 398	55.94 446	83.86 238	41.19 377	50.42 445	26.05 475	75.38 397	66.27 440

icg_test_0407_2		63.88 310	65.59 288	58.75 384	72.47 296	48.64 275	53.19 439	72.98 268	45.33 363	68.91 337	79.37 331	61.91 190	51.11 443	55.06 263	81.11 322	76.49 337

IMVS_0407		67.26 265	67.35 263	66.97 292	72.47 296	48.64 275	69.03 261	72.98 268	45.33 363	68.91 337	79.37 331	61.91 190	75.77 241	55.06 263	81.11 322	76.49 337

IMVS_0404		62.18 334	63.05 322	59.58 376	72.47 296	48.64 275	55.47 425	72.98 268	45.33 363	55.80 449	79.37 331	49.84 314	53.60 438	55.06 263	81.11 322	76.49 337

IMVS_0403		67.07 270	67.08 268	67.03 290	72.47 296	48.64 275	68.44 281	72.98 268	45.33 363	68.63 345	79.37 331	60.38 215	75.97 237	55.06 263	81.11 322	76.49 337

Patchmatch-RL test		59.95 358	59.12 360	62.44 345	72.46 300	54.61 223	59.63 389	47.51 462	41.05 405	74.58 237	74.30 389	31.06 440	65.31 391	51.61 296	79.85 351	67.39 432

CL-MVSNet_self_test		62.44 329	63.40 316	59.55 377	72.34 301	32.38 450	56.39 417	64.84 366	51.21 280	67.46 358	81.01 297	50.75 309	63.51 401	38.47 409	88.12 180	82.75 208

fmvsm_s_conf0.5_n_8		72.87 152	72.85 157	72.93 160	72.25 302	59.01 180	72.35 192	80.13 165	56.32 188	75.74 204	84.12 226	60.14 218	75.05 256	71.71 84	82.90 288	84.75 130

SD_0403		61.63 340	62.83 325	58.03 392	72.21 303	32.43 449	69.33 252	69.00 327	44.54 376	62.01 409	79.42 328	55.27 278	66.88 376	36.07 432	77.63 379	74.78 357

Vis-MVSNet (Re-imp)		62.74 325	63.21 319	61.34 361	72.19 304	31.56 455	67.31 297	53.87 428	53.60 245	69.88 322	83.37 247	40.52 382	70.98 323	41.40 386	86.78 215	81.48 245

thres100view900		61.17 344	61.09 341	61.39 359	72.14 305	35.01 435	65.42 327	56.99 409	55.23 205	70.71 310	79.90 317	32.07 428	72.09 302	35.61 435	81.73 308	77.08 333

fmvsm_s_conf0.5_n_11		71.06 191	70.91 202	71.51 191	72.09 306	59.40 170	73.49 177	79.97 168	50.98 282	68.33 348	81.50 290	61.82 193	72.64 288	69.54 102	80.43 340	82.51 217

ab-mvs		64.11 307	65.13 298	61.05 363	71.99 307	38.03 410	67.59 288	68.79 336	49.08 315	65.32 374	86.26 181	58.02 255	66.85 379	39.33 400	79.79 354	78.27 307

RRT-MVS		70.33 204	70.73 207	69.14 246	71.93 308	45.24 332	75.10 150	75.08 250	60.85 141	78.62 138	87.36 140	49.54 316	78.64 185	60.16 203	77.90 376	83.55 174

thres600view7		61.82 337	61.38 339	63.12 335	71.81 309	34.93 436	64.64 342	56.99 409	54.78 213	70.33 314	79.74 319	32.07 428	72.42 295	38.61 407	83.46 281	82.02 230

fmvsm_s_conf0.5_n_4		70.18 210	69.83 219	71.24 196	71.65 310	58.59 188	69.29 254	71.66 286	48.69 322	71.62 293	82.11 276	59.94 221	70.03 336	74.52 55	78.96 361	85.10 117

QAPM		69.18 230	69.26 228	68.94 252	71.61 311	52.58 238	80.37 82	78.79 196	49.63 303	73.51 260	85.14 206	53.66 287	79.12 176	55.11 262	75.54 394	75.11 355

WB-MVSnew		53.94 401	54.76 398	51.49 427	71.53 312	28.05 470	58.22 405	50.36 448	37.94 433	59.16 429	70.17 424	49.21 321	51.94 441	24.49 483	71.80 428	74.47 363

KinetiMVS		72.61 159	72.54 166	72.82 167	71.47 313	55.27 214	68.54 277	76.50 231	61.70 133	74.95 226	86.08 190	59.17 233	76.95 226	69.96 97	84.45 261	86.24 84

baseline		73.10 139	73.96 130	70.51 205	71.46 314	46.39 321	72.08 198	84.40 67	55.95 197	76.62 185	86.46 176	67.20 127	78.03 204	64.22 154	87.27 200	87.11 66

fmvsm_s_conf0.5_n_3		72.97 148	74.13 126	69.47 237	71.40 315	58.36 189	73.07 183	80.64 152	56.86 180	75.49 210	84.67 211	67.86 123	72.33 299	75.68 45	81.54 318	77.73 320

viewmacassd2359aftdt		71.41 185	72.29 173	68.78 257	71.32 316	44.81 336	70.11 238	81.51 126	52.64 256	74.95 226	86.79 157	66.02 144	74.50 264	62.43 176	84.86 250	87.03 67

casdiffmvs		73.06 142	73.84 131	70.72 201	71.32 316	46.71 312	70.93 227	84.26 73	55.62 200	77.46 162	87.10 143	67.09 129	77.81 207	63.95 158	86.83 214	87.64 55
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_fmvsmvis_n_1920		72.36 165	72.49 167	71.96 185	71.29 318	64.06 124	72.79 187	81.82 121	40.23 414	81.25 108	81.04 296	70.62 90	68.69 351	69.74 100	83.60 280	83.14 192

Anonymous20231206		54.13 396	55.82 388	49.04 444	70.89 319	35.96 428	51.73 448	50.87 446	34.86 449	62.49 407	79.22 337	42.52 367	44.29 474	27.95 471	81.88 302	66.88 436

fmvsm_s_conf0.1_n_a		67.37 263	66.36 279	70.37 208	70.86 320	61.17 148	74.00 173	57.18 408	40.77 409	68.83 342	80.88 298	63.11 174	67.61 366	66.94 129	74.72 401	82.33 224

viewdifsd2359ckpt13		69.89 216	69.74 220	70.32 211	70.82 321	48.73 271	72.39 191	81.39 131	48.20 327	72.73 276	82.73 264	62.61 178	76.50 233	55.87 253	80.93 327	85.73 102

tfpn200view9		60.35 355	59.97 354	61.51 356	70.78 322	35.35 433	63.27 358	57.47 402	53.00 252	68.31 349	77.09 363	32.45 425	72.09 302	35.61 435	81.73 308	77.08 333

thres400		60.77 352	59.97 354	63.15 334	70.78 322	35.35 433	63.27 358	57.47 402	53.00 252	68.31 349	77.09 363	32.45 425	72.09 302	35.61 435	81.73 308	82.02 230

fmvsm_s_conf0.5_n_10		72.30 167	72.02 179	73.15 150	70.76 324	59.05 178	73.40 180	79.63 175	48.80 321	75.39 216	84.03 230	59.60 228	75.18 255	72.85 73	83.68 279	85.21 114

