High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

mamv4		90.28 1	88.75 1	94.85 1	93.34 1	96.17 1	82.69 58	91.63 1	86.34 1	97.97 1	94.77 3	66.57 126	95.38 1	87.74 1	97.72 1	93.00 7

LCM-MVSNet		86.90 2	88.67 2	81.57 25	91.50 2	63.30 128	84.80 35	87.77 11	86.18 2	96.26 2	96.06 1	90.32 1	84.49 72	68.08 106	97.05 2	96.93 1

MTAPA		83.19 23	83.87 23	81.13 34	91.16 3	78.16 12	84.87 33	80.63 137	72.08 42	84.93 60	90.79 52	74.65 50	84.42 75	80.98 6	94.75 34	80.82 237

mPP-MVS		84.01 14	84.39 16	82.88 7	90.65 4	81.38 4	87.08 13	82.79 91	72.41 40	85.11 59	90.85 51	76.65 32	84.89 66	79.30 21	94.63 38	82.35 199

MP-MVS		83.19 23	83.54 28	82.14 20	90.54 5	79.00 9	86.42 25	83.59 81	71.31 45	81.26 104	90.96 46	74.57 51	84.69 70	78.41 26	94.78 33	82.74 188
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMVS	70.70 6	81.70 37	83.15 36	77.36 84	90.35 6	82.82 3	82.15 60	79.22 168	74.08 24	87.16 33	91.97 23	84.80 2	76.97 210	64.98 138	93.61 65	72.28 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 52	82.86 41	73.07 146	89.93 7	39.21 365	77.15 118	81.28 119	79.74 6	90.87 5	92.73 14	75.03 47	84.93 65	63.83 152	95.19 21	95.07 3

DTE-MVSNet		80.35 53	82.89 40	72.74 164	89.84 8	37.34 385	77.16 117	81.81 109	80.45 4	90.92 4	92.95 10	74.57 51	86.12 31	63.65 153	94.68 37	94.76 6

PEN-MVS		80.46 51	82.91 39	73.11 145	89.83 9	39.02 368	77.06 120	82.61 97	80.04 5	90.60 7	92.85 12	74.93 48	85.21 60	63.15 160	95.15 23	95.09 2

region2R		83.54 18	83.86 24	82.58 15	89.82 10	77.53 18	87.06 16	84.23 71	70.19 54	83.86 74	90.72 56	75.20 44	86.27 23	79.41 19	94.25 55	83.95 146

ACMMPR		83.62 16	83.93 22	82.69 12	89.78 11	77.51 22	87.01 17	84.19 72	70.23 52	84.49 67	90.67 57	75.15 45	86.37 20	79.58 15	94.26 54	84.18 140

MSP-MVS		80.49 50	79.67 63	82.96 6	89.70 12	77.46 23	87.16 12	85.10 44	64.94 99	81.05 107	88.38 121	57.10 243	87.10 9	79.75 12	83.87 251	84.31 137
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 59	81.65 50	72.98 150	89.66 13	39.06 367	76.76 121	80.46 141	78.91 9	90.32 8	91.70 33	68.49 100	84.89 66	63.40 157	95.12 24	95.01 4

PGM-MVS		83.07 26	83.25 35	82.54 16	89.57 14	77.21 24	82.04 62	85.40 37	67.96 65	84.91 63	90.88 49	75.59 40	86.57 16	78.16 27	94.71 36	83.82 148

WR-MVS_H		80.22 55	82.17 46	74.39 121	89.46 15	42.69 336	78.24 104	82.24 101	78.21 13	89.57 10	92.10 21	68.05 105	85.59 50	66.04 130	95.62 10	94.88 5

XVS		83.51 19	83.73 25	82.85 9	89.43 16	77.61 16	86.80 20	84.66 57	72.71 33	82.87 84	90.39 69	73.86 56	86.31 21	78.84 24	94.03 58	84.64 120

X-MVStestdata		76.81 84	74.79 107	82.85 9	89.43 16	77.61 16	86.80 20	84.66 57	72.71 33	82.87 84	9.95 460	73.86 56	86.31 21	78.84 24	94.03 58	84.64 120

CP-MVS		84.12 12	84.55 15	82.80 11	89.42 18	79.74 6	88.19 5	84.43 62	71.96 44	84.70 65	90.56 59	77.12 29	86.18 28	79.24 22	95.36 15	82.49 196

ACMMP_NAP		82.33 32	83.28 33	79.46 51	89.28 19	69.09 80	83.62 47	84.98 46	64.77 100	83.97 73	91.02 45	75.53 43	85.93 38	82.00 3	94.36 50	83.35 167

UniMVSNet_ETH3D		76.74 85	79.02 66	69.92 213	89.27 20	43.81 323	74.47 159	71.70 263	72.33 41	85.50 54	93.65 4	77.98 24	76.88 214	54.60 252	91.64 91	89.08 34

HFP-MVS		83.39 22	84.03 21	81.48 27	89.25 21	75.69 28	87.01 17	84.27 68	70.23 52	84.47 68	90.43 64	76.79 30	85.94 36	79.58 15	94.23 56	82.82 185

ACMMP		84.22 10	84.84 13	82.35 18	89.23 22	76.66 26	87.65 7	85.89 27	71.03 48	85.85 46	90.58 58	78.77 18	85.78 44	79.37 20	95.17 22	84.62 122
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CPTT-MVS		81.51 39	81.76 48	80.76 38	89.20 23	78.75 10	86.48 24	82.03 105	68.80 59	80.92 109	88.52 117	72.00 69	82.39 110	74.80 49	93.04 72	81.14 227

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

ZNCC-MVS		83.12 25	83.68 26	81.45 28	89.14 25	73.28 46	86.32 26	85.97 26	67.39 68	84.02 72	90.39 69	74.73 49	86.46 17	80.73 8	94.43 45	84.60 125

HPM-MVS++		79.89 56	79.80 62	80.18 43	89.02 26	78.44 11	83.49 50	80.18 147	64.71 101	78.11 144	88.39 120	65.46 138	83.14 95	77.64 34	91.20 102	78.94 272

GST-MVS		82.79 29	83.27 34	81.34 31	88.99 27	73.29 45	85.94 28	85.13 42	68.58 63	84.14 71	90.21 79	73.37 60	86.41 18	79.09 23	93.98 61	84.30 139

TSAR-MVS + MP.		79.05 62	78.81 67	79.74 46	88.94 28	67.52 89	86.61 22	81.38 117	51.71 250	77.15 158	91.42 40	65.49 137	87.20 7	79.44 18	87.17 197	84.51 131
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 38	82.28 45	79.40 52	88.91 29	69.16 78	84.67 36	80.01 151	75.34 19	79.80 120	94.91 2	69.79 92	80.25 154	72.63 72	94.46 41	88.78 44

SMA-MVS		82.12 33	82.68 43	80.43 40	88.90 30	69.52 71	85.12 32	84.76 51	63.53 112	84.23 70	91.47 38	72.02 68	87.16 8	79.74 14	94.36 50	84.61 123
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 31	83.46 30	79.76 45	88.88 31	68.44 82	81.57 65	86.33 20	63.17 118	85.38 56	91.26 41	76.33 34	84.67 71	83.30 2	94.96 28	86.17 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		86.32 3	86.33 3	86.29 2	88.64 32	81.19 5	88.84 4	90.72 2	78.27 12	87.95 18	92.53 16	79.37 15	84.79 69	74.51 56	96.15 3	92.88 8

HPM-MVS_fast		84.59 8	85.10 10	83.06 5	88.60 33	75.83 27	86.27 27	86.89 17	73.69 27	86.17 41	91.70 33	78.23 22	85.20 61	79.45 17	94.91 30	88.15 50

SR-MVS		84.51 9	85.27 8	82.25 19	88.52 34	77.71 15	86.81 19	85.25 41	77.42 17	86.15 42	90.24 77	81.69 5	85.94 36	77.77 31	93.58 66	83.09 174

新几何1				69.99 211	88.37 35	71.34 55		62.08 351	43.85 345	74.99 208	86.11 178	52.85 271	70.57 297	50.99 282	83.23 263	68.05 393

HPM-MVS		84.12 12	84.63 14	82.60 14	88.21 36	74.40 35	85.24 31	87.21 15	70.69 51	85.14 58	90.42 65	78.99 17	86.62 15	80.83 7	94.93 29	86.79 68
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 37	73.96 38	83.11 54	84.52 61	66.40 78	87.45 26	89.16 100	81.02 8	80.52 150	74.27 59	95.73 8	80.98 233
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 38	69.15 79	67.85 266	59.59 361	41.06 370	73.05 252	85.72 187	48.03 310			80.65 305	66.92 398

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 60	87.01 39	72.91 47	80.23 81	85.56 32	66.56 77	85.64 49	89.57 89	69.12 96	80.55 149	72.51 74	93.37 68	83.48 160

save fliter					87.00 40	67.23 93	79.24 92	77.94 194	56.65 181

LPG-MVS_test		83.47 20	84.33 17	80.90 36	87.00 40	70.41 64	82.04 62	86.35 18	69.77 56	87.75 19	91.13 42	81.83 3	86.20 26	77.13 40	95.96 6	86.08 80

LGP-MVS_train				80.90 36	87.00 40	70.41 64		86.35 18	69.77 56	87.75 19	91.13 42	81.83 3	86.20 26	77.13 40	95.96 6	86.08 80

EGC-MVSNET		64.77 276	61.17 313	75.60 107	86.90 43	74.47 34	84.04 40	68.62 308	0.60 462	1.13 464	91.61 36	65.32 140	74.15 253	64.01 146	88.28 170	78.17 284

OPM-MVS		80.99 46	81.63 51	79.07 57	86.86 44	69.39 74	79.41 91	84.00 77	65.64 83	85.54 53	89.28 93	76.32 35	83.47 90	74.03 61	93.57 67	84.35 136
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepC-MVS	72.44 4	81.00 45	80.83 55	81.50 26	86.70 45	70.03 68	82.06 61	87.00 16	59.89 144	80.91 110	90.53 60	72.19 65	88.56 2	73.67 64	94.52 40	85.92 85
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 30	83.38 31	80.40 41	86.50 46	69.44 73	82.30 59	86.08 25	66.80 73	86.70 35	89.99 82	81.64 6	85.95 35	74.35 58	96.11 4	85.81 86
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 57	79.99 60	78.49 68	86.46 47	74.79 33	77.15 118	85.39 38	66.73 74	80.39 116	88.85 109	74.43 54	78.33 191	74.73 51	85.79 214	82.35 199

VDDNet		71.60 168	73.13 143	67.02 269	86.29 48	41.11 346	69.97 227	66.50 319	68.72 61	74.74 212	91.70 33	59.90 204	75.81 224	48.58 306	91.72 89	84.15 142

test_0728_SECOND				76.57 92	86.20 49	60.57 157	83.77 45	85.49 33					85.90 40	75.86 43	94.39 46	83.25 169

SR-MVS-dyc-post		84.75 7	85.26 9	83.21 4	86.19 50	79.18 7	87.23 9	86.27 21	77.51 14	87.65 22	90.73 54	79.20 16	85.58 51	78.11 28	94.46 41	84.89 109

RE-MVS-def			85.50 7		86.19 50	79.18 7	87.23 9	86.27 21	77.51 14	87.65 22	90.73 54	81.38 7		78.11 28	94.46 41	84.89 109

DVP-MVS		81.15 42	83.12 37	75.24 113	86.16 52	60.78 154	83.77 45	80.58 139	72.48 38	85.83 47	90.41 66	78.57 19	85.69 47	75.86 43	94.39 46	79.24 268
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 52	60.78 154	83.81 44	85.10 44	72.48 38	85.27 57	89.96 83	78.57 19

SED-MVS		81.78 36	83.48 29	76.67 90	86.12 54	61.06 148	83.62 47	84.72 53	72.61 36	87.38 28	89.70 87	77.48 27	85.89 42	75.29 47	94.39 46	83.08 175

IU-MVS					86.12 54	60.90 152		80.38 143	45.49 327	81.31 103				75.64 46	94.39 46	84.65 119

test_241102_ONE					86.12 54	61.06 148		84.72 53	72.64 35	87.38 28	89.47 90	77.48 27	85.74 46

reproduce-ours		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 32	79.90 10	95.21 18	82.72 189

our_new_method		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 29	78.70 10	88.94 13	91.88 27	79.74 12	86.05 32	79.90 10	95.21 18	82.72 189

