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	63.30 131	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

UA-Net		81.56 39	82.28 47	79.40 51	88.91 28	69.16 77	84.67 40	80.01 168	75.34 18	79.80 123	94.91 2	69.79 101	80.25 160	72.63 76	94.46 40	88.78 43

UniMVSNet_ETH3D		76.74 87	79.02 68	69.92 230	89.27 19	43.81 347	74.47 166	71.70 286	72.33 43	85.50 56	93.65 3	77.98 24	76.88 230	54.60 274	91.64 94	89.08 33

tt0320		71.34 188	73.47 141	64.97 313	79.92 148	40.81 378	65.22 331	69.07 327	66.72 78	76.15 202	93.36 4	70.35 91	66.90 375	49.31 321	91.09 112	87.21 62

OurMVSNet-221017-0		78.57 69	78.53 74	78.67 63	80.48 142	64.16 122	80.24 85	82.06 118	61.89 131	88.77 15	93.32 5	57.15 263	82.60 110	70.08 96	92.80 78	89.25 29

tt0320-xc		71.50 183	73.63 138	65.08 311	79.77 150	40.46 387	64.80 339	68.86 333	67.08 73	76.84 180	93.24 6	70.33 92	66.77 382	49.76 313	92.02 90	88.02 52

K. test v3		73.67 124	73.61 139	73.87 133	79.78 149	55.62 213	74.69 162	62.04 387	66.16 84	84.76 67	93.23 7	49.47 318	80.97 146	65.66 139	86.67 218	85.02 122

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
DTE-MVSNet		80.35 55	82.89 41	72.74 170	89.84 7	37.34 419	77.16 122	81.81 123	80.45 3	90.92 3	92.95 9	74.57 53	86.12 32	63.65 163	94.68 36	94.76 6

Anonymous20231211		75.54 98	77.19 88	70.59 204	77.67 189	45.70 330	74.73 160	80.19 163	68.80 62	82.95 86	92.91 10	66.26 143	76.76 232	58.41 227	92.77 79	89.30 26

PEN-MVS		80.46 53	82.91 39	73.11 151	89.83 8	39.02 399	77.06 125	82.61 106	80.04 4	90.60 6	92.85 11	74.93 50	85.21 63	63.15 170	95.15 22	95.09 2

pmmvs6		71.82 177	73.66 136	66.31 300	75.94 224	42.01 364	66.99 303	72.53 279	63.45 118	76.43 196	92.78 12	72.95 66	69.69 342	51.41 300	90.46 129	87.22 61

PS-CasMVS		80.41 54	82.86 42	73.07 152	89.93 6	39.21 396	77.15 123	81.28 135	79.74 5	90.87 4	92.73 13	75.03 49	84.93 68	63.83 161	95.19 20	95.07 3

gg-mvs-nofinetune		55.75 385	56.75 382	52.72 422	62.87 431	28.04 472	68.92 263	41.36 487	71.09 50	50.80 470	92.63 14	20.74 481	66.86 379	29.97 463	72.41 422	63.25 457

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

v7n		79.37 63	80.41 59	76.28 100	78.67 175	55.81 209	79.22 98	82.51 110	70.72 53	87.54 24	92.44 16	68.00 121	81.34 134	72.84 74	91.72 92	91.69 10

PS-MVSNAJss		77.54 78	77.35 87	78.13 76	84.88 78	66.37 98	78.55 104	79.59 180	53.48 248	86.29 40	92.43 17	62.39 184	80.25 160	67.90 116	90.61 127	87.77 54

test_djsdf		78.88 66	78.27 76	80.70 38	81.42 132	71.24 55	83.98 45	75.72 243	52.27 261	87.37 29	92.25 18	68.04 120	80.56 153	72.28 81	91.15 107	90.32 20

SixPastTwentyTwo		75.77 93	76.34 94	74.06 130	81.69 130	54.84 220	76.47 130	75.49 245	64.10 109	87.73 20	92.24 19	50.45 312	81.30 136	67.41 120	91.46 99	86.04 92

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 40	80.47 8	95.20 19	82.10 229

WR-MVS_H		80.22 57	82.17 48	74.39 124	89.46 14	42.69 360	78.24 109	82.24 115	78.21 12	89.57 9	92.10 20	68.05 119	85.59 52	66.04 135	95.62 9	94.88 5

PMVS	70.70 6	81.70 38	83.15 36	77.36 87	90.35 5	82.82 2	82.15 64	79.22 188	74.08 23	87.16 32	91.97 22	84.80 2	76.97 226	64.98 143	93.61 68	72.28 388
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
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	85.56 106

MVSMamba_PlusPlus		76.88 85	78.21 77	72.88 164	80.83 138	48.71 273	83.28 57	82.79 100	72.78 31	79.17 131	91.94 24	56.47 272	83.95 81	70.51 94	86.15 222	85.99 94

ANet_high		67.08 270	69.94 216	58.51 389	57.55 467	27.09 475	58.43 405	76.80 230	63.56 115	82.40 93	91.93 25	59.82 225	64.98 395	50.10 311	88.86 170	83.46 181

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 33	79.90 9	95.21 17	82.72 211

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 33	79.90 9	95.21 17	82.72 211

mvs_tets		78.93 65	78.67 72	79.72 46	84.81 80	73.93 38	80.65 77	76.50 232	51.98 268	87.40 26	91.86 28	76.09 39	78.53 187	68.58 106	90.20 133	86.69 73

sc_t1		72.50 165	74.23 124	67.33 283	80.05 146	46.99 309	66.58 311	69.48 319	66.28 82	77.62 159	91.83 29	70.98 87	68.62 355	53.86 285	91.40 100	86.37 84

test_0402		78.17 75	79.48 66	74.24 126	83.50 100	59.15 175	72.52 190	74.60 254	75.34 18	88.69 16	91.81 30	75.06 48	82.37 116	65.10 141	88.68 171	81.20 248

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 45	86.65 35	91.75 31	78.20 23	87.04 10	77.93 30	94.32 52	83.47 180
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VDDNet		71.60 181	73.13 151	67.02 292	86.29 47	41.11 373	69.97 242	66.50 353	68.72 64	74.74 231	91.70 32	59.90 223	75.81 241	48.58 328	91.72 92	84.15 161

CP-MVSNet		79.48 61	81.65 52	72.98 156	89.66 12	39.06 398	76.76 126	80.46 158	78.91 8	90.32 7	91.70 32	68.49 112	84.89 69	63.40 167	95.12 23	95.01 4

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 41	91.70 32	78.23 22	85.20 64	79.45 16	94.91 29	88.15 51

EGC-MVSNET		64.77 298	61.17 341	75.60 110	86.90 42	74.47 33	84.04 44	68.62 340	0.60 500	1.13 502	91.61 35	65.32 156	74.15 273	64.01 155	88.28 176	78.17 311

jajsoiax		78.51 70	78.16 78	79.59 48	84.65 83	73.83 40	80.42 80	76.12 238	51.33 279	87.19 31	91.51 36	73.79 60	78.44 192	68.27 109	90.13 137	86.49 81

SMA-MVS		82.12 33	82.68 44	80.43 39	88.90 29	69.52 70	85.12 36	84.76 52	63.53 116	84.23 73	91.47 37	72.02 71	87.16 7	79.74 13	94.36 49	84.61 141
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 14	84.11 19	82.68 12	82.97 112	74.39 35	87.18 11	88.18 7	78.98 7	86.11 44	91.47 37	79.70 14	85.76 47	66.91 130	95.46 13	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 45	88.94 27	67.52 88	86.61 22	81.38 133	51.71 270	77.15 170	91.42 39	65.49 153	87.20 6	79.44 17	87.17 210	84.51 150
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 30	83.46 30	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 36	84.67 74	83.30 1	94.96 27	86.17 89
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
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 59	87.75 18	91.13 41	81.83 3	86.20 27	77.13 40	95.96 5	86.08 90

LGP-MVS_train				80.90 35	87.00 39	70.41 63		86.35 17	69.77 59	87.75 18	91.13 41	81.83 3	86.20 27	77.13 40	95.96 5	86.08 90

ACMH+	66.64 10	81.20 43	82.48 45	77.35 88	81.16 137	62.39 136	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 62

ACMMP_NAP		82.33 32	83.28 33	79.46 50	89.28 18	69.09 79	83.62 51	84.98 47	64.77 104	83.97 76	91.02 44	75.53 45	85.93 39	82.00 2	94.36 49	83.35 187

MP-MVS		83.19 22	83.54 28	82.14 19	90.54 4	79.00 8	86.42 25	83.59 85	71.31 47	81.26 107	90.96 45	74.57 53	84.69 73	78.41 25	94.78 32	82.74 210
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	72.95 157	67.07 395	67.62 86	76.10 140	80.68 151	64.95 100	86.58 36	90.94 46	71.20 84	71.68 316	60.46 198	91.13 109	79.56 285

APD_test2		75.66 96	76.57 91	72.95 157	67.07 395	67.62 86	76.10 140	80.68 151	64.95 100	86.58 36	90.94 46	71.20 84	71.68 316	60.46 198	91.13 109	79.56 285

anonymousdsp		78.60 68	77.80 80	81.00 34	78.01 183	74.34 36	80.09 87	76.12 238	50.51 293	89.19 10	90.88 48	71.45 80	77.78 210	73.38 68	90.60 128	90.90 16

PGM-MVS		83.07 25	83.25 35	82.54 15	89.57 13	77.21 23	82.04 66	85.40 36	67.96 68	84.91 65	90.88 48	75.59 42	86.57 15	78.16 27	94.71 35	83.82 167

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 41	85.11 61	90.85 50	76.65 33	84.89 69	79.30 20	94.63 37	82.35 222

MTAPA		83.19 22	83.87 23	81.13 33	91.16 2	78.16 11	84.87 37	80.63 154	72.08 44	84.93 62	90.79 51	74.65 52	84.42 78	80.98 5	94.75 33	80.82 260

MIMVSNet1		66.57 278	69.23 231	58.59 388	81.26 136	37.73 415	64.06 351	57.62 402	57.02 179	78.40 143	90.75 52	62.65 178	58.10 425	41.77 385	89.58 150	79.95 280

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 53	78.11 28	94.46 40	84.89 123

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	84.89 123

region2R		83.54 17	83.86 24	82.58 14	89.82 9	77.53 17	87.06 16	84.23 75	70.19 57	83.86 77	90.72 55	75.20 46	86.27 24	79.41 18	94.25 54	83.95 165

ACMMPR		83.62 15	83.93 21	82.69 11	89.78 10	77.51 21	87.01 17	84.19 76	70.23 55	84.49 70	90.67 56	75.15 47	86.37 19	79.58 14	94.26 53	84.18 159

ACMMP		84.22 9	84.84 12	82.35 17	89.23 21	76.66 25	87.65 7	85.89 26	71.03 51	85.85 46	90.58 57	78.77 18	85.78 46	79.37 19	95.17 21	84.62 140
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 11	84.55 14	82.80 10	89.42 17	79.74 5	88.19 5	84.43 66	71.96 46	84.70 68	90.56 58	77.12 29	86.18 29	79.24 21	95.36 14	82.49 219

Baseline_NR-MVSNet		70.62 202	73.19 149	62.92 343	76.97 201	34.44 440	68.84 265	70.88 307	60.25 145	79.50 127	90.53 59	61.82 194	69.11 349	54.67 273	95.27 15	85.22 112

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 96
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 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 95

Anonymous20240529		72.56 161	73.79 134	68.86 256	76.89 209	45.21 334	68.80 270	77.25 224	67.16 72	76.89 176	90.44 62	65.95 147	74.19 272	50.75 305	90.00 138	87.18 65

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 55	84.47 71	90.43 63	76.79 30	85.94 37	79.58 14	94.23 55	82.82 207

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 54	85.14 60	90.42 64	78.99 17	86.62 14	80.83 6	94.93 28	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 44	83.12 37	75.24 116	86.16 54	60.78 157	83.77 49	80.58 156	72.48 37	85.83 47	90.41 65	78.57 19	85.69 49	75.86 43	94.39 45	79.24 292
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 24	85.83 47	90.41 65	75.58 43	85.69 49	77.43 35	94.74 34	84.31 156

SteuartSystems-ACMMP		83.07 25	83.64 27	81.35 29	85.14 75	71.00 57	85.53 33	84.78 51	70.91 52	85.64 49	90.41 65	75.55 44	87.69 4	79.75 11	95.08 24	85.36 111
Skip Steuart: Steuart Systems R&D Blog.
ZNCC-MVS		83.12 24	83.68 26	81.45 27	89.14 24	73.28 45	86.32 26	85.97 25	67.39 71	84.02 75	90.39 68	74.73 51	86.46 16	80.73 7	94.43 44	84.60 143

XVS		83.51 18	83.73 25	82.85 8	89.43 15	77.61 15	86.80 20	84.66 58	72.71 32	82.87 87	90.39 68	73.86 58	86.31 22	78.84 23	94.03 61	84.64 138

DVP-MVS++		81.24 42	82.74 43	76.76 92	83.14 105	60.90 155	91.64 1	85.49 32	74.03 24	84.93 62	90.38 70	66.82 134	85.90 41	77.43 35	90.78 123	83.49 177

test_one_0601					85.84 66	61.45 145		85.63 30	75.27 20	85.62 52	90.38 70	76.72 32

FC-MVSNet-test		73.32 136	74.78 110	68.93 254	79.21 160	36.57 422	71.82 214	79.54 182	57.63 174	82.57 92	90.38 70	59.38 232	78.99 179	57.91 232	94.56 38	91.23 12

GBi-Net		68.30 247	68.79 237	66.81 294	73.14 282	40.68 381	71.96 204	73.03 266	54.81 210	74.72 232	90.36 73	48.63 330	75.20 253	47.12 341	85.37 233	84.54 146

test1		68.30 247	68.79 237	66.81 294	73.14 282	40.68 381	71.96 204	73.03 266	54.81 210	74.72 232	90.36 73	48.63 330	75.20 253	47.12 341	85.37 233	84.54 146

FMVSNet1		71.06 192	72.48 169	66.81 294	77.65 190	40.68 381	71.96 204	73.03 266	61.14 136	79.45 128	90.36 73	60.44 215	75.20 253	50.20 310	88.05 182	84.54 146

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 42	90.24 76	81.69 5	85.94 37	77.77 31	93.58 69	83.09 196