AstraMVS		67.11 268	66.84 276	67.92 270	70.75 325	51.36 244	64.77 339	67.06 349	49.03 317	75.40 213	82.05 277	51.26 305	70.65 325	58.89 219	82.32 296	81.77 240

MSDG		67.47 261	67.48 262	67.46 280	70.70 326	54.69 222	66.90 305	78.17 208	60.88 140	70.41 312	74.76 382	61.22 204	73.18 281	47.38 339	76.87 384	74.49 362

testing91		55.74 385	55.29 394	57.08 397	70.63 327	30.85 460	54.94 431	56.31 419	50.34 293	57.08 437	70.10 426	24.50 468	65.86 386	36.98 423	76.75 385	74.53 361

test_yl		65.11 291	65.09 300	65.18 308	70.59 328	40.86 375	63.22 360	72.79 272	57.91 166	68.88 339	79.07 342	42.85 364	74.89 258	45.50 358	84.97 240	79.81 280

DCV-MVSNet		65.11 291	65.09 300	65.18 308	70.59 328	40.86 375	63.22 360	72.79 272	57.91 166	68.88 339	79.07 342	42.85 364	74.89 258	45.50 358	84.97 240	79.81 280

test_fmvsm_n_1920		69.63 219	68.45 243	73.16 148	70.56 330	65.86 104	70.26 236	78.35 204	37.69 434	74.29 244	78.89 344	61.10 206	68.10 360	65.87 137	79.07 359	85.53 106

OpenMVS	62.51 15	68.76 238	68.75 238	68.78 257	70.56 330	53.91 228	78.29 107	77.35 220	48.85 320	70.22 315	83.52 243	52.65 295	76.93 227	55.31 260	81.99 300	75.49 349

viewdifsd2359ckpt07		70.24 206	71.30 196	67.05 289	70.55 332	43.90 345	67.15 299	77.48 219	53.60 245	75.49 210	85.35 200	71.42 81	72.13 301	59.03 216	81.60 316	85.12 116

DELS-MVS		68.83 236	68.31 245	70.38 207	70.55 332	48.31 280	63.78 353	82.13 116	54.00 235	68.96 332	75.17 380	58.95 236	80.06 165	58.55 222	82.74 292	82.76 207
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
FE-MVSNET		62.77 323	64.36 304	57.97 394	70.52 334	33.96 442	61.66 368	67.88 345	50.67 288	73.18 268	82.58 269	48.03 333	68.22 358	43.21 370	81.55 317	71.74 392

testing222		53.37 403	52.50 413	55.98 405	70.51 335	29.68 465	56.20 420	51.85 441	46.19 351	56.76 441	68.94 437	19.18 489	65.39 390	25.87 478	76.98 383	72.87 378

fmvsm_l_conf0.5_n_9		70.73 199	71.08 199	69.67 234	70.44 336	58.80 183	70.21 237	75.11 249	48.15 329	73.50 261	82.69 267	65.69 149	68.05 362	70.87 89	83.02 286	82.16 226

testing11		53.13 405	52.26 415	55.75 406	70.44 336	31.73 454	54.75 432	52.40 439	44.81 374	52.36 464	68.40 444	21.83 478	65.74 389	32.64 451	72.73 419	69.78 412

LCM-MVSNet-Re		69.10 232	71.57 192	61.70 354	70.37 338	34.30 441	61.45 370	79.62 176	56.81 181	89.59 8	88.16 130	68.44 112	72.94 284	42.30 378	87.33 196	77.85 317

UBG		49.18 434	49.35 435	48.66 446	70.36 339	26.56 478	50.53 454	45.61 468	37.43 436	53.37 460	65.97 458	23.03 474	54.20 436	26.29 473	71.54 429	65.20 447

patch_mono-2		62.73 326	64.08 308	58.68 386	70.36 339	55.87 207	60.84 377	64.11 373	41.23 402	64.04 390	78.22 351	60.00 219	48.80 451	54.17 280	83.71 277	71.37 396

ETVMVS		50.32 427	49.87 434	51.68 425	70.30 341	26.66 476	52.33 447	43.93 473	43.54 386	54.91 453	67.95 446	20.01 486	60.17 412	22.47 488	73.40 414	68.22 426

SCA		58.57 369	58.04 371	60.17 372	70.17 342	41.07 373	65.19 331	53.38 434	43.34 390	61.00 418	73.48 396	45.20 344	69.38 346	40.34 397	70.31 438	70.05 409

WBMVS		53.38 402	54.14 402	51.11 429	70.16 343	26.66 476	50.52 455	51.64 444	39.32 419	63.08 405	77.16 362	23.53 471	55.56 430	31.99 452	79.88 350	71.11 402

ET-MVSNet_ETH3D		63.32 314	60.69 348	71.20 197	70.15 344	55.66 210	65.02 335	64.32 371	43.28 391	68.99 331	72.05 407	25.46 464	78.19 202	54.16 281	82.80 290	79.74 283

testing99		55.16 391	54.56 400	56.98 399	70.13 345	30.58 462	54.55 434	54.11 427	49.53 307	56.76 441	70.14 425	22.76 475	65.79 388	36.99 422	76.04 390	74.57 360

guyue		66.95 274	66.74 277	67.56 278	70.12 346	51.14 246	65.05 334	68.68 337	49.98 301	74.64 235	80.83 299	50.77 308	70.34 332	57.72 233	82.89 289	81.21 246

viewmanbaseed2359cas		70.24 206	70.83 204	68.48 262	69.99 347	44.55 340	69.48 248	81.01 144	50.87 284	73.61 258	84.84 209	64.00 167	74.31 269	60.24 200	83.43 282	86.56 78

APD_test1		75.04 107	75.38 106	74.02 131	69.89 348	70.15 65	76.46 131	79.71 173	65.50 87	82.99 85	88.60 115	66.94 130	72.35 296	59.77 210	88.54 172	79.56 284

PVSNet_BlendedMVS		65.38 289	64.30 305	68.61 260	69.81 349	49.36 267	65.60 325	78.96 190	45.50 357	59.98 422	78.61 346	51.82 299	78.20 200	44.30 362	84.11 270	78.27 307

PVSNet_Blended		62.90 321	61.64 335	66.69 296	69.81 349	49.36 267	61.23 373	78.96 190	42.04 395	59.98 422	68.86 440	51.82 299	78.20 200	44.30 362	77.77 378	72.52 382

