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	63.30 161	84.80 39	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 76	68.08 117	97.05 1	96.93 1

MTAPA		83.19 22	83.87 23	81.13 33	91.16 2	78.16 15	84.87 37	80.63 157	72.08 44	84.93 68	90.79 51	74.65 54	84.42 79	80.98 5	94.75 33	80.82 268

mPP-MVS		84.01 13	84.39 15	82.88 6	90.65 3	81.38 4	87.08 13	82.79 102	72.41 41	85.11 67	90.85 50	76.65 33	84.89 70	79.30 20	94.63 37	82.35 229

MP-MVS		83.19 22	83.54 28	82.14 19	90.54 4	79.00 12	86.42 25	83.59 87	71.31 47	81.26 120	90.96 45	74.57 55	84.69 74	78.41 25	94.78 32	82.74 217
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMVS	70.70 6	81.70 38	83.15 36	77.36 87	90.35 5	82.82 2	82.15 64	79.22 191	74.08 23	87.16 34	91.97 22	84.80 2	76.97 228	64.98 150	93.61 70	72.28 408
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 54	82.86 41	73.07 155	89.93 6	39.21 441	77.15 124	81.28 138	79.74 5	90.87 4	92.73 13	75.03 50	84.93 69	63.83 168	95.19 20	95.07 3

DTE-MVSNet		80.35 55	82.89 40	72.74 173	89.84 7	37.34 465	77.16 123	81.81 126	80.45 3	90.92 3	92.95 9	74.57 55	86.12 32	63.65 171	94.68 36	94.76 6

PEN-MVS		80.46 53	82.91 39	73.11 153	89.83 8	39.02 445	77.06 126	82.61 108	80.04 4	90.60 6	92.85 11	74.93 51	85.21 64	63.15 178	95.15 22	95.09 2

region2R		83.54 17	83.86 24	82.58 14	89.82 9	77.53 21	87.06 16	84.23 77	70.19 57	83.86 85	90.72 55	75.20 47	86.27 24	79.41 18	94.25 54	83.95 169

ACMMPR		83.62 15	83.93 21	82.69 11	89.78 10	77.51 25	87.01 17	84.19 78	70.23 55	84.49 76	90.67 56	75.15 48	86.37 19	79.58 14	94.26 53	84.18 163

MSP-MVS		80.49 52	79.67 65	82.96 5	89.70 11	77.46 27	87.16 12	85.10 44	64.94 102	81.05 123	88.38 123	57.10 273	87.10 8	79.75 11	83.87 302	84.31 160
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	72.98 159	89.66 12	39.06 444	76.76 127	80.46 161	78.91 8	90.32 7	91.70 32	68.49 115	84.89 70	63.40 175	95.12 23	95.01 4

PGM-MVS		83.07 25	83.25 35	82.54 15	89.57 13	77.21 28	82.04 66	85.40 37	67.96 68	84.91 71	90.88 48	75.59 42	86.57 15	78.16 27	94.71 35	83.82 172

WR-MVS_H		80.22 57	82.17 48	74.39 125	89.46 14	42.69 405	78.24 109	82.24 118	78.21 12	89.57 9	92.10 20	68.05 122	85.59 53	66.04 142	95.62 9	94.88 5

XVS		83.51 18	83.73 25	82.85 8	89.43 15	77.61 19	86.80 20	84.66 60	72.71 32	82.87 95	90.39 68	73.86 60	86.31 22	78.84 23	94.03 60	84.64 142

X-MVStestdata		76.81 87	74.79 111	82.85 8	89.43 15	77.61 19	86.80 20	84.66 60	72.71 32	82.87 95	9.95 546	73.86 60	86.31 22	78.84 23	94.03 60	84.64 142

CP-MVS		84.12 11	84.55 14	82.80 10	89.42 17	79.74 9	88.19 5	84.43 68	71.96 46	84.70 74	90.56 58	77.12 29	86.18 29	79.24 21	95.36 14	82.49 226

ACMMP_NAP		82.33 32	83.28 33	79.46 50	89.28 18	69.09 93	83.62 51	84.98 48	64.77 104	83.97 83	91.02 44	75.53 45	85.93 39	82.00 2	94.36 49	83.35 193

UniMVSNet_ETH3D		76.74 88	79.02 68	69.92 240	89.27 19	43.81 392	74.47 169	71.70 294	72.33 43	85.50 61	93.65 3	77.98 24	76.88 232	54.60 291	91.64 98	89.08 34

HFP-MVS		83.39 21	84.03 20	81.48 26	89.25 20	75.69 35	87.01 17	84.27 74	70.23 55	84.47 77	90.43 63	76.79 30	85.94 37	79.58 14	94.23 55	82.82 214

ACMMP		84.22 9	84.84 12	82.35 17	89.23 21	76.66 31	87.65 7	85.89 27	71.03 51	85.85 51	90.58 57	78.77 18	85.78 46	79.37 19	95.17 21	84.62 144
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 40	81.76 50	80.76 37	89.20 22	78.75 13	86.48 24	82.03 122	68.80 62	80.92 125	88.52 119	72.00 75	82.39 117	74.80 50	93.04 77	81.14 258

FOURS1					89.19 23	77.84 17	91.64 1	89.11 2	84.05 2	91.57 2

ZNCC-MVS		83.12 24	83.68 26	81.45 27	89.14 24	73.28 55	86.32 26	85.97 25	67.39 71	84.02 82	90.39 68	74.73 53	86.46 16	80.73 7	94.43 44	84.60 147

HPM-MVS++		79.89 58	79.80 64	80.18 42	89.02 25	78.44 14	83.49 54	80.18 167	64.71 105	78.11 165	88.39 122	65.46 157	83.14 100	77.64 34	91.20 112	78.94 306

GST-MVS		82.79 28	83.27 34	81.34 30	88.99 26	73.29 54	85.94 32	85.13 42	68.58 66	84.14 81	90.21 78	73.37 64	86.41 17	79.09 22	93.98 63	84.30 162

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 45	88.94 27	67.52 105	86.61 22	81.38 136	51.71 276	77.15 188	91.42 39	65.49 156	87.20 6	79.44 17	87.17 231	84.51 154
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 39	82.28 47	79.40 51	88.91 28	69.16 90	84.67 40	80.01 171	75.34 18	79.80 137	94.91 2	69.79 104	80.25 162	72.63 79	94.46 40	88.78 44

SMA-MVS		82.12 33	82.68 44	80.43 39	88.90 29	69.52 82	85.12 36	84.76 54	63.53 116	84.23 80	91.47 37	72.02 74	87.16 7	79.74 13	94.36 49	84.61 145
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 30	79.76 44	88.88 30	68.44 96	81.57 69	86.33 19	63.17 122	85.38 64	91.26 40	76.33 36	84.67 75	83.30 1	94.96 27	86.17 91
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 5	88.84 4	90.72 1	78.27 11	87.95 18	92.53 15	79.37 15	84.79 73	74.51 59	96.15 2	92.88 7

HPM-MVS_fast		84.59 7	85.10 9	83.06 4	88.60 32	75.83 33	86.27 27	86.89 16	73.69 26	86.17 46	91.70 32	78.23 22	85.20 65	79.45 16	94.91 29	88.15 52

SR-MVS		84.51 8	85.27 7	82.25 18	88.52 33	77.71 18	86.81 19	85.25 41	77.42 16	86.15 47	90.24 76	81.69 5	85.94 37	77.77 31	93.58 71	83.09 202

新几何1				69.99 237	88.37 34	71.34 64		62.08 399	43.85 404	74.99 250	86.11 192	52.85 304	70.57 335	50.99 322	83.23 318	68.05 455

HPM-MVS		84.12 11	84.63 13	82.60 13	88.21 35	74.40 44	85.24 35	87.21 14	70.69 54	85.14 66	90.42 64	78.99 17	86.62 14	80.83 6	94.93 28	86.79 72
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 34	83.31 32	78.49 68	88.17 36	73.96 47	83.11 58	84.52 66	66.40 81	87.45 27	89.16 101	81.02 8	80.52 158	74.27 62	95.73 7	80.98 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 37	69.15 92	67.85 304	59.59 417	41.06 439	73.05 304	85.72 201	48.03 347			80.65 374	66.92 462

XVG-ACMP-BASELINE		80.54 51	81.06 55	78.98 59	87.01 38	72.91 56	80.23 86	85.56 32	66.56 80	85.64 54	89.57 90	69.12 108	80.55 157	72.51 81	93.37 73	83.48 184

save fliter					87.00 39	67.23 111	79.24 97	77.94 217	56.65 191

LPG-MVS_test		83.47 19	84.33 16	80.90 35	87.00 39	70.41 75	82.04 66	86.35 17	69.77 59	87.75 20	91.13 41	81.83 3	86.20 27	77.13 40	95.96 5	86.08 92

LGP-MVS_train				80.90 35	87.00 39	70.41 75		86.35 17	69.77 59	87.75 20	91.13 41	81.83 3	86.20 27	77.13 40	95.96 5	86.08 92

EGC-MVSNET		64.77 316	61.17 367	75.60 110	86.90 42	74.47 43	84.04 44	68.62 348	0.60 548	1.13 551	91.61 35	65.32 159	74.15 277	64.01 162	88.28 192	78.17 320

OPM-MVS		80.99 48	81.63 53	79.07 56	86.86 43	69.39 85	79.41 96	84.00 83	65.64 86	85.54 58	89.28 94	76.32 37	83.47 95	74.03 67	93.57 72	84.35 159
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 79	82.06 65	87.00 15	59.89 149	80.91 126	90.53 59	72.19 71	88.56 1	73.67 70	94.52 39	85.92 98
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 31	80.40 40	86.50 45	69.44 84	82.30 63	86.08 24	66.80 76	86.70 38	89.99 83	81.64 6	85.95 36	74.35 61	96.11 3	85.81 99
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 68	86.46 46	74.79 41	77.15 124	85.39 38	66.73 77	80.39 133	88.85 111	74.43 58	78.33 200	74.73 52	85.79 252	82.35 229

VDDNet		71.60 184	73.13 154	67.02 304	86.29 47	41.11 418	69.97 253	66.50 362	68.72 64	74.74 255	91.70 32	59.90 228	75.81 244	48.58 350	91.72 96	84.15 165

MED-MVS test				78.47 70	86.27 48	64.31 146	86.10 28	84.54 64	64.93 103	85.54 58	88.38 123		86.37 19	74.09 63	94.20 58	84.73 138

MED-MVS		81.77 37	82.86 41	78.51 67	86.27 48	64.31 146	86.10 28	84.54 64	72.46 39	85.54 58	90.03 80	72.97 67	86.37 19	74.09 63	93.74 67	84.86 130

TestfortrainingZip a		82.48 31	83.93 21	78.11 77	86.27 48	64.11 152	86.10 28	85.02 46	72.46 39	86.32 44	90.03 80	76.75 31	85.37 56	78.23 26	94.22 56	84.86 130

test_0728_SECOND				76.57 95	86.20 51	60.57 192	83.77 49	85.49 33					85.90 41	75.86 43	94.39 45	83.25 195

SR-MVS-dyc-post		84.75 6	85.26 8	83.21 3	86.19 52	79.18 10	87.23 9	86.27 20	77.51 13	87.65 23	90.73 53	79.20 16	85.58 54	78.11 28	94.46 40	84.89 127

RE-MVS-def			85.50 6		86.19 52	79.18 10	87.23 9	86.27 20	77.51 13	87.65 23	90.73 53	81.38 7		78.11 28	94.46 40	84.89 127

DVP-MVS		81.15 44	83.12 37	75.24 117	86.16 54	60.78 189	83.77 49	80.58 159	72.48 37	85.83 52	90.41 65	78.57 19	85.69 49	75.86 43	94.39 45	79.24 300
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	60.78 189	83.81 48	85.10 44	72.48 37	85.27 65	89.96 84	78.57 19

SED-MVS		81.78 36	83.48 29	76.67 93	86.12 56	61.06 183	83.62 51	84.72 56	72.61 35	87.38 29	89.70 88	77.48 27	85.89 43	75.29 47	94.39 45	83.08 203

IU-MVS					86.12 56	60.90 187		80.38 163	45.49 378	81.31 119				75.64 46	94.39 45	84.65 141

test_241102_ONE					86.12 56	61.06 183		84.72 56	72.64 34	87.38 29	89.47 91	77.48 27	85.74 48

reproduce-ours		84.97 3	85.93 3	82.10 20	86.11 59	77.53 21	87.08 13	85.81 29	78.70 9	88.94 12	91.88 26	79.74 12	86.05 33	79.90 9	95.21 17	82.72 218

our_new_method		84.97 3	85.93 3	82.10 20	86.11 59	77.53 21	87.08 13	85.81 29	78.70 9	88.94 12	91.88 26	79.74 12	86.05 33	79.90 9	95.21 17	82.72 218

XVG-OURS		79.51 60	79.82 63	78.58 65	86.11 59	74.96 40	76.33 140	84.95 50	66.89 74	82.75 98	88.99 107	66.82 137	78.37 198	74.80 50	90.76 134	82.40 228

test_part2					85.90 62	66.44 121				84.61 75

原ACMM1				73.90 134	85.90 62	65.15 138		81.67 128	50.97 292	74.25 271	86.16 188	61.60 201	83.54 92	56.75 260	91.08 120	73.00 394

testdata				64.13 337	85.87 64	63.34 160		61.80 403	47.83 347	76.42 217	86.60 174	48.83 339	62.31 417	54.46 293	81.26 358	66.74 466

CNVR-MVS		78.49 71	78.59 73	78.16 74	85.86 65	67.40 107	78.12 112	81.50 131	63.92 110	77.51 177	86.56 175	68.43 117	84.82 72	73.83 68	91.61 100	82.26 233

test_one_0601					85.84 66	61.45 177		85.63 31	75.27 20	85.62 57	90.38 70	76.72 32

NCCC		78.25 74	78.04 80	78.89 61	85.61 67	69.45 83	79.80 93	80.99 149	65.77 85	75.55 231	86.25 185	67.42 129	85.42 55	70.10 99	90.88 129	81.81 247

reproduce_model		84.87 5	85.80 5	82.05 22	85.52 68	78.14 16	87.69 6	85.36 39	79.26 6	89.12 11	92.10 20	77.52 26	85.92 40	80.47 8	95.20 19	82.10 236

TEST9					85.47 69	69.32 87	76.42 135	78.69 202	53.73 245	76.97 190	86.74 164	66.84 136	81.10 142

train_agg		76.38 90	76.55 94	75.86 106	85.47 69	69.32 87	76.42 135	78.69 202	54.00 240	76.97 190	86.74 164	66.60 142	81.10 142	72.50 82	91.56 101	77.15 340

DPE-MVS		82.00 35	83.02 38	78.95 60	85.36 71	67.25 109	82.91 59	84.98 48	73.52 28	85.43 62	90.03 80	76.37 35	86.97 12	74.56 57	94.02 62	82.62 222
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	65.18 136	82.74 61	85.49 33	65.45 89	78.23 162	89.11 102	60.83 214	86.15 30	71.09 90	90.94 123	84.82 134

plane_prior7					85.18 72	66.21 124

SymmetryMVS		74.00 121	72.85 161	77.43 86	85.17 74	70.01 80	79.92 91	68.48 349	58.60 161	75.21 244	84.02 234	52.85 304	81.82 128	61.45 194	89.99 152	80.47 279

SteuartSystems-ACMMP		83.07 25	83.64 27	81.35 29	85.14 75	71.00 68	85.53 33	84.78 53	70.91 52	85.64 54	90.41 65	75.55 44	87.69 4	79.75 11	95.08 24	85.36 113
Skip Steuart: Steuart Systems R&D Blog.
test_8					85.09 76	67.89 100	76.26 142	78.66 204	54.00 240	76.89 194	86.72 167	66.60 142	80.89 152

test-260524					85.04 77	63.52 157		84.79 52		83.97 83		74.92 52	85.60 52	74.59 56	93.74 67

WR-MVS		71.20 193	72.48 172	67.36 294	84.98 78	35.70 478	64.43 372	68.66 347	65.05 99	81.49 117	86.43 180	57.57 266	76.48 238	50.36 328	93.32 75	89.90 22

PS-MVSNAJss		77.54 79	77.35 88	78.13 76	84.88 79	66.37 122	78.55 104	79.59 183	53.48 252	86.29 45	92.43 17	62.39 188	80.25 162	67.90 122	90.61 135	87.77 55