ACMH	63.62 14	77.50 81	80.11 61	69.68 234	79.61 152	56.28 203	78.81 101	83.62 84	63.41 120	87.14 33	90.23 77	76.11 38	73.32 281	67.58 117	94.44 43	79.44 289
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GST-MVS		82.79 28	83.27 34	81.34 30	88.99 26	73.29 44	85.94 32	85.13 41	68.58 66	84.14 74	90.21 78	73.37 62	86.41 17	79.09 22	93.98 64	84.30 158

3Dnovator+	73.19 2	81.08 46	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	59.80 226	87.58 5	73.06 71	91.34 102	89.01 35

MED-MVS		81.81 36	82.91 39	78.51 67	86.27 48	64.31 119	86.10 28	84.54 62	72.46 39	85.54 53	90.03 80	72.97 65	86.37 19	74.09 60	94.20 58	84.86 126

TestfortrainingZip a		82.48 31	83.93 21	78.11 77	86.27 48	64.11 124	86.10 28	85.02 45	72.46 39	86.32 39	90.03 80	76.75 31	85.37 55	78.23 26	94.22 56	84.86 126

DPE-MVS		82.00 35	83.02 38	78.95 60	85.36 71	67.25 91	82.91 59	84.98 47	73.52 28	85.43 57	90.03 80	76.37 35	86.97 12	74.56 54	94.02 63	82.62 215
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 29	83.38 31	80.40 40	86.50 45	69.44 72	82.30 63	86.08 24	66.80 76	86.70 34	89.99 83	81.64 6	85.95 36	74.35 58	96.11 3	85.81 97
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test0726					86.16 54	60.78 157	83.81 48	85.10 43	72.48 37	85.27 59	89.96 84	78.57 19

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 85	69.14 104	85.26 60	66.15 132	91.24 104	87.61 57

TransMVSNet (Re)		69.62 221	71.63 189	63.57 328	76.51 213	35.93 430	65.75 323	71.29 298	61.05 137	75.02 225	89.90 86	65.88 149	70.41 332	49.79 312	89.48 152	84.38 154

RPSCF		75.76 94	74.37 120	79.93 43	74.81 241	77.53 17	77.53 117	79.30 185	59.44 151	78.88 134	89.80 87	71.26 83	73.09 284	57.45 237	80.89 329	89.17 32

SED-MVS		81.78 37	83.48 29	76.67 93	86.12 56	61.06 151	83.62 51	84.72 54	72.61 35	87.38 27	89.70 88	77.48 27	85.89 43	75.29 47	94.39 45	83.08 197

test_241102_TWO								84.80 50	72.61 35	84.93 62	89.70 88	77.73 25	85.89 43	75.29 47	94.22 56	83.25 189

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 80	85.64 49	89.57 90	69.12 105	80.55 155	72.51 78	93.37 71	83.48 179

test_241102_ONE					86.12 56	61.06 151		84.72 54	72.64 34	87.38 27	89.47 91	77.48 27	85.74 48

FIs		72.56 161	73.80 133	68.84 257	78.74 174	37.74 414	71.02 226	79.83 171	56.12 191	80.88 115	89.45 92	58.18 247	78.28 199	56.63 244	93.36 72	90.51 19

pm-mvs1		68.40 245	69.85 218	64.04 322	73.10 285	39.94 391	64.61 345	70.50 310	55.52 203	73.97 254	89.33 93	63.91 170	68.38 357	49.68 315	88.02 183	83.81 168

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 86	85.54 53	89.28 94	76.32 37	83.47 94	74.03 64	93.57 70	84.35 155
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v8		75.07 106	75.64 102	73.35 143	73.42 276	47.46 299	75.20 150	81.45 130	60.05 146	85.64 49	89.26 95	58.08 253	81.80 129	69.71 101	87.97 185	90.79 17

TranMVSNet+NR-MVSNet		76.13 91	77.66 82	71.56 190	84.61 84	42.57 362	70.98 227	78.29 208	68.67 65	83.04 83	89.26 95	72.99 64	80.75 151	55.58 260	95.47 12	91.35 11

SSC-MVS		61.79 339	66.08 283	48.89 446	76.91 206	10.00 504	53.56 439	47.37 464	68.20 67	76.56 189	89.21 97	54.13 286	57.59 427	54.75 271	74.07 411	79.08 295

nrg030		74.87 113	75.99 99	71.52 191	74.90 237	49.88 266	74.10 173	82.58 107	54.55 220	83.50 81	89.21 97	71.51 78	75.74 244	61.24 189	92.34 86	88.94 38

SF-MVS		80.72 50	81.80 49	77.48 84	82.03 125	64.40 118	83.41 55	88.46 5	65.28 94	84.29 72	89.18 99	73.73 61	83.22 98	76.01 42	93.77 66	84.81 132

v10		75.69 95	76.20 96	74.16 128	74.44 255	48.69 274	75.84 146	82.93 98	59.02 156	85.92 45	89.17 100	58.56 244	82.74 108	70.73 90	89.14 162	91.05 13

ACMM	69.25 9	82.11 34	83.31 32	78.49 68	88.17 36	73.96 37	83.11 58	84.52 64	66.40 81	87.45 25	89.16 101	81.02 8	80.52 156	74.27 59	95.73 7	80.98 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZD-MVS					83.91 94	69.36 74		81.09 142	58.91 158	82.73 91	89.11 102	75.77 41	86.63 13	72.73 75	92.93 77

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 89	78.23 145	89.11 102	60.83 210	86.15 30	71.09 86	90.94 115	84.82 130

plane_prior4											89.11 102

mvs5depth		66.35 282	67.98 254	61.47 359	62.43 433	51.05 247	69.38 252	69.24 322	56.74 184	73.62 258	89.06 105	46.96 339	58.63 421	55.87 254	88.49 173	74.73 359

lessismore_v0				72.75 169	79.60 153	56.83 202		57.37 405		83.80 78	89.01 106	47.45 337	78.74 184	64.39 152	86.49 221	82.69 213

XVG-OURS		79.51 60	79.82 63	78.58 65	86.11 59	74.96 31	76.33 138	84.95 49	66.89 74	82.75 90	88.99 107	66.82 134	78.37 196	74.80 49	90.76 126	82.40 221

APD-MVS		81.13 45	81.73 51	79.36 52	84.47 86	70.53 62	83.85 47	83.70 83	69.43 61	83.67 79	88.96 108	75.89 40	86.41 17	72.62 77	92.95 76	81.14 250
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Gipuma		69.55 223	72.83 160	59.70 375	63.63 429	53.97 227	80.08 88	75.93 241	64.24 108	73.49 263	88.93 109	57.89 257	62.46 404	59.75 212	91.55 98	62.67 460
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 46	74.79 32	77.15 123	85.39 37	66.73 77	80.39 119	88.85 110	74.43 56	78.33 198	74.73 51	85.79 227	82.35 222

casdiffmvs_mvg		75.26 102	76.18 97	72.52 175	72.87 293	49.47 267	72.94 187	84.71 56	59.49 150	80.90 114	88.81 111	70.07 96	79.71 168	67.40 121	88.39 175	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 49	80.10 145	65.47 106	80.94 74	78.74 198	71.22 49	72.40 284	88.70 112	60.51 214	87.70 3	77.40 37	89.13 163	85.48 108

VDD-MVS		70.81 199	71.44 195	68.91 255	79.07 167	46.51 318	67.82 288	70.83 308	61.23 135	74.07 250	88.69 113	59.86 224	75.62 246	51.11 302	90.28 132	84.61 141

test2506		61.23 344	60.85 347	62.38 347	78.80 172	27.88 473	67.33 297	37.42 492	54.23 229	67.55 358	88.68 114	17.87 495	74.39 268	46.33 351	89.41 154	84.86 126

ECVR-MVS		64.82 296	65.22 294	63.60 327	78.80 172	31.14 459	66.97 304	56.47 416	54.23 229	69.94 322	88.68 114	37.23 404	74.81 261	45.28 362	89.41 154	84.86 126

mmtdpeth		68.76 239	70.55 211	63.40 334	67.06 398	56.26 204	68.73 274	71.22 302	55.47 204	70.09 319	88.64 116	65.29 157	56.89 429	58.94 219	89.50 151	77.04 337

APD_test1		75.04 107	75.38 106	74.02 131	69.89 349	70.15 65	76.46 131	79.71 174	65.50 88	82.99 85	88.60 117	66.94 131	72.35 297	59.77 210	88.54 172	79.56 285

CPTT-MVS		81.51 40	81.76 50	80.76 37	89.20 22	78.75 9	86.48 24	82.03 119	68.80 62	80.92 112	88.52 118	72.00 72	82.39 115	74.80 49	93.04 75	81.14 250

test1111		64.62 299	65.19 295	62.93 342	79.01 168	29.91 465	65.45 327	54.41 427	54.09 234	71.47 304	88.48 119	37.02 405	74.29 271	46.83 346	89.94 142	84.58 144

Vis-MVSNet		74.85 114	74.56 113	75.72 107	81.63 131	64.64 116	76.35 136	79.06 190	62.85 125	73.33 266	88.41 120	62.54 182	79.59 171	63.94 160	82.92 288	82.94 201
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 42	89.02 25	78.44 10	83.49 54	80.18 164	64.71 105	78.11 148	88.39 121	65.46 154	83.14 99	77.64 34	91.20 105	78.94 298

MED-MVS test				78.47 70	86.27 48	64.31 119	86.10 28	84.54 62	64.93 103	85.54 53	88.38 122		86.37 19	74.09 60	94.20 58	84.73 134

ME-MVS		81.36 41	82.39 46	78.28 73	84.42 89	64.31 119	82.78 60	85.02 45	71.25 48	84.81 66	88.38 122	76.53 34	85.81 45	74.09 60	94.20 58	84.73 134

MSP-MVS		80.49 52	79.67 65	82.96 5	89.70 11	77.46 22	87.16 12	85.10 43	64.94 102	81.05 110	88.38 122	57.10 265	87.10 8	79.75 11	83.87 273	84.31 156
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		68.71 241	70.37 212	63.72 326	76.13 219	38.06 410	64.10 350	71.48 292	56.60 188	74.10 249	88.31 125	64.78 163	69.72 341	47.69 339	90.15 135	83.37 186

ambc				70.10 225	77.74 187	50.21 256	74.28 171	77.93 215		79.26 129	88.29 126	54.11 287	79.77 167	64.43 151	91.10 111	80.30 275

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 53	81.75 130	73.75 66	93.78 65

AllTest		77.66 77	77.43 83	78.35 71	79.19 162	70.81 58	78.60 103	88.64 3	65.37 92	80.09 121	88.17 128	70.33 92	78.43 193	55.60 257	90.90 119	85.81 97

TestCases				78.35 71	79.19 162	70.81 58		88.64 3	65.37 92	80.09 121	88.17 128	70.33 92	78.43 193	55.60 257	90.90 119	85.81 97

LCM-MVSNet-Re		69.10 233	71.57 193	61.70 355	70.37 339	34.30 442	61.45 371	79.62 177	56.81 182	89.59 8	88.16 130	68.44 113	72.94 285	42.30 379	87.33 197	77.85 318

MG-MVS		70.47 204	71.34 196	67.85 273	79.26 158	40.42 388	74.67 163	75.15 249	58.41 163	68.74 345	88.14 131	56.08 275	83.69 88	59.90 208	81.71 312	79.43 290

IS-MVSNet		75.10 105	75.42 105	74.15 129	79.23 159	48.05 287	79.43 94	78.04 212	70.09 58	79.17 131	88.02 132	53.04 292	83.60 89	58.05 231	93.76 67	90.79 17

Elysia		77.52 79	77.43 83	77.78 80	79.01 168	60.26 163	76.55 128	84.34 68	67.82 69	78.73 136	87.94 133	58.68 242	83.79 84	74.70 52	89.10 165	89.28 27

StellarMVS		77.52 79	77.43 83	77.78 80	79.01 168	60.26 163	76.55 128	84.34 68	67.82 69	78.73 136	87.94 133	58.68 242	83.79 84	74.70 52	89.10 165	89.28 27

tt0805		76.12 92	78.43 75	69.20 244	81.32 134	41.37 370	76.72 127	77.64 217	63.78 113	82.06 95	87.88 135	79.78 11	79.05 177	64.33 153	92.40 84	87.17 66

tfpnnormal		66.48 279	67.93 255	62.16 350	73.40 277	36.65 421	63.45 356	64.99 365	55.97 197	72.82 276	87.80 136	57.06 266	69.10 350	48.31 332	87.54 189	80.72 265

casdiffseed414692147		74.13 118	74.76 111	72.25 183	73.89 269	49.89 265	75.54 148	82.35 113	58.57 162	77.77 153	87.76 137	69.09 106	78.46 190	59.77 210	88.10 181	88.41 47

FE-MVSNET2		68.70 242	69.85 218	65.22 308	74.82 240	37.95 412	67.28 299	73.47 262	53.40 249	77.65 158	87.72 138	59.72 227	73.17 283	46.39 349	88.23 177	84.56 145

BridgeMVS		73.59 126	74.06 128	72.17 185	77.48 192	47.72 294	81.43 71	82.20 116	54.38 224	79.19 130	87.68 139	54.41 284	83.57 90	63.98 157	85.78 228	85.22 112

WB-MVS		60.04 358	64.19 308	47.59 449	76.09 220	10.22 503	52.44 447	46.74 466	65.17 97	74.07 250	87.48 140	53.48 289	55.28 433	49.36 319	72.84 419	77.28 324

RRT-MVS		70.33 205	70.73 208	69.14 247	71.93 309	45.24 333	75.10 151	75.08 251	60.85 141	78.62 138	87.36 141	49.54 317	78.64 185	60.16 203	77.90 377	83.55 175

MGCNet		75.45 99	74.66 112	77.83 79	75.58 229	61.53 144	78.29 107	77.18 226	63.15 124	69.97 321	87.20 142	57.54 260	87.05 9	74.05 63	88.96 168	84.89 123

CDPH-MVS		77.33 82	77.06 90	78.14 75	84.21 91	63.98 126	76.07 142	83.45 86	54.20 231	77.68 157	87.18 143	69.98 97	85.37 55	68.01 113	92.72 81	85.08 120

casdiffmvs		73.06 143	73.84 132	70.72 202	71.32 317	46.71 313	70.93 228	84.26 73	55.62 201	77.46 163	87.10 144	67.09 130	77.81 208	63.95 158	86.83 215	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	72.93 160	83.50 100	45.79 326	69.47 250	80.14 165	65.22 95	81.74 101	87.08 145	61.82 194	81.07 142	56.21 250	94.98 25	91.93 8