OpenMVS_ROB	54.93 17	63.23 317	63.28 317	63.07 336	69.81 349	45.34 331	68.52 278	67.14 347	43.74 383	70.61 311	79.22 337	47.90 335	72.66 287	48.75 324	73.84 413	71.21 400

test_fmvsmconf0.01_n		73.91 120	73.64 136	74.71 117	69.79 352	66.25 99	75.90 144	79.90 169	46.03 353	76.48 193	85.02 207	67.96 122	73.97 273	74.47 57	87.22 205	83.90 165

fmvsm_s_conf0.5_n_a		67.00 273	65.95 287	70.17 219	69.72 353	61.16 149	73.34 181	56.83 411	40.96 406	68.36 347	80.08 315	62.84 175	67.57 367	66.90 131	74.50 405	81.78 239

usedtu_dtu_shiyan1		61.16 345	60.92 343	61.90 350	69.70 354	36.41 424	58.57 400	68.86 332	44.94 372	65.02 377	75.67 372	43.00 361	70.28 333	40.83 391	81.68 312	78.99 295

FE-MVSNET3		61.16 345	60.92 343	61.90 350	69.70 354	36.41 424	58.57 400	68.86 332	44.94 372	65.02 377	75.67 372	43.00 361	70.28 333	40.82 392	81.68 312	78.99 295

FMVSNet3		65.00 294	65.16 295	64.52 316	69.47 356	37.56 416	66.63 308	70.38 310	51.55 272	74.72 231	83.27 252	37.89 400	74.44 266	47.12 340	85.37 232	81.57 244

myMVS_eth3d28		51.35 420	51.99 417	49.44 440	69.21 357	22.51 490	49.82 458	49.11 454	49.00 318	55.03 452	70.31 421	22.73 476	52.88 440	24.33 485	78.39 370	72.92 376

MS-PatchMatch		55.59 387	54.89 397	57.68 395	69.18 358	49.05 270	61.00 375	62.93 380	35.98 445	58.36 432	68.93 438	36.71 406	66.59 383	37.62 417	63.30 467	57.39 474

baseline1		57.82 373	58.36 369	56.19 403	69.17 359	30.76 461	62.94 362	55.21 421	46.04 352	63.83 395	78.47 347	41.20 376	63.68 399	39.44 399	68.99 446	74.13 365

v148		69.38 226	69.39 224	69.36 239	69.14 360	44.56 339	68.83 266	72.70 276	54.79 212	78.59 139	84.12 226	54.69 280	76.74 232	59.40 214	82.20 297	86.79 69

test_fmvsmconf0.1_n		73.26 137	72.82 160	74.56 119	69.10 361	66.18 101	74.65 163	79.34 183	45.58 356	75.54 208	83.91 236	67.19 128	73.88 276	73.26 69	86.86 212	83.63 173

fmvsm_s_conf0.1_n		66.60 276	65.54 289	69.77 232	68.99 362	59.15 175	72.12 197	56.74 413	40.72 411	68.25 351	80.14 314	61.18 205	66.92 373	67.34 126	74.40 406	83.23 190

Syy-MVS		54.13 396	55.45 391	50.18 433	68.77 363	23.59 486	55.02 428	44.55 471	43.80 380	58.05 434	64.07 463	46.22 339	58.83 418	46.16 351	72.36 422	68.12 428

myMVS_eth3d		50.36 426	50.52 430	49.88 434	68.77 363	22.69 488	55.02 428	44.55 471	43.80 380	58.05 434	64.07 463	14.16 500	58.83 418	33.90 446	72.36 422	68.12 428

test_fmvsmconf_n		72.91 150	72.40 171	74.46 120	68.62 365	66.12 102	74.21 171	78.80 195	45.64 355	74.62 236	83.25 254	66.80 136	73.86 277	72.97 72	86.66 218	83.39 183

CANet_DTU		64.04 308	63.83 310	64.66 314	68.39 366	42.97 357	73.45 179	74.50 254	52.05 266	54.78 454	75.44 378	43.99 352	70.42 330	53.49 287	78.41 369	80.59 268

EU-MVSNet		60.82 350	60.80 347	60.86 367	68.37 367	41.16 371	72.27 193	68.27 342	26.96 479	69.08 329	75.71 371	32.09 427	67.44 368	55.59 258	78.90 362	73.97 366

PVSNet	43.83 21	51.56 418	51.17 422	52.73 420	68.34 368	38.27 405	48.22 462	53.56 432	36.41 442	54.29 457	64.94 462	34.60 413	54.20 436	30.34 459	69.87 441	65.71 443

EPNet		69.10 232	67.32 264	74.46 120	68.33 369	61.27 147	77.56 115	63.57 376	60.95 139	56.62 443	82.75 263	51.53 302	81.24 137	54.36 278	90.20 133	80.88 258
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.1_n_2		69.14 231	68.42 244	71.28 194	68.30 370	57.60 197	65.06 333	69.91 313	48.24 325	74.56 239	82.84 262	55.55 276	69.73 339	70.66 92	80.69 335	86.52 79

fmvsm_s_conf0.5_n		66.34 282	65.27 292	69.57 236	68.20 371	59.14 177	71.66 214	56.48 414	40.92 407	67.78 353	79.46 326	61.23 202	66.90 374	67.39 122	74.32 409	82.66 213

IB-MVS	49.67 18	59.69 360	56.96 379	67.90 271	68.19 372	50.30 255	61.42 371	65.18 363	47.57 337	55.83 447	67.15 456	23.77 470	79.60 170	43.56 368	79.97 348	73.79 369
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
viewdifsd2359ckpt11		69.22 227	69.68 221	67.83 274	68.17 373	46.57 315	66.42 312	68.93 328	50.60 290	77.47 161	83.95 234	68.16 115	73.84 278	58.49 223	84.92 245	83.10 193

viewmsd2359difaftdt		69.22 227	69.68 221	67.83 274	68.17 373	46.57 315	66.42 312	68.93 328	50.60 290	77.48 160	83.94 235	68.16 115	73.84 278	58.49 223	84.92 245	83.10 193

MVS		60.62 353	59.97 354	62.58 344	68.13 375	47.28 301	68.59 274	73.96 257	32.19 463	59.94 424	68.86 440	50.48 310	77.64 211	41.85 383	75.74 391	62.83 457

blended_shiyan8		62.19 333	61.77 331	63.46 330	68.01 376	40.65 383	60.47 381	69.13 325	47.24 342	66.44 365	70.55 417	43.75 355	71.91 309	43.18 371	87.19 207	77.81 319

eth_miper_zixun_eth		69.42 224	68.73 240	71.50 192	67.99 377	46.42 319	67.58 289	78.81 193	50.72 287	78.13 147	80.34 309	50.15 313	80.34 158	60.18 202	84.65 254	87.74 54

blended_shiyan6		62.20 332	61.77 331	63.47 329	67.98 378	40.64 384	60.46 382	69.15 322	47.24 342	66.43 366	70.57 416	43.73 356	71.93 308	43.16 372	87.24 201	77.85 317