LTVRE_ROB	75.46 1	84.22 9	84.98 11	81.94 23	84.82 80	75.40 36	91.60 3	87.80 8	73.52 28	88.90 14	93.06 8	71.39 85	81.53 134	81.53 4	92.15 93	88.91 40
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
mvs_tets		78.93 65	78.67 72	79.72 46	84.81 81	73.93 48	80.65 77	76.50 236	51.98 274	87.40 28	91.86 28	76.09 39	78.53 189	68.58 112	90.20 143	86.69 75

APDe-MVS		82.88 27	84.14 18	79.08 55	84.80 82	66.72 117	86.54 23	85.11 43	72.00 45	86.65 39	91.75 31	78.20 23	87.04 10	77.93 30	94.32 52	83.47 185
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSLP-MVS++		74.48 117	75.78 101	70.59 212	84.66 83	62.40 166	78.65 102	84.24 76	60.55 144	77.71 173	81.98 288	63.12 176	77.64 214	62.95 180	88.14 195	71.73 414

jajsoiax		78.51 70	78.16 79	79.59 48	84.65 84	73.83 50	80.42 80	76.12 243	51.33 285	87.19 33	91.51 36	73.79 62	78.44 194	68.27 115	90.13 147	86.49 83

TranMVSNet+NR-MVSNet		76.13 92	77.66 83	71.56 196	84.61 85	42.57 407	70.98 237	78.29 211	68.67 65	83.04 91	89.26 95	72.99 66	80.75 153	55.58 277	95.47 12	91.35 11

旧先验1					84.55 86	60.36 194		63.69 388			87.05 150	54.65 293			83.34 316	69.66 436

APD-MVS		81.13 45	81.73 51	79.36 52	84.47 87	70.53 74	83.85 47	83.70 85	69.43 61	83.67 87	88.96 108	75.89 40	86.41 17	72.62 80	92.95 78	81.14 258
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
plane_prior1					84.46 88

agg_prior					84.44 89	66.02 127		78.62 205		76.95 192			80.34 160

ME-MVS		81.36 41	82.39 46	78.28 73	84.42 90	64.31 146	82.78 60	85.02 46	71.25 48	84.81 72	88.38 123	76.53 34	85.81 45	74.09 63	94.20 58	84.73 138

DeepPCF-MVS	71.07 5	78.48 72	77.14 90	82.52 16	84.39 91	77.04 29	76.35 138	84.05 81	56.66 190	80.27 134	85.31 207	68.56 112	87.03 11	67.39 129	91.26 109	83.50 181

CDPH-MVS		77.33 83	77.06 91	78.14 75	84.21 92	63.98 154	76.07 145	83.45 88	54.20 235	77.68 174	87.18 145	69.98 100	85.37 56	68.01 119	92.72 83	85.08 123

plane_prior6					84.18 93	65.31 135						60.83 214

114514_t		73.40 137	73.33 151	73.64 138	84.15 94	57.11 234	78.20 110	80.02 170	43.76 407	72.55 311	86.07 195	64.00 171	83.35 98	60.14 215	91.03 121	80.45 280

ZD-MVS					83.91 95	69.36 86		81.09 145	58.91 159	82.73 99	89.11 102	75.77 41	86.63 13	72.73 78	92.93 79

DeepC-MVS_fast	69.89 7	77.17 84	76.33 96	79.70 47	83.90 96	67.94 99	80.06 89	83.75 84	56.73 189	74.88 254	85.32 206	65.54 155	87.79 2	65.61 147	91.14 115	83.35 193
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
h-mvs33		73.08 144	71.61 194	77.48 84	83.89 97	72.89 57	70.47 245	71.12 312	54.28 231	77.89 166	83.41 248	49.04 336	80.98 147	63.62 172	90.77 133	78.58 311

NormalMVS		76.15 91	75.08 109	79.36 52	83.87 98	70.01 80	79.92 91	84.34 70	58.60 161	75.21 244	84.02 234	52.85 304	81.82 128	61.45 194	95.50 10	86.24 87

lecture		83.41 20	85.02 10	78.58 65	83.87 98	67.26 108	84.47 41	88.27 6	73.64 27	87.35 32	91.96 23	78.55 21	82.92 105	81.59 3	95.50 10	85.56 108

SD-MVS		80.28 56	81.55 54	76.47 98	83.57 100	67.83 102	83.39 56	85.35 40	64.42 106	86.14 48	87.07 149	74.02 59	80.97 148	77.70 33	92.32 90	80.62 276
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		74.91 111	75.57 104	72.93 163	83.50 101	45.79 367	69.47 263	80.14 168	65.22 95	81.74 113	87.08 147	61.82 198	81.07 144	56.21 267	94.98 25	91.93 8

NR-MVSNet		73.62 127	74.05 131	72.33 184	83.50 101	43.71 393	65.65 346	77.32 225	64.32 107	75.59 230	87.08 147	62.45 187	81.34 136	54.90 286	95.63 8	91.93 8

test_0402		78.17 75	79.48 66	74.24 127	83.50 101	59.15 209	72.52 197	74.60 259	75.34 18	88.69 17	91.81 30	75.06 49	82.37 118	65.10 148	88.68 186	81.20 256

OPU-MVS				78.65 64	83.44 104	66.85 115	83.62 51				86.12 191	66.82 137	86.01 35	61.72 192	89.79 159	83.08 203

NP-MVS					83.34 105	63.07 163					85.97 196

DVP-MVS++		81.24 42	82.74 43	76.76 92	83.14 106	60.90 187	91.64 1	85.49 33	74.03 24	84.93 68	90.38 70	66.82 137	85.90 41	77.43 35	90.78 131	83.49 182

MSC_two_6792asdad				79.02 57	83.14 106	67.03 113		80.75 151					86.24 25	77.27 38	94.85 30	83.78 174

No_MVS				79.02 57	83.14 106	67.03 113		80.75 151					86.24 25	77.27 38	94.85 30	83.78 174

UniMVSNet (Re)		75.00 109	75.48 105	73.56 143	83.14 106	47.92 326	70.41 247	81.04 147	63.67 114	79.54 140	86.37 181	62.83 181	81.82 128	57.10 257	95.25 16	90.94 15

hse-mvs2		72.32 170	70.66 213	77.31 89	83.10 110	71.77 60	69.19 272	71.45 301	54.28 231	77.89 166	78.26 369	49.04 336	79.23 176	63.62 172	89.13 177	80.92 265

UniMVSNet_NR-MVSNet		74.90 112	75.65 102	72.64 176	83.04 111	45.79 367	69.26 269	78.81 197	66.66 79	81.74 113	86.88 154	63.26 175	81.07 144	56.21 267	94.98 25	91.05 13

HyFIR lowres test		63.01 342	60.47 378	70.61 211	83.04 111	54.10 261	59.93 427	72.24 292	33.67 497	69.00 372	75.63 396	38.69 416	76.93 230	36.60 462	75.45 443	80.81 270

COLMAP_ROB	72.78 3	83.75 14	84.11 19	82.68 12	82.97 113	74.39 45	87.18 11	88.18 7	78.98 7	86.11 49	91.47 37	79.70 14	85.76 47	66.91 137	95.46 13	87.89 54
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		70.22 214	67.88 265	77.22 90	82.96 114	71.61 61	69.08 275	71.39 302	49.17 325	71.70 327	78.07 374	37.62 425	79.21 177	61.81 189	89.15 175	80.82 268

DP-MVS Recon		73.57 130	72.69 165	76.23 101	82.85 115	63.39 159	74.32 171	82.96 99	57.75 171	70.35 351	81.98 288	64.34 170	84.41 80	49.69 333	89.95 153	80.89 266

APD-MVS_3200maxsize		83.57 16	84.33 16	81.31 31	82.83 116	73.53 53	85.50 34	87.45 13	74.11 22	86.45 43	90.52 61	80.02 10	84.48 77	77.73 32	94.34 51	85.93 97

PVSNet_Blended_VisFu		70.04 218	68.88 243	73.53 144	82.71 117	63.62 156	74.81 159	81.95 124	48.53 336	67.16 403	79.18 358	51.42 315	78.38 197	54.39 295	79.72 396	78.60 310

DPM-MVS		69.98 220	69.22 239	72.26 186	82.69 118	58.82 216	70.53 244	81.23 140	47.79 348	64.16 436	80.21 327	51.32 316	83.12 101	60.14 215	84.95 270	74.83 372

EG-PatchMatch MVS		70.70 205	70.88 207	70.16 230	82.64 119	58.80 217	71.48 227	73.64 266	54.98 212	76.55 210	81.77 294	61.10 211	78.94 182	54.87 287	80.84 369	72.74 400

HQP-NCC					82.37 120		77.32 120		59.08 153	71.58 333

ACMP_Plane					82.37 120		77.32 120		59.08 153	71.58 333

HQP-MVS		75.24 104	75.01 110	75.94 104	82.37 120	58.80 217	77.32 120	84.12 79	59.08 153	71.58 333	85.96 197	58.09 258	85.30 59	67.38 131	89.16 173	83.73 177

test12				76.51 96	82.28 123	60.94 186		81.64 129		73.60 288		64.88 164	85.19 66		90.42 139	83.38 191

RoMa-HiRes		73.61 128	73.51 143	73.92 133	82.27 124	81.71 3	77.59 114	64.83 379	51.32 287	88.72 16	83.92 239	60.47 219	61.70 420	60.01 218	92.44 85	78.34 314

TAMVS		65.31 306	63.75 331	69.97 239	82.23 125	59.76 202	66.78 329	63.37 391	45.20 387	69.79 364	79.37 350	47.42 351	72.17 306	34.48 484	85.15 265	77.99 325

test_prior				75.27 116	82.15 126	59.85 201		84.33 73					83.39 97			82.58 223

SF-MVS		80.72 50	81.80 49	77.48 84	82.03 127	64.40 144	83.41 55	88.46 5	65.28 94	84.29 79	89.18 99	73.73 63	83.22 99	76.01 42	93.77 65	84.81 136

AdaColmap		74.22 118	74.56 115	73.20 149	81.95 128	60.97 185	79.43 94	80.90 150	65.57 87	72.54 312	81.76 295	70.98 90	85.26 61	47.88 359	90.00 150	73.37 390

PAPM_NR		73.91 123	74.16 128	73.16 150	81.90 129	53.50 266	81.28 72	81.40 134	66.17 83	73.30 295	83.31 254	59.96 226	83.10 102	58.45 241	81.66 349	82.87 211

DP-MVS		78.44 73	79.29 67	75.90 105	81.86 130	65.33 134	79.05 99	84.63 62	74.83 21	80.41 132	86.27 183	71.68 76	83.45 96	62.45 184	92.40 87	78.92 307

F-COLMAP		75.29 102	73.99 132	79.18 54	81.73 131	71.90 59	81.86 68	82.98 98	59.86 150	72.27 315	84.00 236	64.56 168	83.07 103	51.48 316	87.19 229	82.56 224

SixPastTwentyTwo		75.77 94	76.34 95	74.06 131	81.69 132	54.84 255	76.47 131	75.49 250	64.10 109	87.73 22	92.24 19	50.45 323	81.30 138	67.41 127	91.46 104	86.04 94

Vis-MVSNet		74.85 115	74.56 115	75.72 107	81.63 133	64.64 142	76.35 138	79.06 193	62.85 126	73.33 294	88.41 121	62.54 186	79.59 173	63.94 167	82.92 320	82.94 207
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DKM-HiRes		70.49 208	69.89 221	72.31 185	81.51 134	80.92 7	73.23 188	58.80 422	49.23 323	84.44 78	81.39 303	49.91 326	61.22 423	59.28 229	91.22 111	74.79 373

test_djsdf		78.88 66	78.27 77	80.70 38	81.42 135	71.24 65	83.98 45	75.72 248	52.27 267	87.37 31	92.25 18	68.04 123	80.56 155	72.28 84	91.15 114	90.32 20

3Dnovator+	73.19 2	81.08 46	80.48 58	82.87 7	81.41 136	72.03 58	84.38 43	86.23 23	77.28 17	80.65 129	90.18 79	59.80 231	87.58 5	73.06 74	91.34 107	89.01 36

tt0805		76.12 93	78.43 76	69.20 254	81.32 137	41.37 415	76.72 128	77.64 220	63.78 113	82.06 105	87.88 137	79.78 11	79.05 179	64.33 160	92.40 87	87.17 67

MCST-MVS		73.42 132	73.34 150	73.63 139	81.28 138	59.17 208	74.80 161	83.13 93	45.50 376	72.84 305	83.78 244	65.15 161	80.99 146	64.54 157	89.09 181	80.73 272

MIMVSNet1		66.57 290	69.23 238	58.59 425	81.26 139	37.73 461	64.06 377	57.62 427	57.02 182	78.40 160	90.75 52	62.65 182	58.10 444	41.77 411	89.58 163	79.95 288

ACMH+	66.64 10	81.20 43	82.48 45	77.35 88	81.16 140	62.39 167	80.51 78	87.80 8	73.02 30	87.57 25	91.08 43	80.28 9	82.44 115	64.82 152	96.10 4	87.21 63

RoMa-SfM		70.84 201	70.47 215	71.95 192	80.95 141	81.09 6	76.44 134	62.08 399	46.25 367	87.14 35	80.63 319	55.60 287	58.69 436	54.19 298	90.98 122	76.07 359

MVSMamba_PlusPlus		76.88 86	78.21 78	72.88 167	80.83 142	48.71 310	83.28 57	82.79 102	72.78 31	79.17 146	91.94 24	56.47 281	83.95 82	70.51 98	86.15 245	85.99 96

MVS_111021_HR		72.98 151	72.97 160	72.99 158	80.82 143	65.47 132	68.81 284	72.77 282	57.67 173	75.76 225	82.38 279	71.01 89	77.17 222	61.38 196	86.15 245	76.32 354

9.14			80.22 60		80.68 144		80.35 83	87.69 11	59.90 148	83.00 92	88.20 128	74.57 55	81.75 132	73.75 69	93.78 64

OMC-MVS		79.41 62	78.79 70	81.28 32	80.62 145	70.71 73	80.91 75	84.76 54	62.54 128	81.77 111	86.65 171	71.46 82	83.53 93	67.95 121	92.44 85	89.60 24

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 63	80.48 146	64.16 150	80.24 85	82.06 121	61.89 132	88.77 15	93.32 5	57.15 271	82.60 112	70.08 100	92.80 80	89.25 30

CDS-MVSNet		64.33 325	62.66 351	69.35 251	80.44 147	58.28 225	65.26 353	65.66 370	44.36 399	67.30 402	75.54 398	43.27 375	71.77 318	37.68 448	84.44 292	78.01 324
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DKM		69.82 224	69.29 234	71.40 201	80.33 148	80.76 8	73.05 190	60.16 413	47.00 358	85.42 63	79.91 335	48.29 346	58.24 441	57.18 254	92.25 91	75.19 370

PLC	62.01 16	71.79 181	70.28 217	76.33 99	80.31 149	68.63 95	78.18 111	81.24 139	54.57 223	67.09 404	80.63 319	59.44 236	81.74 133	46.91 366	84.17 299	78.63 309
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DenseAffine		67.25 277	66.08 296	70.76 209	80.22 150	77.51 25	70.65 243	58.59 424	45.98 372	81.51 116	76.48 388	41.58 393	62.36 415	49.23 341	90.48 137	72.40 405

MM		78.15 76	77.68 82	79.55 49	80.10 151	65.47 132	80.94 74	78.74 201	71.22 49	72.40 314	88.70 113	60.51 218	87.70 3	77.40 37	89.13 177	85.48 110

sc_t1		72.50 168	74.23 126	67.33 295	80.05 152	46.99 349	66.58 332	69.48 327	66.28 82	77.62 176	91.83 29	70.98 90	68.62 363	53.86 303	91.40 105	86.37 86

CHOSEN 1792x2688		58.09 402	56.30 418	63.45 353	79.95 153	50.93 285	54.07 479	65.59 371	28.56 518	61.53 460	74.33 412	41.09 399	66.52 394	33.91 488	67.69 503	72.92 395

tt0320		71.34 191	73.47 144	64.97 330	79.92 154	40.81 423	65.22 354	69.07 335	66.72 78	76.15 222	93.36 4	70.35 94	66.90 383	49.31 340	91.09 119	87.21 63

K. test v3		73.67 126	73.61 141	73.87 135	79.78 155	55.62 248	74.69 165	62.04 402	66.16 84	84.76 73	93.23 7	49.47 330	80.97 148	65.66 146	86.67 241	85.02 126

tt0320-xc		71.50 186	73.63 140	65.08 328	79.77 156	40.46 432	64.80 362	68.86 341	67.08 73	76.84 198	93.24 6	70.33 95	66.77 390	49.76 332	92.02 94	88.02 53