NR-MVSNet		73.62 125	74.05 129	72.33 180	83.50 100	43.71 348	65.65 324	77.32 222	64.32 107	75.59 207	87.08 145	62.45 183	81.34 134	54.90 269	95.63 8	91.93 8

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 43	87.07 147	74.02 57	80.97 146	77.70 33	92.32 87	80.62 268
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 85	60.36 162		63.69 376			87.05 148	54.65 282			83.34 284	69.66 415

ttmdpeth		56.40 382	55.45 392	59.25 379	55.63 477	40.69 380	58.94 396	49.72 452	36.22 444	65.39 373	86.97 149	23.16 474	56.69 430	42.30 379	80.74 335	80.36 274

PatchT		53.35 405	56.47 384	43.99 465	64.19 424	17.46 496	59.15 391	43.10 477	52.11 266	54.74 456	86.95 150	29.97 450	49.98 449	43.62 368	74.40 407	64.53 455

wuyk23d		61.97 336	66.25 281	49.12 444	58.19 465	60.77 159	66.32 315	52.97 437	55.93 199	90.62 5	86.91 151	73.07 63	35.98 492	20.63 493	91.63 95	50.62 481

UniMVSNet_NR-MVSNet		74.90 111	75.65 101	72.64 173	83.04 110	45.79 326	69.26 256	78.81 194	66.66 79	81.74 101	86.88 152	63.26 172	81.07 142	56.21 250	94.98 25	91.05 13

E5new		73.42 129	74.46 115	70.29 213	74.61 248	47.14 304	71.85 212	83.01 92	56.07 192	77.28 166	86.81 153	71.54 76	77.15 221	64.59 146	84.39 265	86.59 75

E6new		73.42 129	74.46 115	70.29 213	74.60 250	47.14 304	71.86 210	82.99 94	56.07 192	77.28 166	86.81 153	71.55 74	77.14 223	64.59 146	84.39 265	86.59 75

E6		73.42 129	74.46 115	70.29 213	74.60 250	47.14 304	71.86 210	82.99 94	56.07 192	77.28 166	86.81 153	71.55 74	77.14 223	64.59 146	84.39 265	86.59 75

E5		73.42 129	74.46 115	70.29 213	74.61 248	47.14 304	71.85 212	83.01 92	56.07 192	77.28 166	86.81 153	71.54 76	77.15 221	64.59 146	84.39 265	86.59 75

EPP-MVSNet		73.86 123	73.38 144	75.31 114	78.19 179	53.35 233	80.45 79	77.32 222	65.11 98	76.47 195	86.80 157	49.47 318	83.77 86	53.89 283	92.72 81	88.81 42

viewmacassd2359aftdt		71.41 186	72.29 174	68.78 258	71.32 317	44.81 337	70.11 239	81.51 127	52.64 257	74.95 227	86.79 158	66.02 145	74.50 265	62.43 176	84.86 251	87.03 68

TinyColmap		67.98 253	69.28 228	64.08 320	67.98 379	46.82 310	70.04 240	75.26 247	53.05 251	77.36 164	86.79 158	59.39 231	72.59 293	45.64 357	88.01 184	72.83 380

test_prior2							75.57 147		58.92 157	76.53 192	86.78 160	67.83 125		69.81 98	92.76 80

RPMNet		65.77 287	65.08 303	67.84 274	66.37 403	48.24 283	70.93 228	86.27 20	54.66 216	61.35 414	86.77 161	33.29 418	85.67 51	55.93 252	70.17 440	69.62 416

TEST9					85.47 69	69.32 75	76.42 133	78.69 199	53.73 241	76.97 172	86.74 162	66.84 133	81.10 140

train_agg		76.38 89	76.55 93	75.86 106	85.47 69	69.32 75	76.42 133	78.69 199	54.00 236	76.97 172	86.74 162	66.60 139	81.10 140	72.50 79	91.56 97	77.15 331

balanced_ft_v1		71.65 180	72.22 177	69.92 230	74.26 256	45.74 328	81.54 70	79.66 175	53.65 244	79.77 124	86.74 162	51.20 307	80.64 152	58.70 221	84.47 261	83.40 183

test_8					85.09 76	67.89 84	76.26 139	78.66 201	54.00 236	76.89 176	86.72 165	66.60 139	80.89 150

MVS_Test		69.84 218	70.71 209	67.24 285	67.49 388	43.25 355	69.87 244	81.22 138	52.69 256	71.57 300	86.68 166	62.09 190	74.51 264	66.05 134	78.74 364	83.96 164

CR-MVSNet		58.96 365	58.49 367	60.36 372	66.37 403	48.24 283	70.93 228	56.40 417	32.87 463	61.35 414	86.66 167	33.19 419	63.22 403	48.50 329	70.17 440	69.62 416

Patchmtry		60.91 350	63.01 324	54.62 412	66.10 409	26.27 481	67.47 292	56.40 417	54.05 235	72.04 291	86.66 167	33.19 419	60.17 413	43.69 367	87.45 193	77.42 322

OMC-MVS		79.41 62	78.79 70	81.28 32	80.62 141	70.71 61	80.91 75	84.76 52	62.54 127	81.77 99	86.65 169	71.46 79	83.53 92	67.95 115	92.44 83	89.60 23

VPNet		65.58 289	67.56 260	59.65 376	79.72 151	30.17 464	60.27 385	62.14 383	54.19 232	71.24 306	86.63 170	58.80 240	67.62 366	44.17 366	90.87 122	81.18 249

IterMVS-LS		73.01 145	73.12 152	72.66 172	73.79 271	49.90 261	71.63 216	78.44 204	58.22 164	80.51 117	86.63 170	58.15 249	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.
testdata				64.13 319	85.87 64	63.34 130		61.80 388	47.83 335	76.42 197	86.60 172	48.83 327	62.31 406	54.46 276	81.26 322	66.74 440

LFMVS		67.06 272	67.89 256	64.56 316	78.02 182	38.25 407	70.81 231	59.60 394	65.18 96	71.06 308	86.56 173	43.85 354	75.22 251	46.35 350	89.63 147	80.21 278

CNVR-MVS		78.49 71	78.59 73	78.16 74	85.86 65	67.40 89	78.12 112	81.50 128	63.92 110	77.51 160	86.56 173	68.43 114	84.82 71	73.83 65	91.61 96	82.26 226

E4		72.74 156	73.54 140	70.35 210	74.85 239	46.82 310	69.53 247	82.80 99	55.60 202	76.23 199	86.50 175	69.87 99	77.45 214	63.72 162	82.77 292	86.76 72

FMVSNet2		67.48 260	68.21 251	65.29 307	73.14 282	38.94 400	68.81 268	71.21 303	54.81 210	76.73 183	86.48 176	48.63 330	74.60 263	47.98 336	86.11 225	82.35 222

baseline		73.10 140	73.96 131	70.51 206	71.46 315	46.39 322	72.08 199	84.40 67	55.95 198	76.62 186	86.46 177	67.20 128	78.03 205	64.22 154	87.27 201	87.11 67

WR-MVS		71.20 190	72.48 169	67.36 282	84.98 77	35.70 432	64.43 347	68.66 339	65.05 99	81.49 104	86.43 178	57.57 259	76.48 235	50.36 309	93.32 73	89.90 21

UniMVSNet (Re)		75.00 108	75.48 104	73.56 141	83.14 105	47.92 289	70.41 236	81.04 144	63.67 114	79.54 126	86.37 179	62.83 177	81.82 126	57.10 241	95.25 16	90.94 15

PC_three_1452									46.98 346	81.83 98	86.28 180	66.55 141	84.47 77	63.31 169	90.78 123	83.49 177

DP-MVS		78.44 73	79.29 67	75.90 105	81.86 128	65.33 108	79.05 99	84.63 60	74.83 21	80.41 118	86.27 181	71.68 73	83.45 95	62.45 175	92.40 84	78.92 299

ab-mvs		64.11 308	65.13 299	61.05 364	71.99 308	38.03 411	67.59 289	68.79 337	49.08 316	65.32 375	86.26 182	58.02 256	66.85 380	39.33 401	79.79 355	78.27 308

NCCC		78.25 74	78.04 79	78.89 61	85.61 67	69.45 71	79.80 93	80.99 146	65.77 85	75.55 208	86.25 183	67.42 126	85.42 54	70.10 95	90.88 121	81.81 239

FA-MVS(test-final)		71.27 189	71.06 201	71.92 187	73.96 266	52.32 239	76.45 132	76.12 238	59.07 155	74.04 252	86.18 184	52.18 298	79.43 173	59.75 212	81.76 306	84.03 163

ITE_SJBPF				80.35 41	76.94 202	73.60 41		80.48 157	66.87 75	83.64 80	86.18 184	70.25 95	79.90 166	61.12 192	88.95 169	87.56 58

原ACMM1				73.90 132	85.90 62	65.15 112		81.67 125	50.97 284	74.25 246	86.16 186	61.60 197	83.54 91	56.75 243	91.08 113	73.00 376

fmvsm_s_conf0.5_n_9		74.56 115	74.30 122	75.34 113	77.17 195	64.87 114	72.62 189	76.17 237	54.54 221	78.32 144	86.14 187	65.14 160	75.72 245	73.10 70	85.55 231	85.42 109

UGNet		70.20 210	69.05 233	73.65 135	76.24 217	63.64 127	75.87 145	72.53 279	61.48 134	60.93 420	86.14 187	52.37 297	77.12 225	50.67 306	85.21 238	80.17 279
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 64	83.44 103	66.85 95	83.62 51				86.12 189	66.82 134	86.01 35	61.72 183	89.79 146	83.08 197

新几何1				69.99 227	88.37 34	71.34 54		62.08 385	43.85 380	74.99 226	86.11 190	52.85 293	70.57 328	50.99 304	83.23 286	68.05 431

KinetiMVS		72.61 160	72.54 167	72.82 167	71.47 314	55.27 214	68.54 278	76.50 232	61.70 133	74.95 227	86.08 191	59.17 234	76.95 227	69.96 97	84.45 262	86.24 85

mvs_anonymous		65.08 294	65.49 291	63.83 323	63.79 426	37.60 416	66.52 312	69.82 316	43.44 388	73.46 264	86.08 191	58.79 241	71.75 315	51.90 296	75.63 394	82.15 228

114514_t		73.40 134	73.33 148	73.64 136	84.15 93	57.11 199	78.20 110	80.02 167	43.76 383	72.55 281	86.07 193	64.00 168	83.35 97	60.14 205	91.03 114	80.45 272

NP-MVS					83.34 104	63.07 133					85.97 194

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	71.58 297	85.96 195	58.09 251	85.30 58	67.38 124	89.16 159	83.73 172

Anonymous202405211		66.02 284	66.89 275	63.43 333	74.22 259	38.14 408	59.00 394	66.13 355	63.33 121	69.76 325	85.95 196	51.88 299	70.50 329	44.23 365	87.52 190	81.64 244

Anonymous20240521		63.55 312	66.07 284	55.99 405	66.18 408	44.04 345	68.77 271	68.80 336	46.99 345	72.57 280	85.84 197	39.87 387	50.22 448	53.40 291	92.23 88	73.71 371

JIA-IIPM		54.03 399	51.62 419	61.25 363	59.14 458	55.21 219	59.10 393	47.72 461	50.85 286	50.31 474	85.81 198	20.10 486	63.97 398	36.16 431	55.41 488	64.55 454

test222					87.30 37	69.15 78	67.85 287	59.59 395	41.06 405	73.05 274	85.72 199	48.03 334			80.65 337	66.92 436

KD-MVS_self_test		66.38 280	67.51 261	62.97 341	61.76 437	34.39 441	58.11 408	75.30 246	50.84 287	77.12 171	85.42 200	56.84 268	69.44 346	51.07 303	91.16 106	85.08 120

E2		71.98 174	72.60 164	70.13 223	74.09 263	46.61 314	69.15 259	82.56 108	54.40 222	75.32 219	85.35 201	68.51 110	77.34 216	62.30 177	81.74 308	86.44 82

E3		71.98 174	72.60 164	70.13 223	74.09 263	46.61 314	69.15 259	82.56 108	54.40 222	75.31 220	85.35 201	68.51 110	77.34 216	62.30 177	81.75 307	86.44 82

viewdifsd2359ckpt07		70.24 207	71.30 197	67.05 290	70.55 333	43.90 346	67.15 300	77.48 220	53.60 246	75.49 211	85.35 201	71.42 81	72.13 302	59.03 217	81.60 317	85.12 117

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	56.73 185	74.88 230	85.32 204	65.54 152	87.79 2	65.61 140	91.14 108	83.35 187
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 16	84.39 90	77.04 24	76.35 136	84.05 79	56.66 186	80.27 120	85.31 205	68.56 109	87.03 11	67.39 122	91.26 103	83.50 176

v2v482		72.55 163	72.58 166	72.43 177	72.92 292	46.72 312	71.41 219	79.13 189	55.27 205	81.17 109	85.25 206	55.41 278	81.13 139	67.25 128	85.46 232	89.43 25

QAPM		69.18 231	69.26 229	68.94 253	71.61 312	52.58 238	80.37 82	78.79 197	49.63 304	73.51 261	85.14 207	53.66 288	79.12 176	55.11 263	75.54 395	75.11 356

test_fmvsmconf0.01_n		73.91 121	73.64 137	74.71 117	69.79 353	66.25 99	75.90 144	79.90 170	46.03 354	76.48 194	85.02 208	67.96 123	73.97 274	74.47 57	87.22 206	83.90 166

FE-MVS		68.29 249	66.96 273	72.26 181	74.16 261	54.24 225	77.55 116	73.42 264	57.65 173	72.66 279	84.91 209	32.02 431	81.49 133	48.43 330	81.85 304	81.04 252

viewmanbaseed2359cas		70.24 207	70.83 205	68.48 263	69.99 348	44.55 341	69.48 249	81.01 145	50.87 285	73.61 259	84.84 210	64.00 168	74.31 270	60.24 200	83.43 283	86.56 79

v1144		73.29 137	73.39 143	73.01 154	74.12 262	48.11 285	72.01 202	81.08 143	53.83 240	81.77 99	84.68 211	58.07 254	81.91 125	68.10 110	86.86 213	88.99 37

fmvsm_s_conf0.5_n_3		72.97 149	74.13 127	69.47 238	71.40 316	58.36 189	73.07 184	80.64 153	56.86 181	75.49 211	84.67 212	67.86 124	72.33 300	75.68 45	81.54 319	77.73 321