TinyColmap		67.98 252	69.28 227	64.08 319	67.98 378	46.82 309	70.04 239	75.26 246	53.05 250	77.36 163	86.79 157	59.39 230	72.59 292	45.64 356	88.01 183	72.83 379

EPNet_dtu		58.93 366	58.52 365	60.16 373	67.91 380	47.70 294	69.97 241	58.02 400	49.73 302	47.28 480	73.02 401	38.14 396	62.34 404	36.57 426	85.99 225	70.43 407
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 374	57.02 378	59.17 379	67.89 381	34.93 436	58.91 396	57.25 406	50.24 295	64.01 391	71.46 411	32.49 424	71.39 318	31.31 455	79.57 356	71.19 401

fmvsm_s_conf0.5_n_2		68.93 234	68.23 249	71.02 198	67.78 382	57.58 198	64.74 340	69.56 317	48.16 328	74.38 243	82.32 273	56.00 275	69.68 342	70.65 93	80.52 339	85.80 100

SSC-MVS3.2		57.01 377	59.50 358	49.57 439	67.73 383	25.95 482	46.68 468	51.75 443	51.41 276	63.84 394	79.66 322	53.28 290	50.34 446	37.85 414	83.28 284	72.41 384

our_test_3		56.46 380	56.51 382	56.30 402	67.70 384	39.66 394	55.36 427	52.34 440	40.57 413	63.85 393	69.91 429	40.04 385	58.22 422	43.49 369	75.29 399	71.03 404

ppachtmachnet_test		60.26 356	59.61 357	62.20 347	67.70 384	44.33 342	58.18 406	60.96 389	40.75 410	65.80 370	72.57 403	41.23 375	63.92 398	46.87 344	82.42 295	78.33 305

VortexMVS		65.93 284	66.04 285	65.58 305	67.63 386	47.55 296	64.81 337	72.75 275	47.37 340	75.17 222	79.62 324	49.28 320	71.00 322	55.20 261	82.51 294	78.21 309

MVS_Test		69.84 217	70.71 208	67.24 284	67.49 387	43.25 354	69.87 243	81.22 137	52.69 255	71.57 299	86.68 165	62.09 189	74.51 263	66.05 134	78.74 363	83.96 163

fmvsm_l_conf0.5_n		67.48 259	66.88 275	69.28 242	67.41 388	62.04 137	70.69 231	69.85 314	39.46 418	69.59 325	81.09 295	58.15 248	68.73 350	67.51 119	78.16 374	77.07 335

blend_shiyan4		57.39 375	55.27 395	63.73 324	67.25 389	41.75 367	60.08 386	69.15 322	47.57 337	64.19 388	67.14 457	20.46 482	72.34 297	40.73 393	60.88 474	77.11 331

thisisatest0515		60.48 354	57.86 372	68.34 265	67.25 389	46.42 319	60.58 380	62.14 382	40.82 408	63.58 401	69.12 434	26.28 460	78.34 196	48.83 323	82.13 298	80.26 275

V42		71.06 191	70.83 204	71.72 187	67.25 389	47.14 303	65.94 317	80.35 161	51.35 277	83.40 82	83.23 255	59.25 232	78.80 182	65.91 136	80.81 332	89.23 30

fmvsm_l_conf0.5_n_a		66.66 275	65.97 286	68.72 259	67.09 392	61.38 145	70.03 240	69.15 322	38.59 426	68.41 346	80.36 308	56.56 270	68.32 357	66.10 133	77.45 380	76.46 341

GA-MVS		62.91 320	61.66 334	66.66 297	67.09 392	44.49 341	61.18 374	69.36 320	51.33 278	69.33 328	74.47 386	36.83 405	74.94 257	50.60 306	74.72 401	80.57 269

gbinet_0.2-2-1-0.02		62.58 327	61.83 330	64.86 313	67.07 394	41.37 369	61.56 369	67.91 344	49.27 310	66.62 364	67.23 455	41.53 373	74.46 265	45.94 353	89.31 158	78.74 299

testf1		75.66 96	76.57 91	72.95 157	67.07 394	67.62 86	76.10 140	80.68 150	64.95 99	86.58 36	90.94 46	71.20 84	71.68 315	60.46 198	91.13 109	79.56 284

APD_test2		75.66 96	76.57 91	72.95 157	67.07 394	67.62 86	76.10 140	80.68 150	64.95 99	86.58 36	90.94 46	71.20 84	71.68 315	60.46 198	91.13 109	79.56 284

mmtdpeth		68.76 238	70.55 210	63.40 333	67.06 397	56.26 204	68.73 273	71.22 301	55.47 203	70.09 318	88.64 114	65.29 156	56.89 428	58.94 218	89.50 151	77.04 336

HY-MVS	49.31 19	57.96 372	57.59 375	59.10 382	66.85 398	36.17 426	65.13 332	65.39 362	39.24 422	54.69 456	78.14 353	44.28 351	67.18 372	33.75 447	70.79 434	73.95 367

wanda-best-256-512		61.16 345	60.55 349	62.98 337	66.67 399	39.85 392	58.66 398	68.87 330	46.67 347	64.46 381	67.75 447	41.94 369	71.84 310	42.67 375	87.24 201	77.26 326

FE-blended-shiyan7		61.16 345	60.55 349	62.98 337	66.67 399	39.85 392	58.66 398	68.87 330	46.67 347	64.46 381	67.75 447	41.94 369	71.84 310	42.67 375	87.24 201	77.26 326

usedtu_blend_shiyan5		63.30 315	63.13 320	63.78 323	66.67 399	41.75 367	68.57 276	73.64 258	57.20 177	64.46 381	67.75 447	41.94 369	72.34 297	40.72 394	87.24 201	77.26 326

CR-MVSNet		58.96 364	58.49 366	60.36 371	66.37 402	48.24 282	70.93 227	56.40 416	32.87 462	61.35 413	86.66 166	33.19 418	63.22 402	48.50 328	70.17 439	69.62 415

RPMNet		65.77 286	65.08 302	67.84 273	66.37 402	48.24 282	70.93 227	86.27 20	54.66 215	61.35 413	86.77 160	33.29 417	85.67 52	55.93 251	70.17 439	69.62 415

IterMVS		63.12 318	62.48 328	65.02 311	66.34 404	52.86 234	63.81 351	62.25 381	46.57 349	71.51 301	80.40 307	44.60 349	66.82 380	51.38 300	75.47 395	75.38 352
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		69.82 218	69.89 216	69.61 235	66.24 405	43.48 350	68.12 284	79.61 178	51.43 273	77.72 154	80.18 313	54.61 282	78.15 203	63.62 164	87.50 190	87.20 63

tpm2		56.12 382	54.64 399	60.55 370	66.24 405	36.01 427	68.14 283	56.77 412	33.60 460	58.25 433	75.52 377	30.25 446	74.33 268	33.27 448	69.76 443	71.32 397