VPNet		65.58 304	67.56 268	59.65 412	79.72 157	30.17 510	60.27 422	62.14 397	54.19 236	71.24 342	86.63 172	58.80 247	67.62 374	44.17 389	90.87 130	81.18 257

ACMH	63.62 14	77.50 82	80.11 61	69.68 244	79.61 158	56.28 238	78.81 101	83.62 86	63.41 120	87.14 35	90.23 77	76.11 38	73.32 286	67.58 124	94.44 43	79.44 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 172	79.60 159	56.83 237		57.37 431		83.80 86	89.01 106	47.45 350	78.74 186	64.39 159	86.49 244	82.69 220

MVS_111021_LR		72.10 175	71.82 188	72.95 160	79.53 160	73.90 49	70.45 246	66.64 361	56.87 184	76.81 199	81.76 295	68.78 110	71.76 319	61.81 189	83.74 307	73.18 392

Test_1112_low_res		58.78 395	58.69 392	59.04 420	79.41 161	38.13 455	57.62 451	66.98 360	34.74 490	59.62 476	77.56 378	42.92 382	63.65 411	38.66 436	70.73 484	75.35 367

CSCG		74.12 120	74.39 121	73.33 146	79.35 162	61.66 174	77.45 119	81.98 123	62.47 130	79.06 148	80.19 329	61.83 197	78.79 185	59.83 221	87.35 214	79.54 296

MVP-Stereo		61.56 367	59.22 387	68.58 273	79.28 163	60.44 193	69.20 271	71.57 297	43.58 411	56.42 492	78.37 368	39.57 411	76.46 239	34.86 480	60.16 525	68.86 446
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 209	71.34 199	67.85 284	79.26 164	40.42 433	74.67 166	75.15 254	58.41 164	68.74 386	88.14 132	56.08 284	83.69 89	59.90 219	81.71 346	79.43 298

IS-MVSNet		75.10 106	75.42 106	74.15 130	79.23 165	48.05 324	79.43 94	78.04 215	70.09 58	79.17 146	88.02 134	53.04 303	83.60 90	58.05 246	93.76 66	90.79 17

TestfortrainingZip				73.58 141	79.21 166	57.65 230	86.10 28	81.22 141	72.34 42	72.08 322	83.19 264	58.95 244	83.71 88		84.76 278	79.38 299

FC-MVSNet-test		73.32 139	74.78 112	68.93 265	79.21 166	36.57 468	71.82 222	79.54 185	57.63 176	82.57 101	90.38 70	59.38 238	78.99 181	57.91 247	94.56 38	91.23 12

AllTest		77.66 78	77.43 84	78.35 71	79.19 168	70.81 70	78.60 103	88.64 3	65.37 92	80.09 135	88.17 129	70.33 95	78.43 195	55.60 274	90.90 127	85.81 99

TestCases				78.35 71	79.19 168	70.81 70		88.64 3	65.37 92	80.09 135	88.17 129	70.33 95	78.43 195	55.60 274	90.90 127	85.81 99

xiu_mvs_v1_base_debu		67.87 264	67.07 279	70.26 226	79.13 170	61.90 171	67.34 312	71.25 307	47.98 344	67.70 396	74.19 416	61.31 204	72.62 295	56.51 262	78.26 416	76.27 355

xiu_mvs_v1_base		67.87 264	67.07 279	70.26 226	79.13 170	61.90 171	67.34 312	71.25 307	47.98 344	67.70 396	74.19 416	61.31 204	72.62 295	56.51 262	78.26 416	76.27 355

xiu_mvs_v1_base_debi		67.87 264	67.07 279	70.26 226	79.13 170	61.90 171	67.34 312	71.25 307	47.98 344	67.70 396	74.19 416	61.31 204	72.62 295	56.51 262	78.26 416	76.27 355

VDD-MVS		70.81 203	71.44 198	68.91 266	79.07 173	46.51 359	67.82 305	70.83 316	61.23 136	74.07 276	88.69 114	59.86 229	75.62 249	51.11 320	90.28 142	84.61 145

Elysia		77.52 80	77.43 84	77.78 80	79.01 174	60.26 195	76.55 129	84.34 70	67.82 69	78.73 152	87.94 135	58.68 249	83.79 85	74.70 54	89.10 179	89.28 28

StellarMVS		77.52 80	77.43 84	77.78 80	79.01 174	60.26 195	76.55 129	84.34 70	67.82 69	78.73 152	87.94 135	58.68 249	83.79 85	74.70 54	89.10 179	89.28 28

test1111		64.62 318	65.19 311	62.93 363	79.01 174	29.91 512	65.45 350	54.41 453	54.09 238	71.47 340	88.48 120	37.02 427	74.29 275	46.83 368	89.94 154	84.58 148

TSAR-MVS + GP.		73.08 144	71.60 195	77.54 83	78.99 177	70.73 72	74.96 156	69.38 328	60.73 143	74.39 268	78.44 367	57.72 265	82.78 109	60.16 213	89.60 161	79.11 302

test2506		61.23 370	60.85 373	62.38 370	78.80 178	27.88 520	67.33 315	37.42 539	54.23 233	67.55 399	88.68 115	17.87 542	74.39 272	46.33 373	89.41 167	84.86 130

ECVR-MVS		64.82 314	65.22 310	63.60 347	78.80 178	31.14 505	66.97 325	56.47 442	54.23 233	69.94 361	88.68 115	37.23 426	74.81 265	45.28 385	89.41 167	84.86 130

FIs		72.56 164	73.80 135	68.84 268	78.74 180	37.74 460	71.02 236	79.83 174	56.12 195	80.88 128	89.45 92	58.18 254	78.28 201	56.63 261	93.36 74	90.51 19

v7n		79.37 63	80.41 59	76.28 100	78.67 181	55.81 244	79.22 98	82.51 112	70.72 53	87.54 26	92.44 16	68.00 124	81.34 136	72.84 77	91.72 96	91.69 10

LS3D		80.99 48	80.85 56	81.41 28	78.37 182	71.37 63	87.45 8	85.87 28	77.48 15	81.98 106	89.95 85	69.14 107	85.26 61	66.15 139	91.24 110	87.61 58

CNLPA		73.44 131	73.03 158	74.66 119	78.27 183	75.29 37	75.99 146	78.49 206	65.39 91	75.67 228	83.22 263	61.23 207	66.77 390	53.70 304	85.33 261	81.92 244

SSM_0404		72.51 167	72.15 182	73.60 140	78.20 184	55.86 243	74.41 170	79.83 174	53.69 246	73.98 279	84.18 226	62.26 191	82.50 113	58.21 243	84.60 284	82.43 227

EPP-MVSNet		73.86 125	73.38 147	75.31 114	78.19 185	53.35 268	80.45 79	77.32 225	65.11 98	76.47 215	86.80 159	49.47 330	83.77 87	53.89 301	92.72 83	88.81 43

PCF-MVS	63.80 13	72.70 161	71.69 189	75.72 107	78.10 186	60.01 199	73.04 191	81.50 131	45.34 381	79.66 139	84.35 224	65.15 161	82.65 111	48.70 348	89.38 170	84.50 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		73.14 142	73.77 137	71.26 203	78.09 187	52.64 273	74.32 171	79.56 184	56.32 193	76.35 218	83.36 253	70.76 92	77.96 208	63.32 176	81.84 339	83.18 198

PMatch-SfM		67.96 263	66.40 291	72.63 177	78.06 188	75.26 38	71.85 219	59.63 415	46.07 369	86.78 37	82.02 285	26.32 501	66.37 395	57.00 258	89.87 156	76.27 355

LFMVS		67.06 283	67.89 264	64.56 333	78.02 189	38.25 453	70.81 241	59.60 416	65.18 96	71.06 344	86.56 175	43.85 369	75.22 255	46.35 372	89.63 160	80.21 286

anonymousdsp		78.60 68	77.80 81	81.00 34	78.01 190	74.34 46	80.09 87	76.12 243	50.51 301	89.19 10	90.88 48	71.45 83	77.78 212	73.38 71	90.60 136	90.90 16

BH-untuned		69.39 232	69.46 229	69.18 255	77.96 191	56.88 235	68.47 297	77.53 221	56.77 187	77.79 169	79.63 342	60.30 223	80.20 165	46.04 376	80.65 374	70.47 427

1112_ss		59.48 389	58.99 390	60.96 394	77.84 192	42.39 408	61.42 406	68.45 350	37.96 467	59.93 473	67.46 495	45.11 361	65.07 404	40.89 418	71.81 475	75.41 365

PS-MVSNAJ		64.27 326	63.73 332	65.90 320	77.82 193	51.42 279	63.33 386	72.33 290	45.09 390	61.60 459	68.04 489	62.39 188	73.95 280	49.07 343	73.87 459	72.34 406

ambc				70.10 234	77.74 194	50.21 292	74.28 174	77.93 218		79.26 144	88.29 127	54.11 298	79.77 169	64.43 158	91.10 118	80.30 283

xiu_mvs_v2_base		64.43 323	63.96 329	65.85 321	77.72 195	51.32 281	63.63 383	72.31 291	45.06 391	61.70 458	69.66 470	62.56 184	73.93 281	49.06 344	73.91 458	72.31 407

Anonymous20231211		75.54 99	77.19 89	70.59 212	77.67 196	45.70 371	74.73 163	80.19 166	68.80 62	82.95 94	92.91 10	66.26 146	76.76 235	58.41 242	92.77 81	89.30 27

FMVSNet1		71.06 195	72.48 172	66.81 306	77.65 197	40.68 426	71.96 211	73.03 273	61.14 137	79.45 143	90.36 73	60.44 220	75.20 257	50.20 329	88.05 198	84.54 150

ArgMatch-SfM		64.74 317	63.70 333	67.83 286	77.62 198	76.78 30	67.30 317	58.21 425	36.64 477	81.94 108	73.41 425	38.67 417	56.92 448	50.66 325	88.89 184	69.81 433

FPMVS		59.43 390	60.07 380	57.51 436	77.62 198	71.52 62	62.33 395	50.92 473	57.40 177	69.40 369	80.00 333	39.14 414	61.92 419	37.47 452	66.36 508	39.09 539

BridgeMVS		73.59 129	74.06 130	72.17 190	77.48 200	47.72 332	81.43 71	82.20 119	54.38 228	79.19 145	87.68 141	54.41 295	83.57 91	63.98 164	85.78 253	85.22 115

testing3		58.28 400	58.38 397	58.00 431	77.45 201	26.12 530	60.78 415	43.00 519	56.02 200	70.18 355	75.76 392	13.27 550	67.24 380	48.02 357	80.89 366	80.65 275

LuminaMVS		71.15 194	70.79 210	72.24 189	77.20 202	58.34 224	72.18 204	76.20 241	54.91 213	77.74 171	81.93 291	49.17 335	76.31 240	62.12 188	85.66 255	82.07 237

PMatch-Up-SfM		68.45 253	66.90 285	73.11 153	77.17 203	76.10 32	71.60 226	62.67 394	47.32 354	87.78 19	82.41 278	24.19 515	66.58 393	58.86 235	90.11 148	76.66 347

fmvsm_s_conf0.5_n_9		74.56 116	74.30 124	75.34 113	77.17 203	64.87 140	72.62 196	76.17 242	54.54 225	78.32 161	86.14 189	65.14 163	75.72 248	73.10 73	85.55 256	85.42 111

usedtu_dtu_shiyan2		62.25 355	62.27 354	62.18 372	77.08 205	52.84 271	62.56 393	56.33 445	52.43 266	64.22 434	83.26 257	48.47 345	58.06 445	25.75 528	90.34 141	75.64 361

Effi-MVS+-dtu		75.43 101	72.28 178	84.91 2	77.05 206	83.58 1	78.47 105	77.70 219	57.68 172	74.89 253	78.13 373	64.80 165	84.26 81	56.46 265	85.32 262	86.88 71

CLD-MVS		72.88 155	72.36 176	74.43 124	77.03 207	54.30 259	68.77 287	83.43 89	52.12 271	76.79 201	74.44 411	69.54 106	83.91 83	55.88 270	93.25 76	85.09 122
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 89	76.00 99	78.06 78	77.02 208	64.77 141	80.78 76	82.66 107	60.39 145	74.15 272	83.30 255	69.65 105	82.07 124	69.27 108	86.75 240	87.36 61

SPE-MVS-test		74.89 113	74.23 126	76.86 91	77.01 209	62.94 164	78.98 100	84.61 63	58.62 160	70.17 356	80.80 315	66.74 141	81.96 126	61.74 191	89.40 169	85.69 106

Baseline_NR-MVSNet		70.62 206	73.19 152	62.92 364	76.97 210	34.44 486	68.84 280	70.88 315	60.25 146	79.50 142	90.53 59	61.82 198	69.11 357	54.67 290	95.27 15	85.22 115

ITE_SJBPF				80.35 41	76.94 211	73.60 51		80.48 160	66.87 75	83.64 88	86.18 186	70.25 98	79.90 168	61.12 201	88.95 183	87.56 59

mamba_0408		70.32 211	69.35 231	73.24 148	76.92 212	55.22 250	56.61 458	79.27 189	52.14 269	73.08 300	83.14 265	60.53 216	82.50 113	57.51 250	84.91 273	81.99 240

SSM_04072		67.23 278	69.35 231	60.89 395	76.92 212	55.22 250	56.61 458	79.27 189	52.14 269	73.08 300	83.14 265	60.53 216	45.46 507	57.51 250	84.91 273	81.99 240

SSM_0407		72.15 174	71.85 186	73.06 156	76.92 212	55.22 250	73.59 180	79.83 174	53.69 246	73.08 300	84.18 226	62.26 191	81.98 125	58.21 243	84.91 273	81.99 240

SSC-MVS		61.79 363	66.08 296	48.89 487	76.91 215	10.00 553	53.56 481	47.37 494	68.20 67	76.56 209	89.21 97	54.13 297	57.59 446	54.75 288	74.07 457	79.08 303

jason		64.47 322	62.84 348	69.34 252	76.91 215	59.20 205	67.15 321	65.67 369	35.29 486	65.16 420	76.74 386	44.67 363	70.68 332	54.74 289	79.28 400	78.14 321
jason: jason.
ETV-MVS		72.72 160	72.16 181	74.38 126	76.90 217	55.95 240	73.34 186	84.67 59	62.04 131	72.19 318	70.81 454	65.90 151	85.24 63	58.64 237	84.96 269	81.95 243

Anonymous20240529		72.56 164	73.79 136	68.86 267	76.89 218	45.21 375	68.80 286	77.25 227	67.16 72	76.89 194	90.44 62	65.95 150	74.19 276	50.75 323	90.00 150	87.18 66

EC-MVSNet		77.08 85	77.39 87	76.14 103	76.86 219	56.87 236	80.32 84	87.52 12	63.45 118	74.66 259	84.52 220	69.87 102	84.94 68	69.76 104	89.59 162	86.60 76

Casviewmamba		77.76 77	78.57 74	75.31 114	76.72 220	53.06 269	76.28 141	85.90 26	62.98 125	81.96 107	88.90 110	75.35 46	82.88 107	68.97 109	90.11 148	89.98 21

ArgMatch-Sym		63.94 330	63.05 345	66.61 311	76.68 221	75.81 34	65.98 339	57.57 428	35.60 485	80.60 130	69.62 472	43.62 373	55.74 451	49.14 342	88.61 187	68.29 449

PM-MVS		64.49 321	63.61 334	67.14 300	76.68 221	75.15 39	68.49 296	42.85 520	51.17 289	77.85 168	80.51 321	45.76 355	66.31 396	52.83 311	76.35 434	59.96 509

mvsmamba		68.87 243	67.30 276	73.57 142	76.58 223	53.70 265	84.43 42	74.25 262	45.38 380	76.63 205	84.55 219	35.85 433	85.27 60	49.54 336	78.49 411	81.75 250

TransMVSNet (Re)		69.62 227	71.63 192	63.57 348	76.51 224	35.93 476	65.75 345	71.29 306	61.05 138	75.02 249	89.90 86	65.88 152	70.41 339	49.79 331	89.48 165	84.38 158

GDP-MVS		70.84 201	69.24 237	75.62 109	76.44 225	55.65 246	74.62 168	82.78 104	49.63 313	72.10 320	83.79 243	31.86 464	82.84 108	64.93 151	87.01 234	88.39 50

BH-RMVSNet		68.69 250	68.20 260	70.14 231	76.40 226	53.90 264	64.62 367	73.48 268	58.01 168	73.91 283	81.78 293	59.09 242	78.22 202	48.59 349	77.96 420	78.31 316