BP-MVS1		71.60 181	70.06 214	76.20 102	74.07 265	55.22 215	74.29 170	73.44 263	57.29 176	73.87 257	84.65 213	32.57 424	83.49 93	72.43 80	87.94 186	89.89 22

MVStest1		55.38 390	54.97 397	56.58 402	43.72 499	40.07 390	59.13 392	47.09 465	34.83 451	76.53 192	84.65 213	13.55 502	53.30 440	55.04 268	80.23 345	76.38 343

3Dnovator	65.95 11	71.50 183	71.22 199	72.34 179	73.16 281	63.09 132	78.37 106	78.32 206	57.67 171	72.22 287	84.61 215	54.77 280	78.47 189	60.82 195	81.07 327	75.45 351

v1192		73.40 134	73.42 142	73.32 145	74.65 247	48.67 275	72.21 196	81.73 124	52.76 255	81.85 97	84.56 216	57.12 264	82.24 120	68.58 106	87.33 197	89.06 34

mvsmamba		68.87 236	67.30 267	73.57 140	76.58 212	53.70 230	84.43 42	74.25 256	45.38 362	76.63 185	84.55 217	35.85 410	85.27 59	49.54 317	78.49 368	81.75 242

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	74.66 235	84.52 218	69.87 99	84.94 67	69.76 99	89.59 149	86.60 74

USDC		62.80 323	63.10 322	61.89 353	65.19 415	43.30 354	67.42 293	74.20 257	35.80 448	72.25 286	84.48 219	45.67 342	71.95 308	37.95 414	84.97 241	70.42 409

viewcassd2359sk11		71.41 186	71.89 181	69.98 228	73.50 273	46.46 319	68.91 264	82.39 112	53.62 245	74.57 239	84.41 220	67.40 127	77.27 218	61.35 188	80.89 329	86.21 88

tttt0517		69.46 224	67.79 259	74.46 120	75.34 230	52.72 236	75.05 152	63.27 380	54.69 215	78.87 135	84.37 221	26.63 459	81.15 138	63.95 158	87.93 187	89.51 24

PCF-MVS	63.80 13	72.70 158	71.69 186	75.72 107	78.10 180	60.01 166	73.04 185	81.50 128	45.34 363	79.66 125	84.35 222	65.15 158	82.65 109	48.70 326	89.38 157	84.50 151
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v1240		73.06 143	73.14 150	72.84 166	74.74 243	47.27 303	71.88 209	81.11 140	51.80 269	82.28 94	84.21 223	56.22 274	82.34 117	68.82 105	87.17 210	88.91 39

SSM_0407		72.15 171	71.85 183	73.06 153	76.92 203	55.22 215	73.59 177	79.83 171	53.69 242	73.08 270	84.18 224	62.26 187	81.98 123	58.21 228	84.91 248	81.99 233

SSM_0404		72.51 164	72.15 179	73.60 138	78.20 178	55.86 208	74.41 167	79.83 171	53.69 242	73.98 253	84.18 224	62.26 187	82.50 111	58.21 228	84.60 257	82.43 220

fmvsm_l_conf0.5_n_3		71.98 174	71.68 187	72.88 164	72.84 294	64.15 123	73.48 179	77.11 227	48.97 320	71.31 305	84.18 224	67.98 122	71.60 318	68.86 104	80.43 341	82.89 203

fmvsm_s_conf0.5_n_8		72.87 153	72.85 158	72.93 160	72.25 303	59.01 180	72.35 193	80.13 166	56.32 189	75.74 205	84.12 227	60.14 219	75.05 257	71.71 84	82.90 289	84.75 133

v148		69.38 227	69.39 225	69.36 240	69.14 361	44.56 340	68.83 267	72.70 277	54.79 213	78.59 139	84.12 227	54.69 281	76.74 233	59.40 215	82.20 298	86.79 70

v144192		72.99 147	73.06 154	72.77 168	74.58 252	47.48 298	71.90 208	80.44 159	51.57 272	81.46 105	84.11 229	58.04 255	82.12 121	67.98 114	87.47 192	88.70 44

fmvsm_s_conf0.5_n_5		71.46 185	71.62 190	70.99 200	73.89 269	59.95 167	73.02 186	73.08 265	45.15 369	77.30 165	84.06 230	64.73 164	70.08 336	71.20 85	82.10 300	82.92 202

fmvsm_s_conf0.5_n_10		72.30 168	72.02 180	73.15 150	70.76 325	59.05 178	73.40 181	79.63 176	48.80 322	75.39 217	84.03 231	59.60 229	75.18 256	72.85 73	83.68 280	85.21 115

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	75.21 221	84.02 232	52.85 293	81.82 126	61.45 185	95.50 10	86.24 85

SymmetryMVS		74.00 120	72.85 158	77.43 86	85.17 74	70.01 68	79.92 91	68.48 341	58.60 160	75.21 221	84.02 232	52.85 293	81.82 126	61.45 185	89.99 140	80.47 271

F-COLMAP		75.29 101	73.99 130	79.18 54	81.73 129	71.90 49	81.86 68	82.98 96	59.86 149	72.27 285	84.00 234	64.56 165	83.07 102	51.48 298	87.19 208	82.56 217

viewdifsd2359ckpt11		69.22 228	69.68 222	67.83 275	68.17 374	46.57 316	66.42 313	68.93 329	50.60 291	77.47 162	83.95 235	68.16 116	73.84 279	58.49 224	84.92 246	83.10 194

viewmsd2359difaftdt		69.22 228	69.68 222	67.83 275	68.17 374	46.57 316	66.42 313	68.93 329	50.60 291	77.48 161	83.94 236	68.16 116	73.84 279	58.49 224	84.92 246	83.10 194

test_fmvsmconf0.1_n		73.26 138	72.82 161	74.56 119	69.10 362	66.18 101	74.65 164	79.34 184	45.58 357	75.54 209	83.91 237	67.19 129	73.88 277	73.26 69	86.86 213	83.63 174

v1921920		72.96 150	72.98 156	72.89 163	74.67 244	47.58 296	71.92 207	80.69 150	51.70 271	81.69 103	83.89 238	56.58 270	82.25 119	68.34 108	87.36 194	88.82 41

MIMVSNet		54.39 396	56.12 387	49.20 442	72.57 296	30.91 460	59.98 388	48.43 460	41.66 399	55.94 447	83.86 239	41.19 378	50.42 446	26.05 476	75.38 398	66.27 441

GDP-MVS		70.84 198	69.24 230	75.62 109	76.44 214	55.65 211	74.62 165	82.78 102	49.63 304	72.10 289	83.79 240	31.86 432	82.84 106	64.93 144	87.01 212	88.39 49

MCST-MVS		73.42 129	73.34 147	73.63 137	81.28 135	59.17 174	74.80 158	83.13 91	45.50 358	72.84 275	83.78 241	65.15 158	80.99 144	64.54 150	89.09 167	80.73 264

dcpmvs_2		71.02 195	72.65 163	66.16 301	76.06 223	50.49 252	71.97 203	79.36 183	50.34 294	82.81 89	83.63 242	64.38 166	67.27 371	61.54 184	83.71 278	80.71 266

E3new		70.94 197	71.30 197	69.86 232	72.98 291	46.34 323	68.74 273	82.28 114	53.01 252	73.95 255	83.57 243	66.41 142	77.21 219	60.68 196	80.06 347	86.03 93

OpenMVS	62.51 15	68.76 239	68.75 239	68.78 258	70.56 331	53.91 228	78.29 107	77.35 221	48.85 321	70.22 316	83.52 244	52.65 296	76.93 228	55.31 261	81.99 301	75.49 350

h-mvs33		73.08 141	71.61 191	77.48 84	83.89 96	72.89 47	70.47 234	71.12 304	54.28 227	77.89 149	83.41 245	49.04 324	80.98 145	63.62 164	90.77 125	78.58 303

TAPA-MVS	65.27 12	75.16 104	74.29 123	77.77 82	74.86 238	68.08 82	77.89 113	84.04 80	55.15 207	76.19 201	83.39 246	66.91 132	80.11 164	60.04 207	90.14 136	85.13 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet5		55.08 393	55.54 391	53.71 415	65.80 410	33.50 447	56.22 420	52.50 439	43.72 385	61.06 417	83.38 247	25.46 465	54.87 434	30.11 462	81.64 316	72.75 381

VNet		64.01 310	65.15 298	60.57 370	73.28 279	35.61 433	57.60 410	67.08 349	54.61 217	66.76 364	83.37 248	56.28 273	66.87 378	42.19 381	85.20 239	79.23 293

Vis-MVSNet (Re-imp)		62.74 326	63.21 320	61.34 362	72.19 305	31.56 456	67.31 298	53.87 429	53.60 246	69.88 323	83.37 248	40.52 383	70.98 324	41.40 387	86.78 216	81.48 246

GeoE		73.14 139	73.77 135	71.26 196	78.09 181	52.64 237	74.32 168	79.56 181	56.32 189	76.35 198	83.36 250	70.76 89	77.96 206	63.32 168	81.84 305	83.18 192

PAPM_NR		73.91 121	74.16 126	73.16 148	81.90 127	53.50 231	81.28 72	81.40 131	66.17 83	73.30 267	83.31 251	59.96 221	83.10 101	58.45 226	81.66 315	82.87 205

CS-MVS		76.51 88	76.00 98	78.06 78	77.02 199	64.77 115	80.78 76	82.66 105	60.39 144	74.15 247	83.30 252	69.65 102	82.07 122	69.27 103	86.75 217	87.36 60

FMVSNet3		65.00 295	65.16 296	64.52 317	69.47 357	37.56 417	66.63 309	70.38 311	51.55 273	74.72 232	83.27 253	37.89 401	74.44 267	47.12 341	85.37 233	81.57 245

usedtu_dtu_shiyan2		62.25 332	62.27 330	62.18 349	77.08 196	52.84 235	62.56 364	56.33 419	52.43 260	64.22 388	83.26 254	48.47 333	58.06 426	25.75 480	90.34 131	75.64 348

test_fmvsmconf_n		72.91 151	72.40 172	74.46 120	68.62 366	66.12 102	74.21 172	78.80 196	45.64 356	74.62 237	83.25 255	66.80 137	73.86 278	72.97 72	86.66 219	83.39 184

viewdifsd2359ckpt09		72.87 153	72.43 171	74.17 127	74.45 253	51.70 240	76.39 135	84.50 65	49.48 309	75.34 218	83.23 256	63.12 173	82.43 114	56.99 242	88.41 174	88.37 50

V42		71.06 192	70.83 205	71.72 188	67.25 390	47.14 304	65.94 318	80.35 162	51.35 278	83.40 82	83.23 256	59.25 233	78.80 182	65.91 136	80.81 333	89.23 30

test20.03		55.74 386	57.51 377	50.42 433	59.89 453	32.09 453	50.63 454	49.01 457	50.11 298	65.07 377	83.23 256	45.61 343	48.11 457	30.22 461	83.82 274	71.07 404

CNLPA		73.44 128	73.03 155	74.66 118	78.27 177	75.29 29	75.99 143	78.49 203	65.39 91	75.67 206	83.22 259	61.23 203	66.77 382	53.70 286	85.33 236	81.92 237

TestfortrainingZip				73.58 139	79.21 160	57.65 196	86.10 28	81.22 138	72.34 42	72.08 290	83.19 260	58.95 237	83.71 87		84.76 252	79.38 291

mamba_0408		70.32 206	69.35 226	73.24 146	76.92 203	55.22 215	56.61 416	79.27 186	52.14 263	73.08 270	83.14 261	60.53 212	82.50 111	57.51 235	84.91 248	81.99 233

SSM_04072		67.23 267	69.35 226	60.89 367	76.92 203	55.22 215	56.61 416	79.27 186	52.14 263	73.08 270	83.14 261	60.53 212	45.46 466	57.51 235	84.91 248	81.99 233

fmvsm_s_conf0.1_n_2		69.14 232	68.42 245	71.28 195	68.30 371	57.60 197	65.06 334	69.91 314	48.24 326	74.56 240	82.84 263	55.55 277	69.73 340	70.66 92	80.69 336	86.52 80

EPNet		69.10 233	67.32 265	74.46 120	68.33 370	61.27 148	77.56 115	63.57 377	60.95 139	56.62 444	82.75 264	51.53 303	81.24 137	54.36 279	90.20 133	80.88 259
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt13		69.89 217	69.74 221	70.32 212	70.82 322	48.73 272	72.39 192	81.39 132	48.20 328	72.73 277	82.73 265	62.61 179	76.50 234	55.87 254	80.93 328	85.73 103

SDMVSNet		66.36 281	67.85 258	61.88 354	73.04 288	46.14 325	58.54 403	71.36 295	51.42 275	68.93 336	82.72 266	65.62 151	62.22 407	54.41 277	84.67 253	77.28 324

sd_testset		63.55 312	65.38 292	58.07 392	73.04 288	38.83 402	57.41 411	65.44 362	51.42 275	68.93 336	82.72 266	63.76 171	58.11 424	41.05 389	84.67 253	77.28 324

fmvsm_l_conf0.5_n_9		70.73 200	71.08 200	69.67 235	70.44 337	58.80 183	70.21 238	75.11 250	48.15 330	73.50 262	82.69 268	65.69 150	68.05 363	70.87 89	83.02 287	82.16 227

IterMVS-SCA-FT		67.68 258	66.07 284	72.49 176	73.34 278	58.20 193	63.80 353	65.55 361	48.10 331	76.91 175	82.64 269	45.20 345	78.84 181	61.20 190	77.89 378	80.44 273

FE-MVSNET		62.77 324	64.36 305	57.97 395	70.52 335	33.96 443	61.66 369	67.88 346	50.67 289	73.18 269	82.58 270	48.03 334	68.22 359	43.21 371	81.55 318	71.74 393

DIV-MVS_self_test		68.27 250	68.26 248	68.29 267	64.98 419	43.67 349	65.89 319	74.67 252	50.04 300	76.86 178	82.43 271	48.74 328	75.38 247	60.94 193	89.81 144	85.81 97

cl____		68.26 252	68.26 248	68.29 267	64.98 419	43.67 349	65.89 319	74.67 252	50.04 300	76.86 178	82.42 272	48.74 328	75.38 247	60.92 194	89.81 144	85.80 101