Anonymous20240521		63.55 311	66.07 283	55.99 404	66.18 407	44.04 344	68.77 270	68.80 335	46.99 344	72.57 279	85.84 196	39.87 386	50.22 447	53.40 290	92.23 88	73.71 370

Patchmtry		60.91 349	63.01 323	54.62 411	66.10 408	26.27 480	67.47 291	56.40 416	54.05 234	72.04 290	86.66 166	33.19 418	60.17 412	43.69 366	87.45 192	77.42 321

FMVSNet5		55.08 392	55.54 390	53.71 414	65.80 409	33.50 446	56.22 419	52.50 438	43.72 384	61.06 416	83.38 246	25.46 464	54.87 433	30.11 461	81.64 315	72.75 380

1314		59.83 359	58.86 363	62.74 343	65.71 410	44.78 337	68.59 274	72.63 277	33.54 461	61.05 417	67.29 454	43.62 357	71.26 319	49.49 317	67.84 453	72.19 388

MonoMVSNet		62.75 324	63.42 315	60.73 368	65.60 411	40.77 378	72.49 190	70.56 308	52.49 257	75.07 223	79.42 328	39.52 390	69.97 338	46.59 347	69.06 445	71.44 395

MDTV_nov1_ep13			54.05 404		65.54 412	29.30 467	59.00 393	55.22 420	35.96 446	52.44 462	75.98 369	30.77 443	59.62 414	38.21 410	73.33 416

baseline2		55.57 388	52.74 409	64.05 320	65.26 413	44.11 343	62.38 364	54.43 425	39.03 423	51.21 467	67.35 453	33.66 416	72.45 294	37.14 420	64.22 465	75.60 348

USDC		62.80 322	63.10 321	61.89 352	65.19 414	43.30 353	67.42 292	74.20 256	35.80 447	72.25 285	84.48 218	45.67 341	71.95 307	37.95 413	84.97 240	70.42 408

tpm		50.60 424	52.42 414	45.14 459	65.18 415	26.29 479	60.30 383	43.50 474	37.41 437	57.01 438	79.09 341	30.20 448	42.32 479	32.77 450	66.36 459	66.81 438

PatchmatchNet		54.60 394	54.27 401	55.59 407	65.17 416	39.08 396	66.92 304	51.80 442	39.89 415	58.39 431	73.12 400	31.69 434	58.33 421	43.01 374	58.38 482	69.38 419
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_ehance_all_eth		68.36 245	68.16 252	68.98 250	65.14 417	43.34 352	67.07 301	78.92 192	49.11 314	76.21 199	77.72 357	53.48 288	77.92 206	61.16 191	84.59 257	85.68 104

cl____		68.26 251	68.26 247	68.29 266	64.98 418	43.67 348	65.89 318	74.67 251	50.04 299	76.86 177	82.42 271	48.74 327	75.38 246	60.92 194	89.81 144	85.80 100

DIV-MVS_self_test		68.27 249	68.26 247	68.29 266	64.98 418	43.67 348	65.89 318	74.67 251	50.04 299	76.86 177	82.43 270	48.74 327	75.38 246	60.94 193	89.81 144	85.81 96

tpm cat1		54.02 399	52.63 411	58.19 390	64.85 420	39.86 391	66.26 315	57.28 405	32.16 464	56.90 439	70.39 420	32.75 422	65.30 392	34.29 443	58.79 479	69.41 418

viewmambaseed2359dif		65.63 287	65.13 298	67.11 288	64.57 421	44.73 338	64.12 348	72.48 281	43.08 392	71.59 294	81.17 293	58.90 238	72.46 293	52.94 291	77.33 381	84.13 161

XXY-MVS		55.19 390	57.40 377	48.56 447	64.45 422	34.84 438	51.54 449	53.59 430	38.99 424	63.79 396	79.43 327	56.59 268	45.57 463	36.92 424	71.29 431	65.25 446

PatchT		53.35 404	56.47 383	43.99 464	64.19 423	17.46 495	59.15 390	43.10 476	52.11 265	54.74 455	86.95 149	29.97 449	49.98 448	43.62 367	74.40 406	64.53 454

D2MVS		62.58 327	61.05 342	67.20 285	63.85 424	47.92 288	56.29 418	69.58 316	39.32 419	70.07 319	78.19 352	34.93 412	72.68 286	53.44 288	83.74 275	81.00 254

mvs_anonymous		65.08 293	65.49 290	63.83 322	63.79 425	37.60 415	66.52 311	69.82 315	43.44 387	73.46 263	86.08 190	58.79 240	71.75 314	51.90 295	75.63 393	82.15 227

diffmvs_AUTHOR		68.27 249	68.59 242	67.32 283	63.76 426	45.37 330	65.31 328	77.19 224	49.25 311	72.68 277	82.19 275	59.62 227	71.17 320	65.75 138	81.53 319	85.42 108

CostFormer		57.35 376	56.14 385	60.97 364	63.76 426	38.43 403	67.50 290	60.22 391	37.14 439	59.12 430	76.34 368	32.78 421	71.99 305	39.12 403	69.27 444	72.47 383

Gipuma		69.55 222	72.83 159	59.70 374	63.63 428	53.97 227	80.08 88	75.93 240	64.24 108	73.49 262	88.93 107	57.89 256	62.46 403	59.75 211	91.55 98	62.67 459
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
cl22		67.14 267	66.51 278	69.03 249	63.20 429	43.46 351	66.88 306	76.25 234	49.22 312	74.48 240	77.88 356	45.49 343	77.40 214	60.64 197	84.59 257	86.24 84

gg-mvs-nofinetune		55.75 384	56.75 381	52.72 421	62.87 430	28.04 471	68.92 262	41.36 486	71.09 49	50.80 469	92.63 14	20.74 480	66.86 378	29.97 462	72.41 421	63.25 456

gm-plane-assit					62.51 431	33.91 444			37.25 438		62.71 468		72.74 285	38.70 405

mvs5depth		66.35 281	67.98 253	61.47 358	62.43 432	51.05 247	69.38 251	69.24 321	56.74 183	73.62 257	89.06 103	46.96 338	58.63 420	55.87 253	88.49 173	74.73 358

MVS-HIRNet		45.53 443	47.29 441	40.24 471	62.29 433	26.82 475	56.02 422	37.41 492	29.74 474	43.69 492	81.27 291	33.96 414	55.48 431	24.46 484	56.79 483	38.43 492

diffmvs		67.42 262	67.50 261	67.20 285	62.26 434	45.21 333	64.87 336	77.04 227	48.21 326	71.74 291	79.70 321	58.40 245	71.17 320	64.99 142	80.27 343	85.22 111
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CHOSEN 280x420		41.62 455	39.89 460	46.80 452	61.81 435	51.59 241	33.56 491	35.74 493	27.48 478	37.64 496	53.53 482	23.24 472	42.09 480	27.39 472	58.64 480	46.72 484