XVG-OURS		79.51 58	79.82 61	78.58 66	86.11 57	74.96 32	76.33 132	84.95 48	66.89 71	82.75 87	88.99 106	66.82 119	78.37 189	74.80 49	90.76 123	82.40 198

test_part2					85.90 60	66.44 98				84.61 66

原ACMM1				73.90 128	85.90 60	65.15 113		81.67 111	50.97 263	74.25 226	86.16 174	61.60 178	83.54 87	56.75 222	91.08 110	73.00 342

testdata				64.13 294	85.87 62	63.34 127		61.80 354	47.83 307	76.42 185	86.60 161	48.83 304	62.31 369	54.46 254	81.26 292	66.74 402

CNVR-MVS		78.49 69	78.59 71	78.16 72	85.86 63	67.40 90	78.12 107	81.50 113	63.92 106	77.51 154	86.56 162	68.43 102	84.82 68	73.83 62	91.61 93	82.26 203

test_one_0601					85.84 64	61.45 142		85.63 31	75.27 21	85.62 52	90.38 71	76.72 31

NCCC		78.25 72	78.04 77	78.89 62	85.61 65	69.45 72	79.80 88	80.99 129	65.77 82	75.55 195	86.25 171	67.42 112	85.42 52	70.10 91	90.88 118	81.81 216

reproduce_model		84.87 6	85.80 6	82.05 23	85.52 66	78.14 13	87.69 6	85.36 39	79.26 7	89.12 12	92.10 21	77.52 26	85.92 39	80.47 9	95.20 20	82.10 206

TEST9					85.47 67	69.32 76	76.42 128	78.69 179	53.73 228	76.97 160	86.74 152	66.84 118	81.10 135

train_agg		76.38 87	76.55 91	75.86 103	85.47 67	69.32 76	76.42 128	78.69 179	54.00 223	76.97 160	86.74 152	66.60 124	81.10 135	72.50 75	91.56 94	77.15 299

DPE-MVS		82.00 35	83.02 38	78.95 61	85.36 69	67.25 92	82.91 55	84.98 46	73.52 29	85.43 55	90.03 81	76.37 33	86.97 13	74.56 54	94.02 60	82.62 193
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HQP_MVS		78.77 65	78.78 69	78.72 63	85.18 70	65.18 111	82.74 56	85.49 33	65.45 86	78.23 141	89.11 101	60.83 191	86.15 29	71.09 82	90.94 112	84.82 114

plane_prior7					85.18 70	66.21 101

SymmetryMVS		74.00 117	72.85 150	77.43 83	85.17 72	70.01 69	79.92 86	68.48 309	58.60 156	75.21 203	84.02 215	52.85 271	81.82 121	61.45 172	89.99 136	80.47 248

SteuartSystems-ACMMP		83.07 26	83.64 27	81.35 30	85.14 73	71.00 58	85.53 29	84.78 50	70.91 49	85.64 49	90.41 66	75.55 42	87.69 5	79.75 12	95.08 25	85.36 99
Skip Steuart: Steuart Systems R&D Blog.
test_8					85.09 74	67.89 85	76.26 133	78.66 181	54.00 223	76.89 164	86.72 154	66.60 124	80.89 145

WR-MVS		71.20 175	72.48 159	67.36 260	84.98 75	35.70 395	64.43 322	68.66 307	65.05 96	81.49 101	86.43 166	57.57 237	76.48 218	50.36 287	93.32 70	89.90 22

PS-MVSNAJss		77.54 76	77.35 85	78.13 74	84.88 76	66.37 99	78.55 99	79.59 160	53.48 232	86.29 40	92.43 18	62.39 166	80.25 154	67.90 111	90.61 124	87.77 53

LTVRE_ROB	75.46 1	84.22 10	84.98 12	81.94 24	84.82 77	75.40 29	91.60 3	87.80 9	73.52 29	88.90 15	93.06 9	71.39 74	81.53 127	81.53 5	92.15 86	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 63	78.67 70	79.72 47	84.81 78	73.93 39	80.65 72	76.50 211	51.98 248	87.40 27	91.86 29	76.09 37	78.53 181	68.58 101	90.20 129	86.69 70

APDe-MVS		82.88 28	84.14 19	79.08 56	84.80 79	66.72 97	86.54 23	85.11 43	72.00 43	86.65 36	91.75 32	78.20 23	87.04 11	77.93 30	94.32 53	83.47 161
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSLP-MVS++		74.48 114	75.78 98	70.59 196	84.66 80	62.40 132	78.65 97	84.24 70	60.55 139	77.71 151	81.98 256	63.12 157	77.64 205	62.95 161	88.14 172	71.73 359

jajsoiax		78.51 68	78.16 76	79.59 49	84.65 81	73.83 41	80.42 75	76.12 217	51.33 259	87.19 32	91.51 37	73.79 58	78.44 185	68.27 104	90.13 133	86.49 74

TranMVSNet+NR-MVSNet		76.13 89	77.66 80	71.56 183	84.61 82	42.57 338	70.98 213	78.29 188	68.67 62	83.04 80	89.26 94	72.99 62	80.75 146	55.58 238	95.47 13	91.35 12

旧先验1					84.55 83	60.36 159		63.69 342			87.05 143	54.65 260			83.34 261	69.66 380

APD-MVS		81.13 43	81.73 49	79.36 53	84.47 84	70.53 63	83.85 43	83.70 79	69.43 58	83.67 76	88.96 107	75.89 38	86.41 18	72.62 73	92.95 73	81.14 227
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
plane_prior1					84.46 85

agg_prior					84.44 86	66.02 104		78.62 182		76.95 162			80.34 152

DeepPCF-MVS	71.07 5	78.48 70	77.14 87	82.52 17	84.39 87	77.04 25	76.35 130	84.05 75	56.66 180	80.27 117	85.31 190	68.56 99	87.03 12	67.39 117	91.26 100	83.50 157

CDPH-MVS		77.33 80	77.06 88	78.14 73	84.21 88	63.98 123	76.07 136	83.45 82	54.20 218	77.68 152	87.18 138	69.98 89	85.37 53	68.01 108	92.72 78	85.08 106

plane_prior6					84.18 89	65.31 110						60.83 191

114514_t		73.40 127	73.33 140	73.64 132	84.15 90	57.11 193	78.20 105	80.02 150	43.76 348	72.55 258	86.07 181	64.00 152	83.35 93	60.14 190	91.03 111	80.45 249

ZD-MVS					83.91 91	69.36 75		81.09 125	58.91 154	82.73 88	89.11 101	75.77 39	86.63 14	72.73 71	92.93 74

DeepC-MVS_fast	69.89 7	77.17 81	76.33 93	79.70 48	83.90 92	67.94 84	80.06 84	83.75 78	56.73 179	74.88 211	85.32 189	65.54 136	87.79 3	65.61 135	91.14 105	83.35 167
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
h-mvs33		73.08 134	71.61 176	77.48 81	83.89 93	72.89 48	70.47 220	71.12 281	54.28 214	77.89 145	83.41 225	49.04 301	80.98 140	63.62 154	90.77 122	78.58 276

NormalMVS		76.15 88	75.08 105	79.36 53	83.87 94	70.01 69	79.92 86	84.34 64	58.60 156	75.21 203	84.02 215	52.85 271	81.82 121	61.45 172	95.50 11	86.24 76

lecture		83.41 21	85.02 11	78.58 66	83.87 94	67.26 91	84.47 37	88.27 7	73.64 28	87.35 31	91.96 24	78.55 21	82.92 100	81.59 4	95.50 11	85.56 94

SD-MVS		80.28 54	81.55 52	76.47 95	83.57 96	67.83 86	83.39 52	85.35 40	64.42 102	86.14 43	87.07 142	74.02 55	80.97 141	77.70 33	92.32 84	80.62 245
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		74.91 108	75.57 101	72.93 154	83.50 97	45.79 305	69.47 234	80.14 148	65.22 92	81.74 98	87.08 140	61.82 176	81.07 137	56.21 229	94.98 26	91.93 9

NR-MVSNet		73.62 122	74.05 122	72.33 174	83.50 97	43.71 324	65.65 299	77.32 201	64.32 103	75.59 194	87.08 140	62.45 165	81.34 129	54.90 247	95.63 9	91.93 9

test_0402		78.17 73	79.48 64	74.24 123	83.50 97	59.15 171	72.52 181	74.60 233	75.34 19	88.69 17	91.81 31	75.06 46	82.37 111	65.10 136	88.68 166	81.20 225

OPU-MVS				78.65 65	83.44 100	66.85 96	83.62 47				86.12 177	66.82 119	86.01 34	61.72 170	89.79 142	83.08 175

NP-MVS					83.34 101	63.07 130					85.97 182

DVP-MVS++		81.24 40	82.74 42	76.76 89	83.14 102	60.90 152	91.64 1	85.49 33	74.03 25	84.93 60	90.38 71	66.82 119	85.90 40	77.43 35	90.78 120	83.49 158

MSC_two_6792asdad				79.02 58	83.14 102	67.03 94		80.75 131					86.24 24	77.27 38	94.85 31	83.78 150

No_MVS				79.02 58	83.14 102	67.03 94		80.75 131					86.24 24	77.27 38	94.85 31	83.78 150

UniMVSNet (Re)		75.00 106	75.48 102	73.56 136	83.14 102	47.92 279	70.41 222	81.04 127	63.67 110	79.54 122	86.37 167	62.83 160	81.82 121	57.10 221	95.25 17	90.94 16

hse-mvs2		72.32 158	70.66 192	77.31 86	83.10 106	71.77 51	69.19 242	71.45 270	54.28 214	77.89 145	78.26 325	49.04 301	79.23 168	63.62 154	89.13 158	80.92 234

UniMVSNet_NR-MVSNet		74.90 109	75.65 99	72.64 167	83.04 107	45.79 305	69.26 240	78.81 174	66.66 76	81.74 98	86.88 147	63.26 156	81.07 137	56.21 229	94.98 26	91.05 14

HyFIR lowres test		63.01 297	60.47 320	70.61 195	83.04 107	54.10 220	59.93 358	72.24 262	33.67 422	69.00 307	75.63 347	38.69 362	76.93 212	36.60 393	75.45 363	80.81 239

COLMAP_ROB	72.78 3	83.75 15	84.11 20	82.68 13	82.97 109	74.39 36	87.18 11	88.18 8	78.98 8	86.11 44	91.47 38	79.70 14	85.76 45	66.91 125	95.46 14	87.89 52
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		70.22 191	67.88 235	77.22 87	82.96 110	71.61 52	69.08 243	71.39 271	49.17 288	71.70 269	78.07 330	37.62 370	79.21 169	61.81 167	89.15 156	80.82 237

DP-MVS Recon		73.57 124	72.69 154	76.23 98	82.85 111	63.39 126	74.32 161	82.96 89	57.75 164	70.35 290	81.98 256	64.34 151	84.41 76	49.69 292	89.95 137	80.89 235

APD-MVS_3200maxsize		83.57 17	84.33 17	81.31 32	82.83 112	73.53 44	85.50 30	87.45 14	74.11 23	86.45 39	90.52 62	80.02 10	84.48 73	77.73 32	94.34 52	85.93 84

PVSNet_Blended_VisFu		70.04 195	68.88 214	73.53 137	82.71 113	63.62 125	74.81 149	81.95 107	48.53 298	67.16 337	79.18 314	51.42 282	78.38 188	54.39 256	79.72 322	78.60 275

DPM-MVS		69.98 197	69.22 210	72.26 175	82.69 114	58.82 177	70.53 219	81.23 121	47.79 308	64.16 355	80.21 285	51.32 283	83.12 96	60.14 190	84.95 232	74.83 323

EG-PatchMatch MVS		70.70 184	70.88 186	70.16 207	82.64 115	58.80 178	71.48 203	73.64 238	54.98 197	76.55 178	81.77 260	61.10 188	78.94 174	54.87 248	80.84 300	72.74 348

HQP-NCC					82.37 116		77.32 114		59.08 148	71.58 273

ACMP_Plane					82.37 116		77.32 114		59.08 148	71.58 273

HQP-MVS		75.24 101	75.01 106	75.94 101	82.37 116	58.80 178	77.32 114	84.12 73	59.08 148	71.58 273	85.96 183	58.09 229	85.30 55	67.38 119	89.16 154	83.73 153

test12				76.51 93	82.28 119	60.94 151		81.64 112		73.60 239		64.88 145	85.19 62		90.42 127	83.38 165

TAMVS		65.31 269	63.75 289	69.97 212	82.23 120	59.76 166	66.78 285	63.37 345	45.20 335	69.79 300	79.37 306	47.42 313	72.17 275	34.48 407	85.15 227	77.99 289

test_prior				75.27 112	82.15 121	59.85 165		84.33 67					83.39 92			82.58 194

SF-MVS		80.72 48	81.80 47	77.48 81	82.03 122	64.40 119	83.41 51	88.46 6	65.28 91	84.29 69	89.18 98	73.73 59	83.22 94	76.01 42	93.77 63	84.81 116