MVS_111021_HR		72.98 148	72.97 157	72.99 155	80.82 139	65.47 106	68.81 268	72.77 275	57.67 171	75.76 204	82.38 273	71.01 86	77.17 220	61.38 187	86.15 222	76.32 344

fmvsm_s_conf0.5_n_2		68.93 235	68.23 250	71.02 199	67.78 383	57.58 198	64.74 341	69.56 318	48.16 329	74.38 244	82.32 274	56.00 276	69.68 343	70.65 93	80.52 340	85.80 101

pmmvs-eth3d		64.41 305	63.27 319	67.82 277	75.81 227	60.18 165	69.49 248	62.05 386	38.81 426	74.13 248	82.23 275	43.76 355	68.65 353	42.53 378	80.63 339	74.63 360

diffmvs_AUTHOR		68.27 250	68.59 243	67.32 284	63.76 427	45.37 331	65.31 329	77.19 225	49.25 312	72.68 278	82.19 276	59.62 228	71.17 321	65.75 138	81.53 320	85.42 109

fmvsm_s_conf0.5_n_4		70.18 211	69.83 220	71.24 197	71.65 311	58.59 188	69.29 255	71.66 287	48.69 323	71.62 294	82.11 277	59.94 222	70.03 337	74.52 55	78.96 362	85.10 118

AstraMVS		67.11 269	66.84 277	67.92 271	70.75 326	51.36 244	64.77 340	67.06 350	49.03 318	75.40 214	82.05 278	51.26 306	70.65 326	58.89 220	82.32 297	81.77 241

MGCFI-Net		71.70 179	73.10 153	67.49 280	73.23 280	43.08 356	72.06 200	82.43 111	54.58 218	75.97 203	82.00 279	72.42 67	75.22 251	57.84 233	87.34 196	84.18 159

alignmvs		70.54 203	71.00 202	69.15 246	73.50 273	48.04 288	69.85 245	79.62 177	53.94 239	76.54 191	82.00 279	59.00 236	74.68 262	57.32 238	87.21 207	84.72 136

MSLP-MVS++		74.48 116	75.78 100	70.59 204	84.66 82	62.40 135	78.65 102	84.24 74	60.55 143	77.71 156	81.98 281	63.12 173	77.64 212	62.95 171	88.14 179	71.73 394

DP-MVS Recon		73.57 127	72.69 162	76.23 101	82.85 114	63.39 129	74.32 168	82.96 97	57.75 169	70.35 314	81.98 281	64.34 167	84.41 79	49.69 314	89.95 141	80.89 258

LuminaMVS		71.15 191	70.79 207	72.24 184	77.20 194	58.34 190	72.18 197	76.20 236	54.91 209	77.74 154	81.93 283	49.17 323	76.31 237	62.12 179	85.66 230	82.07 230

BH-RMVSNet		68.69 243	68.20 252	70.14 222	76.40 215	53.90 229	64.62 344	73.48 261	58.01 166	73.91 256	81.78 284	59.09 235	78.22 200	48.59 327	77.96 376	78.31 307

EG-PatchMatch MVS		70.70 201	70.88 204	70.16 221	82.64 118	58.80 183	71.48 217	73.64 259	54.98 208	76.55 190	81.77 285	61.10 207	78.94 180	54.87 270	80.84 332	72.74 382

MVS_111021_LR		72.10 172	71.82 185	72.95 157	79.53 154	73.90 39	70.45 235	66.64 352	56.87 180	76.81 181	81.76 286	68.78 107	71.76 314	61.81 180	83.74 276	73.18 374

AdaColmap		74.22 117	74.56 113	73.20 147	81.95 126	60.97 153	79.43 94	80.90 147	65.57 87	72.54 282	81.76 286	70.98 87	85.26 60	47.88 337	90.00 138	73.37 372

fmvsm_s_conf0.5_n_6		70.08 212	69.97 215	70.39 207	72.99 290	58.93 181	68.84 265	76.40 234	49.08 316	68.75 344	81.65 288	57.34 261	71.97 307	70.91 88	83.81 275	80.26 276

sasdasda		72.29 169	73.38 144	69.04 248	74.23 257	47.37 300	73.93 175	83.18 88	54.36 225	76.61 187	81.64 289	72.03 69	75.34 249	57.12 239	87.28 199	84.40 152

canonicalmvs		72.29 169	73.38 144	69.04 248	74.23 257	47.37 300	73.93 175	83.18 88	54.36 225	76.61 187	81.64 289	72.03 69	75.34 249	57.12 239	87.28 199	84.40 152

fmvsm_s_conf0.5_n_11		71.06 192	70.91 203	71.51 192	72.09 307	59.40 170	73.49 178	79.97 169	50.98 283	68.33 349	81.50 291	61.82 194	72.64 289	69.54 102	80.43 341	82.51 218

MVS-HIRNet		45.53 444	47.29 442	40.24 472	62.29 434	26.82 476	56.02 423	37.41 493	29.74 475	43.69 493	81.27 292	33.96 415	55.48 432	24.46 485	56.79 484	38.43 493

CMPMVS	48.73 20	61.54 343	60.89 346	63.52 329	61.08 441	51.55 242	68.07 286	68.00 344	33.88 457	65.87 370	81.25 293	37.91 400	67.71 364	49.32 320	82.60 294	71.31 399
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
viewmambaseed2359dif		65.63 288	65.13 299	67.11 289	64.57 422	44.73 339	64.12 349	72.48 282	43.08 393	71.59 295	81.17 294	58.90 239	72.46 294	52.94 292	77.33 382	84.13 162

testgi		54.00 401	56.86 381	45.45 458	58.20 464	25.81 484	49.05 460	49.50 454	45.43 361	67.84 353	81.17 294	51.81 302	43.20 479	29.30 466	79.41 358	67.34 435

fmvsm_l_conf0.5_n		67.48 260	66.88 276	69.28 243	67.41 389	62.04 138	70.69 232	69.85 315	39.46 419	69.59 326	81.09 296	58.15 249	68.73 351	67.51 119	78.16 375	77.07 336

test_fmvsmvis_n_1920		72.36 166	72.49 168	71.96 186	71.29 319	64.06 125	72.79 188	81.82 122	40.23 415	81.25 108	81.04 297	70.62 90	68.69 352	69.74 100	83.60 281	83.14 193

CL-MVSNet_self_test		62.44 330	63.40 317	59.55 378	72.34 302	32.38 451	56.39 418	64.84 367	51.21 281	67.46 359	81.01 298	50.75 310	63.51 402	38.47 410	88.12 180	82.75 209

fmvsm_s_conf0.1_n_a		67.37 264	66.36 280	70.37 209	70.86 321	61.17 149	74.00 174	57.18 409	40.77 410	68.83 343	80.88 299	63.11 175	67.61 367	66.94 129	74.72 402	82.33 225

guyue		66.95 275	66.74 278	67.56 279	70.12 347	51.14 246	65.05 335	68.68 338	49.98 302	74.64 236	80.83 300	50.77 309	70.34 333	57.72 234	82.89 290	81.21 247

SPE-MVS-test		74.89 112	74.23 124	76.86 91	77.01 200	62.94 134	78.98 100	84.61 61	58.62 159	70.17 318	80.80 301	66.74 138	81.96 124	61.74 182	89.40 156	85.69 104

thisisatest0530		67.05 273	65.16 296	72.73 171	73.10 285	50.55 251	71.26 224	63.91 375	50.22 297	74.46 242	80.75 302	26.81 458	80.25 160	59.43 214	86.50 220	87.37 59

PHI-MVS		74.92 109	74.36 121	76.61 94	76.40 215	62.32 137	80.38 81	83.15 90	54.16 233	73.23 268	80.75 302	62.19 189	83.86 83	68.02 112	90.92 118	83.65 173

fmvsm_s_conf0.5_n_7		67.30 265	66.92 274	68.43 264	72.78 295	58.22 192	60.90 377	72.51 281	49.62 306	63.66 400	80.65 304	58.56 244	68.63 354	62.83 172	80.76 334	78.45 305

PLC	62.01 16	71.79 178	70.28 213	76.33 99	80.31 144	68.63 80	78.18 111	81.24 136	54.57 219	67.09 363	80.63 305	59.44 230	81.74 131	46.91 344	84.17 270	78.63 301
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PM-MVS		64.49 302	63.61 314	67.14 288	76.68 211	75.15 30	68.49 280	42.85 479	51.17 282	77.85 151	80.51 306	45.76 341	66.31 386	52.83 293	76.35 388	59.96 469

CANet		73.00 146	71.84 184	76.48 97	75.82 226	61.28 147	74.81 156	80.37 161	63.17 122	62.43 409	80.50 307	61.10 207	85.16 66	64.00 156	84.34 269	83.01 200

IterMVS		63.12 319	62.48 329	65.02 312	66.34 405	52.86 234	63.81 352	62.25 382	46.57 350	71.51 302	80.40 308	44.60 350	66.82 381	51.38 301	75.47 396	75.38 353
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_l_conf0.5_n_a		66.66 276	65.97 287	68.72 260	67.09 393	61.38 146	70.03 241	69.15 323	38.59 427	68.41 347	80.36 309	56.56 271	68.32 358	66.10 133	77.45 381	76.46 342

eth_miper_zixun_eth		69.42 225	68.73 241	71.50 193	67.99 378	46.42 320	67.58 290	78.81 194	50.72 288	78.13 147	80.34 310	50.15 314	80.34 158	60.18 202	84.65 255	87.74 55

DPM-MVS		69.98 215	69.22 232	72.26 181	82.69 117	58.82 182	70.53 233	81.23 137	47.79 336	64.16 390	80.21 311	51.32 305	83.12 100	60.14 205	84.95 245	74.83 357

LF4IMVS		67.50 259	67.31 266	68.08 270	58.86 460	61.93 139	71.43 218	75.90 242	44.67 376	72.42 283	80.20 312	57.16 262	70.44 330	58.99 218	86.12 224	71.88 391

CSCG		74.12 119	74.39 119	73.33 144	79.35 156	61.66 143	77.45 118	81.98 120	62.47 129	79.06 133	80.19 313	61.83 193	78.79 183	59.83 209	87.35 195	79.54 288

c3_l		69.82 219	69.89 217	69.61 236	66.24 406	43.48 351	68.12 285	79.61 179	51.43 274	77.72 155	80.18 314	54.61 283	78.15 204	63.62 164	87.50 191	87.20 64

fmvsm_s_conf0.1_n		66.60 277	65.54 290	69.77 233	68.99 363	59.15 175	72.12 198	56.74 414	40.72 412	68.25 352	80.14 315	61.18 206	66.92 374	67.34 126	74.40 407	83.23 191

fmvsm_s_conf0.5_n_a		67.00 274	65.95 288	70.17 220	69.72 354	61.16 150	73.34 182	56.83 412	40.96 407	68.36 348	80.08 316	62.84 176	67.57 368	66.90 131	74.50 406	81.78 240

FPMVS		59.43 363	60.07 354	57.51 397	77.62 191	71.52 52	62.33 366	50.92 446	57.40 175	69.40 328	80.00 317	39.14 393	61.92 408	37.47 419	66.36 460	39.09 492

thres100view900		61.17 345	61.09 342	61.39 360	72.14 306	35.01 436	65.42 328	56.99 410	55.23 206	70.71 311	79.90 318	32.07 429	72.09 303	35.61 436	81.73 309	77.08 334

new-patchmatchnet		52.89 409	55.76 390	44.26 464	59.94 452	6.31 505	37.36 489	50.76 448	41.10 404	64.28 387	79.82 319	44.77 348	48.43 456	36.24 430	87.61 188	78.03 314

thres600view7		61.82 338	61.38 340	63.12 336	71.81 310	34.93 437	64.64 343	56.99 410	54.78 214	70.33 315	79.74 320	32.07 429	72.42 296	38.61 408	83.46 282	82.02 231

testing3-2		56.85 379	57.62 375	54.53 413	75.84 225	22.23 493	51.26 453	49.10 456	61.04 138	63.74 398	79.73 321	22.29 478	59.44 416	31.16 458	84.43 264	81.92 237

diffmvs		67.42 263	67.50 262	67.20 286	62.26 435	45.21 334	64.87 337	77.04 228	48.21 327	71.74 292	79.70 322	58.40 246	71.17 321	64.99 142	80.27 344	85.22 112
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		57.01 378	59.50 359	49.57 440	67.73 384	25.95 483	46.68 469	51.75 444	51.41 277	63.84 395	79.66 323	53.28 291	50.34 447	37.85 415	83.28 285	72.41 385

BH-untuned		69.39 226	69.46 224	69.18 245	77.96 184	56.88 200	68.47 281	77.53 218	56.77 183	77.79 152	79.63 324	60.30 218	80.20 163	46.04 353	80.65 337	70.47 407

VortexMVS		65.93 285	66.04 286	65.58 306	67.63 387	47.55 297	64.81 338	72.75 276	47.37 341	75.17 223	79.62 325	49.28 321	71.00 323	55.20 262	82.51 295	78.21 310

PAPM		61.79 339	60.37 353	66.05 302	76.09 220	41.87 365	69.30 254	76.79 231	40.64 413	53.80 460	79.62 325	44.38 351	82.92 104	29.64 465	73.11 418	73.36 373

fmvsm_s_conf0.5_n		66.34 283	65.27 293	69.57 237	68.20 372	59.14 177	71.66 215	56.48 415	40.92 408	67.78 354	79.46 327	61.23 203	66.90 375	67.39 122	74.32 410	82.66 214

XXY-MVS		55.19 391	57.40 378	48.56 448	64.45 423	34.84 439	51.54 450	53.59 431	38.99 425	63.79 397	79.43 328	56.59 269	45.57 464	36.92 425	71.29 432	65.25 447

SD_0403		61.63 341	62.83 326	58.03 393	72.21 304	32.43 450	69.33 253	69.00 328	44.54 377	62.01 410	79.42 329	55.27 279	66.88 377	36.07 433	77.63 380	74.78 358

MonoMVSNet		62.75 325	63.42 316	60.73 369	65.60 412	40.77 379	72.49 191	70.56 309	52.49 258	75.07 224	79.42 329	39.52 391	69.97 339	46.59 348	69.06 446	71.44 396

MDA-MVSNet-bldmvs		62.34 331	61.73 334	64.16 318	61.64 438	49.90 261	48.11 464	57.24 408	53.31 250	80.95 111	79.39 331	49.00 326	61.55 409	45.92 355	80.05 348	81.03 253

icg_test_0407_2		63.88 311	65.59 289	58.75 385	72.47 297	48.64 276	53.19 440	72.98 269	45.33 364	68.91 338	79.37 332	61.91 191	51.11 444	55.06 264	81.11 323	76.49 338

IMVS_0407		67.26 266	67.35 264	66.97 293	72.47 297	48.64 276	69.03 262	72.98 269	45.33 364	68.91 338	79.37 332	61.91 191	75.77 242	55.06 264	81.11 323	76.49 338