KD-MVS_self_test		66.38 279	67.51 260	62.97 340	61.76 436	34.39 440	58.11 407	75.30 245	50.84 286	77.12 170	85.42 199	56.84 267	69.44 345	51.07 302	91.16 106	85.08 119

MDA-MVSNet-bldmvs		62.34 330	61.73 333	64.16 317	61.64 437	49.90 261	48.11 463	57.24 407	53.31 249	80.95 111	79.39 330	49.00 325	61.55 408	45.92 354	80.05 347	81.03 252

miper_enhance_ethall		65.86 285	65.05 303	68.28 268	61.62 438	42.62 360	64.74 340	77.97 212	42.52 393	73.42 264	72.79 402	49.66 315	77.68 210	58.12 229	84.59 257	84.54 144

WTY-MVS		49.39 433	50.31 432	46.62 453	61.22 439	32.00 453	46.61 469	49.77 450	33.87 457	54.12 458	69.55 432	41.96 368	45.40 466	31.28 456	64.42 464	62.47 461

CMPMVS	48.73 20	61.54 342	60.89 345	63.52 328	61.08 440	51.55 242	68.07 285	68.00 343	33.88 456	65.87 369	81.25 292	37.91 399	67.71 363	49.32 319	82.60 293	71.31 398
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
0.4-1-1-0.1		51.02 422	48.31 437	59.15 380	60.95 441	37.94 412	53.17 444	59.12 398	39.52 417	47.88 478	50.31 488	20.36 484	69.99 337	35.79 434	67.66 455	69.51 417

test-LLR		50.43 425	50.69 429	49.64 437	60.76 442	41.87 364	53.18 440	45.48 469	43.41 388	49.41 474	60.47 476	29.22 452	44.73 471	42.09 381	72.14 425	62.33 463

test-mter		48.56 436	48.20 439	49.64 437	60.76 442	41.87 364	53.18 440	45.48 469	31.91 468	49.41 474	60.47 476	18.34 491	44.73 471	42.09 381	72.14 425	62.33 463

GG-mvs-BLEND				52.24 422	60.64 444	29.21 468	69.73 245	42.41 479		45.47 483	52.33 485	20.43 483	68.16 359	25.52 481	65.42 461	59.36 470

tpmvs		55.84 383	55.45 391	57.01 398	60.33 445	33.20 447	65.89 318	59.29 395	47.52 339	56.04 445	73.60 395	31.05 441	68.06 361	40.64 395	64.64 463	69.77 413

UWE-MVS-28		44.18 450	44.37 455	43.61 465	60.10 446	16.96 496	52.62 445	33.27 495	36.79 441	48.86 476	69.47 433	19.96 487	45.65 462	13.40 495	64.83 462	68.23 425

miper_lstm_enhance		61.97 335	61.63 336	62.98 337	60.04 447	45.74 327	47.53 465	70.95 304	44.04 378	73.06 272	78.84 345	39.72 387	60.33 411	55.82 255	84.64 255	82.88 203

dmvs_re		49.91 430	50.77 428	47.34 449	59.98 448	38.86 400	53.18 440	53.58 431	39.75 416	55.06 451	61.58 472	36.42 407	44.40 473	29.15 469	68.23 449	58.75 471

PVSNet_0	36.71 22	41.12 456	40.78 459	42.14 467	59.97 449	40.13 388	40.97 480	42.24 483	30.81 472	44.86 487	49.41 489	40.70 381	45.12 468	23.15 487	34.96 494	41.16 490

dmvs_testset		45.26 444	47.51 440	38.49 474	59.96 450	14.71 498	58.50 403	43.39 475	41.30 401	51.79 466	56.48 480	39.44 391	49.91 450	21.42 490	55.35 488	50.85 479

new-patchmatchnet		52.89 408	55.76 389	44.26 463	59.94 451	6.31 504	37.36 488	50.76 447	41.10 403	64.28 386	79.82 318	44.77 347	48.43 455	36.24 429	87.61 187	78.03 313

test20.03		55.74 385	57.51 376	50.42 432	59.89 452	32.09 452	50.63 453	49.01 456	50.11 297	65.07 376	83.23 255	45.61 342	48.11 456	30.22 460	83.82 273	71.07 403

MVSTER		63.29 316	61.60 337	68.36 264	59.77 453	46.21 323	60.62 379	71.32 295	41.83 397	75.40 213	79.12 340	30.25 446	75.85 238	56.30 248	79.81 352	83.03 198

reproduce_monomvs		58.94 365	58.14 370	61.35 360	59.70 454	40.98 374	60.24 385	63.51 377	45.85 354	68.95 333	75.31 379	18.27 492	65.82 387	51.47 298	79.97 348	77.26 326

N_pmnet		52.06 414	51.11 423	54.92 408	59.64 455	71.03 56	37.42 487	61.62 388	33.68 458	57.12 436	72.10 404	37.94 398	31.03 493	29.13 470	71.35 430	62.70 458

test_vis1_n_1920		52.96 406	53.50 405	51.32 428	59.15 456	44.90 335	56.13 421	64.29 372	30.56 473	59.87 426	60.68 474	40.16 384	47.47 457	48.25 332	62.46 469	61.58 465

JIA-IIPM		54.03 398	51.62 418	61.25 362	59.14 457	55.21 219	59.10 392	47.72 460	50.85 285	50.31 473	85.81 197	20.10 485	63.97 397	36.16 430	55.41 487	64.55 453

0.3-1-1-0.015		49.68 431	46.67 443	58.69 385	58.94 458	37.51 417	51.35 451	59.18 396	38.35 428	44.62 489	47.14 491	18.49 490	69.68 342	35.13 439	66.84 458	68.87 423

LF4IMVS		67.50 258	67.31 265	68.08 269	58.86 459	61.93 138	71.43 217	75.90 241	44.67 375	72.42 282	80.20 311	57.16 261	70.44 329	58.99 217	86.12 223	71.88 390

UnsupCasMVSNet_bld		50.01 429	51.03 425	46.95 450	58.61 460	32.64 448	48.31 461	53.27 435	34.27 455	60.47 420	71.53 410	41.40 374	47.07 459	30.68 458	60.78 475	61.13 466

dongtai		31.66 461	32.98 464	27.71 478	58.58 461	12.61 500	45.02 473	14.24 504	41.90 396	47.93 477	43.91 493	10.65 503	41.81 483	14.06 494	20.53 497	28.72 494

dp		44.09 451	44.88 452	41.72 470	58.53 462	23.18 487	54.70 433	42.38 481	34.80 451	44.25 490	65.61 460	24.48 469	44.80 470	29.77 463	49.42 490	57.18 475

testgi		54.00 400	56.86 380	45.45 457	58.20 463	25.81 483	49.05 459	49.50 453	45.43 360	67.84 352	81.17 293	51.81 301	43.20 478	29.30 465	79.41 357	67.34 434

wuyk23d		61.97 335	66.25 280	49.12 443	58.19 464	60.77 158	66.32 314	52.97 436	55.93 198	90.62 5	86.91 150	73.07 62	35.98 491	20.63 492	91.63 95	50.62 480