AdaColmap		74.22 115	74.56 110	73.20 142	81.95 123	60.97 150	79.43 89	80.90 130	65.57 84	72.54 259	81.76 261	70.98 79	85.26 57	47.88 315	90.00 134	73.37 338

PAPM_NR		73.91 118	74.16 119	73.16 143	81.90 124	53.50 225	81.28 67	81.40 116	66.17 80	73.30 246	83.31 231	59.96 202	83.10 97	58.45 206	81.66 287	82.87 183

DP-MVS		78.44 71	79.29 65	75.90 102	81.86 125	65.33 109	79.05 94	84.63 59	74.83 22	80.41 115	86.27 169	71.68 70	83.45 91	62.45 165	92.40 81	78.92 273

F-COLMAP		75.29 99	73.99 123	79.18 55	81.73 126	71.90 50	81.86 64	82.98 88	59.86 145	72.27 262	84.00 217	64.56 149	83.07 98	51.48 276	87.19 196	82.56 195

SixPastTwentyTwo		75.77 91	76.34 92	74.06 126	81.69 127	54.84 214	76.47 125	75.49 224	64.10 105	87.73 21	92.24 20	50.45 289	81.30 131	67.41 115	91.46 96	86.04 82

Vis-MVSNet		74.85 112	74.56 110	75.72 104	81.63 128	64.64 117	76.35 130	79.06 170	62.85 121	73.33 245	88.41 119	62.54 164	79.59 165	63.94 151	82.92 265	82.94 179
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_djsdf		78.88 64	78.27 74	80.70 39	81.42 129	71.24 56	83.98 41	75.72 222	52.27 241	87.37 30	92.25 19	68.04 106	80.56 147	72.28 77	91.15 104	90.32 21

3Dnovator+	73.19 2	81.08 44	80.48 56	82.87 8	81.41 130	72.03 49	84.38 39	86.23 24	77.28 18	80.65 113	90.18 80	59.80 207	87.58 6	73.06 68	91.34 99	89.01 36

tt0805		76.12 90	78.43 73	69.20 225	81.32 131	41.37 344	76.72 122	77.64 197	63.78 109	82.06 92	87.88 132	79.78 11	79.05 171	64.33 144	92.40 81	87.17 65

MCST-MVS		73.42 126	73.34 139	73.63 133	81.28 132	59.17 170	74.80 151	83.13 87	45.50 325	72.84 253	83.78 222	65.15 142	80.99 139	64.54 141	89.09 162	80.73 241

MIMVSNet1		66.57 256	69.23 209	58.59 352	81.26 133	37.73 382	64.06 326	57.62 365	57.02 173	78.40 139	90.75 53	62.65 161	58.10 388	41.77 356	89.58 146	79.95 257

ACMH+	66.64 10	81.20 41	82.48 44	77.35 85	81.16 134	62.39 133	80.51 73	87.80 9	73.02 31	87.57 24	91.08 44	80.28 9	82.44 109	64.82 140	96.10 5	87.21 61

MVSMamba_PlusPlus		76.88 83	78.21 75	72.88 158	80.83 135	48.71 263	83.28 53	82.79 91	72.78 32	79.17 127	91.94 25	56.47 250	83.95 78	70.51 90	86.15 209	85.99 83

MVS_111021_HR		72.98 141	72.97 149	72.99 149	80.82 136	65.47 107	68.81 249	72.77 252	57.67 166	75.76 191	82.38 248	71.01 78	77.17 208	61.38 174	86.15 209	76.32 311

9.14			80.22 58		80.68 137		80.35 78	87.69 12	59.90 143	83.00 81	88.20 124	74.57 51	81.75 125	73.75 63	93.78 62

OMC-MVS		79.41 60	78.79 68	81.28 33	80.62 138	70.71 62	80.91 70	84.76 51	62.54 123	81.77 96	86.65 158	71.46 72	83.53 88	67.95 110	92.44 80	89.60 24

OurMVSNet-221017-0		78.57 67	78.53 72	78.67 64	80.48 139	64.16 120	80.24 80	82.06 104	61.89 127	88.77 16	93.32 6	57.15 241	82.60 106	70.08 92	92.80 75	89.25 30

CDS-MVSNet		64.33 284	62.66 304	69.35 222	80.44 140	58.28 186	65.26 305	65.66 325	44.36 343	67.30 336	75.54 348	43.27 332	71.77 282	37.68 383	84.44 245	78.01 288
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	62.01 16	71.79 166	70.28 195	76.33 96	80.31 141	68.63 81	78.18 106	81.24 120	54.57 208	67.09 338	80.63 279	59.44 209	81.74 126	46.91 322	84.17 248	78.63 274
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MM		78.15 74	77.68 79	79.55 50	80.10 142	65.47 107	80.94 69	78.74 178	71.22 46	72.40 261	88.70 111	60.51 195	87.70 4	77.40 37	89.13 158	85.48 96

sc_t1		72.50 156	74.23 117	67.33 261	80.05 143	46.99 295	66.58 288	69.48 296	66.28 79	77.62 153	91.83 30	70.98 79	68.62 319	53.86 263	91.40 97	86.37 75

CHOSEN 1792x2688		58.09 340	56.30 353	63.45 303	79.95 144	50.93 241	54.07 402	65.59 326	28.56 438	61.53 378	74.33 361	41.09 346	66.52 348	33.91 410	67.69 421	72.92 343

tt0320		71.34 173	73.47 133	64.97 289	79.92 145	40.81 351	65.22 306	69.07 301	66.72 75	76.15 189	93.36 5	70.35 83	66.90 338	49.31 299	91.09 109	87.21 61

K. test v3		73.67 121	73.61 132	73.87 129	79.78 146	55.62 207	74.69 155	62.04 353	66.16 81	84.76 64	93.23 8	49.47 295	80.97 141	65.66 134	86.67 205	85.02 108

tt0320-xc		71.50 170	73.63 131	65.08 287	79.77 147	40.46 358	64.80 314	68.86 303	67.08 70	76.84 168	93.24 7	70.33 84	66.77 345	49.76 291	92.02 87	88.02 51

VPNet		65.58 267	67.56 238	59.65 342	79.72 148	30.17 426	60.27 355	62.14 349	54.19 219	71.24 282	86.63 159	58.80 218	67.62 329	44.17 342	90.87 119	81.18 226

ACMH	63.62 14	77.50 79	80.11 59	69.68 215	79.61 149	56.28 197	78.81 96	83.62 80	63.41 116	87.14 34	90.23 78	76.11 36	73.32 259	67.58 112	94.44 44	79.44 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 163	79.60 150	56.83 196		57.37 368		83.80 75	89.01 105	47.45 312	78.74 178	64.39 143	86.49 208	82.69 191

MVS_111021_LR		72.10 162	71.82 170	72.95 151	79.53 151	73.90 40	70.45 221	66.64 318	56.87 174	76.81 169	81.76 261	68.78 97	71.76 283	61.81 167	83.74 254	73.18 340

Test_1112_low_res		58.78 336	58.69 333	59.04 349	79.41 152	38.13 378	57.62 374	66.98 317	34.74 415	59.62 394	77.56 334	42.92 335	63.65 364	38.66 374	70.73 402	75.35 320

CSCG		74.12 116	74.39 112	73.33 139	79.35 153	61.66 140	77.45 113	81.98 106	62.47 125	79.06 129	80.19 287	61.83 175	78.79 177	59.83 194	87.35 187	79.54 265

MVP-Stereo		61.56 314	59.22 328	68.58 242	79.28 154	60.44 158	69.20 241	71.57 266	43.58 351	56.42 410	78.37 324	39.57 357	76.46 219	34.86 406	60.16 439	68.86 388
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 187	71.34 181	67.85 253	79.26 155	40.42 359	74.67 156	75.15 228	58.41 158	68.74 321	88.14 128	56.08 253	83.69 84	59.90 193	81.71 286	79.43 267

IS-MVSNet		75.10 103	75.42 103	74.15 125	79.23 156	48.05 277	79.43 89	78.04 192	70.09 55	79.17 127	88.02 129	53.04 270	83.60 85	58.05 211	93.76 64	90.79 18

FC-MVSNet-test		73.32 129	74.78 108	68.93 235	79.21 157	36.57 387	71.82 200	79.54 162	57.63 169	82.57 89	90.38 71	59.38 211	78.99 173	57.91 212	94.56 39	91.23 13

AllTest		77.66 75	77.43 81	78.35 70	79.19 158	70.81 59	78.60 98	88.64 4	65.37 89	80.09 118	88.17 125	70.33 84	78.43 186	55.60 235	90.90 116	85.81 86

TestCases				78.35 70	79.19 158	70.81 59		88.64 4	65.37 89	80.09 118	88.17 125	70.33 84	78.43 186	55.60 235	90.90 116	85.81 86

xiu_mvs_v1_base_debu		67.87 232	67.07 248	70.26 203	79.13 160	61.90 137	67.34 273	71.25 276	47.98 304	67.70 330	74.19 365	61.31 181	72.62 266	56.51 224	78.26 338	76.27 312

xiu_mvs_v1_base		67.87 232	67.07 248	70.26 203	79.13 160	61.90 137	67.34 273	71.25 276	47.98 304	67.70 330	74.19 365	61.31 181	72.62 266	56.51 224	78.26 338	76.27 312

xiu_mvs_v1_base_debi		67.87 232	67.07 248	70.26 203	79.13 160	61.90 137	67.34 273	71.25 276	47.98 304	67.70 330	74.19 365	61.31 181	72.62 266	56.51 224	78.26 338	76.27 312

VDD-MVS		70.81 182	71.44 180	68.91 236	79.07 163	46.51 299	67.82 267	70.83 285	61.23 131	74.07 230	88.69 112	59.86 205	75.62 229	51.11 280	90.28 128	84.61 123

Elysia		77.52 77	77.43 81	77.78 77	79.01 164	60.26 160	76.55 123	84.34 64	67.82 66	78.73 132	87.94 130	58.68 220	83.79 81	74.70 52	89.10 160	89.28 28

StellarMVS		77.52 77	77.43 81	77.78 77	79.01 164	60.26 160	76.55 123	84.34 64	67.82 66	78.73 132	87.94 130	58.68 220	83.79 81	74.70 52	89.10 160	89.28 28

test1111		64.62 277	65.19 273	62.93 311	79.01 164	29.91 427	65.45 302	54.41 389	54.09 221	71.47 280	88.48 118	37.02 372	74.29 251	46.83 324	89.94 138	84.58 126

TSAR-MVS + GP.		73.08 134	71.60 177	77.54 80	78.99 167	70.73 61	74.96 146	69.38 297	60.73 138	74.39 223	78.44 323	57.72 236	82.78 103	60.16 188	89.60 144	79.11 270

test2506		61.23 316	60.85 317	62.38 316	78.80 168	27.88 435	67.33 276	37.42 454	54.23 216	67.55 333	88.68 113	17.87 458	74.39 248	46.33 328	89.41 150	84.86 112

ECVR-MVS		64.82 274	65.22 272	63.60 300	78.80 168	31.14 421	66.97 281	56.47 379	54.23 216	69.94 298	88.68 113	37.23 371	74.81 243	45.28 338	89.41 150	84.86 112

FIs		72.56 152	73.80 126	68.84 238	78.74 170	37.74 381	71.02 212	79.83 153	56.12 185	80.88 112	89.45 91	58.18 225	78.28 192	56.63 223	93.36 69	90.51 20

v7n		79.37 61	80.41 57	76.28 97	78.67 171	55.81 203	79.22 93	82.51 99	70.72 50	87.54 25	92.44 17	68.00 107	81.34 129	72.84 70	91.72 89	91.69 11

LS3D		80.99 46	80.85 54	81.41 29	78.37 172	71.37 54	87.45 8	85.87 28	77.48 16	81.98 93	89.95 84	69.14 95	85.26 57	66.15 127	91.24 101	87.61 56

CNLPA		73.44 125	73.03 147	74.66 115	78.27 173	75.29 30	75.99 137	78.49 183	65.39 88	75.67 193	83.22 237	61.23 184	66.77 345	53.70 264	85.33 223	81.92 214

SSM_0404		72.51 155	72.15 166	73.60 134	78.20 174	55.86 202	74.41 160	79.83 153	53.69 229	73.98 233	84.18 208	62.26 169	82.50 107	58.21 208	84.60 240	82.43 197