IMVS_0404		62.18 335	63.05 323	59.58 377	72.47 297	48.64 276	55.47 426	72.98 269	45.33 364	55.80 450	79.37 332	49.84 315	53.60 439	55.06 264	81.11 323	76.49 338

IMVS_0403		67.07 271	67.08 269	67.03 291	72.47 297	48.64 276	68.44 282	72.98 269	45.33 364	68.63 346	79.37 332	60.38 216	75.97 238	55.06 264	81.11 323	76.49 338

TAMVS		65.31 291	63.75 312	69.97 229	82.23 123	59.76 169	66.78 308	63.37 379	45.20 368	69.79 324	79.37 332	47.42 338	72.17 301	34.48 443	85.15 240	77.99 316

PAPR		69.20 230	68.66 242	70.82 201	75.15 234	47.77 292	75.31 149	81.11 140	49.62 306	66.33 368	79.27 337	61.53 198	82.96 103	48.12 334	81.50 321	81.74 243

Anonymous20231206		54.13 397	55.82 389	49.04 445	70.89 320	35.96 429	51.73 449	50.87 447	34.86 450	62.49 408	79.22 338	42.52 368	44.29 475	27.95 472	81.88 303	66.88 437

OpenMVS_ROB	54.93 17	63.23 318	63.28 318	63.07 337	69.81 350	45.34 332	68.52 279	67.14 348	43.74 384	70.61 312	79.22 338	47.90 336	72.66 288	48.75 325	73.84 414	71.21 401

PVSNet_Blended_VisFu		70.04 213	68.88 236	73.53 142	82.71 116	63.62 128	74.81 156	81.95 121	48.53 325	67.16 362	79.18 340	51.42 304	78.38 195	54.39 278	79.72 356	78.60 302

MVSTER		63.29 317	61.60 338	68.36 265	59.77 454	46.21 324	60.62 380	71.32 296	41.83 398	75.40 214	79.12 341	30.25 447	75.85 239	56.30 249	79.81 353	83.03 199

tpm		50.60 425	52.42 415	45.14 460	65.18 416	26.29 480	60.30 384	43.50 475	37.41 438	57.01 439	79.09 342	30.20 449	42.32 480	32.77 451	66.36 460	66.81 439

test_yl		65.11 292	65.09 301	65.18 309	70.59 329	40.86 376	63.22 361	72.79 273	57.91 167	68.88 340	79.07 343	42.85 365	74.89 259	45.50 359	84.97 241	79.81 281

DCV-MVSNet		65.11 292	65.09 301	65.18 309	70.59 329	40.86 376	63.22 361	72.79 273	57.91 167	68.88 340	79.07 343	42.85 365	74.89 259	45.50 359	84.97 241	79.81 281

test_fmvsm_n_1920		69.63 220	68.45 244	73.16 148	70.56 331	65.86 104	70.26 237	78.35 205	37.69 435	74.29 245	78.89 345	61.10 207	68.10 361	65.87 137	79.07 360	85.53 107

miper_lstm_enhance		61.97 336	61.63 337	62.98 338	60.04 448	45.74 328	47.53 466	70.95 305	44.04 379	73.06 273	78.84 346	39.72 388	60.33 412	55.82 256	84.64 256	82.88 204

PVSNet_BlendedMVS		65.38 290	64.30 306	68.61 261	69.81 350	49.36 268	65.60 326	78.96 191	45.50 358	59.98 423	78.61 347	51.82 300	78.20 201	44.30 363	84.11 271	78.27 308

baseline1		57.82 374	58.36 370	56.19 404	69.17 360	30.76 462	62.94 363	55.21 422	46.04 353	63.83 396	78.47 348	41.20 377	63.68 400	39.44 400	68.99 447	74.13 366

TSAR-MVS + GP.		73.08 141	71.60 192	77.54 83	78.99 171	70.73 60	74.96 153	69.38 320	60.73 142	74.39 243	78.44 349	57.72 258	82.78 107	60.16 203	89.60 148	79.11 294

MVP-Stereo		61.56 342	59.22 360	68.58 262	79.28 157	60.44 161	69.20 257	71.57 289	43.58 386	56.42 445	78.37 350	39.57 390	76.46 236	34.86 441	60.16 477	68.86 425
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
hse-mvs2		72.32 167	70.66 210	77.31 89	83.10 109	71.77 50	69.19 258	71.45 293	54.28 227	77.89 149	78.26 351	49.04 324	79.23 174	63.62 164	89.13 163	80.92 257

patch_mono-2		62.73 327	64.08 309	58.68 387	70.36 340	55.87 207	60.84 378	64.11 374	41.23 403	64.04 391	78.22 352	60.00 220	48.80 452	54.17 281	83.71 278	71.37 397

D2MVS		62.58 328	61.05 343	67.20 286	63.85 425	47.92 289	56.29 419	69.58 317	39.32 420	70.07 320	78.19 353	34.93 413	72.68 287	53.44 289	83.74 276	81.00 255

HY-MVS	49.31 19	57.96 373	57.59 376	59.10 383	66.85 399	36.17 427	65.13 333	65.39 363	39.24 423	54.69 457	78.14 354	44.28 352	67.18 373	33.75 448	70.79 435	73.95 368

Effi-MVS+-dtu		75.43 100	72.28 175	84.91 2	77.05 197	83.58 1	78.47 105	77.70 216	57.68 170	74.89 229	78.13 355	64.80 162	84.26 80	56.46 248	85.32 237	86.88 69

AUN-MVS		70.22 209	67.88 257	77.22 90	82.96 113	71.61 51	69.08 261	71.39 294	49.17 314	71.70 293	78.07 356	37.62 403	79.21 175	61.81 180	89.15 161	80.82 260

cl22		67.14 268	66.51 279	69.03 250	63.20 430	43.46 352	66.88 307	76.25 235	49.22 313	74.48 241	77.88 357	45.49 344	77.40 215	60.64 197	84.59 258	86.24 85

miper_ehance_all_eth		68.36 246	68.16 253	68.98 251	65.14 418	43.34 353	67.07 302	78.92 193	49.11 315	76.21 200	77.72 358	53.48 289	77.92 207	61.16 191	84.59 258	85.68 105

DSMNet-mixed		43.18 455	44.66 454	38.75 474	54.75 481	28.88 470	57.06 413	27.42 499	13.47 497	47.27 482	77.67 359	38.83 394	39.29 489	25.32 483	60.12 478	48.08 483

Test_1112_low_res		58.78 368	58.69 365	59.04 384	79.41 155	38.13 409	57.62 409	66.98 351	34.74 453	59.62 429	77.56 360	42.92 364	63.65 401	38.66 407	70.73 436	75.35 354

API-MVS		70.97 196	71.51 194	69.37 239	75.20 232	55.94 206	80.99 73	76.84 229	62.48 128	71.24 306	77.51 361	61.51 199	80.96 149	52.04 294	85.76 229	71.22 400

pmmvs4		60.78 352	59.04 362	66.00 303	73.06 287	57.67 195	64.53 346	60.22 392	36.91 441	65.96 369	77.27 362	39.66 389	68.54 356	38.87 405	74.89 401	71.80 392

WBMVS		53.38 403	54.14 403	51.11 430	70.16 344	26.66 477	50.52 456	51.64 445	39.32 420	63.08 406	77.16 363	23.53 472	55.56 431	31.99 453	79.88 351	71.11 403

tfpn200view9		60.35 356	59.97 355	61.51 357	70.78 323	35.35 434	63.27 359	57.47 403	53.00 253	68.31 350	77.09 364	32.45 426	72.09 303	35.61 436	81.73 309	77.08 334

thres400		60.77 353	59.97 355	63.15 335	70.78 323	35.35 434	63.27 359	57.47 403	53.00 253	68.31 350	77.09 364	32.45 426	72.09 303	35.61 436	81.73 309	82.02 231

Effi-MVS+		72.10 172	72.28 175	71.58 189	74.21 260	50.33 254	74.72 161	82.73 103	62.62 126	70.77 310	76.83 366	69.96 98	80.97 146	60.20 201	78.43 369	83.45 182

MVSFormer		69.93 216	69.03 234	72.63 174	74.93 235	59.19 172	83.98 45	75.72 243	52.27 261	63.53 403	76.74 367	43.19 360	80.56 153	72.28 81	78.67 366	78.14 312

jason		64.47 303	62.84 325	69.34 242	76.91 206	59.20 171	67.15 300	65.67 358	35.29 449	65.16 376	76.74 367	44.67 349	70.68 325	54.74 272	79.28 359	78.14 312
jason: jason.
CostFormer		57.35 377	56.14 386	60.97 365	63.76 427	38.43 404	67.50 291	60.22 392	37.14 440	59.12 431	76.34 369	32.78 422	71.99 306	39.12 404	69.27 445	72.47 384

MDTV_nov1_ep13			54.05 405		65.54 413	29.30 468	59.00 394	55.22 421	35.96 447	52.44 463	75.98 370	30.77 444	59.62 415	38.21 411	73.33 417

testing3		58.28 371	58.38 369	58.00 394	77.45 193	26.12 482	60.78 379	43.00 478	56.02 196	70.18 317	75.76 371	13.27 503	67.24 372	48.02 335	80.89 329	80.65 267

EU-MVSNet		60.82 351	60.80 348	60.86 368	68.37 368	41.16 372	72.27 194	68.27 343	26.96 480	69.08 330	75.71 372	32.09 428	67.44 369	55.59 259	78.90 363	73.97 367

usedtu_dtu_shiyan1		61.16 346	60.92 344	61.90 351	69.70 355	36.41 425	58.57 401	68.86 333	44.94 373	65.02 378	75.67 373	43.00 362	70.28 334	40.83 392	81.68 313	78.99 296

FE-MVSNET3		61.16 346	60.92 344	61.90 351	69.70 355	36.41 425	58.57 401	68.86 333	44.94 373	65.02 378	75.67 373	43.00 362	70.28 334	40.82 393	81.68 313	78.99 296

HyFIR lowres test		63.01 320	60.47 352	70.61 203	83.04 110	54.10 226	59.93 389	72.24 285	33.67 460	69.00 331	75.63 375	38.69 395	76.93 228	36.60 426	75.45 397	80.81 262

Fast-Effi-MVS+		68.81 238	68.30 247	70.35 210	74.66 246	48.61 280	66.06 317	78.32 206	50.62 290	71.48 303	75.54 376	68.75 108	79.59 171	50.55 308	78.73 365	82.86 206

CDS-MVSNet		64.33 306	62.66 328	69.35 241	80.44 143	58.28 191	65.26 330	65.66 359	44.36 378	67.30 361	75.54 376	43.27 359	71.77 313	37.68 416	84.44 263	78.01 315
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm2		56.12 383	54.64 400	60.55 371	66.24 406	36.01 428	68.14 284	56.77 413	33.60 461	58.25 434	75.52 378	30.25 447	74.33 269	33.27 449	69.76 444	71.32 398

CANet_DTU		64.04 309	63.83 311	64.66 315	68.39 367	42.97 358	73.45 180	74.50 255	52.05 267	54.78 455	75.44 379	43.99 353	70.42 331	53.49 288	78.41 370	80.59 269

reproduce_monomvs		58.94 366	58.14 371	61.35 361	59.70 455	40.98 375	60.24 386	63.51 378	45.85 355	68.95 334	75.31 380	18.27 493	65.82 388	51.47 299	79.97 349	77.26 327

DELS-MVS		68.83 237	68.31 246	70.38 208	70.55 333	48.31 281	63.78 354	82.13 117	54.00 236	68.96 333	75.17 381	58.95 237	80.06 165	58.55 223	82.74 293	82.76 208
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		52.49 413	52.58 413	52.21 424	54.99 480	32.38 451	55.45 427	53.84 430	32.15 466	55.49 451	74.81 382	38.08 398	57.37 428	34.02 445	74.40 407	66.88 437

MSDG		67.47 262	67.48 263	67.46 281	70.70 327	54.69 222	66.90 306	78.17 209	60.88 140	70.41 313	74.76 383	61.22 205	73.18 282	47.38 340	76.87 385	74.49 363

UnsupCasMVSNet_eth		52.26 414	53.29 409	49.16 443	55.08 479	33.67 446	50.03 458	58.79 400	37.67 436	63.43 405	74.75 384	41.82 373	45.83 462	38.59 409	59.42 479	67.98 432

Fast-Effi-MVS+-dtu		70.00 214	68.74 240	73.77 134	73.47 275	64.53 117	71.36 220	78.14 211	55.81 200	68.84 342	74.71 385	65.36 155	75.75 243	52.00 295	79.00 361	81.03 253

TR-MVS		64.59 300	63.54 315	67.73 278	75.75 228	50.83 250	63.39 357	70.29 312	49.33 310	71.55 301	74.55 386	50.94 308	78.46 190	40.43 397	75.69 393	73.89 369

GA-MVS		62.91 321	61.66 335	66.66 298	67.09 393	44.49 342	61.18 375	69.36 321	51.33 279	69.33 329	74.47 387	36.83 406	74.94 258	50.60 307	74.72 402	80.57 270

CLD-MVS		72.88 152	72.36 173	74.43 123	77.03 198	54.30 224	68.77 271	83.43 87	52.12 265	76.79 182	74.44 388	69.54 103	83.91 82	55.88 253	93.25 74	85.09 119
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CHOSEN 1792x2688		58.09 372	56.30 385	63.45 332	79.95 147	50.93 249	54.07 437	65.59 360	28.56 476	61.53 413	74.33 389	41.09 379	66.52 385	33.91 446	67.69 455	72.92 377

Patchmatch-RL test		59.95 359	59.12 361	62.44 346	72.46 301	54.61 223	59.63 390	47.51 463	41.05 406	74.58 238	74.30 390	31.06 441	65.31 392	51.61 297	79.85 352	67.39 433

cdsmvs_eth3d_5k		17.71 465	23.62 466	0.00 486	0.00 509	0.00 511	0.00 497	70.17 313	0.00 504	0.00 505	74.25 391	68.16 116	0.00 505	0.00 503	0.00 503	0.00 501

lupinMVS		63.36 314	61.49 339	68.97 252	74.93 235	59.19 172	65.80 322	64.52 371	34.68 455	63.53 403	74.25 391	43.19 360	70.62 327	53.88 284	78.67 366	77.10 333

xiu_mvs_v1_base_debu		67.87 254	67.07 270	70.26 217	79.13 164	61.90 140	67.34 294	71.25 299	47.98 332	67.70 355	74.19 393	61.31 200	72.62 290	56.51 245	78.26 372	76.27 345

xiu_mvs_v1_base		67.87 254	67.07 270	70.26 217	79.13 164	61.90 140	67.34 294	71.25 299	47.98 332	67.70 355	74.19 393	61.31 200	72.62 290	56.51 245	78.26 372	76.27 345

xiu_mvs_v1_base_debi		67.87 254	67.07 270	70.26 217	79.13 164	61.90 140	67.34 294	71.25 299	47.98 332	67.70 355	74.19 393	61.31 200	72.62 290	56.51 245	78.26 372	76.27 345

tpmvs		55.84 384	55.45 392	57.01 399	60.33 446	33.20 448	65.89 319	59.29 396	47.52 340	56.04 446	73.60 396	31.05 442	68.06 362	40.64 396	64.64 464	69.77 414