PHI-MVS		74.92 110	74.36 123	76.61 94	76.40 226	62.32 168	80.38 81	83.15 92	54.16 237	73.23 296	80.75 316	62.19 193	83.86 84	68.02 118	90.92 126	83.65 178

UGNet		70.20 215	69.05 240	73.65 137	76.24 228	63.64 155	75.87 148	72.53 286	61.48 135	60.93 467	86.14 189	52.37 308	77.12 227	50.67 324	85.21 263	80.17 287
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		58.68 396	57.72 402	61.57 382	76.21 229	73.59 52	61.83 398	49.00 486	47.30 355	61.08 463	68.97 480	50.16 324	59.01 433	36.06 471	68.84 496	52.10 520

VPA-MVSNet		68.71 248	70.37 216	63.72 346	76.13 230	38.06 456	64.10 376	71.48 300	56.60 192	74.10 274	88.31 126	64.78 166	69.72 349	47.69 361	90.15 145	83.37 192

WB-MVS		60.04 384	64.19 327	47.59 490	76.09 231	10.22 552	52.44 489	46.74 496	65.17 97	74.07 276	87.48 142	53.48 300	55.28 454	49.36 338	72.84 466	77.28 333

PAPM		61.79 363	60.37 379	66.05 317	76.09 231	41.87 410	69.30 267	76.79 235	40.64 447	53.80 507	79.62 343	44.38 365	82.92 105	29.64 511	73.11 465	73.36 391

BH-w/o		64.81 315	64.29 326	66.36 314	76.08 233	54.71 256	65.61 347	75.23 253	50.10 308	71.05 345	71.86 445	54.33 296	79.02 180	38.20 442	76.14 436	65.36 480

dcpmvs_2		71.02 198	72.65 166	66.16 316	76.06 234	50.49 288	71.97 210	79.36 186	50.34 303	82.81 97	83.63 245	64.38 169	67.27 379	61.54 193	83.71 310	80.71 274

pmmvs6		71.82 180	73.66 138	66.31 315	75.94 235	42.01 409	66.99 324	72.53 286	63.45 118	76.43 216	92.78 12	72.95 68	69.69 350	51.41 318	90.46 138	87.22 62

testing3-2		56.85 416	57.62 404	54.53 453	75.84 236	22.23 541	51.26 496	49.10 484	61.04 139	63.74 444	79.73 339	22.29 524	59.44 430	31.16 504	84.43 293	81.92 244

CANet		73.00 149	71.84 187	76.48 97	75.82 237	61.28 179	74.81 159	80.37 164	63.17 122	62.43 456	80.50 322	61.10 211	85.16 67	64.00 163	84.34 298	83.01 206

pmmvs-eth3d		64.41 324	63.27 341	67.82 289	75.81 238	60.18 197	69.49 261	62.05 401	38.81 460	74.13 273	82.23 281	43.76 370	68.65 361	42.53 401	80.63 376	74.63 376

TR-MVS		64.59 319	63.54 336	67.73 290	75.75 239	50.83 286	63.39 385	70.29 320	49.33 319	71.55 337	74.55 409	50.94 319	78.46 192	40.43 424	75.69 439	73.89 386

MGCNet		75.45 100	74.66 114	77.83 79	75.58 240	61.53 175	78.29 107	77.18 229	63.15 124	69.97 360	87.20 144	57.54 267	87.05 9	74.05 66	88.96 182	84.89 127

tttt0517		69.46 230	67.79 267	74.46 121	75.34 241	52.72 272	75.05 155	63.27 392	54.69 219	78.87 150	84.37 223	26.63 499	81.15 140	63.95 165	87.93 203	89.51 25

cascas		64.59 319	62.77 350	70.05 236	75.27 242	50.02 294	61.79 399	71.61 296	42.46 426	63.68 445	68.89 483	49.33 332	80.35 159	47.82 360	84.05 301	79.78 291

API-MVS		70.97 199	71.51 197	69.37 249	75.20 243	55.94 241	80.99 73	76.84 233	62.48 129	71.24 342	77.51 379	61.51 203	80.96 151	52.04 312	85.76 254	71.22 420

EIA-MVS		68.59 252	67.16 277	72.90 165	75.18 244	55.64 247	69.39 264	81.29 137	52.44 265	64.53 425	70.69 455	60.33 222	82.30 120	54.27 297	76.31 435	80.75 271

PAPR		69.20 237	68.66 249	70.82 208	75.15 245	47.77 330	75.31 152	81.11 143	49.62 315	66.33 411	79.27 355	61.53 202	82.96 104	48.12 356	81.50 356	81.74 251

MVSFormer		69.93 221	69.03 241	72.63 177	74.93 246	59.19 206	83.98 45	75.72 248	52.27 267	63.53 449	76.74 386	43.19 376	80.56 155	72.28 84	78.67 408	78.14 321

lupinMVS		63.36 335	61.49 364	68.97 263	74.93 246	59.19 206	65.80 344	64.52 383	34.68 492	63.53 449	74.25 414	43.19 376	70.62 334	53.88 302	78.67 408	77.10 342

nrg030		74.87 114	75.99 100	71.52 197	74.90 248	49.88 302	74.10 176	82.58 109	54.55 224	83.50 89	89.21 97	71.51 81	75.74 247	61.24 198	92.34 89	88.94 39

TAPA-MVS	65.27 12	75.16 105	74.29 125	77.77 82	74.86 249	68.08 97	77.89 113	84.04 82	55.15 211	76.19 221	83.39 249	66.91 135	80.11 166	60.04 217	90.14 146	85.13 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
E4		72.74 159	73.54 142	70.35 219	74.85 250	46.82 351	69.53 260	82.80 101	55.60 206	76.23 219	86.50 177	69.87 102	77.45 216	63.72 169	82.77 324	86.76 74

FE-MVSNET2		68.70 249	69.85 223	65.22 325	74.82 251	37.95 458	67.28 319	73.47 269	53.40 253	77.65 175	87.72 140	59.72 232	73.17 288	46.39 371	88.23 193	84.56 149

RPSCF		75.76 95	74.37 122	79.93 43	74.81 252	77.53 21	77.53 118	79.30 188	59.44 152	78.88 149	89.80 87	71.26 86	73.09 289	57.45 252	80.89 366	89.17 33

EI-MVSNet-Vis-set		72.78 158	71.87 185	75.54 111	74.77 253	59.02 213	72.24 202	71.56 298	63.92 110	78.59 155	71.59 446	66.22 147	78.60 188	67.58 124	80.32 380	89.00 37

v1240		73.06 146	73.14 153	72.84 169	74.74 254	47.27 342	71.88 216	81.11 143	51.80 275	82.28 103	84.21 225	56.22 283	82.34 119	68.82 111	87.17 231	88.91 40

v1921920		72.96 153	72.98 159	72.89 166	74.67 255	47.58 335	71.92 214	80.69 153	51.70 277	81.69 115	83.89 241	56.58 279	82.25 121	68.34 114	87.36 213	88.82 42

EI-MVSNet-UG-set		72.63 162	71.68 190	75.47 112	74.67 255	58.64 221	72.02 208	71.50 299	63.53 116	78.58 157	71.39 450	65.98 149	78.53 189	67.30 134	80.18 384	89.23 31

Fast-Effi-MVS+		68.81 245	68.30 254	70.35 219	74.66 257	48.61 317	66.06 338	78.32 209	50.62 298	71.48 339	75.54 398	68.75 111	79.59 173	50.55 327	78.73 407	82.86 212

v1192		73.40 137	73.42 145	73.32 147	74.65 258	48.67 312	72.21 203	81.73 127	52.76 260	81.85 109	84.56 218	57.12 272	82.24 122	68.58 112	87.33 216	89.06 35

E5new		73.42 132	74.46 117	70.29 222	74.61 259	47.14 344	71.85 219	83.01 94	56.07 196	77.28 183	86.81 155	71.54 79	77.15 223	64.59 153	84.39 294	86.59 77

E5		73.42 132	74.46 117	70.29 222	74.61 259	47.14 344	71.85 219	83.01 94	56.07 196	77.28 183	86.81 155	71.54 79	77.15 223	64.59 153	84.39 294	86.59 77

E6new		73.42 132	74.46 117	70.29 222	74.60 261	47.14 344	71.86 217	82.99 96	56.07 196	77.28 183	86.81 155	71.55 77	77.14 225	64.59 153	84.39 294	86.59 77

E6		73.42 132	74.46 117	70.29 222	74.60 261	47.14 344	71.86 217	82.99 96	56.07 196	77.28 183	86.81 155	71.55 77	77.14 225	64.59 153	84.39 294	86.59 77

v144192		72.99 150	73.06 157	72.77 171	74.58 263	47.48 337	71.90 215	80.44 162	51.57 278	81.46 118	84.11 231	58.04 262	82.12 123	67.98 120	87.47 209	88.70 45

viewdifsd2359ckpt09		72.87 156	72.43 174	74.17 128	74.45 264	51.70 276	76.39 137	84.50 67	49.48 318	75.34 241	83.23 259	63.12 176	82.43 116	56.99 259	88.41 190	88.37 51

MAR-MVS		67.72 267	66.16 295	72.40 182	74.45 264	64.99 139	74.87 157	77.50 222	48.67 335	65.78 416	68.58 487	57.01 275	77.79 211	46.68 369	81.92 335	74.42 382
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 96	76.20 97	74.16 129	74.44 266	48.69 311	75.84 149	82.93 100	59.02 157	85.92 50	89.17 100	58.56 251	82.74 110	70.73 94	89.14 176	91.05 13

balanced_ft_v1		71.65 183	72.22 180	69.92 240	74.26 267	45.74 369	81.54 70	79.66 178	53.65 248	79.77 138	86.74 164	51.20 318	80.64 154	58.70 236	84.47 289	83.40 189

sasdasda		72.29 172	73.38 147	69.04 258	74.23 268	47.37 339	73.93 178	83.18 90	54.36 229	76.61 207	81.64 299	72.03 72	75.34 252	57.12 255	87.28 218	84.40 156

canonicalmvs		72.29 172	73.38 147	69.04 258	74.23 268	47.37 339	73.93 178	83.18 90	54.36 229	76.61 207	81.64 299	72.03 72	75.34 252	57.12 255	87.28 218	84.40 156

Anonymous202405211		66.02 298	66.89 286	63.43 354	74.22 270	38.14 454	59.00 435	66.13 366	63.33 121	69.76 365	85.95 198	51.88 310	70.50 336	44.23 388	87.52 207	81.64 252

Effi-MVS+		72.10 175	72.28 178	71.58 195	74.21 271	50.33 290	74.72 164	82.73 105	62.62 127	70.77 346	76.83 385	69.96 101	80.97 148	60.20 211	78.43 412	83.45 188

FE-MVS		68.29 258	66.96 283	72.26 186	74.16 272	54.24 260	77.55 117	73.42 271	57.65 175	72.66 309	84.91 211	32.02 463	81.49 135	48.43 352	81.85 338	81.04 260

v1144		73.29 140	73.39 146	73.01 157	74.12 273	48.11 322	72.01 209	81.08 146	53.83 244	81.77 111	84.68 213	58.07 261	81.91 127	68.10 116	86.86 235	88.99 38

E2		71.98 177	72.60 167	70.13 232	74.09 274	46.61 355	69.15 273	82.56 110	54.40 226	75.32 242	85.35 203	68.51 113	77.34 218	62.30 186	81.74 342	86.44 84

E3		71.98 177	72.60 167	70.13 232	74.09 274	46.61 355	69.15 273	82.56 110	54.40 226	75.31 243	85.35 203	68.51 113	77.34 218	62.30 186	81.75 341	86.44 84

BP-MVS1		71.60 184	70.06 218	76.20 102	74.07 276	55.22 250	74.29 173	73.44 270	57.29 179	73.87 285	84.65 215	32.57 453	83.49 94	72.43 83	87.94 202	89.89 23

ALIKED-LG		64.85 313	64.54 322	65.79 322	74.03 277	74.67 42	73.55 181	67.52 356	36.17 480	78.83 151	83.08 267	34.08 439	59.10 432	42.05 409	91.51 103	63.61 493

ALIKED-MNN		63.44 334	63.42 337	63.48 350	73.99 278	70.97 69	71.80 223	66.48 363	32.46 502	71.87 324	81.60 301	36.54 430	58.50 438	42.45 402	93.63 69	60.97 507

FA-MVS(test-final)		71.27 192	71.06 204	71.92 193	73.96 279	52.32 275	76.45 133	76.12 243	59.07 156	74.04 278	86.18 186	52.18 309	79.43 175	59.75 224	81.76 340	84.03 167

EI-MVSNet		69.61 228	69.01 242	71.41 200	73.94 280	49.90 297	71.31 232	71.32 304	58.22 165	75.40 237	70.44 458	58.16 255	75.85 242	62.51 182	79.81 391	88.48 46

CVMVSNet		59.21 391	58.44 396	61.51 383	73.94 280	47.76 331	71.31 232	64.56 382	26.91 526	60.34 469	70.44 458	36.24 432	67.65 373	53.57 305	68.66 497	69.12 443

casdiffseed414692147		74.13 119	74.76 113	72.25 188	73.89 282	49.89 301	75.54 151	82.35 115	58.57 163	77.77 170	87.76 139	69.09 109	78.46 192	59.77 222	88.10 197	88.41 48

fmvsm_s_conf0.5_n_5		71.46 188	71.62 193	70.99 207	73.89 282	59.95 200	73.02 192	73.08 272	45.15 388	77.30 182	84.06 232	64.73 167	70.08 344	71.20 88	82.10 333	82.92 208

IterMVS-LS		73.01 148	73.12 155	72.66 175	73.79 284	49.90 297	71.63 225	78.44 207	58.22 165	80.51 131	86.63 172	58.15 256	79.62 171	62.51 182	88.20 194	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
hybridcas		73.97 122	75.17 108	70.38 216	73.56 285	47.22 343	72.99 193	82.30 116	56.94 183	79.54 140	88.05 133	72.64 69	76.88 232	63.11 179	87.43 211	87.04 69

UWE-MVS		52.94 450	52.70 453	53.65 456	73.56 285	27.49 522	57.30 454	49.57 480	38.56 462	62.79 454	71.42 449	19.49 536	60.41 424	24.33 534	77.33 426	73.06 393

viewcassd2359sk11		71.41 189	71.89 184	69.98 238	73.50 287	46.46 360	68.91 279	82.39 114	53.62 249	74.57 263	84.41 222	67.40 130	77.27 220	61.35 197	80.89 366	86.21 90

alignmvs		70.54 207	71.00 205	69.15 256	73.50 287	48.04 325	69.85 256	79.62 180	53.94 243	76.54 211	82.00 286	59.00 243	74.68 266	57.32 253	87.21 227	84.72 140

Fast-Effi-MVS+-dtu		70.00 219	68.74 247	73.77 136	73.47 289	64.53 143	71.36 230	78.14 214	55.81 204	68.84 383	74.71 407	65.36 158	75.75 246	52.00 313	79.00 403	81.03 261

v8		75.07 107	75.64 103	73.35 145	73.42 290	47.46 338	75.20 153	81.45 133	60.05 147	85.64 54	89.26 95	58.08 260	81.80 131	69.71 106	87.97 201	90.79 17

tfpnnormal		66.48 291	67.93 263	62.16 373	73.40 291	36.65 467	63.45 384	64.99 376	55.97 201	72.82 306	87.80 138	57.06 274	69.10 358	48.31 354	87.54 206	80.72 273

IterMVS-SCA-FT		67.68 268	66.07 298	72.49 180	73.34 292	58.20 227	63.80 380	65.55 372	48.10 343	76.91 193	82.64 274	45.20 359	78.84 183	61.20 199	77.89 422	80.44 281

VNet		64.01 329	65.15 314	60.57 398	73.28 293	35.61 479	57.60 452	67.08 358	54.61 221	66.76 406	83.37 251	56.28 282	66.87 386	42.19 405	85.20 264	79.23 301

MGCFI-Net		71.70 182	73.10 156	67.49 292	73.23 294	43.08 401	72.06 207	82.43 113	54.58 222	75.97 223	82.00 286	72.42 70	75.22 255	57.84 248	87.34 215	84.18 163

3Dnovator	65.95 11	71.50 186	71.22 202	72.34 183	73.16 295	63.09 162	78.37 106	78.32 209	57.67 173	72.22 317	84.61 217	54.77 291	78.47 191	60.82 204	81.07 364	75.45 364