EPP-MVSNet		73.86 120	73.38 136	75.31 111	78.19 175	53.35 227	80.45 74	77.32 201	65.11 95	76.47 183	86.80 148	49.47 295	83.77 83	53.89 261	92.72 78	88.81 43

PCF-MVS	63.80 13	72.70 149	71.69 171	75.72 104	78.10 176	60.01 163	73.04 176	81.50 113	45.34 330	79.66 121	84.35 206	65.15 142	82.65 105	48.70 304	89.38 153	84.50 132
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		73.14 132	73.77 128	71.26 188	78.09 177	52.64 230	74.32 161	79.56 161	56.32 183	76.35 186	83.36 230	70.76 81	77.96 199	63.32 158	81.84 281	83.18 172

LFMVS		67.06 250	67.89 234	64.56 291	78.02 178	38.25 376	70.81 217	59.60 360	65.18 93	71.06 284	86.56 162	43.85 329	75.22 234	46.35 327	89.63 143	80.21 255

anonymousdsp		78.60 66	77.80 78	81.00 35	78.01 179	74.34 37	80.09 82	76.12 217	50.51 269	89.19 11	90.88 49	71.45 73	77.78 203	73.38 65	90.60 125	90.90 17

BH-untuned		69.39 207	69.46 202	69.18 226	77.96 180	56.88 194	68.47 260	77.53 198	56.77 177	77.79 148	79.63 298	60.30 199	80.20 157	46.04 330	80.65 305	70.47 372

1112_ss		59.48 330	58.99 331	60.96 332	77.84 181	42.39 339	61.42 344	68.45 310	37.96 395	59.93 391	67.46 419	45.11 322	65.07 357	40.89 362	71.81 394	75.41 318

PS-MVSNAJ		64.27 285	63.73 290	65.90 281	77.82 182	51.42 235	63.33 333	72.33 260	45.09 338	61.60 377	68.04 416	62.39 166	73.95 255	49.07 300	73.87 379	72.34 352

ambc				70.10 209	77.74 183	50.21 248	74.28 164	77.93 195		79.26 125	88.29 123	54.11 265	79.77 161	64.43 142	91.10 108	80.30 252

xiu_mvs_v2_base		64.43 282	63.96 287	65.85 282	77.72 184	51.32 237	63.63 330	72.31 261	45.06 339	61.70 376	69.66 401	62.56 162	73.93 256	49.06 301	73.91 378	72.31 353

Anonymous20231211		75.54 96	77.19 86	70.59 196	77.67 185	45.70 308	74.73 153	80.19 146	68.80 59	82.95 83	92.91 11	66.26 128	76.76 216	58.41 207	92.77 76	89.30 27

FMVSNet1		71.06 177	72.48 159	66.81 271	77.65 186	40.68 354	71.96 194	73.03 243	61.14 132	79.45 124	90.36 74	60.44 196	75.20 236	50.20 288	88.05 174	84.54 127

FPMVS		59.43 331	60.07 322	57.51 359	77.62 187	71.52 53	62.33 340	50.92 408	57.40 170	69.40 304	80.00 291	39.14 360	61.92 371	37.47 386	66.36 423	39.09 454

balanced_conf03		73.59 123	74.06 121	72.17 178	77.48 188	47.72 284	81.43 66	82.20 102	54.38 211	79.19 126	87.68 134	54.41 262	83.57 86	63.98 148	85.78 215	85.22 100

testing3		58.28 339	58.38 337	58.00 357	77.45 189	26.12 444	60.78 351	43.00 440	56.02 186	70.18 293	75.76 345	13.27 466	67.24 335	48.02 313	80.89 298	80.65 244

LuminaMVS		71.15 176	70.79 189	72.24 177	77.20 190	58.34 185	72.18 187	76.20 215	54.91 198	77.74 149	81.93 258	49.17 300	76.31 220	62.12 166	85.66 217	82.07 207

fmvsm_s_conf0.5_n_9		74.56 113	74.30 115	75.34 110	77.17 191	64.87 115	72.62 180	76.17 216	54.54 210	78.32 140	86.14 175	65.14 144	75.72 228	73.10 67	85.55 218	85.42 97

Effi-MVS+-dtu		75.43 98	72.28 163	84.91 3	77.05 192	83.58 2	78.47 100	77.70 196	57.68 165	74.89 210	78.13 329	64.80 146	84.26 77	56.46 227	85.32 224	86.88 67

CLD-MVS		72.88 145	72.36 162	74.43 120	77.03 193	54.30 218	68.77 252	83.43 83	52.12 245	76.79 170	74.44 360	69.54 94	83.91 79	55.88 232	93.25 71	85.09 105
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 86	76.00 96	78.06 75	77.02 194	64.77 116	80.78 71	82.66 96	60.39 140	74.15 227	83.30 232	69.65 93	82.07 117	69.27 98	86.75 204	87.36 59

SPE-MVS-test		74.89 110	74.23 117	76.86 88	77.01 195	62.94 131	78.98 95	84.61 60	58.62 155	70.17 294	80.80 275	66.74 123	81.96 119	61.74 169	89.40 152	85.69 92

Baseline_NR-MVSNet		70.62 185	73.19 141	62.92 312	76.97 196	34.44 403	68.84 246	70.88 284	60.25 141	79.50 123	90.53 60	61.82 176	69.11 313	54.67 251	95.27 16	85.22 100

ITE_SJBPF				80.35 42	76.94 197	73.60 42		80.48 140	66.87 72	83.64 77	86.18 172	70.25 87	79.90 160	61.12 178	88.95 164	87.56 57

mamba_0408		70.32 189	69.35 204	73.24 141	76.92 198	55.22 209	56.61 381	79.27 166	52.14 243	73.08 248	83.14 238	60.53 193	82.50 107	57.51 215	84.91 233	81.99 210

SSM_04072		67.23 245	69.35 204	60.89 333	76.92 198	55.22 209	56.61 381	79.27 166	52.14 243	73.08 248	83.14 238	60.53 193	45.46 428	57.51 215	84.91 233	81.99 210

SSM_0407		72.15 161	71.85 168	73.06 147	76.92 198	55.22 209	73.59 170	79.83 153	53.69 229	73.08 248	84.18 208	62.26 169	81.98 118	58.21 208	84.91 233	81.99 210

SSC-MVS		61.79 311	66.08 261	48.89 408	76.91 201	10.00 466	53.56 404	47.37 426	68.20 64	76.56 177	89.21 96	54.13 264	57.59 389	54.75 249	74.07 377	79.08 271

jason		64.47 281	62.84 301	69.34 223	76.91 201	59.20 167	67.15 278	65.67 324	35.29 411	65.16 348	76.74 341	44.67 324	70.68 294	54.74 250	79.28 325	78.14 285
jason: jason.
ETV-MVS		72.72 148	72.16 165	74.38 122	76.90 203	55.95 199	73.34 173	84.67 56	62.04 126	72.19 265	70.81 387	65.90 132	85.24 59	58.64 204	84.96 231	81.95 213

Anonymous20240529		72.56 152	73.79 127	68.86 237	76.89 204	45.21 312	68.80 251	77.25 203	67.16 69	76.89 164	90.44 63	65.95 131	74.19 252	50.75 283	90.00 134	87.18 64

EC-MVSNet		77.08 82	77.39 84	76.14 100	76.86 205	56.87 195	80.32 79	87.52 13	63.45 114	74.66 216	84.52 203	69.87 91	84.94 64	69.76 95	89.59 145	86.60 71

PM-MVS		64.49 280	63.61 291	67.14 266	76.68 206	75.15 31	68.49 259	42.85 441	51.17 262	77.85 147	80.51 280	45.76 316	66.31 349	52.83 271	76.35 354	59.96 431

mvsmamba		68.87 215	67.30 245	73.57 135	76.58 207	53.70 224	84.43 38	74.25 235	45.38 329	76.63 173	84.55 202	35.85 377	85.27 56	49.54 295	78.49 334	81.75 219

TransMVSNet (Re)		69.62 202	71.63 174	63.57 301	76.51 208	35.93 393	65.75 298	71.29 275	61.05 133	75.02 207	89.90 85	65.88 133	70.41 301	49.79 290	89.48 148	84.38 135

GDP-MVS		70.84 181	69.24 208	75.62 106	76.44 209	55.65 205	74.62 158	82.78 93	49.63 280	72.10 266	83.79 221	31.86 399	82.84 102	64.93 139	87.01 199	88.39 49

BH-RMVSNet		68.69 221	68.20 230	70.14 208	76.40 210	53.90 223	64.62 319	73.48 239	58.01 161	73.91 235	81.78 259	59.09 214	78.22 193	48.59 305	77.96 342	78.31 280

PHI-MVS		74.92 107	74.36 114	76.61 91	76.40 210	62.32 134	80.38 76	83.15 86	54.16 220	73.23 247	80.75 276	62.19 171	83.86 80	68.02 107	90.92 115	83.65 154

UGNet		70.20 192	69.05 211	73.65 131	76.24 212	63.64 124	75.87 139	72.53 256	61.48 130	60.93 385	86.14 175	52.37 275	77.12 209	50.67 284	85.21 225	80.17 256
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		58.68 337	57.72 342	61.57 322	76.21 213	73.59 43	61.83 341	49.00 420	47.30 313	61.08 381	68.97 407	50.16 290	59.01 381	36.06 401	68.84 414	52.10 441

VPA-MVSNet		68.71 220	70.37 194	63.72 299	76.13 214	38.06 379	64.10 325	71.48 269	56.60 182	74.10 229	88.31 122	64.78 147	69.72 307	47.69 317	90.15 131	83.37 166

WB-MVS		60.04 326	64.19 285	47.59 411	76.09 215	10.22 465	52.44 411	46.74 428	65.17 94	74.07 230	87.48 135	53.48 267	55.28 395	49.36 297	72.84 385	77.28 295

PAPM		61.79 311	60.37 321	66.05 279	76.09 215	41.87 341	69.30 238	76.79 210	40.64 378	53.80 425	79.62 299	44.38 326	82.92 100	29.64 429	73.11 384	73.36 339

BH-w/o		64.81 275	64.29 284	66.36 276	76.08 217	54.71 215	65.61 300	75.23 227	50.10 275	71.05 285	71.86 381	54.33 263	79.02 172	38.20 379	76.14 356	65.36 408

dcpmvs_2		71.02 179	72.65 155	66.16 278	76.06 218	50.49 244	71.97 193	79.36 163	50.34 270	82.81 86	83.63 223	64.38 150	67.27 334	61.54 171	83.71 256	80.71 243

pmmvs6		71.82 165	73.66 129	66.31 277	75.94 219	42.01 340	66.99 280	72.53 256	63.45 114	76.43 184	92.78 13	72.95 63	69.69 308	51.41 278	90.46 126	87.22 60

testing3-2		56.85 346	57.62 343	54.53 375	75.84 220	22.23 455	51.26 416	49.10 418	61.04 134	63.74 363	79.73 295	22.29 445	59.44 379	31.16 422	84.43 246	81.92 214

CANet		73.00 139	71.84 169	76.48 94	75.82 221	61.28 144	74.81 149	80.37 144	63.17 118	62.43 374	80.50 281	61.10 188	85.16 63	64.00 147	84.34 247	83.01 178

pmmvs-eth3d		64.41 283	63.27 296	67.82 255	75.81 222	60.18 162	69.49 232	62.05 352	38.81 390	74.13 228	82.23 250	43.76 330	68.65 317	42.53 349	80.63 307	74.63 326

TR-MVS		64.59 278	63.54 292	67.73 256	75.75 223	50.83 242	63.39 332	70.29 289	49.33 285	71.55 277	74.55 358	50.94 285	78.46 184	40.43 364	75.69 359	73.89 335

MVS_0304		75.45 97	74.66 109	77.83 76	75.58 224	61.53 141	78.29 102	77.18 205	63.15 120	69.97 297	87.20 137	57.54 238	87.05 10	74.05 60	88.96 163	84.89 109

tttt0517		69.46 205	67.79 237	74.46 117	75.34 225	52.72 229	75.05 145	63.27 346	54.69 204	78.87 131	84.37 205	26.63 426	81.15 133	63.95 149	87.93 179	89.51 25

cascas		64.59 278	62.77 303	70.05 210	75.27 226	50.02 250	61.79 342	71.61 265	42.46 360	63.68 364	68.89 410	49.33 297	80.35 151	47.82 316	84.05 250	79.78 260

API-MVS		70.97 180	71.51 179	69.37 220	75.20 227	55.94 200	80.99 68	76.84 208	62.48 124	71.24 282	77.51 335	61.51 180	80.96 144	52.04 272	85.76 216	71.22 365