SCA		58.57 370	58.04 372	60.17 373	70.17 343	41.07 374	65.19 332	53.38 435	43.34 391	61.00 419	73.48 397	45.20 345	69.38 347	40.34 398	70.31 439	70.05 410

Patchmatch-test		47.93 438	49.96 434	41.84 469	57.42 468	24.26 486	48.75 461	41.49 486	39.30 422	56.79 441	73.48 397	30.48 446	33.87 493	29.29 467	72.61 421	67.39 433

MDA-MVSNet_test_wron		52.57 412	53.49 408	49.81 437	54.24 482	36.47 423	40.48 483	46.58 467	38.13 430	75.47 213	73.32 399	41.05 381	43.85 477	40.98 390	71.20 433	69.10 423

YYNet1		52.58 411	53.50 406	49.85 436	54.15 483	36.45 424	40.53 482	46.55 468	38.09 431	75.52 210	73.31 400	41.08 380	43.88 476	41.10 388	71.14 434	69.21 421

PatchmatchNet		54.60 395	54.27 402	55.59 408	65.17 417	39.08 397	66.92 305	51.80 443	39.89 416	58.39 432	73.12 401	31.69 435	58.33 422	43.01 375	58.38 483	69.38 420
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPNet_dtu		58.93 367	58.52 366	60.16 374	67.91 381	47.70 295	69.97 242	58.02 401	49.73 303	47.28 481	73.02 402	38.14 397	62.34 405	36.57 427	85.99 226	70.43 408
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_enhance_ethall		65.86 286	65.05 304	68.28 269	61.62 439	42.62 361	64.74 341	77.97 213	42.52 394	73.42 265	72.79 403	49.66 316	77.68 211	58.12 230	84.59 258	84.54 146

ppachtmachnet_test		60.26 357	59.61 358	62.20 348	67.70 385	44.33 343	58.18 407	60.96 390	40.75 411	65.80 371	72.57 404	41.23 376	63.92 399	46.87 345	82.42 296	78.33 306

N_pmnet		52.06 415	51.11 424	54.92 409	59.64 456	71.03 56	37.42 488	61.62 389	33.68 459	57.12 437	72.10 405	37.94 399	31.03 494	29.13 471	71.35 431	62.70 459

ADS-MVSNet2		48.76 436	47.25 443	53.29 420	55.90 475	40.54 386	47.34 467	54.99 424	31.41 471	50.48 471	72.06 406	31.23 438	54.26 436	25.93 477	55.93 485	65.07 449

ADS-MVSNet		44.62 449	45.58 448	41.73 470	55.90 475	20.83 494	47.34 467	39.94 490	31.41 471	50.48 471	72.06 406	31.23 438	39.31 488	25.93 477	55.93 485	65.07 449

ET-MVSNet_ETH3D		63.32 315	60.69 349	71.20 198	70.15 345	55.66 210	65.02 336	64.32 372	43.28 392	68.99 332	72.05 408	25.46 465	78.19 203	54.16 282	82.80 291	79.74 284

BH-w/o		64.81 297	64.29 307	66.36 299	76.08 222	54.71 221	65.61 325	75.23 248	50.10 299	71.05 309	71.86 409	54.33 285	79.02 178	38.20 412	76.14 390	65.36 446

EI-MVSNet-Vis-set		72.78 155	71.87 182	75.54 111	74.77 242	59.02 179	72.24 195	71.56 290	63.92 110	78.59 139	71.59 410	66.22 144	78.60 186	67.58 117	80.32 343	89.00 36

UnsupCasMVSNet_bld		50.01 430	51.03 426	46.95 451	58.61 461	32.64 449	48.31 462	53.27 436	34.27 456	60.47 421	71.53 411	41.40 375	47.07 460	30.68 459	60.78 476	61.13 467

thres200		57.55 375	57.02 379	59.17 380	67.89 382	34.93 437	58.91 397	57.25 407	50.24 296	64.01 392	71.46 412	32.49 425	71.39 319	31.31 456	79.57 357	71.19 402

UWE-MVS		52.94 408	52.70 411	53.65 416	73.56 272	27.49 474	57.30 412	49.57 453	38.56 428	62.79 407	71.42 413	19.49 489	60.41 411	24.33 486	77.33 382	73.06 375

EI-MVSNet-UG-set		72.63 159	71.68 187	75.47 112	74.67 244	58.64 187	72.02 201	71.50 291	63.53 116	78.58 141	71.39 414	65.98 146	78.53 187	67.30 127	80.18 346	89.23 30

ETV-MVS		72.72 157	72.16 178	74.38 125	76.90 208	55.95 205	73.34 182	84.67 57	62.04 130	72.19 288	70.81 415	65.90 148	85.24 62	58.64 222	84.96 244	81.95 236

EIA-MVS		68.59 244	67.16 268	72.90 162	75.18 233	55.64 212	69.39 251	81.29 134	52.44 259	64.53 381	70.69 416	60.33 217	82.30 118	54.27 280	76.31 389	80.75 263

blended_shiyan6		62.20 333	61.77 332	63.47 330	67.98 379	40.64 385	60.46 383	69.15 323	47.24 343	66.43 367	70.57 417	43.73 357	71.93 309	43.16 373	87.24 202	77.85 318

blended_shiyan8		62.19 334	61.77 332	63.46 331	68.01 377	40.65 384	60.47 382	69.13 326	47.24 343	66.44 366	70.55 418	43.75 356	71.91 310	43.18 372	87.19 208	77.81 320

EI-MVSNet		69.61 222	69.01 235	71.41 194	73.94 267	49.90 261	71.31 222	71.32 296	58.22 164	75.40 214	70.44 419	58.16 248	75.85 239	62.51 173	79.81 353	88.48 45

CVMVSNet		59.21 364	58.44 368	61.51 357	73.94 267	47.76 293	71.31 222	64.56 370	26.91 482	60.34 422	70.44 419	36.24 409	67.65 365	53.57 287	68.66 449	69.12 422

tpm cat1		54.02 400	52.63 412	58.19 391	64.85 421	39.86 392	66.26 316	57.28 406	32.16 465	56.90 440	70.39 421	32.75 423	65.30 393	34.29 444	58.79 480	69.41 419

myMVS_eth3d28		51.35 421	51.99 418	49.44 441	69.21 358	22.51 491	49.82 459	49.11 455	49.00 319	55.03 453	70.31 422	22.73 477	52.88 441	24.33 486	78.39 371	72.92 377

PMMVS2		37.74 459	40.87 459	28.36 478	42.41 501	5.35 506	24.61 493	27.75 498	32.15 466	47.85 480	70.27 423	35.85 410	29.51 496	19.08 494	67.85 453	50.22 482

EPMVS		45.74 443	46.53 446	43.39 467	54.14 484	22.33 492	55.02 429	35.00 495	34.69 454	51.09 469	70.20 424	25.92 463	42.04 482	37.19 420	55.50 487	65.78 443

WB-MVSnew		53.94 402	54.76 399	51.49 428	71.53 313	28.05 471	58.22 406	50.36 449	37.94 434	59.16 430	70.17 425	49.21 322	51.94 442	24.49 484	71.80 429	74.47 364

testing99		55.16 392	54.56 401	56.98 400	70.13 346	30.58 463	54.55 435	54.11 428	49.53 308	56.76 442	70.14 426	22.76 476	65.79 389	36.99 423	76.04 391	74.57 361

testing91		55.74 386	55.29 395	57.08 398	70.63 328	30.85 461	54.94 432	56.31 420	50.34 294	57.08 438	70.10 427	24.50 469	65.86 387	36.98 424	76.75 386	74.53 362

KD-MVS_2432*1600		52.05 416	51.58 420	53.44 418	52.11 488	31.20 457	44.88 475	64.83 368	41.53 400	64.37 385	70.03 428	15.61 499	64.20 396	36.25 428	74.61 404	64.93 451

miper_refine_blended		52.05 416	51.58 420	53.44 418	52.11 488	31.20 457	44.88 475	64.83 368	41.53 400	64.37 385	70.03 428	15.61 499	64.20 396	36.25 428	74.61 404	64.93 451

our_test_3		56.46 381	56.51 383	56.30 403	67.70 385	39.66 395	55.36 428	52.34 441	40.57 414	63.85 394	69.91 430	40.04 386	58.22 423	43.49 370	75.29 400	71.03 405

xiu_mvs_v2_base		64.43 304	63.96 310	65.85 305	77.72 188	51.32 245	63.63 355	72.31 284	45.06 372	61.70 411	69.66 431	62.56 180	73.93 276	49.06 323	73.91 412	72.31 387

tpmrst		50.15 429	51.38 422	46.45 455	56.05 473	24.77 485	64.40 348	49.98 450	36.14 445	53.32 462	69.59 432	35.16 412	48.69 453	39.24 402	58.51 482	65.89 442

WTY-MVS		49.39 434	50.31 433	46.62 454	61.22 440	32.00 454	46.61 470	49.77 451	33.87 458	54.12 459	69.55 433	41.96 369	45.40 467	31.28 457	64.42 465	62.47 462

UWE-MVS-28		44.18 451	44.37 456	43.61 466	60.10 447	16.96 497	52.62 446	33.27 496	36.79 442	48.86 477	69.47 434	19.96 488	45.65 463	13.40 496	64.83 463	68.23 426

thisisatest0515		60.48 355	57.86 373	68.34 266	67.25 390	46.42 320	60.58 381	62.14 383	40.82 409	63.58 402	69.12 435	26.28 461	78.34 197	48.83 324	82.13 299	80.26 276

patchmatchnet-post											68.99 436	31.32 437	69.38 347

PatchMatch-RL		58.68 369	57.72 374	61.57 356	76.21 218	73.59 42	61.83 367	49.00 458	47.30 342	61.08 416	68.97 437	50.16 313	59.01 418	36.06 434	68.84 448	52.10 479

testing222		53.37 404	52.50 414	55.98 406	70.51 336	29.68 466	56.20 421	51.85 442	46.19 352	56.76 442	68.94 438	19.18 490	65.39 391	25.87 479	76.98 384	72.87 379

MS-PatchMatch		55.59 388	54.89 398	57.68 396	69.18 359	49.05 271	61.00 376	62.93 381	35.98 446	58.36 433	68.93 439	36.71 407	66.59 384	37.62 418	63.30 468	57.39 475

cascas		64.59 300	62.77 327	70.05 226	75.27 231	50.02 258	61.79 368	71.61 288	42.46 395	63.68 399	68.89 440	49.33 320	80.35 157	47.82 338	84.05 272	79.78 283

MVS		60.62 354	59.97 355	62.58 345	68.13 376	47.28 302	68.59 275	73.96 258	32.19 464	59.94 425	68.86 441	50.48 311	77.64 212	41.85 384	75.74 392	62.83 458

PVSNet_Blended		62.90 322	61.64 336	66.69 297	69.81 350	49.36 268	61.23 374	78.96 191	42.04 396	59.98 423	68.86 441	51.82 300	78.20 201	44.30 363	77.77 379	72.52 383

test_fmvs3		56.78 380	55.99 388	59.12 382	53.96 486	48.09 286	58.76 398	66.22 354	27.54 478	76.66 184	68.69 443	25.32 467	51.31 443	53.42 290	73.38 416	77.97 317

MAR-MVS		67.72 257	66.16 282	72.40 178	74.45 253	64.99 113	74.87 154	77.50 219	48.67 324	65.78 372	68.58 444	57.01 267	77.79 209	46.68 347	81.92 302	74.42 365
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		53.13 406	52.26 416	55.75 407	70.44 337	31.73 455	54.75 433	52.40 440	44.81 375	52.36 465	68.40 445	21.83 479	65.74 390	32.64 452	72.73 420	69.78 413

PS-MVSNAJ		64.27 307	63.73 313	65.90 304	77.82 186	51.42 243	63.33 358	72.33 283	45.09 371	61.60 412	68.04 446	62.39 184	73.95 275	49.07 322	73.87 413	72.34 386

ETVMVS		50.32 428	49.87 435	51.68 426	70.30 342	26.66 477	52.33 448	43.93 474	43.54 387	54.91 454	67.95 447	20.01 487	60.17 413	22.47 489	73.40 415	68.22 427

wanda-best-256-512		61.16 346	60.55 350	62.98 338	66.67 400	39.85 393	58.66 399	68.87 331	46.67 348	64.46 382	67.75 448	41.94 370	71.84 311	42.67 376	87.24 202	77.26 327

FE-blended-shiyan7		61.16 346	60.55 350	62.98 338	66.67 400	39.85 393	58.66 399	68.87 331	46.67 348	64.46 382	67.75 448	41.94 370	71.84 311	42.67 376	87.24 202	77.26 327

usedtu_blend_shiyan5		63.30 316	63.13 321	63.78 324	66.67 400	41.75 368	68.57 277	73.64 259	57.20 178	64.46 382	67.75 448	41.94 370	72.34 298	40.72 395	87.24 202	77.26 327

test0.0.03 1		47.72 439	48.31 438	45.93 456	55.53 478	29.39 467	46.40 471	41.21 488	43.41 389	55.81 449	67.65 451	29.22 453	43.77 478	25.73 481	69.87 442	64.62 453

1112_ss		59.48 362	58.99 363	60.96 366	77.84 185	42.39 363	61.42 372	68.45 342	37.96 433	59.93 426	67.46 452	45.11 347	65.07 394	40.89 391	71.81 428	75.41 352

ab-mvs-re		5.62 467	7.50 470	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	67.46 452	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

baseline2		55.57 389	52.74 410	64.05 321	65.26 414	44.11 344	62.38 365	54.43 426	39.03 424	51.21 468	67.35 454	33.66 417	72.45 295	37.14 421	64.22 466	75.60 349