0.4-1-1-0.2		49.48 432	46.57 444	58.21 389	58.02 465	36.93 419	50.24 456	59.18 396	37.97 431	44.94 485	46.16 492	20.52 481	69.54 344	34.84 441	67.28 457	68.17 427

ANet_high		67.08 269	69.94 215	58.51 388	57.55 466	27.09 474	58.43 404	76.80 229	63.56 115	82.40 93	91.93 25	59.82 224	64.98 394	50.10 310	88.86 170	83.46 180

Patchmatch-test		47.93 437	49.96 433	41.84 468	57.42 467	24.26 485	48.75 460	41.49 485	39.30 421	56.79 440	73.48 396	30.48 445	33.87 492	29.29 466	72.61 420	67.39 432

test_vis1_n		51.27 421	50.41 431	53.83 413	56.99 468	50.01 259	56.75 413	60.53 390	25.68 484	59.74 427	57.86 479	29.40 451	47.41 458	43.10 373	63.66 466	64.08 455

new_pmnet		37.55 459	39.80 461	30.79 476	56.83 469	16.46 497	39.35 484	30.65 496	25.59 485	45.26 484	61.60 471	24.54 467	28.02 496	21.60 489	52.80 489	47.90 483

pmmvs3		46.71 440	45.09 450	51.55 426	56.76 470	48.25 281	55.78 424	39.53 490	24.13 489	50.35 472	63.40 465	15.90 497	51.08 444	29.29 466	70.69 436	55.33 477

sss		47.59 439	48.32 436	45.40 458	56.73 471	33.96 442	45.17 472	48.51 458	32.11 467	52.37 463	65.79 459	40.39 383	41.91 482	31.85 453	61.97 471	60.35 467

tpmrst		50.15 428	51.38 421	46.45 454	56.05 472	24.77 484	64.40 347	49.98 449	36.14 444	53.32 461	69.59 431	35.16 411	48.69 452	39.24 401	58.51 481	65.89 441

TESTMET0.1,1		45.17 445	44.93 451	45.89 456	56.02 473	38.31 404	53.18 440	41.94 484	27.85 476	44.86 487	56.47 481	17.93 493	41.50 484	38.08 412	68.06 450	57.85 472

ADS-MVSNet2		48.76 435	47.25 442	53.29 419	55.90 474	40.54 385	47.34 466	54.99 423	31.41 470	50.48 470	72.06 405	31.23 437	54.26 435	25.93 476	55.93 484	65.07 448

ADS-MVSNet		44.62 448	45.58 447	41.73 469	55.90 474	20.83 493	47.34 466	39.94 489	31.41 470	50.48 470	72.06 405	31.23 437	39.31 487	25.93 476	55.93 484	65.07 448

ttmdpeth		56.40 381	55.45 391	59.25 378	55.63 476	40.69 379	58.94 395	49.72 451	36.22 443	65.39 372	86.97 148	23.16 473	56.69 429	42.30 378	80.74 334	80.36 273

test0.0.03 1		47.72 438	48.31 437	45.93 455	55.53 477	29.39 466	46.40 470	41.21 487	43.41 388	55.81 448	67.65 450	29.22 452	43.77 477	25.73 480	69.87 441	64.62 452

UnsupCasMVSNet_eth		52.26 413	53.29 408	49.16 442	55.08 478	33.67 445	50.03 457	58.79 399	37.67 435	63.43 404	74.75 383	41.82 372	45.83 461	38.59 408	59.42 478	67.98 431

pmmvs5		52.49 412	52.58 412	52.21 423	54.99 479	32.38 450	55.45 426	53.84 429	32.15 465	55.49 450	74.81 381	38.08 397	57.37 427	34.02 444	74.40 406	66.88 436

DSMNet-mixed		43.18 454	44.66 453	38.75 473	54.75 480	28.88 469	57.06 412	27.42 498	13.47 496	47.27 481	77.67 358	38.83 393	39.29 488	25.32 482	60.12 477	48.08 482

MDA-MVSNet_test_wron		52.57 411	53.49 407	49.81 436	54.24 481	36.47 422	40.48 482	46.58 466	38.13 429	75.47 212	73.32 398	41.05 380	43.85 476	40.98 389	71.20 432	69.10 422

YYNet1		52.58 410	53.50 405	49.85 435	54.15 482	36.45 423	40.53 481	46.55 467	38.09 430	75.52 209	73.31 399	41.08 379	43.88 475	41.10 387	71.14 433	69.21 420

EPMVS		45.74 442	46.53 445	43.39 466	54.14 483	22.33 491	55.02 428	35.00 494	34.69 453	51.09 468	70.20 423	25.92 462	42.04 481	37.19 419	55.50 486	65.78 442

test_cas_vis1_n_1920		50.90 423	50.92 426	50.83 431	54.12 484	47.80 290	51.44 450	54.61 424	26.95 480	63.95 392	60.85 473	37.86 401	44.97 469	45.53 357	62.97 468	59.72 469

test_fmvs3		56.78 379	55.99 387	59.12 381	53.96 485	48.09 285	58.76 397	66.22 353	27.54 477	76.66 183	68.69 442	25.32 466	51.31 442	53.42 289	73.38 415	77.97 316

test_fmvs1_n		52.70 409	52.01 416	54.76 409	53.83 486	50.36 253	55.80 423	65.90 355	24.96 486	65.39 372	60.64 475	27.69 455	48.46 453	45.88 355	67.99 451	65.46 444

KD-MVS_2432*1600		52.05 415	51.58 419	53.44 417	52.11 487	31.20 456	44.88 474	64.83 367	41.53 399	64.37 384	70.03 427	15.61 498	64.20 395	36.25 427	74.61 403	64.93 450

miper_refine_blended		52.05 415	51.58 419	53.44 417	52.11 487	31.20 456	44.88 474	64.83 367	41.53 399	64.37 384	70.03 427	15.61 498	64.20 395	36.25 427	74.61 403	64.93 450

test_fmvs2		54.80 393	54.11 403	56.88 400	51.76 489	49.95 260	56.70 414	65.80 356	26.22 482	69.42 326	65.25 461	31.82 432	49.98 448	49.63 315	70.36 437	70.71 405

E-PMN		45.17 445	45.36 448	44.60 461	50.07 490	42.75 358	38.66 485	42.29 482	46.39 350	39.55 493	51.15 486	26.00 461	45.37 467	37.68 415	76.41 386	45.69 486

PMMVS		44.69 447	43.95 456	46.92 451	50.05 491	53.47 232	48.08 464	42.40 480	22.36 492	44.01 491	53.05 484	42.60 366	45.49 464	31.69 454	61.36 473	41.79 489

test_fmvs1		51.51 419	50.86 427	53.48 416	49.72 492	49.35 269	54.11 435	64.96 365	24.64 488	63.66 399	59.61 478	28.33 454	48.45 454	45.38 360	67.30 456	62.66 460