GBi-Net		68.30 256	68.79 244	66.81 306	73.14 296	40.68 426	71.96 211	73.03 273	54.81 214	74.72 256	90.36 73	48.63 342	75.20 257	47.12 363	85.37 258	84.54 150

test1		68.30 256	68.79 244	66.81 306	73.14 296	40.68 426	71.96 211	73.03 273	54.81 214	74.72 256	90.36 73	48.63 342	75.20 257	47.12 363	85.37 258	84.54 150

FMVSNet2		67.48 270	68.21 258	65.29 324	73.14 296	38.94 446	68.81 284	71.21 311	54.81 214	76.73 203	86.48 178	48.63 342	74.60 267	47.98 358	86.11 248	82.35 229

thisisatest0530		67.05 284	65.16 312	72.73 174	73.10 299	50.55 287	71.26 234	63.91 387	50.22 306	74.46 266	80.75 316	26.81 498	80.25 162	59.43 226	86.50 243	87.37 60

pm-mvs1		68.40 254	69.85 223	64.04 340	73.10 299	39.94 436	64.61 368	70.50 318	55.52 207	73.97 280	89.33 93	63.91 173	68.38 365	49.68 334	88.02 199	83.81 173

pmmvs4		60.78 378	59.04 389	66.00 319	73.06 301	57.67 229	64.53 370	60.22 411	36.91 475	65.96 413	77.27 381	39.66 410	68.54 364	38.87 434	74.89 447	71.80 412

SDMVSNet		66.36 293	67.85 266	61.88 377	73.04 302	46.14 366	58.54 445	71.36 303	51.42 281	68.93 377	82.72 271	65.62 154	62.22 418	54.41 294	84.67 280	77.28 333

sd_testset		63.55 332	65.38 308	58.07 429	73.04 302	38.83 448	57.41 453	65.44 373	51.42 281	68.93 377	82.72 271	63.76 174	58.11 443	41.05 416	84.67 280	77.28 333

dtuonlycased		61.79 363	62.24 355	60.43 402	73.00 304	39.07 443	61.74 400	60.61 407	33.09 500	74.10 274	80.34 325	59.20 240	60.39 425	38.34 440	79.76 395	81.83 246

fmvsm_s_conf0.5_n_6		70.08 217	69.97 219	70.39 215	72.99 305	58.93 215	68.84 280	76.40 239	49.08 327	68.75 385	81.65 298	57.34 269	71.97 312	70.91 92	83.81 305	80.26 284

E3new		70.94 200	71.30 200	69.86 242	72.98 306	46.34 364	68.74 289	82.28 117	53.01 256	73.95 281	83.57 246	66.41 145	77.21 221	60.68 206	80.06 385	86.03 95

v2v482		72.55 166	72.58 169	72.43 181	72.92 307	46.72 353	71.41 229	79.13 192	55.27 209	81.17 122	85.25 208	55.41 289	81.13 141	67.25 135	85.46 257	89.43 26

casdiffmvs_mvg		75.26 103	76.18 98	72.52 179	72.87 308	49.47 304	72.94 194	84.71 58	59.49 151	80.90 127	88.81 112	70.07 99	79.71 170	67.40 128	88.39 191	88.40 49
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		71.98 177	71.68 190	72.88 167	72.84 309	64.15 151	73.48 183	77.11 230	48.97 331	71.31 341	84.18 226	67.98 125	71.60 323	68.86 110	80.43 378	82.89 209

fmvsm_s_conf0.5_n_7		67.30 275	66.92 284	68.43 275	72.78 310	58.22 226	60.90 413	72.51 288	49.62 315	63.66 446	80.65 318	58.56 251	68.63 362	62.83 181	80.76 371	78.45 313

MIMVSNet		54.39 437	56.12 422	49.20 483	72.57 311	30.91 506	59.98 425	48.43 489	41.66 433	55.94 494	83.86 242	41.19 398	50.42 471	26.05 524	75.38 444	66.27 471

icg_test_0407_2		63.88 331	65.59 304	58.75 421	72.47 312	48.64 313	53.19 482	72.98 276	45.33 382	68.91 379	79.37 350	61.91 195	51.11 466	55.06 281	81.11 360	76.49 348

IMVS_0407		67.26 276	67.35 273	66.97 305	72.47 312	48.64 313	69.03 276	72.98 276	45.33 382	68.91 379	79.37 350	61.91 195	75.77 245	55.06 281	81.11 360	76.49 348

IMVS_0404		62.18 358	63.05 345	59.58 413	72.47 312	48.64 313	55.47 468	72.98 276	45.33 382	55.80 497	79.37 350	49.84 327	53.60 460	55.06 281	81.11 360	76.49 348

IMVS_0403		67.07 282	67.08 278	67.03 303	72.47 312	48.64 313	68.44 298	72.98 276	45.33 382	68.63 387	79.37 350	60.38 221	75.97 241	55.06 281	81.11 360	76.49 348

Patchmatch-RL test		59.95 385	59.12 388	62.44 369	72.46 316	54.61 258	59.63 429	47.51 493	41.05 440	74.58 262	74.30 413	31.06 473	65.31 402	51.61 315	79.85 390	67.39 457

CL-MVSNet_self_test		62.44 353	63.40 339	59.55 414	72.34 317	32.38 497	56.39 460	64.84 378	51.21 288	67.46 400	81.01 311	50.75 321	63.51 412	38.47 439	88.12 196	82.75 216

fmvsm_s_conf0.5_n_8		72.87 156	72.85 161	72.93 163	72.25 318	59.01 214	72.35 200	80.13 169	56.32 193	75.74 226	84.12 229	60.14 224	75.05 261	71.71 87	82.90 321	84.75 137

SD_0403		61.63 366	62.83 349	58.03 430	72.21 319	32.43 496	69.33 266	69.00 336	44.54 397	62.01 457	79.42 347	55.27 290	66.88 385	36.07 470	77.63 424	74.78 374

Vis-MVSNet (Re-imp)		62.74 348	63.21 342	61.34 388	72.19 320	31.56 502	67.31 316	53.87 455	53.60 250	69.88 362	83.37 251	40.52 403	70.98 331	41.40 413	86.78 239	81.48 254

thres100view900		61.17 371	61.09 368	61.39 386	72.14 321	35.01 482	65.42 351	56.99 436	55.23 210	70.71 347	79.90 336	32.07 461	72.09 308	35.61 473	81.73 343	77.08 343

fmvsm_s_conf0.5_n_11		71.06 195	70.91 206	71.51 198	72.09 322	59.40 203	73.49 182	79.97 172	50.98 291	68.33 390	81.50 302	61.82 198	72.64 294	69.54 107	80.43 378	82.51 225

ab-mvs		64.11 327	65.13 315	61.05 392	71.99 323	38.03 457	67.59 306	68.79 345	49.08 327	65.32 419	86.26 184	58.02 263	66.85 388	39.33 429	79.79 394	78.27 317

RRT-MVS		70.33 210	70.73 211	69.14 257	71.93 324	45.24 374	75.10 154	75.08 256	60.85 142	78.62 154	87.36 143	49.54 329	78.64 187	60.16 213	77.90 421	83.55 180

thres600view7		61.82 362	61.38 365	63.12 357	71.81 325	34.93 483	64.64 366	56.99 436	54.78 218	70.33 352	79.74 338	32.07 461	72.42 301	38.61 437	83.46 314	82.02 238

ALIKED-NN		61.86 361	61.18 366	63.92 341	71.72 326	71.04 66	69.24 270	66.41 364	29.80 515	64.25 433	81.10 308	35.56 435	58.35 439	41.25 414	91.30 108	62.35 501

fmvsm_s_conf0.5_n_4		70.18 216	69.83 225	71.24 204	71.65 327	58.59 222	69.29 268	71.66 295	48.69 334	71.62 330	82.11 283	59.94 227	70.03 345	74.52 58	78.96 404	85.10 121

QAPM		69.18 238	69.26 236	68.94 264	71.61 328	52.58 274	80.37 82	78.79 200	49.63 313	73.51 289	85.14 209	53.66 299	79.12 178	55.11 280	75.54 441	75.11 371

WB-MVSnew		53.94 443	54.76 441	51.49 469	71.53 329	28.05 518	58.22 448	50.36 476	37.94 468	59.16 477	70.17 464	49.21 334	51.94 464	24.49 532	71.80 476	74.47 381

KinetiMVS		72.61 163	72.54 170	72.82 170	71.47 330	55.27 249	68.54 294	76.50 236	61.70 134	74.95 251	86.08 193	59.17 241	76.95 229	69.96 101	84.45 290	86.24 87

baseline		73.10 143	73.96 133	70.51 214	71.46 331	46.39 363	72.08 206	84.40 69	55.95 202	76.62 206	86.46 179	67.20 131	78.03 207	64.22 161	87.27 220	87.11 68

fmvsm_s_conf0.5_n_3		72.97 152	74.13 129	69.47 248	71.40 332	58.36 223	73.07 189	80.64 156	56.86 185	75.49 234	84.67 214	67.86 127	72.33 305	75.68 45	81.54 354	77.73 330

viewmacassd2359aftdt		71.41 189	72.29 177	68.78 269	71.32 333	44.81 379	70.11 250	81.51 130	52.64 262	74.95 251	86.79 160	66.02 148	74.50 269	62.43 185	84.86 277	87.03 70

casdiffmvs		73.06 146	73.84 134	70.72 210	71.32 333	46.71 354	70.93 238	84.26 75	55.62 205	77.46 180	87.10 146	67.09 133	77.81 210	63.95 165	86.83 237	87.64 57
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		72.36 169	72.49 171	71.96 191	71.29 335	64.06 153	72.79 195	81.82 125	40.23 449	81.25 121	81.04 310	70.62 93	68.69 360	69.74 105	83.60 313	83.14 199

Anonymous20231206		54.13 438	55.82 427	49.04 486	70.89 336	35.96 475	51.73 492	50.87 474	34.86 487	62.49 455	79.22 356	42.52 386	44.29 517	27.95 519	81.88 336	66.88 463

fmvsm_s_conf0.1_n_a		67.37 274	66.36 292	70.37 218	70.86 337	61.17 181	74.00 177	57.18 435	40.77 444	68.83 384	80.88 312	63.11 178	67.61 375	66.94 136	74.72 448	82.33 232

viewdifsd2359ckpt13		69.89 222	69.74 226	70.32 221	70.82 338	48.73 309	72.39 199	81.39 135	48.20 340	72.73 307	82.73 270	62.61 183	76.50 237	55.87 271	80.93 365	85.73 105

tfpn200view9		60.35 382	59.97 381	61.51 383	70.78 339	35.35 480	63.27 387	57.47 429	53.00 257	68.31 391	77.09 383	32.45 456	72.09 308	35.61 473	81.73 343	77.08 343

thres400		60.77 379	59.97 381	63.15 356	70.78 339	35.35 480	63.27 387	57.47 429	53.00 257	68.31 391	77.09 383	32.45 456	72.09 308	35.61 473	81.73 343	82.02 238

fmvsm_s_conf0.5_n_10		72.30 171	72.02 183	73.15 152	70.76 341	59.05 212	73.40 185	79.63 179	48.80 333	75.39 240	84.03 233	59.60 235	75.18 260	72.85 76	83.68 312	85.21 118

AstraMVS		67.11 280	66.84 288	67.92 282	70.75 342	51.36 280	64.77 363	67.06 359	49.03 329	75.40 237	82.05 284	51.26 317	70.65 333	58.89 234	82.32 330	81.77 249

MSDG		67.47 272	67.48 271	67.46 293	70.70 343	54.69 257	66.90 327	78.17 212	60.88 141	70.41 350	74.76 405	61.22 209	73.18 287	47.38 362	76.87 430	74.49 380

testing91		55.74 427	55.29 436	57.08 438	70.63 344	30.85 507	54.94 474	56.31 446	50.34 303	57.08 485	70.10 466	24.50 513	65.86 397	36.98 457	76.75 431	74.53 379

test_yl		65.11 308	65.09 317	65.18 326	70.59 345	40.86 421	63.22 389	72.79 280	57.91 169	68.88 381	79.07 361	42.85 383	74.89 263	45.50 382	84.97 266	79.81 289

DCV-MVSNet		65.11 308	65.09 317	65.18 326	70.59 345	40.86 421	63.22 389	72.79 280	57.91 169	68.88 381	79.07 361	42.85 383	74.89 263	45.50 382	84.97 266	79.81 289

test_fmvsm_n_1920		69.63 226	68.45 251	73.16 150	70.56 347	65.86 128	70.26 248	78.35 208	37.69 469	74.29 270	78.89 363	61.10 211	68.10 369	65.87 144	79.07 402	85.53 109

OpenMVS	62.51 15	68.76 246	68.75 246	68.78 269	70.56 347	53.91 263	78.29 107	77.35 224	48.85 332	70.22 353	83.52 247	52.65 307	76.93 230	55.31 278	81.99 334	75.49 363

viewdifsd2359ckpt07		70.24 212	71.30 200	67.05 302	70.55 349	43.90 391	67.15 321	77.48 223	53.60 250	75.49 234	85.35 203	71.42 84	72.13 307	59.03 231	81.60 351	85.12 120

DELS-MVS		68.83 244	68.31 253	70.38 216	70.55 349	48.31 318	63.78 381	82.13 120	54.00 240	68.96 374	75.17 403	58.95 244	80.06 167	58.55 238	82.74 325	82.76 215
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		62.77 346	64.36 323	57.97 432	70.52 351	33.96 489	61.66 402	67.88 354	50.67 297	73.18 297	82.58 275	48.03 347	68.22 367	43.21 394	81.55 352	71.74 413

testing222		53.37 445	52.50 456	55.98 446	70.51 352	29.68 513	56.20 463	51.85 468	46.19 368	56.76 489	68.94 481	19.18 537	65.39 401	25.87 527	76.98 429	72.87 397

fmvsm_l_conf0.5_n_9		70.73 204	71.08 203	69.67 245	70.44 353	58.80 217	70.21 249	75.11 255	48.15 342	73.50 290	82.69 273	65.69 153	68.05 371	70.87 93	83.02 319	82.16 234

testing11		53.13 447	52.26 458	55.75 447	70.44 353	31.73 501	54.75 475	52.40 466	44.81 394	52.36 513	68.40 488	21.83 525	65.74 400	32.64 498	72.73 467	69.78 434

LCM-MVSNet-Re		69.10 240	71.57 196	61.70 380	70.37 355	34.30 488	61.45 405	79.62 180	56.81 186	89.59 8	88.16 131	68.44 116	72.94 290	42.30 403	87.33 216	77.85 327

UBG		49.18 478	49.35 479	48.66 488	70.36 356	26.56 527	50.53 498	45.61 500	37.43 471	53.37 509	65.97 502	23.03 520	54.20 458	26.29 522	71.54 477	65.20 483

patch_mono-2		62.73 349	64.08 328	58.68 424	70.36 356	55.87 242	60.84 414	64.11 386	41.23 437	64.04 437	78.22 370	60.00 225	48.80 483	54.17 299	83.71 310	71.37 417

ETVMVS		50.32 470	49.87 478	51.68 467	70.30 358	26.66 525	52.33 491	43.93 511	43.54 412	54.91 501	67.95 490	20.01 534	60.17 427	22.47 537	73.40 462	68.22 451

SCA		58.57 399	58.04 400	60.17 407	70.17 359	41.07 419	65.19 355	53.38 461	43.34 417	61.00 466	73.48 422	45.20 359	69.38 355	40.34 425	70.31 487	70.05 430

WBMVS		53.38 444	54.14 445	51.11 471	70.16 360	26.66 525	50.52 499	51.64 471	39.32 454	63.08 452	77.16 382	23.53 517	55.56 452	31.99 499	79.88 389	71.11 423

ET-MVSNet_ETH3D		63.32 337	60.69 375	71.20 205	70.15 361	55.66 245	65.02 359	64.32 384	43.28 418	68.99 373	72.05 441	25.46 507	78.19 205	54.16 300	82.80 323	79.74 292

testing99		55.16 433	54.56 443	56.98 440	70.13 362	30.58 509	54.55 477	54.11 454	49.53 317	56.76 489	70.14 465	22.76 521	65.79 399	36.99 456	76.04 437	74.57 377

guyue		66.95 286	66.74 289	67.56 291	70.12 363	51.14 282	65.05 358	68.68 346	49.98 311	74.64 260	80.83 314	50.77 320	70.34 340	57.72 249	82.89 322	81.21 255

viewmanbaseed2359cas		70.24 212	70.83 208	68.48 274	69.99 364	44.55 384	69.48 262	81.01 148	50.87 293	73.61 287	84.84 212	64.00 171	74.31 274	60.24 210	83.43 315	86.56 81