EIA-MVS		68.59 222	67.16 246	72.90 156	75.18 228	55.64 206	69.39 235	81.29 118	52.44 240	64.53 351	70.69 388	60.33 198	82.30 113	54.27 258	76.31 355	80.75 240

PAPR		69.20 209	68.66 220	70.82 193	75.15 229	47.77 282	75.31 142	81.11 123	49.62 282	66.33 340	79.27 311	61.53 179	82.96 99	48.12 312	81.50 291	81.74 220

MVSFormer		69.93 198	69.03 212	72.63 168	74.93 230	59.19 168	83.98 41	75.72 222	52.27 241	63.53 368	76.74 341	43.19 333	80.56 147	72.28 77	78.67 332	78.14 285

lupinMVS		63.36 292	61.49 311	68.97 233	74.93 230	59.19 168	65.80 297	64.52 337	34.68 417	63.53 368	74.25 363	43.19 333	70.62 296	53.88 262	78.67 332	77.10 300

nrg030		74.87 111	75.99 97	71.52 184	74.90 232	49.88 257	74.10 166	82.58 98	54.55 209	83.50 78	89.21 96	71.51 71	75.74 227	61.24 175	92.34 83	88.94 39

TAPA-MVS	65.27 12	75.16 102	74.29 116	77.77 79	74.86 233	68.08 83	77.89 108	84.04 76	55.15 196	76.19 188	83.39 226	66.91 117	80.11 158	60.04 192	90.14 132	85.13 103
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RPSCF		75.76 92	74.37 113	79.93 44	74.81 234	77.53 18	77.53 112	79.30 165	59.44 147	78.88 130	89.80 86	71.26 75	73.09 261	57.45 217	80.89 298	89.17 33

EI-MVSNet-Vis-set		72.78 147	71.87 167	75.54 108	74.77 235	59.02 174	72.24 185	71.56 267	63.92 106	78.59 135	71.59 382	66.22 129	78.60 180	67.58 112	80.32 310	89.00 37

v1240		73.06 136	73.14 142	72.84 160	74.74 236	47.27 293	71.88 199	81.11 123	51.80 249	82.28 91	84.21 207	56.22 252	82.34 112	68.82 100	87.17 197	88.91 40

v1921920		72.96 143	72.98 148	72.89 157	74.67 237	47.58 286	71.92 197	80.69 133	51.70 251	81.69 100	83.89 219	56.58 248	82.25 114	68.34 103	87.36 186	88.82 42

EI-MVSNet-UG-set		72.63 150	71.68 172	75.47 109	74.67 237	58.64 182	72.02 191	71.50 268	63.53 112	78.58 137	71.39 386	65.98 130	78.53 181	67.30 122	80.18 313	89.23 31

Fast-Effi-MVS+		68.81 217	68.30 225	70.35 202	74.66 239	48.61 270	66.06 292	78.32 186	50.62 268	71.48 279	75.54 348	68.75 98	79.59 165	50.55 286	78.73 331	82.86 184

v1192		73.40 127	73.42 134	73.32 140	74.65 240	48.67 265	72.21 186	81.73 110	52.76 237	81.85 94	84.56 201	57.12 242	82.24 115	68.58 101	87.33 189	89.06 35

v144192		72.99 140	73.06 146	72.77 162	74.58 241	47.48 288	71.90 198	80.44 142	51.57 252	81.46 102	84.11 213	58.04 233	82.12 116	67.98 109	87.47 184	88.70 45

MAR-MVS		67.72 235	66.16 260	72.40 172	74.45 242	64.99 114	74.87 147	77.50 199	48.67 297	65.78 344	68.58 414	57.01 245	77.79 202	46.68 325	81.92 278	74.42 331
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 93	76.20 94	74.16 124	74.44 243	48.69 264	75.84 140	82.93 90	59.02 152	85.92 45	89.17 99	58.56 222	82.74 104	70.73 86	89.14 157	91.05 14

sasdasda		72.29 159	73.38 136	69.04 229	74.23 244	47.37 290	73.93 168	83.18 84	54.36 212	76.61 175	81.64 264	72.03 66	75.34 232	57.12 219	87.28 191	84.40 133

canonicalmvs		72.29 159	73.38 136	69.04 229	74.23 244	47.37 290	73.93 168	83.18 84	54.36 212	76.61 175	81.64 264	72.03 66	75.34 232	57.12 219	87.28 191	84.40 133

Anonymous202405211		66.02 262	66.89 253	63.43 304	74.22 246	38.14 377	59.00 363	66.13 321	63.33 117	69.76 301	85.95 184	51.88 277	70.50 298	44.23 341	87.52 182	81.64 221

Effi-MVS+		72.10 162	72.28 163	71.58 182	74.21 247	50.33 246	74.72 154	82.73 94	62.62 122	70.77 286	76.83 340	69.96 90	80.97 141	60.20 186	78.43 335	83.45 163

FE-MVS		68.29 227	66.96 251	72.26 175	74.16 248	54.24 219	77.55 111	73.42 241	57.65 168	72.66 256	84.91 194	32.02 398	81.49 128	48.43 308	81.85 280	81.04 229

v1144		73.29 130	73.39 135	73.01 148	74.12 249	48.11 275	72.01 192	81.08 126	53.83 227	81.77 96	84.68 196	58.07 232	81.91 120	68.10 105	86.86 200	88.99 38

BP-MVS1		71.60 168	70.06 196	76.20 99	74.07 250	55.22 209	74.29 163	73.44 240	57.29 171	73.87 236	84.65 198	32.57 391	83.49 89	72.43 76	87.94 178	89.89 23

FA-MVS(test-final)		71.27 174	71.06 184	71.92 180	73.96 251	52.32 232	76.45 127	76.12 217	59.07 151	74.04 232	86.18 172	52.18 276	79.43 167	59.75 196	81.76 282	84.03 144

EI-MVSNet		69.61 203	69.01 213	71.41 186	73.94 252	49.90 253	71.31 208	71.32 273	58.22 159	75.40 200	70.44 389	58.16 226	75.85 222	62.51 163	79.81 319	88.48 46

CVMVSNet		59.21 332	58.44 336	61.51 323	73.94 252	47.76 283	71.31 208	64.56 336	26.91 444	60.34 387	70.44 389	36.24 376	67.65 328	53.57 265	68.66 415	69.12 386

fmvsm_s_conf0.5_n_5		71.46 172	71.62 175	70.99 192	73.89 254	59.95 164	73.02 177	73.08 242	45.15 336	77.30 157	84.06 214	64.73 148	70.08 303	71.20 81	82.10 276	82.92 180

IterMVS-LS		73.01 138	73.12 144	72.66 166	73.79 255	49.90 253	71.63 202	78.44 184	58.22 159	80.51 114	86.63 159	58.15 227	79.62 163	62.51 163	88.20 171	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UWE-MVS		52.94 375	52.70 378	53.65 378	73.56 256	27.49 436	57.30 377	49.57 415	38.56 392	62.79 372	71.42 385	19.49 453	60.41 374	24.33 449	77.33 348	73.06 341

alignmvs		70.54 186	71.00 185	69.15 227	73.50 257	48.04 278	69.85 230	79.62 157	53.94 226	76.54 179	82.00 254	59.00 215	74.68 244	57.32 218	87.21 195	84.72 118

Fast-Effi-MVS+-dtu		70.00 196	68.74 218	73.77 130	73.47 258	64.53 118	71.36 206	78.14 191	55.81 190	68.84 318	74.71 357	65.36 139	75.75 226	52.00 273	79.00 327	81.03 230

v8		75.07 104	75.64 100	73.35 138	73.42 259	47.46 289	75.20 143	81.45 115	60.05 142	85.64 49	89.26 94	58.08 231	81.80 124	69.71 97	87.97 177	90.79 18

tfpnnormal		66.48 257	67.93 233	62.16 318	73.40 260	36.65 386	63.45 331	64.99 331	55.97 187	72.82 254	87.80 133	57.06 244	69.10 314	48.31 310	87.54 181	80.72 242

IterMVS-SCA-FT		67.68 236	66.07 262	72.49 170	73.34 261	58.20 188	63.80 328	65.55 327	48.10 303	76.91 163	82.64 245	45.20 320	78.84 175	61.20 176	77.89 344	80.44 250

VNet		64.01 288	65.15 276	60.57 336	73.28 262	35.61 396	57.60 375	67.08 315	54.61 206	66.76 339	83.37 228	56.28 251	66.87 341	42.19 352	85.20 226	79.23 269

MGCFI-Net		71.70 167	73.10 145	67.49 258	73.23 263	43.08 332	72.06 190	82.43 100	54.58 207	75.97 190	82.00 254	72.42 64	75.22 234	57.84 213	87.34 188	84.18 140

3Dnovator	65.95 11	71.50 170	71.22 182	72.34 173	73.16 264	63.09 129	78.37 101	78.32 186	57.67 166	72.22 264	84.61 200	54.77 258	78.47 183	60.82 181	81.07 297	75.45 317

GBi-Net		68.30 225	68.79 215	66.81 271	73.14 265	40.68 354	71.96 194	73.03 243	54.81 199	74.72 213	90.36 74	48.63 307	75.20 236	47.12 319	85.37 220	84.54 127

test1		68.30 225	68.79 215	66.81 271	73.14 265	40.68 354	71.96 194	73.03 243	54.81 199	74.72 213	90.36 74	48.63 307	75.20 236	47.12 319	85.37 220	84.54 127

FMVSNet2		67.48 238	68.21 229	65.29 284	73.14 265	38.94 369	68.81 249	71.21 280	54.81 199	76.73 171	86.48 164	48.63 307	74.60 245	47.98 314	86.11 212	82.35 199

thisisatest0530		67.05 251	65.16 274	72.73 165	73.10 268	50.55 243	71.26 210	63.91 341	50.22 273	74.46 222	80.75 276	26.81 425	80.25 154	59.43 198	86.50 207	87.37 58

pm-mvs1		68.40 223	69.85 200	64.04 297	73.10 268	39.94 362	64.61 320	70.50 287	55.52 192	73.97 234	89.33 92	63.91 154	68.38 321	49.68 293	88.02 175	83.81 149

pmmvs4		60.78 320	59.04 330	66.00 280	73.06 270	57.67 190	64.53 321	60.22 358	36.91 403	65.96 341	77.27 336	39.66 356	68.54 320	38.87 372	74.89 367	71.80 358

SDMVSNet		66.36 259	67.85 236	61.88 320	73.04 271	46.14 304	58.54 368	71.36 272	51.42 255	68.93 312	82.72 242	65.62 135	62.22 370	54.41 255	84.67 236	77.28 295

sd_testset		63.55 290	65.38 270	58.07 355	73.04 271	38.83 371	57.41 376	65.44 328	51.42 255	68.93 312	82.72 242	63.76 155	58.11 387	41.05 360	84.67 236	77.28 295

fmvsm_s_conf0.5_n_6		70.08 194	69.97 197	70.39 199	72.99 273	58.93 176	68.84 246	76.40 213	49.08 290	68.75 320	81.65 263	57.34 239	71.97 280	70.91 84	83.81 253	80.26 253

v2v482		72.55 154	72.58 156	72.43 171	72.92 274	46.72 297	71.41 205	79.13 169	55.27 194	81.17 106	85.25 191	55.41 256	81.13 134	67.25 123	85.46 219	89.43 26

casdiffmvs_mvg		75.26 100	76.18 95	72.52 169	72.87 275	49.47 258	72.94 178	84.71 55	59.49 146	80.90 111	88.81 110	70.07 88	79.71 162	67.40 116	88.39 169	88.40 48
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_l_conf0.5_n_3		71.98 164	71.68 172	72.88 158	72.84 276	64.15 121	73.48 171	77.11 206	48.97 294	71.31 281	84.18 208	67.98 108	71.60 287	68.86 99	80.43 309	82.89 181

fmvsm_s_conf0.5_n_7		67.30 243	66.92 252	68.43 244	72.78 277	58.22 187	60.90 349	72.51 258	49.62 282	63.66 365	80.65 278	58.56 222	68.63 318	62.83 162	80.76 302	78.45 278

MIMVSNet		54.39 363	56.12 355	49.20 404	72.57 278	30.91 422	59.98 357	48.43 422	41.66 364	55.94 412	83.86 220	41.19 345	50.42 408	26.05 440	75.38 364	66.27 403

icg_test_0407_2		63.88 289	65.59 267	58.75 350	72.47 279	48.64 266	53.19 405	72.98 246	45.33 331	68.91 314	79.37 306	61.91 173	51.11 406	55.06 242	81.11 293	76.49 305