1314		59.83 360	58.86 364	62.74 344	65.71 411	44.78 338	68.59 275	72.63 278	33.54 462	61.05 418	67.29 455	43.62 358	71.26 320	49.49 318	67.84 454	72.19 389

gbinet_0.2-2-1-0.02		62.58 328	61.83 331	64.86 314	67.07 395	41.37 370	61.56 370	67.91 345	49.27 311	66.62 365	67.23 456	41.53 374	74.46 266	45.94 354	89.31 158	78.74 300

IB-MVS	49.67 18	59.69 361	56.96 380	67.90 272	68.19 373	50.30 255	61.42 372	65.18 364	47.57 338	55.83 448	67.15 457	23.77 471	79.60 170	43.56 369	79.97 349	73.79 370
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
blend_shiyan4		57.39 376	55.27 396	63.73 325	67.25 390	41.75 368	60.08 387	69.15 323	47.57 338	64.19 389	67.14 458	20.46 483	72.34 298	40.73 394	60.88 475	77.11 332

UBG		49.18 435	49.35 436	48.66 447	70.36 340	26.56 479	50.53 455	45.61 469	37.43 437	53.37 461	65.97 459	23.03 475	54.20 437	26.29 474	71.54 430	65.20 448

sss		47.59 440	48.32 437	45.40 459	56.73 472	33.96 443	45.17 473	48.51 459	32.11 468	52.37 464	65.79 460	40.39 384	41.91 483	31.85 454	61.97 472	60.35 468

dp		44.09 452	44.88 453	41.72 471	58.53 463	23.18 488	54.70 434	42.38 482	34.80 452	44.25 491	65.61 461	24.48 470	44.80 471	29.77 464	49.42 491	57.18 476

test_fmvs2		54.80 394	54.11 404	56.88 401	51.76 490	49.95 260	56.70 415	65.80 357	26.22 483	69.42 327	65.25 462	31.82 433	49.98 449	49.63 316	70.36 438	70.71 406

PVSNet	43.83 21	51.56 419	51.17 423	52.73 421	68.34 369	38.27 406	48.22 463	53.56 433	36.41 443	54.29 458	64.94 463	34.60 414	54.20 437	30.34 460	69.87 442	65.71 444

Syy-MVS		54.13 397	55.45 392	50.18 434	68.77 364	23.59 487	55.02 429	44.55 472	43.80 381	58.05 435	64.07 464	46.22 340	58.83 419	46.16 352	72.36 423	68.12 429

myMVS_eth3d		50.36 427	50.52 431	49.88 435	68.77 364	22.69 489	55.02 429	44.55 472	43.80 381	58.05 435	64.07 464	14.16 501	58.83 419	33.90 447	72.36 423	68.12 429

pmmvs3		46.71 441	45.09 451	51.55 427	56.76 471	48.25 282	55.78 425	39.53 491	24.13 490	50.35 473	63.40 466	15.90 498	51.08 445	29.29 467	70.69 437	55.33 478

test_f		43.79 453	45.63 447	38.24 476	42.29 502	38.58 403	34.76 491	47.68 462	22.22 494	67.34 360	63.15 467	31.82 433	30.60 495	39.19 403	62.28 471	45.53 488

test_vis3_rt		51.94 418	51.04 425	54.65 411	46.32 497	50.13 257	44.34 477	78.17 209	23.62 491	68.95 334	62.81 468	21.41 480	38.52 490	41.49 386	72.22 425	75.30 355

gm-plane-assit					62.51 432	33.91 445			37.25 439		62.71 469		72.74 286	38.70 406

MVE	27.91 23	36.69 461	35.64 464	39.84 473	43.37 500	35.85 431	19.49 494	24.61 500	24.68 488	39.05 495	62.63 470	38.67 396	27.10 498	21.04 492	47.25 493	56.56 477
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
mvsany_test3		43.76 454	41.01 458	52.01 425	48.09 495	57.74 194	42.47 479	23.85 502	23.30 492	64.80 380	62.17 471	27.12 457	40.59 486	29.17 469	48.11 492	57.69 474

new_pmnet		37.55 460	39.80 462	30.79 477	56.83 470	16.46 498	39.35 485	30.65 497	25.59 486	45.26 485	61.60 472	24.54 468	28.02 497	21.60 490	52.80 490	47.90 484

dmvs_re		49.91 431	50.77 429	47.34 450	59.98 449	38.86 401	53.18 441	53.58 432	39.75 417	55.06 452	61.58 473	36.42 408	44.40 474	29.15 470	68.23 450	58.75 472

test_cas_vis1_n_1920		50.90 424	50.92 427	50.83 432	54.12 485	47.80 291	51.44 451	54.61 425	26.95 481	63.95 393	60.85 474	37.86 402	44.97 470	45.53 358	62.97 469	59.72 470

test_vis1_n_1920		52.96 407	53.50 406	51.32 429	59.15 457	44.90 336	56.13 422	64.29 373	30.56 474	59.87 427	60.68 475	40.16 385	47.47 458	48.25 333	62.46 470	61.58 466

test_fmvs1_n		52.70 410	52.01 417	54.76 410	53.83 487	50.36 253	55.80 424	65.90 356	24.96 487	65.39 373	60.64 476	27.69 456	48.46 454	45.88 356	67.99 452	65.46 445

test-LLR		50.43 426	50.69 430	49.64 438	60.76 443	41.87 365	53.18 441	45.48 470	43.41 389	49.41 475	60.47 477	29.22 453	44.73 472	42.09 382	72.14 426	62.33 464

test-mter		48.56 437	48.20 440	49.64 438	60.76 443	41.87 365	53.18 441	45.48 470	31.91 469	49.41 475	60.47 477	18.34 492	44.73 472	42.09 382	72.14 426	62.33 464

test_fmvs1		51.51 420	50.86 428	53.48 417	49.72 493	49.35 270	54.11 436	64.96 366	24.64 489	63.66 400	59.61 479	28.33 455	48.45 455	45.38 361	67.30 457	62.66 461

test_vis1_n		51.27 422	50.41 432	53.83 414	56.99 469	50.01 259	56.75 414	60.53 391	25.68 485	59.74 428	57.86 480	29.40 452	47.41 459	43.10 374	63.66 467	64.08 456

dmvs_testset		45.26 445	47.51 441	38.49 475	59.96 451	14.71 499	58.50 404	43.39 476	41.30 402	51.79 467	56.48 481	39.44 392	49.91 451	21.42 491	55.35 489	50.85 480

TESTMET0.1,1		45.17 446	44.93 452	45.89 457	56.02 474	38.31 405	53.18 441	41.94 485	27.85 477	44.86 488	56.47 482	17.93 494	41.50 485	38.08 413	68.06 451	57.85 473

CHOSEN 280x420		41.62 456	39.89 461	46.80 453	61.81 436	51.59 241	33.56 492	35.74 494	27.48 479	37.64 497	53.53 483	23.24 473	42.09 481	27.39 473	58.64 481	46.72 485

mvsany_test1		37.88 458	35.74 463	44.28 463	47.28 496	49.90 261	36.54 490	24.37 501	19.56 496	45.76 483	53.46 484	32.99 421	37.97 491	26.17 475	35.52 494	44.99 489

PMMVS		44.69 448	43.95 457	46.92 452	50.05 492	53.47 232	48.08 465	42.40 481	22.36 493	44.01 492	53.05 485	42.60 367	45.49 465	31.69 455	61.36 474	41.79 490

GG-mvs-BLEND				52.24 423	60.64 445	29.21 469	69.73 246	42.41 480		45.47 484	52.33 486	20.43 484	68.16 360	25.52 482	65.42 462	59.36 471

E-PMN		45.17 446	45.36 449	44.60 462	50.07 491	42.75 359	38.66 486	42.29 483	46.39 351	39.55 494	51.15 487	26.00 462	45.37 468	37.68 416	76.41 387	45.69 487

test_vis1_rt		46.70 442	45.24 450	51.06 431	44.58 498	51.04 248	39.91 484	67.56 347	21.84 495	51.94 466	50.79 488	33.83 416	39.77 487	35.25 439	61.50 473	62.38 463

0.4-1-1-0.1		51.02 423	48.31 438	59.15 381	60.95 442	37.94 413	53.17 445	59.12 399	39.52 418	47.88 479	50.31 489	20.36 485	69.99 338	35.79 435	67.66 456	69.51 418

PVSNet_0	36.71 22	41.12 457	40.78 460	42.14 468	59.97 450	40.13 389	40.97 481	42.24 484	30.81 473	44.86 488	49.41 490	40.70 382	45.12 469	23.15 488	34.96 495	41.16 491

EMVS		44.61 450	44.45 455	45.10 461	48.91 494	43.00 357	37.92 487	41.10 489	46.75 347	38.00 496	48.43 491	26.42 460	46.27 461	37.11 422	75.38 398	46.03 486

0.3-1-1-0.015		49.68 432	46.67 444	58.69 386	58.94 459	37.51 418	51.35 452	59.18 397	38.35 429	44.62 490	47.14 492	18.49 491	69.68 343	35.13 440	66.84 459	68.87 424

0.4-1-1-0.2		49.48 433	46.57 445	58.21 390	58.02 466	36.93 420	50.24 457	59.18 397	37.97 432	44.94 486	46.16 493	20.52 482	69.54 345	34.84 442	67.28 458	68.17 428

dongtai		31.66 462	32.98 465	27.71 479	58.58 462	12.61 501	45.02 474	14.24 505	41.90 397	47.93 478	43.91 494	10.65 504	41.81 484	14.06 495	20.53 498	28.72 495

test_method		19.26 464	19.12 468	19.71 480	9.09 505	1.91 508	7.79 496	53.44 434	1.42 499	10.27 501	35.80 495	17.42 496	25.11 499	12.44 497	24.38 497	32.10 494

kuosan		22.02 463	23.52 467	17.54 481	41.56 503	11.24 502	41.99 480	13.39 506	26.13 484	28.87 498	30.75 496	9.72 505	21.94 500	4.77 500	14.49 499	19.43 496

DeepMVS_CX				11.83 482	15.51 504	13.86 500		11.25 507	5.76 498	20.85 500	26.46 497	17.06 497	9.22 501	9.69 499	13.82 500	12.42 497

X-MVStestdata		76.81 86	74.79 109	82.85 8	89.43 15	77.61 15	86.80 20	84.66 58	72.71 32	82.87 87	9.95 498	73.86 58	86.31 22	78.84 23	94.03 61	84.64 138

tmp_tt		11.98 466	14.73 469	3.72 483	2.28 506	4.62 507	19.44 495	14.50 504	0.47 501	21.55 499	9.58 499	25.78 464	4.57 502	11.61 498	27.37 496	1.96 498

test_post1							66.63 309				2.08 500	30.66 445	59.33 417	40.34 398

test_post											1.99 501	30.91 443	54.76 435

test123		4.43 469	5.78 472	0.39 485	0.97 507	0.28 509	46.33 472	0.45 508	0.31 502	0.62 503	1.50 502	0.61 507	0.11 504	0.56 501	0.63 501	0.77 500

testmvs		4.06 470	5.28 473	0.41 484	0.64 508	0.16 510	42.54 478	0.31 509	0.26 503	0.50 504	1.40 503	0.77 506	0.17 503	0.56 501	0.55 502	0.90 499

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

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

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

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

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

pcd_1.5k_mvsjas		5.20 468	6.93 471	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	62.39 184	0.00 505	0.00 503	0.00 503	0.00 501

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

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

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

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

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

WAC-MVS						22.69 489								36.10 432

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

MSC_two_6792asdad				79.02 57	83.14 105	67.03 93		80.75 148					86.24 25	77.27 38	94.85 30	83.78 169

No_MVS				79.02 57	83.14 105	67.03 93		80.75 148					86.24 25	77.27 38	94.85 30	83.78 169

eth-test2					0.00 509

eth-test					0.00 509

IU-MVS					86.12 56	60.90 155		80.38 160	45.49 360	81.31 106				75.64 46	94.39 45	84.65 137

save fliter					87.00 39	67.23 92	79.24 97	77.94 214	56.65 187

test_0728_SECOND				76.57 95	86.20 51	60.57 160	83.77 49	85.49 32					85.90 41	75.86 43	94.39 45	83.25 189

GSMVS																70.05 410

test_part2					85.90 62	66.44 97				84.61 69

sam_mvs1												31.41 436				70.05 410

sam_mvs												31.21 440

MTGPA								80.63 154

MTMP							84.83 38	19.26 503

test9_res														72.12 83	91.37 101	77.40 323

agg_prior2														70.70 91	90.93 117	78.55 304

agg_prior					84.44 88	66.02 103		78.62 202		76.95 174			80.34 158

test_prior4						70.14 66	77.57 114

test_prior				75.27 115	82.15 124	59.85 168		84.33 71					83.39 96			82.58 216

旧先验2							71.17 225		45.11 370	78.54 142			61.28 410	59.19 216

新几何2							71.33 221

无先验							74.82 155	70.94 306	47.75 337				76.85 231	54.47 275		72.09 390

原ACMM2							74.78 159

testdata2													67.30 370	48.34 331

segment_acmp												68.30 115

testdata1							68.34 283		57.24 177

test12				76.51 96	82.28 122	60.94 154		81.64 126		73.60 260		64.88 161	85.19 65		90.42 130	83.38 185

plane_prior7					85.18 72	66.21 100

plane_prior6					84.18 92	65.31 109						60.83 210

plane_prior5								85.49 32					86.15 30	71.09 86	90.94 115	84.82 130

plane_prior3						65.67 105			63.82 112	78.23 145

plane_prior2							82.74 61		65.45 89

plane_prior1					84.46 87

plane_prior						65.18 110	80.06 89		61.88 132						89.91 143

n2								0.00 510

nn								0.00 510

door-mid								55.02 423

test11								82.71 104

door								52.91 438

HQP5-MVS						58.80 183

HQP-NCC					82.37 119		77.32 119		59.08 152	71.58 297

ACMP_Plane					82.37 119		77.32 119		59.08 152	71.58 297

BP-MVS														67.38 124

HQP4-MVS										71.59 295			85.31 57			83.74 171

HQP3-MVS								84.12 77							89.16 159

HQP2-MVS												58.09 251

MDTV_nov1_ep13_2view						18.41 495	53.74 438		31.57 470	44.89 487		29.90 451		32.93 450		71.48 395

ACMMP++_ref															89.47 153

ACMMP++															91.96 91

Test By Simon												62.56 180

High-resolution multi-view benchmark