APD_test1		75.04 108	75.38 107	74.02 132	69.89 365	70.15 77	76.46 132	79.71 177	65.50 88	82.99 93	88.60 118	66.94 134	72.35 302	59.77 222	88.54 188	79.56 293

PVSNet_BlendedMVS		65.38 305	64.30 324	68.61 272	69.81 366	49.36 305	65.60 348	78.96 194	45.50 376	59.98 470	78.61 365	51.82 311	78.20 203	44.30 386	84.11 300	78.27 317

PVSNet_Blended		62.90 344	61.64 361	66.69 309	69.81 366	49.36 305	61.23 408	78.96 194	42.04 429	59.98 470	68.86 484	51.82 311	78.20 203	44.30 386	77.77 423	72.52 402

OpenMVS_ROB	54.93 17	63.23 340	63.28 340	63.07 358	69.81 366	45.34 373	68.52 295	67.14 357	43.74 409	70.61 348	79.22 356	47.90 349	72.66 293	48.75 347	73.84 460	71.21 421

test_fmvsmconf0.01_n		73.91 123	73.64 139	74.71 118	69.79 369	66.25 123	75.90 147	79.90 173	46.03 371	76.48 214	85.02 210	67.96 126	73.97 279	74.47 60	87.22 226	83.90 171

fmvsm_s_conf0.5_n_a		67.00 285	65.95 302	70.17 229	69.72 370	61.16 182	73.34 186	56.83 438	40.96 441	68.36 389	80.08 332	62.84 180	67.57 376	66.90 138	74.50 452	81.78 248

usedtu_dtu_shiyan1		61.16 372	60.92 370	61.90 374	69.70 371	36.41 471	58.57 443	68.86 341	44.94 392	65.02 422	75.67 394	43.00 380	70.28 341	40.83 419	81.68 347	78.99 304

FE-MVSNET3		61.16 372	60.92 370	61.90 374	69.70 371	36.41 471	58.57 443	68.86 341	44.94 392	65.02 422	75.67 394	43.00 380	70.28 341	40.82 420	81.68 347	78.99 304

FMVSNet3		65.00 311	65.16 312	64.52 334	69.47 373	37.56 463	66.63 330	70.38 319	51.55 279	74.72 256	83.27 256	37.89 423	74.44 271	47.12 363	85.37 258	81.57 253

myMVS_eth3d28		51.35 463	51.99 460	49.44 482	69.21 374	22.51 539	49.82 502	49.11 483	49.00 330	55.03 500	70.31 461	22.73 522	52.88 463	24.33 534	78.39 415	72.92 395

SP-DiffGlue		64.90 312	65.69 303	62.51 368	69.18 375	64.39 145	69.79 257	60.46 410	52.50 263	75.70 227	72.08 438	44.17 367	48.59 487	67.84 123	79.52 398	74.54 378

MS-PatchMatch		55.59 429	54.89 440	57.68 434	69.18 375	49.05 308	61.00 411	62.93 393	35.98 482	58.36 480	68.93 482	36.71 429	66.59 392	37.62 450	63.30 516	57.39 515

baseline1		57.82 405	58.36 398	56.19 444	69.17 377	30.76 508	62.94 391	55.21 448	46.04 370	63.83 442	78.47 366	41.20 397	63.68 410	39.44 428	68.99 495	74.13 383

v148		69.38 233	69.39 230	69.36 250	69.14 378	44.56 382	68.83 282	72.70 284	54.79 217	78.59 155	84.12 229	54.69 292	76.74 236	59.40 227	82.20 331	86.79 72

test_fmvsmconf0.1_n		73.26 141	72.82 164	74.56 120	69.10 379	66.18 125	74.65 167	79.34 187	45.58 375	75.54 232	83.91 240	67.19 132	73.88 282	73.26 72	86.86 235	83.63 179

LoFTR		61.29 369	62.50 352	57.67 435	69.07 380	65.66 131	68.96 277	48.59 487	43.15 419	86.65 39	79.95 334	32.68 452	53.14 462	46.21 374	87.20 228	54.22 519

fmvsm_s_conf0.1_n		66.60 288	65.54 305	69.77 243	68.99 381	59.15 209	72.12 205	56.74 440	40.72 446	68.25 393	80.14 331	61.18 210	66.92 382	67.34 133	74.40 453	83.23 197

Syy-MVS		54.13 438	55.45 432	50.18 475	68.77 382	23.59 535	55.02 471	44.55 506	43.80 405	58.05 482	64.07 507	46.22 354	58.83 434	46.16 375	72.36 470	68.12 453

myMVS_eth3d		50.36 469	50.52 474	49.88 476	68.77 382	22.69 537	55.02 471	44.55 506	43.80 405	58.05 482	64.07 507	14.16 548	58.83 434	33.90 489	72.36 470	68.12 453

SP-LightGlue		66.16 297	66.97 282	63.75 344	68.62 384	66.76 116	68.82 283	62.15 396	57.30 178	70.52 349	75.63 396	43.02 379	48.82 482	75.09 49	81.55 352	75.66 360

test_fmvsmconf_n		72.91 154	72.40 175	74.46 121	68.62 384	66.12 126	74.21 175	78.80 199	45.64 374	74.62 261	83.25 258	66.80 140	73.86 283	72.97 75	86.66 242	83.39 190

SP-SuperGlue		66.58 289	67.36 272	64.24 335	68.59 386	66.47 119	68.14 300	61.29 405	58.07 167	71.67 328	75.95 391	46.37 353	50.95 469	74.72 53	81.46 357	75.29 369

SIFT-MNN		59.60 388	58.57 393	62.71 366	68.39 387	69.16 90	63.67 382	48.13 490	45.22 386	73.92 282	73.85 419	30.71 478	50.57 470	39.45 427	83.78 306	68.40 447

CANet_DTU		64.04 328	63.83 330	64.66 332	68.39 387	42.97 403	73.45 184	74.50 261	52.05 273	54.78 502	75.44 401	43.99 368	70.42 338	53.49 306	78.41 414	80.59 277

EU-MVSNet		60.82 377	60.80 374	60.86 396	68.37 389	41.16 417	72.27 201	68.27 351	26.96 524	69.08 371	75.71 393	32.09 460	67.44 377	55.59 276	78.90 405	73.97 384

PVSNet	43.83 21	51.56 461	51.17 466	52.73 461	68.34 390	38.27 452	48.22 506	53.56 459	36.41 478	54.29 505	64.94 506	34.60 438	54.20 458	30.34 506	69.87 490	65.71 475

EPNet		69.10 240	67.32 274	74.46 121	68.33 391	61.27 180	77.56 116	63.57 389	60.95 140	56.62 491	82.75 269	51.53 314	81.24 139	54.36 296	90.20 143	80.88 267
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.1_n_2		69.14 239	68.42 252	71.28 202	68.30 392	57.60 231	65.06 357	69.91 322	48.24 338	74.56 264	82.84 268	55.55 288	69.73 348	70.66 96	80.69 373	86.52 82

fmvsm_s_conf0.5_n		66.34 295	65.27 309	69.57 247	68.20 393	59.14 211	71.66 224	56.48 441	40.92 442	67.78 395	79.46 345	61.23 207	66.90 383	67.39 129	74.32 456	82.66 221

IB-MVS	49.67 18	59.69 387	56.96 412	67.90 283	68.19 394	50.30 291	61.42 406	65.18 375	47.57 350	55.83 495	67.15 500	23.77 516	79.60 172	43.56 392	79.97 387	73.79 388
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		69.22 235	69.68 227	67.83 286	68.17 395	46.57 357	66.42 334	68.93 337	50.60 299	77.47 179	83.95 237	68.16 119	73.84 284	58.49 239	84.92 271	83.10 200

viewmsd2359difaftdt		69.22 235	69.68 227	67.83 286	68.17 395	46.57 357	66.42 334	68.93 337	50.60 299	77.48 178	83.94 238	68.16 119	73.84 284	58.49 239	84.92 271	83.10 200

MVS		60.62 380	59.97 381	62.58 367	68.13 397	47.28 341	68.59 291	73.96 265	32.19 503	59.94 472	68.86 484	50.48 322	77.64 214	41.85 410	75.74 438	62.83 495

blended_shiyan8		62.19 357	61.77 357	63.46 352	68.01 398	40.65 429	60.47 419	69.13 334	47.24 356	66.44 409	70.55 457	43.75 371	71.91 315	43.18 395	87.19 229	77.81 329

eth_miper_zixun_eth		69.42 231	68.73 248	71.50 199	67.99 399	46.42 361	67.58 307	78.81 197	50.72 296	78.13 164	80.34 325	50.15 325	80.34 160	60.18 212	84.65 282	87.74 56

blended_shiyan6		62.20 356	61.77 357	63.47 351	67.98 400	40.64 430	60.46 420	69.15 331	47.24 356	66.43 410	70.57 456	43.73 372	71.93 314	43.16 396	87.24 222	77.85 327

TinyColmap		67.98 262	69.28 235	64.08 338	67.98 400	46.82 351	70.04 251	75.26 252	53.05 255	77.36 181	86.79 160	59.39 237	72.59 298	45.64 380	88.01 200	72.83 398

EPNet_dtu		58.93 394	58.52 394	60.16 408	67.91 402	47.70 333	69.97 253	58.02 426	49.73 312	47.28 529	73.02 430	38.14 419	62.34 416	36.57 463	85.99 250	70.43 428
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 408	57.02 410	59.17 416	67.89 403	34.93 483	58.91 438	57.25 433	50.24 305	64.01 438	71.46 448	32.49 454	71.39 325	31.31 502	79.57 397	71.19 422

fmvsm_s_conf0.5_n_2		68.93 242	68.23 257	71.02 206	67.78 404	57.58 232	64.74 364	69.56 326	48.16 341	74.38 269	82.32 280	56.00 285	69.68 351	70.65 97	80.52 377	85.80 103

SSC-MVS3.2		57.01 415	59.50 386	49.57 481	67.73 405	25.95 531	46.68 513	51.75 470	51.41 283	63.84 441	79.66 341	53.28 302	50.34 473	37.85 447	83.28 317	72.41 404

our_test_3		56.46 420	56.51 416	56.30 443	67.70 406	39.66 440	55.36 470	52.34 467	40.57 448	63.85 440	69.91 469	40.04 406	58.22 442	43.49 393	75.29 446	71.03 425

ppachtmachnet_test		60.26 383	59.61 384	62.20 371	67.70 406	44.33 387	58.18 449	60.96 406	40.75 445	65.80 415	72.57 434	41.23 396	63.92 409	46.87 367	82.42 328	78.33 315

VortexMVS		65.93 299	66.04 300	65.58 323	67.63 408	47.55 336	64.81 361	72.75 283	47.37 353	75.17 247	79.62 343	49.28 333	71.00 330	55.20 279	82.51 327	78.21 319

SIFT-NCM-Cal		58.68 396	57.65 403	61.77 379	67.58 409	68.99 94	62.62 392	43.04 518	44.65 396	75.91 224	72.23 436	33.66 443	49.28 481	34.36 485	84.76 278	67.03 461

MVS_Test		69.84 223	70.71 212	67.24 297	67.49 410	43.25 400	69.87 255	81.22 141	52.69 261	71.57 336	86.68 168	62.09 194	74.51 268	66.05 141	78.74 406	83.96 168

fmvsm_l_conf0.5_n		67.48 270	66.88 287	69.28 253	67.41 411	62.04 169	70.69 242	69.85 323	39.46 453	69.59 366	81.09 309	58.15 256	68.73 359	67.51 126	78.16 419	77.07 345

blend_shiyan4		57.39 411	55.27 437	63.73 345	67.25 412	41.75 413	60.08 424	69.15 331	47.57 350	64.19 435	67.14 501	20.46 530	72.34 303	40.73 421	60.88 523	77.11 341

thisisatest0515		60.48 381	57.86 401	68.34 277	67.25 412	46.42 361	60.58 418	62.14 397	40.82 443	63.58 448	69.12 478	26.28 502	78.34 199	48.83 345	82.13 332	80.26 284

V42		71.06 195	70.83 208	71.72 194	67.25 412	47.14 344	65.94 340	80.35 165	51.35 284	83.40 90	83.23 259	59.25 239	78.80 184	65.91 143	80.81 370	89.23 31

fmvsm_l_conf0.5_n_a		66.66 287	65.97 301	68.72 271	67.09 415	61.38 178	70.03 252	69.15 331	38.59 461	68.41 388	80.36 324	56.56 280	68.32 366	66.10 140	77.45 425	76.46 352

GA-MVS		62.91 343	61.66 360	66.66 310	67.09 415	44.49 386	61.18 410	69.36 329	51.33 285	69.33 370	74.47 410	36.83 428	74.94 262	50.60 326	74.72 448	80.57 278

gbinet_0.2-2-1-0.02		62.58 351	61.83 356	64.86 331	67.07 417	41.37 415	61.56 403	67.91 353	49.27 321	66.62 408	67.23 499	41.53 394	74.46 270	45.94 377	89.31 172	78.74 308

testf1		75.66 97	76.57 92	72.95 160	67.07 417	67.62 103	76.10 143	80.68 154	64.95 100	86.58 41	90.94 46	71.20 87	71.68 321	60.46 208	91.13 116	79.56 293

APD_test2		75.66 97	76.57 92	72.95 160	67.07 417	67.62 103	76.10 143	80.68 154	64.95 100	86.58 41	90.94 46	71.20 87	71.68 321	60.46 208	91.13 116	79.56 293

mmtdpeth		68.76 246	70.55 214	63.40 355	67.06 420	56.26 239	68.73 290	71.22 310	55.47 208	70.09 357	88.64 117	65.29 160	56.89 449	58.94 233	89.50 164	77.04 346

SIFT-NN		56.62 418	55.34 435	60.47 401	67.01 421	67.25 109	61.74 400	45.38 504	42.69 424	64.49 426	71.36 451	28.48 494	47.55 494	36.68 460	80.23 382	66.63 467

SIFT-NN-NCMNet		57.48 409	56.02 424	61.86 378	66.93 422	69.26 89	62.14 397	44.46 508	42.32 428	67.01 405	71.93 443	32.46 455	50.96 468	35.06 479	81.87 337	65.36 480

HY-MVS	49.31 19	57.96 403	57.59 406	59.10 419	66.85 423	36.17 473	65.13 356	65.39 374	39.24 457	54.69 504	78.14 372	44.28 366	67.18 381	33.75 491	70.79 483	73.95 385

wanda-best-256-512		61.16 372	60.55 376	62.98 359	66.67 424	39.85 438	58.66 440	68.87 339	46.67 362	64.46 427	67.75 491	41.94 388	71.84 316	42.67 399	87.24 222	77.26 336

FE-blended-shiyan7		61.16 372	60.55 376	62.98 359	66.67 424	39.85 438	58.66 440	68.87 339	46.67 362	64.46 427	67.75 491	41.94 388	71.84 316	42.67 399	87.24 222	77.26 336

usedtu_blend_shiyan5		63.30 338	63.13 343	63.78 343	66.67 424	41.75 413	68.57 293	73.64 266	57.20 181	64.46 427	67.75 491	41.94 388	72.34 303	40.72 422	87.24 222	77.26 336

CR-MVSNet		58.96 392	58.49 395	60.36 405	66.37 427	48.24 320	70.93 238	56.40 443	32.87 501	61.35 461	86.66 169	33.19 446	63.22 413	48.50 351	70.17 488	69.62 437

RPMNet		65.77 301	65.08 319	67.84 285	66.37 427	48.24 320	70.93 238	86.27 20	54.66 220	61.35 461	86.77 163	33.29 445	85.67 51	55.93 269	70.17 488	69.62 437

IterMVS		63.12 341	62.48 353	65.02 329	66.34 429	52.86 270	63.81 379	62.25 395	46.57 364	71.51 338	80.40 323	44.60 364	66.82 389	51.38 319	75.47 442	75.38 366
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		69.82 224	69.89 221	69.61 246	66.24 430	43.48 396	68.12 302	79.61 182	51.43 280	77.72 172	80.18 330	54.61 294	78.15 206	63.62 172	87.50 208	87.20 65

tpm2		56.12 423	54.64 442	60.55 399	66.24 430	36.01 474	68.14 300	56.77 439	33.60 498	58.25 481	75.52 400	30.25 482	74.33 273	33.27 493	69.76 492	71.32 418