IMVS_0407		67.26 244	67.35 242	66.97 270	72.47 279	48.64 266	69.03 244	72.98 246	45.33 331	68.91 314	79.37 306	61.91 173	75.77 225	55.06 242	81.11 293	76.49 305

IMVS_0404		62.18 307	63.05 299	59.58 343	72.47 279	48.64 266	55.47 391	72.98 246	45.33 331	55.80 415	79.37 306	49.84 292	53.60 401	55.06 242	81.11 293	76.49 305

IMVS_0403		67.07 249	67.08 247	67.03 268	72.47 279	48.64 266	68.44 261	72.98 246	45.33 331	68.63 322	79.37 306	60.38 197	75.97 221	55.06 242	81.11 293	76.49 305

Patchmatch-RL test		59.95 327	59.12 329	62.44 315	72.46 283	54.61 217	59.63 359	47.51 425	41.05 371	74.58 219	74.30 362	31.06 408	65.31 355	51.61 275	79.85 318	67.39 395

CL-MVSNet_self_test		62.44 305	63.40 294	59.55 344	72.34 284	32.38 413	56.39 383	64.84 333	51.21 261	67.46 334	81.01 272	50.75 287	63.51 365	38.47 377	88.12 173	82.75 187

fmvsm_s_conf0.5_n_8		72.87 146	72.85 150	72.93 154	72.25 285	59.01 175	72.35 183	80.13 149	56.32 183	75.74 192	84.12 211	60.14 200	75.05 239	71.71 80	82.90 266	84.75 117

SD_0403		61.63 313	62.83 302	58.03 356	72.21 286	32.43 412	69.33 237	69.00 302	44.54 342	62.01 375	79.42 303	55.27 257	66.88 340	36.07 400	77.63 346	74.78 324

Vis-MVSNet (Re-imp)		62.74 302	63.21 297	61.34 328	72.19 287	31.56 418	67.31 277	53.87 391	53.60 231	69.88 299	83.37 228	40.52 350	70.98 293	41.40 358	86.78 203	81.48 223

thres100view900		61.17 317	61.09 314	61.39 326	72.14 288	35.01 399	65.42 303	56.99 373	55.23 195	70.71 287	79.90 292	32.07 396	72.09 276	35.61 402	81.73 283	77.08 301

ab-mvs		64.11 286	65.13 277	61.05 330	71.99 289	38.03 380	67.59 268	68.79 305	49.08 290	65.32 347	86.26 170	58.02 234	66.85 343	39.33 368	79.79 321	78.27 281

RRT-MVS		70.33 188	70.73 190	69.14 228	71.93 290	45.24 311	75.10 144	75.08 230	60.85 137	78.62 134	87.36 136	49.54 294	78.64 179	60.16 188	77.90 343	83.55 156

thres600view7		61.82 310	61.38 312	63.12 307	71.81 291	34.93 400	64.64 318	56.99 373	54.78 203	70.33 291	79.74 294	32.07 396	72.42 272	38.61 375	83.46 259	82.02 208

fmvsm_s_conf0.5_n_4		70.18 193	69.83 201	71.24 189	71.65 292	58.59 183	69.29 239	71.66 264	48.69 296	71.62 270	82.11 252	59.94 203	70.03 304	74.52 55	78.96 328	85.10 104

QAPM		69.18 210	69.26 207	68.94 234	71.61 293	52.58 231	80.37 77	78.79 177	49.63 280	73.51 240	85.14 192	53.66 266	79.12 170	55.11 241	75.54 361	75.11 322

WB-MVSnew		53.94 369	54.76 366	51.49 390	71.53 294	28.05 433	58.22 371	50.36 411	37.94 396	59.16 395	70.17 395	49.21 299	51.94 404	24.49 447	71.80 395	74.47 330

KinetiMVS		72.61 151	72.54 157	72.82 161	71.47 295	55.27 208	68.54 257	76.50 211	61.70 129	74.95 209	86.08 179	59.17 213	76.95 211	69.96 93	84.45 244	86.24 76

baseline		73.10 133	73.96 124	70.51 198	71.46 296	46.39 302	72.08 189	84.40 63	55.95 188	76.62 174	86.46 165	67.20 113	78.03 198	64.22 145	87.27 193	87.11 66

fmvsm_s_conf0.5_n_3		72.97 142	74.13 120	69.47 219	71.40 297	58.36 184	73.07 175	80.64 136	56.86 175	75.49 198	84.67 197	67.86 110	72.33 274	75.68 45	81.54 289	77.73 292

casdiffmvs		73.06 136	73.84 125	70.72 194	71.32 298	46.71 298	70.93 214	84.26 69	55.62 191	77.46 155	87.10 139	67.09 115	77.81 201	63.95 149	86.83 202	87.64 55
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmvis_n_1920		72.36 157	72.49 158	71.96 179	71.29 299	64.06 122	72.79 179	81.82 108	40.23 380	81.25 105	81.04 271	70.62 82	68.69 316	69.74 96	83.60 258	83.14 173

Anonymous20231206		54.13 364	55.82 357	49.04 407	70.89 300	35.96 392	51.73 413	50.87 409	34.86 412	62.49 373	79.22 312	42.52 339	44.29 437	27.95 436	81.88 279	66.88 399

fmvsm_s_conf0.1_n_a		67.37 242	66.36 258	70.37 201	70.86 301	61.17 146	74.00 167	57.18 372	40.77 375	68.83 319	80.88 273	63.11 158	67.61 330	66.94 124	74.72 368	82.33 202

tfpn200view9		60.35 324	59.97 323	61.51 323	70.78 302	35.35 397	63.27 334	57.47 366	53.00 235	68.31 325	77.09 338	32.45 393	72.09 276	35.61 402	81.73 283	77.08 301

thres400		60.77 321	59.97 323	63.15 306	70.78 302	35.35 397	63.27 334	57.47 366	53.00 235	68.31 325	77.09 338	32.45 393	72.09 276	35.61 402	81.73 283	82.02 208

AstraMVS		67.11 247	66.84 255	67.92 251	70.75 304	51.36 236	64.77 315	67.06 316	49.03 292	75.40 200	82.05 253	51.26 284	70.65 295	58.89 203	82.32 273	81.77 218

MSDG		67.47 240	67.48 241	67.46 259	70.70 305	54.69 216	66.90 283	78.17 189	60.88 136	70.41 289	74.76 355	61.22 186	73.18 260	47.38 318	76.87 351	74.49 329

testing91		55.74 353	55.29 363	57.08 360	70.63 306	30.85 423	54.94 397	56.31 382	50.34 270	57.08 403	70.10 397	24.50 436	65.86 350	36.98 391	76.75 352	74.53 328

test_yl		65.11 270	65.09 279	65.18 285	70.59 307	40.86 349	63.22 336	72.79 250	57.91 162	68.88 316	79.07 317	42.85 336	74.89 241	45.50 335	84.97 228	79.81 258

DCV-MVSNet		65.11 270	65.09 279	65.18 285	70.59 307	40.86 349	63.22 336	72.79 250	57.91 162	68.88 316	79.07 317	42.85 336	74.89 241	45.50 335	84.97 228	79.81 258

test_fmvsm_n_1920		69.63 201	68.45 222	73.16 143	70.56 309	65.86 105	70.26 223	78.35 185	37.69 397	74.29 225	78.89 319	61.10 188	68.10 324	65.87 132	79.07 326	85.53 95

OpenMVS	62.51 15	68.76 218	68.75 217	68.78 239	70.56 309	53.91 222	78.29 102	77.35 200	48.85 295	70.22 292	83.52 224	52.65 274	76.93 212	55.31 239	81.99 277	75.49 316

DELS-MVS		68.83 216	68.31 224	70.38 200	70.55 311	48.31 271	63.78 329	82.13 103	54.00 223	68.96 309	75.17 353	58.95 216	80.06 159	58.55 205	82.74 269	82.76 186
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
testing222		53.37 371	52.50 381	55.98 368	70.51 312	29.68 428	56.20 386	51.85 404	46.19 319	56.76 407	68.94 408	19.18 454	65.39 354	25.87 443	76.98 350	72.87 345

fmvsm_l_conf0.5_n_9		70.73 183	71.08 183	69.67 216	70.44 313	58.80 178	70.21 224	75.11 229	48.15 302	73.50 241	82.69 244	65.69 134	68.05 326	70.87 85	83.02 264	82.16 204

testing11		53.13 373	52.26 383	55.75 369	70.44 313	31.73 417	54.75 398	52.40 402	44.81 340	52.36 430	68.40 415	21.83 446	65.74 353	32.64 416	72.73 386	69.78 378

LCM-MVSNet-Re		69.10 212	71.57 178	61.70 321	70.37 315	34.30 405	61.45 343	79.62 157	56.81 176	89.59 9	88.16 127	68.44 101	72.94 262	42.30 350	87.33 189	77.85 291

UBG		49.18 399	49.35 403	48.66 409	70.36 316	26.56 441	50.53 418	45.61 431	37.43 399	53.37 426	65.97 424	23.03 442	54.20 399	26.29 438	71.54 396	65.20 410

patch_mono-2		62.73 303	64.08 286	58.68 351	70.36 316	55.87 201	60.84 350	64.11 340	41.23 368	64.04 356	78.22 326	60.00 201	48.80 414	54.17 259	83.71 256	71.37 362

ETVMVS		50.32 394	49.87 402	51.68 388	70.30 318	26.66 439	52.33 412	43.93 436	43.54 352	54.91 419	67.95 417	20.01 451	60.17 376	22.47 452	73.40 381	68.22 390

SCA		58.57 338	58.04 340	60.17 339	70.17 319	41.07 347	65.19 307	53.38 397	43.34 356	61.00 384	73.48 369	45.20 320	69.38 311	40.34 365	70.31 405	70.05 375

WBMVS		53.38 370	54.14 370	51.11 392	70.16 320	26.66 439	50.52 419	51.64 407	39.32 384	63.08 371	77.16 337	23.53 439	55.56 393	31.99 417	79.88 317	71.11 368

ET-MVSNet_ETH3D		63.32 293	60.69 319	71.20 190	70.15 321	55.66 204	65.02 311	64.32 338	43.28 357	68.99 308	72.05 380	25.46 432	78.19 196	54.16 260	82.80 268	79.74 261

testing99		55.16 359	54.56 368	56.98 362	70.13 322	30.58 425	54.55 400	54.11 390	49.53 284	56.76 407	70.14 396	22.76 443	65.79 352	36.99 390	76.04 357	74.57 327

guyue		66.95 253	66.74 256	67.56 257	70.12 323	51.14 238	65.05 310	68.68 306	49.98 278	74.64 217	80.83 274	50.77 286	70.34 302	57.72 214	82.89 267	81.21 224

viewmanbaseed2359cas		70.24 190	70.83 187	68.48 243	69.99 324	44.55 318	69.48 233	81.01 128	50.87 264	73.61 238	84.84 195	64.00 152	74.31 250	60.24 185	83.43 260	86.56 72

APD_test1		75.04 105	75.38 104	74.02 127	69.89 325	70.15 66	76.46 126	79.71 156	65.50 85	82.99 82	88.60 116	66.94 116	72.35 273	59.77 195	88.54 167	79.56 262

PVSNet_BlendedMVS		65.38 268	64.30 283	68.61 241	69.81 326	49.36 259	65.60 301	78.96 171	45.50 325	59.98 388	78.61 321	51.82 278	78.20 194	44.30 339	84.11 249	78.27 281

PVSNet_Blended		62.90 299	61.64 308	66.69 274	69.81 326	49.36 259	61.23 346	78.96 171	42.04 361	59.98 388	68.86 411	51.82 278	78.20 194	44.30 339	77.77 345	72.52 349

OpenMVS_ROB	54.93 17	63.23 295	63.28 295	63.07 308	69.81 326	45.34 310	68.52 258	67.14 314	43.74 349	70.61 288	79.22 312	47.90 311	72.66 265	48.75 303	73.84 380	71.21 366

test_fmvsmconf0.01_n		73.91 118	73.64 130	74.71 114	69.79 329	66.25 100	75.90 138	79.90 152	46.03 321	76.48 182	85.02 193	67.96 109	73.97 254	74.47 57	87.22 194	83.90 147

fmvsm_s_conf0.5_n_a		67.00 252	65.95 266	70.17 206	69.72 330	61.16 147	73.34 173	56.83 375	40.96 372	68.36 324	80.08 290	62.84 159	67.57 331	66.90 126	74.50 372	81.78 217