EMVS		44.61 449	44.45 454	45.10 460	48.91 493	43.00 356	37.92 486	41.10 488	46.75 346	38.00 495	48.43 490	26.42 459	46.27 460	37.11 421	75.38 397	46.03 485

mvsany_test3		43.76 453	41.01 457	52.01 424	48.09 494	57.74 194	42.47 478	23.85 501	23.30 491	64.80 379	62.17 470	27.12 456	40.59 485	29.17 468	48.11 491	57.69 473

mvsany_test1		37.88 457	35.74 462	44.28 462	47.28 495	49.90 261	36.54 489	24.37 500	19.56 495	45.76 482	53.46 483	32.99 420	37.97 490	26.17 474	35.52 493	44.99 488

test_vis3_rt		51.94 417	51.04 424	54.65 410	46.32 496	50.13 257	44.34 476	78.17 208	23.62 490	68.95 333	62.81 467	21.41 479	38.52 489	41.49 385	72.22 424	75.30 354

test_vis1_rt		46.70 441	45.24 449	51.06 430	44.58 497	51.04 248	39.91 483	67.56 346	21.84 494	51.94 465	50.79 487	33.83 415	39.77 486	35.25 438	61.50 472	62.38 462

MVStest1		55.38 389	54.97 396	56.58 401	43.72 498	40.07 389	59.13 391	47.09 464	34.83 450	76.53 191	84.65 212	13.55 501	53.30 439	55.04 267	80.23 344	76.38 342

MVE	27.91 23	36.69 460	35.64 463	39.84 472	43.37 499	35.85 430	19.49 493	24.61 499	24.68 487	39.05 494	62.63 469	38.67 395	27.10 497	21.04 491	47.25 492	56.56 476
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 458	40.87 458	28.36 477	42.41 500	5.35 505	24.61 492	27.75 497	32.15 465	47.85 479	70.27 422	35.85 409	29.51 495	19.08 493	67.85 452	50.22 481

test_f		43.79 452	45.63 446	38.24 475	42.29 501	38.58 402	34.76 490	47.68 461	22.22 493	67.34 359	63.15 466	31.82 432	30.60 494	39.19 402	62.28 470	45.53 487

kuosan		22.02 462	23.52 466	17.54 480	41.56 502	11.24 501	41.99 479	13.39 505	26.13 483	28.87 497	30.75 495	9.72 504	21.94 499	4.77 499	14.49 498	19.43 495

DeepMVS_CX				11.83 481	15.51 503	13.86 499		11.25 506	5.76 497	20.85 499	26.46 496	17.06 496	9.22 500	9.69 498	13.82 499	12.42 496

test_method		19.26 463	19.12 467	19.71 479	9.09 504	1.91 507	7.79 495	53.44 433	1.42 498	10.27 500	35.80 494	17.42 495	25.11 498	12.44 496	24.38 496	32.10 493

tmp_tt		11.98 465	14.73 468	3.72 482	2.28 505	4.62 506	19.44 494	14.50 503	0.47 500	21.55 498	9.58 498	25.78 463	4.57 501	11.61 497	27.37 495	1.96 497

test123		4.43 468	5.78 471	0.39 484	0.97 506	0.28 508	46.33 471	0.45 507	0.31 501	0.62 502	1.50 501	0.61 506	0.11 503	0.56 500	0.63 500	0.77 499

testmvs		4.06 469	5.28 472	0.41 483	0.64 507	0.16 509	42.54 477	0.31 508	0.26 502	0.50 503	1.40 502	0.77 505	0.17 502	0.56 500	0.55 501	0.90 498

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

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

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

eth-test2					0.00 508

eth-test					0.00 508

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

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

cdsmvs_eth3d_5k		17.71 464	23.62 465	0.00 485	0.00 508	0.00 510	0.00 496	70.17 312	0.00 503	0.00 504	74.25 390	68.16 115	0.00 504	0.00 502	0.00 502	0.00 500

pcd_1.5k_mvsjas		5.20 467	6.93 470	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	0.00 503	62.39 183	0.00 504	0.00 502	0.00 502	0.00 500

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

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

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

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

ab-mvs-re		5.62 466	7.50 469	0.00 485	0.00 508	0.00 510	0.00 496	0.00 509	0.00 503	0.00 504	67.46 451	0.00 507	0.00 504	0.00 502	0.00 502	0.00 500

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

WAC-MVS						22.69 488								36.10 431

PC_three_1452									46.98 345	81.83 98	86.28 179	66.55 140	84.47 77	63.31 169	90.78 123	83.49 176

test_241102_TWO								84.80 49	72.61 35	84.93 62	89.70 86	77.73 25	85.89 44	75.29 47	94.22 56	83.25 188

test_0728_THIRD									74.03 24	85.83 46	90.41 65	75.58 42	85.69 50	77.43 35	94.74 34	84.31 155

GSMVS																70.05 409

sam_mvs1												31.41 435				70.05 409

sam_mvs												31.21 439

MTGPA								80.63 153

test_post1							66.63 308				2.08 499	30.66 444	59.33 416	40.34 397

test_post											1.99 500	30.91 442	54.76 434

patchmatchnet-post											68.99 435	31.32 436	69.38 346

MTMP							84.83 38	19.26 502

test9_res														72.12 83	91.37 101	77.40 322

agg_prior2														70.70 91	90.93 117	78.55 303

test_prior4						70.14 66	77.57 114

test_prior2							75.57 147		58.92 157	76.53 191	86.78 159	67.83 124		69.81 98	92.76 80

旧先验2							71.17 224		45.11 369	78.54 142			61.28 409	59.19 215

新几何2							71.33 220

无先验							74.82 154	70.94 305	47.75 336				76.85 230	54.47 274		72.09 389

原ACMM2							74.78 158

testdata2													67.30 369	48.34 330

segment_acmp												68.30 114

testdata1							68.34 282		57.24 176

plane_prior5								85.49 32					86.15 31	71.09 86	90.94 115	84.82 127

plane_prior4											89.11 100

plane_prior3						65.67 105			63.82 112	78.23 145

plane_prior2							82.74 61		65.45 88

plane_prior						65.18 110	80.06 89		61.88 132						89.91 143

n2								0.00 509

nn								0.00 509

door-mid								55.02 422

test11								82.71 104

door								52.91 437

HQP5-MVS						58.80 183

BP-MVS														67.38 124

HQP4-MVS										71.59 294			85.31 57			83.74 170

HQP3-MVS								84.12 77							89.16 159

HQP2-MVS												58.09 250

MDTV_nov1_ep13_2view						18.41 494	53.74 437		31.57 469	44.89 486		29.90 450		32.93 449		71.48 394

ACMMP++_ref															89.47 153

ACMMP++															91.96 91

Test By Simon												62.56 179

High-resolution multi-view benchmark