Anonymous20240521		63.55 332	66.07 298	55.99 445	66.18 432	44.04 390	68.77 287	68.80 344	46.99 359	72.57 310	85.84 199	39.87 407	50.22 475	53.40 309	92.23 92	73.71 389

ELoFTR		57.63 407	59.55 385	51.85 466	66.16 433	61.46 176	69.66 259	43.94 510	30.20 514	82.28 103	77.47 380	33.76 442	42.30 524	42.10 406	90.40 140	51.81 521

Patchmtry		60.91 376	63.01 347	54.62 452	66.10 434	26.27 529	67.47 310	56.40 443	54.05 239	72.04 323	86.66 169	33.19 446	60.17 427	43.69 390	87.45 210	77.42 331

SP-MNN		63.33 336	64.30 324	60.41 404	66.01 435	60.04 198	65.58 349	60.61 407	49.33 319	69.45 367	73.75 420	41.65 392	48.61 486	69.96 101	82.36 329	72.57 401

FMVSNet5		55.08 434	55.54 430	53.71 455	65.80 436	33.50 493	56.22 462	52.50 465	43.72 410	61.06 464	83.38 250	25.46 507	54.87 455	30.11 508	81.64 350	72.75 399

1314		59.83 386	58.86 391	62.74 365	65.71 437	44.78 380	68.59 291	72.63 285	33.54 499	61.05 465	67.29 498	43.62 373	71.26 326	49.49 337	67.84 502	72.19 409

SIFT-CM-Cal		57.90 404	56.75 414	61.34 388	65.62 438	67.48 106	60.91 412	44.69 505	44.05 402	73.16 298	71.09 453	30.69 479	50.23 474	33.27 493	87.25 221	66.31 470

MonoMVSNet		62.75 347	63.42 337	60.73 397	65.60 439	40.77 424	72.49 198	70.56 317	52.49 264	75.07 248	79.42 347	39.52 412	69.97 347	46.59 370	69.06 494	71.44 416

SIFT-ConvMatch		58.61 398	57.61 405	61.63 381	65.55 440	67.97 98	62.24 396	42.52 521	44.40 398	77.28 183	73.28 428	30.00 485	50.42 471	36.36 464	86.82 238	66.50 468

MDTV_nov1_ep13			54.05 447		65.54 441	29.30 515	59.00 435	55.22 447	35.96 483	52.44 511	75.98 390	30.77 477	59.62 429	38.21 441	73.33 464

SIFT-UMatch		58.13 401	57.37 409	60.42 403	65.49 442	67.10 112	61.52 404	43.57 513	44.20 400	76.80 200	72.60 432	29.70 488	47.95 493	36.61 461	85.82 251	66.20 472

baseline2		55.57 430	52.74 452	64.05 339	65.26 443	44.11 389	62.38 394	54.43 452	39.03 458	51.21 516	67.35 497	33.66 443	72.45 300	37.14 454	64.22 514	75.60 362

dtuplus		65.20 307	64.80 321	66.40 313	65.25 444	44.86 378	64.55 369	72.19 293	43.76 407	72.09 321	81.87 292	57.49 268	71.49 324	48.79 346	77.23 428	82.85 213

USDC		62.80 345	63.10 344	61.89 376	65.19 445	43.30 399	67.42 311	74.20 264	35.80 484	72.25 316	84.48 221	45.67 356	71.95 313	37.95 446	84.97 266	70.42 429

tpm		50.60 467	52.42 457	45.14 504	65.18 446	26.29 528	60.30 421	43.50 514	37.41 472	57.01 486	79.09 360	30.20 484	42.32 523	32.77 497	66.36 508	66.81 465

PatchmatchNet		54.60 436	54.27 444	55.59 448	65.17 447	39.08 442	66.92 326	51.80 469	39.89 450	58.39 479	73.12 429	31.69 467	58.33 440	43.01 398	58.38 531	69.38 441
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_ehance_all_eth		68.36 255	68.16 261	68.98 262	65.14 448	43.34 398	67.07 323	78.92 196	49.11 326	76.21 220	77.72 376	53.48 300	77.92 209	61.16 200	84.59 285	85.68 107

cl____		68.26 261	68.26 255	68.29 278	64.98 449	43.67 394	65.89 341	74.67 257	50.04 309	76.86 196	82.42 277	48.74 340	75.38 250	60.92 203	89.81 157	85.80 103

DIV-MVS_self_test		68.27 259	68.26 255	68.29 278	64.98 449	43.67 394	65.89 341	74.67 257	50.04 309	76.86 196	82.43 276	48.74 340	75.38 250	60.94 202	89.81 157	85.81 99

SIFT-UM-Cal		57.67 406	56.99 411	59.70 410	64.92 451	66.46 120	59.84 428	46.03 499	44.18 401	76.77 202	71.89 444	29.03 493	48.71 484	33.08 495	87.13 233	63.93 492

SP-NN		62.65 350	63.58 335	59.87 409	64.90 452	59.38 204	64.50 371	60.00 414	50.42 302	66.09 412	73.43 424	43.16 378	46.39 500	71.17 89	78.53 410	73.85 387

tpm cat1		54.02 441	52.63 454	58.19 428	64.85 453	39.86 437	66.26 337	57.28 432	32.16 504	56.90 487	70.39 460	32.75 451	65.30 403	34.29 486	58.79 528	69.41 440

viewmambaseed2359dif		65.63 302	65.13 315	67.11 301	64.57 454	44.73 381	64.12 375	72.48 289	43.08 420	71.59 331	81.17 306	58.90 246	72.46 299	52.94 310	77.33 426	84.13 166

XXY-MVS		55.19 432	57.40 408	48.56 489	64.45 455	34.84 485	51.54 493	53.59 457	38.99 459	63.79 443	79.43 346	56.59 278	45.57 505	36.92 458	71.29 480	65.25 482

onestephybrid01		68.67 251	68.21 258	70.07 235	64.40 456	49.83 303	67.51 308	76.41 238	51.08 290	71.78 325	81.97 290	59.69 233	75.32 254	59.85 220	81.20 359	85.06 125

PatchT		53.35 446	56.47 417	43.99 509	64.19 457	17.46 545	59.15 432	43.10 517	52.11 272	54.74 503	86.95 152	29.97 486	49.98 476	43.62 391	74.40 453	64.53 490

viewmamba		69.26 234	69.34 233	69.03 260	64.17 458	47.67 334	67.23 320	76.95 232	52.82 259	73.15 299	83.23 259	62.99 179	74.06 278	63.71 170	79.80 393	85.36 113

D2MVS		62.58 351	61.05 369	67.20 298	63.85 459	47.92 326	56.29 461	69.58 325	39.32 454	70.07 358	78.19 371	34.93 437	72.68 292	53.44 307	83.74 307	81.00 263

mvs_anonymous		65.08 310	65.49 306	63.83 342	63.79 460	37.60 462	66.52 333	69.82 324	43.44 413	73.46 292	86.08 193	58.79 248	71.75 320	51.90 314	75.63 440	82.15 235

diffmvs_AUTHOR		68.27 259	68.59 250	67.32 296	63.76 461	45.37 372	65.31 352	77.19 228	49.25 322	72.68 308	82.19 282	59.62 234	71.17 327	65.75 145	81.53 355	85.42 111

CostFormer		57.35 412	56.14 421	60.97 393	63.76 461	38.43 450	67.50 309	60.22 411	37.14 474	59.12 478	76.34 389	32.78 449	71.99 311	39.12 433	69.27 493	72.47 403

SIFT-NN-CMatch		57.48 409	56.23 419	61.21 391	63.66 463	67.89 100	60.78 415	40.90 534	41.97 430	71.65 329	71.96 442	32.11 459	49.35 479	38.19 443	84.88 276	66.37 469

Gipuma		69.55 229	72.83 163	59.70 410	63.63 464	53.97 262	80.08 88	75.93 246	64.24 108	73.49 291	88.93 109	57.89 264	62.46 414	59.75 224	91.55 102	62.67 497
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
cl22		67.14 279	66.51 290	69.03 260	63.20 465	43.46 397	66.88 328	76.25 240	49.22 324	74.48 265	77.88 375	45.49 358	77.40 217	60.64 207	84.59 285	86.24 87

SIFT-PCN-Cal		56.03 424	55.47 431	57.69 433	63.19 466	62.93 165	58.63 442	43.46 515	42.37 427	75.62 229	69.51 475	25.32 509	44.67 515	33.77 490	87.41 212	65.45 479

gg-mvs-nofinetune		55.75 426	56.75 414	52.72 462	62.87 467	28.04 519	68.92 278	41.36 529	71.09 50	50.80 518	92.63 14	20.74 527	66.86 387	29.97 509	72.41 469	63.25 494

SIFT-NN-UMatch		57.27 413	56.18 420	60.54 400	62.85 468	66.67 118	61.19 409	41.27 530	43.01 421	70.01 359	72.44 435	32.76 450	49.32 480	38.19 443	83.87 302	65.63 476

SIFT-PointCN		56.55 419	55.82 427	58.75 421	62.59 469	63.48 158	59.22 431	45.58 501	42.97 422	74.44 267	69.65 471	25.00 511	47.28 497	35.25 476	87.73 204	65.49 477

SIFT-NCMNet		56.27 422	55.94 426	57.26 437	62.54 470	64.28 149	59.61 430	41.26 531	43.43 414	78.50 159	69.35 477	32.26 458	45.98 502	27.16 521	89.34 171	61.53 505

gm-plane-assit					62.51 471	33.91 491			37.25 473		62.71 513		72.74 291	38.70 435

SIFT-NN-PointCN		57.17 414	56.12 422	60.35 406	62.47 472	65.79 129	59.98 425	44.36 509	42.73 423	72.13 319	71.16 452	30.84 476	48.08 492	36.92 458	84.45 290	67.17 460

mvs5depth		66.35 294	67.98 262	61.47 385	62.43 473	51.05 283	69.38 265	69.24 330	56.74 188	73.62 286	89.06 105	46.96 352	58.63 437	55.87 271	88.49 189	74.73 375

MVS-HIRNet		45.53 489	47.29 486	40.24 518	62.29 474	26.82 524	56.02 465	37.41 540	29.74 516	43.69 541	81.27 304	33.96 440	55.48 453	24.46 533	56.79 532	38.43 540

diffmvs		67.42 273	67.50 270	67.20 298	62.26 475	45.21 375	64.87 360	77.04 231	48.21 339	71.74 326	79.70 340	58.40 253	71.17 327	64.99 149	80.27 381	85.22 115
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		41.62 502	39.89 507	46.80 495	61.81 476	51.59 277	33.56 539	35.74 542	27.48 521	37.64 546	53.53 529	23.24 518	42.09 525	27.39 520	58.64 529	46.72 528

KD-MVS_self_test		66.38 292	67.51 269	62.97 362	61.76 477	34.39 487	58.11 450	75.30 251	50.84 295	77.12 189	85.42 202	56.84 276	69.44 354	51.07 321	91.16 113	85.08 123

MDA-MVSNet-bldmvs		62.34 354	61.73 359	64.16 336	61.64 478	49.90 297	48.11 507	57.24 434	53.31 254	80.95 124	79.39 349	49.00 338	61.55 421	45.92 378	80.05 386	81.03 261

miper_enhance_ethall		65.86 300	65.05 320	68.28 280	61.62 479	42.62 406	64.74 364	77.97 216	42.52 425	73.42 293	72.79 431	49.66 328	77.68 213	58.12 245	84.59 285	84.54 150

WTY-MVS		49.39 477	50.31 476	46.62 498	61.22 480	32.00 500	46.61 514	49.77 478	33.87 495	54.12 506	69.55 474	41.96 387	45.40 508	31.28 503	64.42 513	62.47 499

CMPMVS	48.73 20	61.54 368	60.89 372	63.52 349	61.08 481	51.55 278	68.07 303	68.00 352	33.88 494	65.87 414	81.25 305	37.91 422	67.71 372	49.32 339	82.60 326	71.31 419
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
0.4-1-1-0.1		51.02 465	48.31 482	59.15 417	60.95 482	37.94 459	53.17 487	59.12 421	39.52 452	47.88 527	50.31 536	20.36 532	69.99 346	35.79 472	67.66 504	69.51 439

test-LLR		50.43 468	50.69 473	49.64 479	60.76 483	41.87 410	53.18 483	45.48 502	43.41 415	49.41 523	60.47 521	29.22 490	44.73 513	42.09 407	72.14 473	62.33 502

test-mter		48.56 481	48.20 484	49.64 479	60.76 483	41.87 410	53.18 483	45.48 502	31.91 508	49.41 523	60.47 521	18.34 539	44.73 513	42.09 407	72.14 473	62.33 502

hybridnocas07		66.30 296	66.22 294	66.51 312	60.68 485	44.53 385	64.01 378	74.60 259	48.26 337	70.21 354	81.74 297	56.61 277	71.06 329	60.70 205	79.20 401	83.94 170

GG-mvs-BLEND				52.24 463	60.64 486	29.21 516	69.73 258	42.41 522		45.47 532	52.33 532	20.43 531	68.16 368	25.52 530	65.42 510	59.36 511

hybrid		65.62 303	65.49 306	66.01 318	60.48 487	44.28 388	64.13 374	74.21 263	46.41 365	69.84 363	80.86 313	55.77 286	70.28 341	59.30 228	78.42 413	83.46 186

tpmvs		55.84 425	55.45 432	57.01 439	60.33 488	33.20 494	65.89 341	59.29 418	47.52 352	56.04 493	73.60 421	31.05 474	68.06 370	40.64 423	64.64 512	69.77 435

UWE-MVS-28		44.18 497	44.37 502	43.61 511	60.10 489	16.96 546	52.62 488	33.27 544	36.79 476	48.86 525	69.47 476	19.96 535	45.65 504	13.40 544	64.83 511	68.23 450

miper_lstm_enhance		61.97 359	61.63 362	62.98 359	60.04 490	45.74 369	47.53 509	70.95 313	44.04 403	73.06 303	78.84 364	39.72 409	60.33 426	55.82 273	84.64 283	82.88 210

dmvs_re		49.91 474	50.77 472	47.34 491	59.98 491	38.86 447	53.18 483	53.58 458	39.75 451	55.06 499	61.58 517	36.42 431	44.40 516	29.15 516	68.23 498	58.75 512

PVSNet_0	36.71 22	41.12 503	40.78 506	42.14 513	59.97 492	40.13 434	40.97 527	42.24 526	30.81 512	44.86 536	49.41 537	40.70 402	45.12 510	23.15 536	34.96 544	41.16 538

dmvs_testset		45.26 490	47.51 485	38.49 521	59.96 493	14.71 548	58.50 446	43.39 516	41.30 436	51.79 515	56.48 526	39.44 413	49.91 478	21.42 539	55.35 537	50.85 522

new-patchmatchnet		52.89 451	55.76 429	44.26 508	59.94 494	6.31 554	37.36 535	50.76 475	41.10 438	64.28 432	79.82 337	44.77 362	48.43 490	36.24 467	87.61 205	78.03 323

test20.03		55.74 427	57.51 407	50.42 474	59.89 495	32.09 499	50.63 497	49.01 485	50.11 307	65.07 421	83.23 259	45.61 357	48.11 491	30.22 507	83.82 304	71.07 424

MVSTER		63.29 339	61.60 363	68.36 276	59.77 496	46.21 365	60.62 417	71.32 304	41.83 432	75.40 237	79.12 359	30.25 482	75.85 242	56.30 266	79.81 391	83.03 205

reproduce_monomvs		58.94 393	58.14 399	61.35 387	59.70 497	40.98 420	60.24 423	63.51 390	45.85 373	68.95 375	75.31 402	18.27 540	65.82 398	51.47 317	79.97 387	77.26 336

N_pmnet		52.06 457	51.11 467	54.92 449	59.64 498	71.03 67	37.42 534	61.62 404	33.68 496	57.12 484	72.10 437	37.94 421	31.03 541	29.13 517	71.35 479	62.70 496

MatchFormer		53.09 448	55.03 438	47.30 492	59.31 499	57.25 233	67.30 317	37.25 541	27.23 522	82.61 100	74.56 408	26.23 503	42.89 522	34.73 482	86.00 249	41.75 537

test_vis1_n_1920		52.96 449	53.50 448	51.32 470	59.15 500	44.90 377	56.13 464	64.29 385	30.56 513	59.87 474	60.68 519	40.16 405	47.47 495	48.25 355	62.46 518	61.58 504

JIA-IIPM		54.03 440	51.62 461	61.25 390	59.14 501	55.21 254	59.10 434	47.72 491	50.85 294	50.31 522	85.81 200	20.10 533	63.97 408	36.16 468	55.41 536	64.55 489