FMVSNet3		65.00 273	65.16 274	64.52 292	69.47 331	37.56 384	66.63 286	70.38 288	51.55 253	74.72 213	83.27 233	37.89 368	74.44 247	47.12 319	85.37 220	81.57 222

myMVS_eth3d28		51.35 388	51.99 385	49.44 403	69.21 332	22.51 453	49.82 421	49.11 417	49.00 293	55.03 418	70.31 392	22.73 444	52.88 403	24.33 449	78.39 337	72.92 343

MS-PatchMatch		55.59 355	54.89 365	57.68 358	69.18 333	49.05 262	61.00 348	62.93 347	35.98 408	58.36 398	68.93 409	36.71 374	66.59 347	37.62 385	63.30 431	57.39 437

baseline1		57.82 342	58.36 338	56.19 366	69.17 334	30.76 424	62.94 338	55.21 384	46.04 320	63.83 361	78.47 322	41.20 344	63.68 363	39.44 367	68.99 413	74.13 332

v148		69.38 208	69.39 203	69.36 221	69.14 335	44.56 317	68.83 248	72.70 254	54.79 202	78.59 135	84.12 211	54.69 259	76.74 217	59.40 199	82.20 274	86.79 68

test_fmvsmconf0.1_n		73.26 131	72.82 153	74.56 116	69.10 336	66.18 102	74.65 157	79.34 164	45.58 324	75.54 196	83.91 218	67.19 114	73.88 257	73.26 66	86.86 200	83.63 155

fmvsm_s_conf0.1_n		66.60 255	65.54 268	69.77 214	68.99 337	59.15 171	72.12 188	56.74 377	40.72 377	68.25 327	80.14 289	61.18 187	66.92 337	67.34 121	74.40 373	83.23 171

Syy-MVS		54.13 364	55.45 360	50.18 396	68.77 338	23.59 449	55.02 394	44.55 434	43.80 346	58.05 400	64.07 429	46.22 315	58.83 382	46.16 329	72.36 389	68.12 391

myMVS_eth3d		50.36 393	50.52 398	49.88 397	68.77 338	22.69 451	55.02 394	44.55 434	43.80 346	58.05 400	64.07 429	14.16 464	58.83 382	33.90 411	72.36 389	68.12 391

test_fmvsmconf_n		72.91 144	72.40 161	74.46 117	68.62 340	66.12 103	74.21 165	78.80 176	45.64 323	74.62 218	83.25 234	66.80 122	73.86 258	72.97 69	86.66 206	83.39 164

CANet_DTU		64.04 287	63.83 288	64.66 290	68.39 341	42.97 334	73.45 172	74.50 234	52.05 247	54.78 420	75.44 351	43.99 328	70.42 300	53.49 266	78.41 336	80.59 246

EU-MVSNet		60.82 319	60.80 318	60.86 334	68.37 342	41.16 345	72.27 184	68.27 311	26.96 442	69.08 306	75.71 346	32.09 395	67.44 332	55.59 237	78.90 329	73.97 333

PVSNet	43.83 21	51.56 386	51.17 390	52.73 383	68.34 343	38.27 375	48.22 425	53.56 395	36.41 405	54.29 423	64.94 428	34.60 381	54.20 399	30.34 424	69.87 408	65.71 406

EPNet		69.10 212	67.32 243	74.46 117	68.33 344	61.27 145	77.56 110	63.57 343	60.95 135	56.62 409	82.75 241	51.53 281	81.24 132	54.36 257	90.20 129	80.88 236
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.1_n_2		69.14 211	68.42 223	71.28 187	68.30 345	57.60 191	65.06 309	69.91 291	48.24 299	74.56 220	82.84 240	55.55 255	69.73 306	70.66 88	80.69 304	86.52 73

fmvsm_s_conf0.5_n		66.34 261	65.27 271	69.57 218	68.20 346	59.14 173	71.66 201	56.48 378	40.92 373	67.78 329	79.46 301	61.23 184	66.90 338	67.39 117	74.32 376	82.66 192

IB-MVS	49.67 18	59.69 329	56.96 348	67.90 252	68.19 347	50.30 247	61.42 344	65.18 330	47.57 310	55.83 413	67.15 423	23.77 438	79.60 164	43.56 345	79.97 315	73.79 336
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
MVS		60.62 322	59.97 323	62.58 314	68.13 348	47.28 292	68.59 255	73.96 237	32.19 426	59.94 390	68.86 411	50.48 288	77.64 205	41.85 355	75.74 358	62.83 420

eth_miper_zixun_eth		69.42 206	68.73 219	71.50 185	67.99 349	46.42 300	67.58 269	78.81 174	50.72 267	78.13 143	80.34 284	50.15 291	80.34 152	60.18 187	84.65 238	87.74 54

TinyColmap		67.98 231	69.28 206	64.08 295	67.98 350	46.82 296	70.04 225	75.26 226	53.05 234	77.36 156	86.79 149	59.39 210	72.59 269	45.64 333	88.01 176	72.83 346

EPNet_dtu		58.93 335	58.52 334	60.16 340	67.91 351	47.70 285	69.97 227	58.02 364	49.73 279	47.28 445	73.02 374	38.14 364	62.34 368	36.57 394	85.99 213	70.43 373
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 343	57.02 347	59.17 346	67.89 352	34.93 400	58.91 366	57.25 370	50.24 272	64.01 357	71.46 384	32.49 392	71.39 288	31.31 420	79.57 323	71.19 367

fmvsm_s_conf0.5_n_2		68.93 214	68.23 228	71.02 191	67.78 353	57.58 192	64.74 316	69.56 295	48.16 301	74.38 224	82.32 249	56.00 254	69.68 309	70.65 89	80.52 308	85.80 90

SSC-MVS3.2		57.01 345	59.50 327	49.57 402	67.73 354	25.95 445	46.68 431	51.75 406	51.41 257	63.84 360	79.66 297	53.28 269	50.34 409	37.85 382	83.28 262	72.41 351

our_test_3		56.46 348	56.51 351	56.30 365	67.70 355	39.66 364	55.36 393	52.34 403	40.57 379	63.85 359	69.91 400	40.04 353	58.22 386	43.49 346	75.29 366	71.03 370

ppachtmachnet_test		60.26 325	59.61 326	62.20 317	67.70 355	44.33 320	58.18 372	60.96 356	40.75 376	65.80 343	72.57 376	41.23 343	63.92 362	46.87 323	82.42 272	78.33 279

VortexMVS		65.93 263	66.04 264	65.58 283	67.63 357	47.55 287	64.81 313	72.75 253	47.37 312	75.17 205	79.62 299	49.28 298	71.00 292	55.20 240	82.51 271	78.21 283

MVS_Test		69.84 199	70.71 191	67.24 263	67.49 358	43.25 331	69.87 229	81.22 122	52.69 238	71.57 276	86.68 155	62.09 172	74.51 246	66.05 129	78.74 330	83.96 145

fmvsm_l_conf0.5_n		67.48 238	66.88 254	69.28 224	67.41 359	62.04 135	70.69 218	69.85 292	39.46 383	69.59 302	81.09 270	58.15 227	68.73 315	67.51 114	78.16 341	77.07 303

thisisatest0515		60.48 323	57.86 341	68.34 246	67.25 360	46.42 300	60.58 353	62.14 349	40.82 374	63.58 367	69.12 405	26.28 428	78.34 190	48.83 302	82.13 275	80.26 253

V42		71.06 177	70.83 187	71.72 181	67.25 360	47.14 294	65.94 293	80.35 145	51.35 258	83.40 79	83.23 235	59.25 212	78.80 176	65.91 131	80.81 301	89.23 31

fmvsm_l_conf0.5_n_a		66.66 254	65.97 265	68.72 240	67.09 362	61.38 143	70.03 226	69.15 300	38.59 391	68.41 323	80.36 283	56.56 249	68.32 322	66.10 128	77.45 347	76.46 309

GA-MVS		62.91 298	61.66 307	66.66 275	67.09 362	44.49 319	61.18 347	69.36 298	51.33 259	69.33 305	74.47 359	36.83 373	74.94 240	50.60 285	74.72 368	80.57 247

testf1		75.66 94	76.57 89	72.95 151	67.07 364	67.62 87	76.10 134	80.68 134	64.95 97	86.58 37	90.94 47	71.20 76	71.68 285	60.46 183	91.13 106	79.56 262

APD_test2		75.66 94	76.57 89	72.95 151	67.07 364	67.62 87	76.10 134	80.68 134	64.95 97	86.58 37	90.94 47	71.20 76	71.68 285	60.46 183	91.13 106	79.56 262

mmtdpeth		68.76 218	70.55 193	63.40 305	67.06 366	56.26 198	68.73 254	71.22 279	55.47 193	70.09 295	88.64 115	65.29 141	56.89 391	58.94 202	89.50 147	77.04 304

HY-MVS	49.31 19	57.96 341	57.59 344	59.10 348	66.85 367	36.17 390	65.13 308	65.39 329	39.24 387	54.69 422	78.14 328	44.28 327	67.18 336	33.75 412	70.79 401	73.95 334

CR-MVSNet		58.96 333	58.49 335	60.36 338	66.37 368	48.24 273	70.93 214	56.40 380	32.87 425	61.35 379	86.66 156	33.19 386	63.22 366	48.50 307	70.17 406	69.62 381

RPMNet		65.77 265	65.08 281	67.84 254	66.37 368	48.24 273	70.93 214	86.27 21	54.66 205	61.35 379	86.77 151	33.29 385	85.67 49	55.93 231	70.17 406	69.62 381

IterMVS		63.12 296	62.48 305	65.02 288	66.34 370	52.86 228	63.81 327	62.25 348	46.57 317	71.51 278	80.40 282	44.60 325	66.82 344	51.38 279	75.47 362	75.38 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		69.82 200	69.89 199	69.61 217	66.24 371	43.48 327	68.12 264	79.61 159	51.43 254	77.72 150	80.18 288	54.61 261	78.15 197	63.62 154	87.50 183	87.20 63

tpm2		56.12 350	54.64 367	60.55 337	66.24 371	36.01 391	68.14 263	56.77 376	33.60 423	58.25 399	75.52 350	30.25 414	74.33 249	33.27 413	69.76 410	71.32 363

Anonymous20240521		63.55 290	66.07 262	55.99 367	66.18 373	44.04 322	68.77 252	68.80 304	46.99 314	72.57 257	85.84 185	39.87 354	50.22 410	53.40 269	92.23 85	73.71 337

Patchmtry		60.91 318	63.01 300	54.62 374	66.10 374	26.27 443	67.47 271	56.40 380	54.05 222	72.04 267	86.66 156	33.19 386	60.17 376	43.69 343	87.45 185	77.42 293

FMVSNet5		55.08 360	55.54 359	53.71 377	65.80 375	33.50 409	56.22 385	52.50 401	43.72 350	61.06 382	83.38 227	25.46 432	54.87 396	30.11 426	81.64 288	72.75 347

1314		59.83 328	58.86 332	62.74 313	65.71 376	44.78 315	68.59 255	72.63 255	33.54 424	61.05 383	67.29 422	43.62 331	71.26 289	49.49 296	67.84 420	72.19 355

MonoMVSNet		62.75 301	63.42 293	60.73 335	65.60 377	40.77 352	72.49 182	70.56 286	52.49 239	75.07 206	79.42 303	39.52 358	69.97 305	46.59 326	69.06 412	71.44 361

MDTV_nov1_ep13			54.05 372		65.54 378	29.30 430	59.00 363	55.22 383	35.96 409	52.44 428	75.98 344	30.77 411	59.62 378	38.21 378	73.33 383

baseline2		55.57 356	52.74 377	64.05 296	65.26 379	44.11 321	62.38 339	54.43 388	39.03 388	51.21 433	67.35 421	33.66 384	72.45 271	37.14 388	64.22 429	75.60 315

USDC		62.80 300	63.10 298	61.89 319	65.19 380	43.30 330	67.42 272	74.20 236	35.80 410	72.25 263	84.48 204	45.67 317	71.95 281	37.95 381	84.97 228	70.42 374

tpm		50.60 391	52.42 382	45.14 422	65.18 381	26.29 442	60.30 354	43.50 437	37.41 400	57.01 404	79.09 316	30.20 416	42.32 442	32.77 415	66.36 423	66.81 401

PatchmatchNet		54.60 362	54.27 369	55.59 370	65.17 382	39.08 366	66.92 282	51.80 405	39.89 381	58.39 397	73.12 373	31.69 402	58.33 385	43.01 348	58.38 445	69.38 384
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_ehance_all_eth		68.36 224	68.16 231	68.98 232	65.14 383	43.34 329	67.07 279	78.92 173	49.11 289	76.21 187	77.72 332	53.48 267	77.92 200	61.16 177	84.59 241	85.68 93

cl____		68.26 230	68.26 226	68.29 247	64.98 384	43.67 325	65.89 294	74.67 231	50.04 276	76.86 166	82.42 247	48.74 305	75.38 230	60.92 180	89.81 140	85.80 90