0.3-1-1-0.015		49.68 475	46.67 489	58.69 423	58.94 502	37.51 464	51.35 495	59.18 419	38.35 463	44.62 538	47.14 539	18.49 538	69.68 351	35.13 478	66.84 507	68.87 445

LF4IMVS		67.50 269	67.31 275	68.08 281	58.86 503	61.93 170	71.43 228	75.90 247	44.67 395	72.42 313	80.20 328	57.16 270	70.44 337	58.99 232	86.12 247	71.88 411

UnsupCasMVSNet_bld		50.01 473	51.03 469	46.95 493	58.61 504	32.64 495	48.31 505	53.27 462	34.27 493	60.47 468	71.53 447	41.40 395	47.07 498	30.68 505	60.78 524	61.13 506

dongtai		31.66 509	32.98 512	27.71 526	58.58 505	12.61 550	45.02 519	14.24 554	41.90 431	47.93 526	43.91 541	10.65 551	41.81 529	14.06 543	20.53 547	28.72 542

dp		44.09 498	44.88 499	41.72 516	58.53 506	23.18 536	54.70 476	42.38 524	34.80 489	44.25 539	65.61 504	24.48 514	44.80 512	29.77 510	49.42 539	57.18 516

testgi		54.00 442	56.86 413	45.45 502	58.20 507	25.81 532	49.05 503	49.50 481	45.43 379	67.84 394	81.17 306	51.81 313	43.20 521	29.30 512	79.41 399	67.34 459

wuyk23d		61.97 359	66.25 293	49.12 485	58.19 508	60.77 191	66.32 336	52.97 463	55.93 203	90.62 5	86.91 153	73.07 65	35.98 538	20.63 541	91.63 99	50.62 523

0.4-1-1-0.2		49.48 476	46.57 490	58.21 427	58.02 509	36.93 466	50.24 500	59.18 419	37.97 466	44.94 534	46.16 540	20.52 529	69.54 353	34.84 481	67.28 506	68.17 452

ANet_high		67.08 281	69.94 220	58.51 426	57.55 510	27.09 523	58.43 447	76.80 234	63.56 115	82.40 102	91.93 25	59.82 230	64.98 405	50.10 330	88.86 185	83.46 186

Patchmatch-test		47.93 482	49.96 477	41.84 514	57.42 511	24.26 534	48.75 504	41.49 528	39.30 456	56.79 488	73.48 422	30.48 481	33.87 539	29.29 513	72.61 468	67.39 457

test_vis1_n		51.27 464	50.41 475	53.83 454	56.99 512	50.01 295	56.75 456	60.53 409	25.68 529	59.74 475	57.86 525	29.40 489	47.41 496	43.10 397	63.66 515	64.08 491

new_pmnet		37.55 507	39.80 508	30.79 524	56.83 513	16.46 547	39.35 531	30.65 545	25.59 530	45.26 533	61.60 516	24.54 512	28.02 545	21.60 538	52.80 538	47.90 526

pmmvs3		46.71 485	45.09 496	51.55 468	56.76 514	48.25 319	55.78 467	39.53 537	24.13 534	50.35 521	63.40 509	15.90 545	51.08 467	29.29 513	70.69 485	55.33 518

sss		47.59 484	48.32 481	45.40 503	56.73 515	33.96 489	45.17 518	48.51 488	32.11 507	52.37 512	65.79 503	40.39 404	41.91 527	31.85 500	61.97 520	60.35 508

tpmrst		50.15 471	51.38 464	46.45 499	56.05 516	24.77 533	64.40 373	49.98 477	36.14 481	53.32 510	69.59 473	35.16 436	48.69 485	39.24 431	58.51 530	65.89 473

TESTMET0.1,1		45.17 491	44.93 497	45.89 501	56.02 517	38.31 451	53.18 483	41.94 527	27.85 519	44.86 536	56.47 527	17.93 541	41.50 530	38.08 445	68.06 499	57.85 513

ADS-MVSNet2		48.76 479	47.25 487	53.29 460	55.90 518	40.54 431	47.34 510	54.99 450	31.41 510	50.48 519	72.06 439	31.23 470	54.26 457	25.93 525	55.93 533	65.07 484

ADS-MVSNet		44.62 494	45.58 493	41.73 515	55.90 518	20.83 542	47.34 510	39.94 536	31.41 510	50.48 519	72.06 439	31.23 470	39.31 534	25.93 525	55.93 533	65.07 484

ttmdpeth		56.40 421	55.45 432	59.25 415	55.63 520	40.69 425	58.94 437	49.72 479	36.22 479	65.39 417	86.97 151	23.16 519	56.69 450	42.30 403	80.74 372	80.36 282

test0.0.03 1		47.72 483	48.31 482	45.93 500	55.53 521	29.39 514	46.40 515	41.21 532	43.41 415	55.81 496	67.65 494	29.22 490	43.77 520	25.73 529	69.87 490	64.62 488

UnsupCasMVSNet_eth		52.26 456	53.29 451	49.16 484	55.08 522	33.67 492	50.03 501	58.79 423	37.67 470	63.43 451	74.75 406	41.82 391	45.83 503	38.59 438	59.42 527	67.98 456

pmmvs5		52.49 455	52.58 455	52.21 464	54.99 523	32.38 497	55.45 469	53.84 456	32.15 505	55.49 498	74.81 404	38.08 420	57.37 447	34.02 487	74.40 453	66.88 463

DSMNet-mixed		43.18 501	44.66 500	38.75 520	54.75 524	28.88 517	57.06 455	27.42 547	13.47 544	47.27 530	77.67 377	38.83 415	39.29 535	25.32 531	60.12 526	48.08 525

MDA-MVSNet_test_wron		52.57 454	53.49 450	49.81 478	54.24 525	36.47 469	40.48 529	46.58 497	38.13 464	75.47 236	73.32 426	41.05 401	43.85 519	40.98 417	71.20 481	69.10 444

YYNet1		52.58 453	53.50 448	49.85 477	54.15 526	36.45 470	40.53 528	46.55 498	38.09 465	75.52 233	73.31 427	41.08 400	43.88 518	41.10 415	71.14 482	69.21 442

EPMVS		45.74 488	46.53 491	43.39 512	54.14 527	22.33 540	55.02 471	35.00 543	34.69 491	51.09 517	70.20 463	25.92 505	42.04 526	37.19 453	55.50 535	65.78 474

test_cas_vis1_n_1920		50.90 466	50.92 470	50.83 473	54.12 528	47.80 329	51.44 494	54.61 451	26.95 525	63.95 439	60.85 518	37.86 424	44.97 511	45.53 381	62.97 517	59.72 510

test_fmvs3		56.78 417	55.99 425	59.12 418	53.96 529	48.09 323	58.76 439	66.22 365	27.54 520	76.66 204	68.69 486	25.32 509	51.31 465	53.42 308	73.38 463	77.97 326

test_fmvs1_n		52.70 452	52.01 459	54.76 450	53.83 530	50.36 289	55.80 466	65.90 367	24.96 531	65.39 417	60.64 520	27.69 496	48.46 488	45.88 379	67.99 500	65.46 478

dtuonly		50.13 472	51.25 465	46.77 496	53.07 531	30.10 511	52.41 490	49.25 482	28.98 517	53.76 508	72.59 433	39.83 408	41.82 528	37.58 451	73.80 461	68.37 448

KD-MVS_2432*1600		52.05 458	51.58 462	53.44 458	52.11 532	31.20 503	44.88 520	64.83 379	41.53 434	64.37 430	70.03 467	15.61 546	64.20 406	36.25 465	74.61 450	64.93 486

miper_refine_blended		52.05 458	51.58 462	53.44 458	52.11 532	31.20 503	44.88 520	64.83 379	41.53 434	64.37 430	70.03 467	15.61 546	64.20 406	36.25 465	74.61 450	64.93 486

test_fmvs2		54.80 435	54.11 446	56.88 441	51.76 534	49.95 296	56.70 457	65.80 368	26.22 527	69.42 368	65.25 505	31.82 465	49.98 476	49.63 335	70.36 486	70.71 426

E-PMN		45.17 491	45.36 494	44.60 506	50.07 535	42.75 404	38.66 532	42.29 525	46.39 366	39.55 542	51.15 533	26.00 504	45.37 509	37.68 448	76.41 433	45.69 533

PMMVS		44.69 493	43.95 503	46.92 494	50.05 536	53.47 267	48.08 508	42.40 523	22.36 539	44.01 540	53.05 531	42.60 385	45.49 506	31.69 501	61.36 522	41.79 536

test_fmvs1		51.51 462	50.86 471	53.48 457	49.72 537	49.35 307	54.11 478	64.96 377	24.64 533	63.66 446	59.61 524	28.33 495	48.45 489	45.38 384	67.30 505	62.66 498

EMVS		44.61 495	44.45 501	45.10 505	48.91 538	43.00 402	37.92 533	41.10 533	46.75 361	38.00 544	48.43 538	26.42 500	46.27 501	37.11 455	75.38 444	46.03 532

mvsany_test3		43.76 500	41.01 504	52.01 465	48.09 539	57.74 228	42.47 524	23.85 550	23.30 537	64.80 424	62.17 515	27.12 497	40.59 531	29.17 515	48.11 540	57.69 514

mvsany_test1		37.88 505	35.74 510	44.28 507	47.28 540	49.90 297	36.54 536	24.37 549	19.56 543	45.76 531	53.46 530	32.99 448	37.97 537	26.17 523	35.52 543	44.99 535

MASt3R-SfM		45.75 487	47.16 488	41.50 517	47.00 541	47.91 328	45.50 517	38.10 538	21.81 542	73.91 283	62.86 511	29.14 492	29.95 543	34.59 483	71.54 477	46.65 529

XFeat-NN		44.60 496	44.89 498	43.74 510	46.61 542	44.56 382	41.07 526	40.59 535	23.40 536	66.73 407	54.97 528	20.65 528	40.41 532	33.52 492	76.49 432	46.25 531

test_vis3_rt		51.94 460	51.04 468	54.65 451	46.32 543	50.13 293	44.34 522	78.17 212	23.62 535	68.95 375	62.81 512	21.41 526	38.52 536	41.49 412	72.22 472	75.30 368

test_vis1_rt		46.70 486	45.24 495	51.06 472	44.58 544	51.04 284	39.91 530	67.56 355	21.84 541	51.94 514	50.79 534	33.83 441	39.77 533	35.25 476	61.50 521	62.38 500

XFeat-MNN		48.68 480	49.35 479	46.65 497	44.49 545	46.89 350	46.91 512	43.80 512	27.16 523	75.21 244	60.05 523	22.65 523	46.52 499	39.33 429	84.57 288	46.53 530

MVStest1		55.38 431	54.97 439	56.58 442	43.72 546	40.07 435	59.13 433	47.09 495	34.83 488	76.53 212	84.65 215	13.55 549	53.30 461	55.04 285	80.23 382	76.38 353

MVE	27.91 23	36.69 508	35.64 511	39.84 519	43.37 547	35.85 477	19.49 542	24.61 548	24.68 532	39.05 543	62.63 514	38.67 417	27.10 546	21.04 540	47.25 541	56.56 517
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 506	40.87 505	28.36 525	42.41 548	5.35 555	24.61 541	27.75 546	32.15 505	47.85 528	70.27 462	35.85 433	29.51 544	19.08 542	67.85 501	50.22 524

test_f		43.79 499	45.63 492	38.24 522	42.29 549	38.58 449	34.76 538	47.68 492	22.22 540	67.34 401	63.15 510	31.82 465	30.60 542	39.19 432	62.28 519	45.53 534

kuosan		22.02 511	23.52 515	17.54 529	41.56 550	11.24 551	41.99 525	13.39 555	26.13 528	28.87 547	30.75 544	9.72 553	21.94 549	4.77 549	14.49 548	19.43 544

PDCNetPlus		38.77 504	39.67 509	36.07 523	38.82 551	27.82 521	36.52 537	51.55 472	22.53 538	37.81 545	50.69 535	7.16 554	32.98 540	28.21 518	83.73 309	47.40 527

DeepMVS_CX				11.83 530	15.51 552	13.86 549		11.25 556	5.76 546	20.85 549	26.46 545	17.06 544	9.22 550	9.69 547	13.82 549	12.42 545

GLUNet-SfM		24.03 510	24.76 513	21.84 527	12.84 553	18.20 544	27.35 540	15.92 552	9.48 545	63.07 453	34.11 543	10.20 552	23.13 548	9.60 548	40.26 542	24.18 543

test_method		19.26 512	19.12 516	19.71 528	9.09 554	1.91 557	7.79 544	53.44 460	1.42 547	10.27 550	35.80 542	17.42 543	25.11 547	12.44 545	24.38 546	32.10 541

tmp_tt		11.98 514	14.73 517	3.72 531	2.28 555	4.62 556	19.44 543	14.50 553	0.47 549	21.55 548	9.58 547	25.78 506	4.57 551	11.61 546	27.37 545	1.96 546

test123		4.43 517	5.78 520	0.39 533	0.97 556	0.28 558	46.33 516	0.45 557	0.31 550	0.62 552	1.50 550	0.61 556	0.11 553	0.56 550	0.63 550	0.77 548

testmvs		4.06 518	5.28 521	0.41 532	0.64 557	0.16 559	42.54 523	0.31 558	0.26 551	0.50 553	1.40 551	0.77 555	0.17 552	0.56 550	0.55 551	0.90 547

mmdepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

monomultidepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test_blank		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

eth-test2					0.00 558

eth-test					0.00 558

uanet_test		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

DCPMVS		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

cdsmvs_eth3d_5k		17.71 513	23.62 514	0.00 534	0.00 558	0.00 560	0.00 545	70.17 321	0.00 552	0.00 554	74.25 414	68.16 119	0.00 554	0.00 552	0.00 552	0.00 549

pcd_1.5k_mvsjas		5.20 516	6.93 519	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	62.39 188	0.00 554	0.00 552	0.00 552	0.00 549

sosnet-low-res		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uncertanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

Regformer		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

ab-mvs-re		5.62 515	7.50 518	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	67.46 495	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

WAC-MVS						22.69 537								36.10 469

PC_three_1452									46.98 360	81.83 110	86.28 182	66.55 144	84.47 78	63.31 177	90.78 131	83.49 182

test_241102_TWO								84.80 51	72.61 35	84.93 68	89.70 88	77.73 25	85.89 43	75.29 47	94.22 56	83.25 195

test_0728_THIRD									74.03 24	85.83 52	90.41 65	75.58 43	85.69 49	77.43 35	94.74 34	84.31 160

GSMVS																70.05 430

sam_mvs1												31.41 468				70.05 430

sam_mvs												31.21 472

MTGPA								80.63 157

test_post1							66.63 330				2.08 548	30.66 480	59.33 431	40.34 425

test_post											1.99 549	30.91 475	54.76 456

patchmatchnet-post											68.99 479	31.32 469	69.38 355

MTMP							84.83 38	19.26 551

test9_res														72.12 86	91.37 106	77.40 332

agg_prior2														70.70 95	90.93 125	78.55 312

test_prior4						70.14 78	77.57 115

test_prior2							75.57 150		58.92 158	76.53 212	86.78 162	67.83 128		69.81 103	92.76 82

旧先验2							71.17 235		45.11 389	78.54 158			61.28 422	59.19 230

新几何2							71.33 231

无先验							74.82 158	70.94 314	47.75 349				76.85 234	54.47 292		72.09 410

原ACMM2							74.78 162

testdata2													67.30 378	48.34 353

segment_acmp												68.30 118

testdata1							68.34 299		57.24 180

plane_prior5								85.49 33					86.15 30	71.09 90	90.94 123	84.82 134

plane_prior4											89.11 102

plane_prior3						65.67 130			63.82 112	78.23 162

plane_prior2							82.74 61		65.45 89

plane_prior						65.18 136	80.06 89		61.88 133						89.91 155

n2								0.00 559

nn								0.00 559

door-mid								55.02 449

test11								82.71 106

door								52.91 464

HQP5-MVS						58.80 217

BP-MVS														67.38 131

HQP4-MVS										71.59 331			85.31 58			83.74 176

HQP3-MVS								84.12 79							89.16 173

HQP2-MVS												58.09 258

MDTV_nov1_ep13_2view						18.41 543	53.74 480		31.57 509	44.89 535		29.90 487		32.93 496		71.48 415

ACMMP++_ref															89.47 166

ACMMP++															91.96 95

Test By Simon												62.56 184

High-resolution multi-view benchmark