DIV-MVS_self_test		68.27 228	68.26 226	68.29 247	64.98 384	43.67 325	65.89 294	74.67 231	50.04 276	76.86 166	82.43 246	48.74 305	75.38 230	60.94 179	89.81 140	85.81 86

tpm cat1		54.02 367	52.63 379	58.19 354	64.85 386	39.86 363	66.26 291	57.28 369	32.16 427	56.90 405	70.39 391	32.75 390	65.30 356	34.29 408	58.79 442	69.41 383

viewmambaseed2359dif		65.63 266	65.13 277	67.11 267	64.57 387	44.73 316	64.12 324	72.48 259	43.08 358	71.59 271	81.17 268	58.90 217	72.46 270	52.94 270	77.33 348	84.13 143

XXY-MVS		55.19 358	57.40 346	48.56 410	64.45 388	34.84 402	51.54 414	53.59 393	38.99 389	63.79 362	79.43 302	56.59 247	45.57 426	36.92 392	71.29 398	65.25 409

PatchT		53.35 372	56.47 352	43.99 427	64.19 389	17.46 458	59.15 360	43.10 439	52.11 246	54.74 421	86.95 145	29.97 417	49.98 411	43.62 344	74.40 373	64.53 417

D2MVS		62.58 304	61.05 315	67.20 264	63.85 390	47.92 279	56.29 384	69.58 294	39.32 384	70.07 296	78.19 327	34.93 380	72.68 264	53.44 267	83.74 254	81.00 232

mvs_anonymous		65.08 272	65.49 269	63.83 298	63.79 391	37.60 383	66.52 289	69.82 293	43.44 353	73.46 243	86.08 179	58.79 219	71.75 284	51.90 274	75.63 360	82.15 205

diffmvs_AUTHOR		68.27 228	68.59 221	67.32 262	63.76 392	45.37 309	65.31 304	77.19 204	49.25 286	72.68 255	82.19 251	59.62 208	71.17 290	65.75 133	81.53 290	85.42 97

CostFormer		57.35 344	56.14 354	60.97 331	63.76 392	38.43 373	67.50 270	60.22 358	37.14 402	59.12 396	76.34 343	32.78 389	71.99 279	39.12 371	69.27 411	72.47 350

Gipuma		69.55 204	72.83 152	59.70 341	63.63 394	53.97 221	80.08 83	75.93 220	64.24 104	73.49 242	88.93 108	57.89 235	62.46 367	59.75 196	91.55 95	62.67 422
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
cl22		67.14 246	66.51 257	69.03 231	63.20 395	43.46 328	66.88 284	76.25 214	49.22 287	74.48 221	77.88 331	45.49 319	77.40 207	60.64 182	84.59 241	86.24 76

gg-mvs-nofinetune		55.75 352	56.75 350	52.72 384	62.87 396	28.04 434	68.92 245	41.36 449	71.09 47	50.80 435	92.63 15	20.74 448	66.86 342	29.97 427	72.41 388	63.25 419

gm-plane-assit					62.51 397	33.91 407			37.25 401		62.71 434		72.74 263	38.70 373

mvs5depth		66.35 260	67.98 232	61.47 325	62.43 398	51.05 239	69.38 236	69.24 299	56.74 178	73.62 237	89.06 104	46.96 314	58.63 384	55.87 233	88.49 168	74.73 325

MVS-HIRNet		45.53 408	47.29 408	40.24 434	62.29 399	26.82 438	56.02 388	37.41 455	29.74 437	43.69 455	81.27 266	33.96 382	55.48 394	24.46 448	56.79 446	38.43 455

diffmvs		67.42 241	67.50 240	67.20 264	62.26 400	45.21 312	64.87 312	77.04 207	48.21 300	71.74 268	79.70 296	58.40 224	71.17 290	64.99 137	80.27 311	85.22 100
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		41.62 420	39.89 425	46.80 415	61.81 401	51.59 233	33.56 454	35.74 456	27.48 441	37.64 459	53.53 448	23.24 440	42.09 443	27.39 437	58.64 443	46.72 447

KD-MVS_self_test		66.38 258	67.51 239	62.97 310	61.76 402	34.39 404	58.11 373	75.30 225	50.84 266	77.12 159	85.42 188	56.84 246	69.44 310	51.07 281	91.16 103	85.08 106

MDA-MVSNet-bldmvs		62.34 306	61.73 306	64.16 293	61.64 403	49.90 253	48.11 426	57.24 371	53.31 233	80.95 108	79.39 305	49.00 303	61.55 372	45.92 331	80.05 314	81.03 230

miper_enhance_ethall		65.86 264	65.05 282	68.28 249	61.62 404	42.62 337	64.74 316	77.97 193	42.52 359	73.42 244	72.79 375	49.66 293	77.68 204	58.12 210	84.59 241	84.54 127

WTY-MVS		49.39 398	50.31 400	46.62 416	61.22 405	32.00 416	46.61 432	49.77 413	33.87 420	54.12 424	69.55 403	41.96 340	45.40 429	31.28 421	64.42 428	62.47 424

CMPMVS	48.73 20	61.54 315	60.89 316	63.52 302	61.08 406	51.55 234	68.07 265	68.00 312	33.88 419	65.87 342	81.25 267	37.91 367	67.71 327	49.32 298	82.60 270	71.31 364
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 392	50.69 397	49.64 400	60.76 407	41.87 341	53.18 406	45.48 432	43.41 354	49.41 440	60.47 442	29.22 420	44.73 434	42.09 353	72.14 392	62.33 426

test-mter		48.56 401	48.20 406	49.64 400	60.76 407	41.87 341	53.18 406	45.48 432	31.91 431	49.41 440	60.47 442	18.34 455	44.73 434	42.09 353	72.14 392	62.33 426

GG-mvs-BLEND				52.24 385	60.64 409	29.21 431	69.73 231	42.41 442		45.47 448	52.33 451	20.43 449	68.16 323	25.52 445	65.42 425	59.36 433

tpmvs		55.84 351	55.45 360	57.01 361	60.33 410	33.20 410	65.89 294	59.29 362	47.52 311	56.04 411	73.60 368	31.05 409	68.06 325	40.64 363	64.64 427	69.77 379

UWE-MVS-28		44.18 415	44.37 420	43.61 428	60.10 411	16.96 459	52.62 410	33.27 458	36.79 404	48.86 442	69.47 404	19.96 452	45.65 425	13.40 459	64.83 426	68.23 389

miper_lstm_enhance		61.97 308	61.63 309	62.98 309	60.04 412	45.74 307	47.53 428	70.95 282	44.04 344	73.06 251	78.84 320	39.72 355	60.33 375	55.82 234	84.64 239	82.88 182

dmvs_re		49.91 397	50.77 396	47.34 412	59.98 413	38.86 370	53.18 406	53.58 394	39.75 382	55.06 417	61.58 438	36.42 375	44.40 436	29.15 434	68.23 416	58.75 434

PVSNet_0	36.71 22	41.12 421	40.78 424	42.14 430	59.97 414	40.13 360	40.97 443	42.24 446	30.81 435	44.86 451	49.41 454	40.70 349	45.12 431	23.15 451	34.96 457	41.16 453

dmvs_testset		45.26 409	47.51 407	38.49 437	59.96 415	14.71 461	58.50 369	43.39 438	41.30 367	51.79 432	56.48 446	39.44 359	49.91 413	21.42 454	55.35 451	50.85 442

new-patchmatchnet		52.89 376	55.76 358	44.26 426	59.94 416	6.31 467	37.36 451	50.76 410	41.10 369	64.28 354	79.82 293	44.77 323	48.43 418	36.24 397	87.61 180	78.03 287

test20.03		55.74 353	57.51 345	50.42 395	59.89 417	32.09 415	50.63 417	49.01 419	50.11 274	65.07 349	83.23 235	45.61 318	48.11 419	30.22 425	83.82 252	71.07 369

MVSTER		63.29 294	61.60 310	68.36 245	59.77 418	46.21 303	60.62 352	71.32 273	41.83 363	75.40 200	79.12 315	30.25 414	75.85 222	56.30 228	79.81 319	83.03 177

reproduce_monomvs		58.94 334	58.14 339	61.35 327	59.70 419	40.98 348	60.24 356	63.51 344	45.85 322	68.95 310	75.31 352	18.27 456	65.82 351	51.47 277	79.97 315	77.26 298

N_pmnet		52.06 382	51.11 391	54.92 371	59.64 420	71.03 57	37.42 450	61.62 355	33.68 421	57.12 402	72.10 377	37.94 366	31.03 456	29.13 435	71.35 397	62.70 421

test_vis1_n_1920		52.96 374	53.50 373	51.32 391	59.15 421	44.90 314	56.13 387	64.29 339	30.56 436	59.87 392	60.68 440	40.16 352	47.47 420	48.25 311	62.46 433	61.58 428

JIA-IIPM		54.03 366	51.62 386	61.25 329	59.14 422	55.21 213	59.10 362	47.72 423	50.85 265	50.31 439	85.81 186	20.10 450	63.97 361	36.16 398	55.41 450	64.55 416

LF4IMVS		67.50 237	67.31 244	68.08 250	58.86 423	61.93 136	71.43 204	75.90 221	44.67 341	72.42 260	80.20 286	57.16 240	70.44 299	58.99 201	86.12 211	71.88 357

UnsupCasMVSNet_bld		50.01 396	51.03 393	46.95 413	58.61 424	32.64 411	48.31 424	53.27 398	34.27 418	60.47 386	71.53 383	41.40 342	47.07 422	30.68 423	60.78 438	61.13 429

dongtai		31.66 426	32.98 429	27.71 441	58.58 425	12.61 463	45.02 436	14.24 467	41.90 362	47.93 443	43.91 456	10.65 467	41.81 446	14.06 458	20.53 460	28.72 457

dp		44.09 416	44.88 417	41.72 433	58.53 426	23.18 450	54.70 399	42.38 444	34.80 414	44.25 453	65.61 426	24.48 437	44.80 433	29.77 428	49.42 453	57.18 438

testgi		54.00 368	56.86 349	45.45 420	58.20 427	25.81 446	49.05 422	49.50 416	45.43 328	67.84 328	81.17 268	51.81 280	43.20 441	29.30 430	79.41 324	67.34 397

wuyk23d		61.97 308	66.25 259	49.12 406	58.19 428	60.77 156	66.32 290	52.97 399	55.93 189	90.62 6	86.91 146	73.07 61	35.98 454	20.63 456	91.63 92	50.62 443

ANet_high		67.08 248	69.94 198	58.51 353	57.55 429	27.09 437	58.43 370	76.80 209	63.56 111	82.40 90	91.93 26	59.82 206	64.98 358	50.10 289	88.86 165	83.46 162

Patchmatch-test		47.93 402	49.96 401	41.84 431	57.42 430	24.26 448	48.75 423	41.49 448	39.30 386	56.79 406	73.48 369	30.48 413	33.87 455	29.29 431	72.61 387	67.39 395

test_vis1_n		51.27 389	50.41 399	53.83 376	56.99 431	50.01 251	56.75 379	60.53 357	25.68 447	59.74 393	57.86 445	29.40 419	47.41 421	43.10 347	63.66 430	64.08 418

new_pmnet		37.55 424	39.80 426	30.79 439	56.83 432	16.46 460	39.35 447	30.65 459	25.59 448	45.26 449	61.60 437	24.54 435	28.02 459	21.60 453	52.80 452	47.90 446

pmmvs3		46.71 405	45.09 415	51.55 389	56.76 433	48.25 272	55.78 390	39.53 453	24.13 452	50.35 438	63.40 431	15.90 461	51.08 407	29.29 431	70.69 403	55.33 440

sss		47.59 404	48.32 404	45.40 421	56.73 434	33.96 406	45.17 435	48.51 421	32.11 430	52.37 429	65.79 425	40.39 351	41.91 445	31.85 418	61.97 435	60.35 430

tpmrst		50.15 395	51.38 389	46.45 417	56.05 435	24.77 447	64.40 323	49.98 412	36.14 407	53.32 427	69.59 402	35.16 379	48.69 415	39.24 369	58.51 444	65.89 404

TESTMET0.1,1		45.17 410	44.93 416	45.89 419	56.02 436	38.31 374	53.18 406	41.94 447	27.85 439	44.86 451	56.47 447	17.93 457	41.50 447	38.08 380	68.06 417	57.85 435

ADS-MVSNet2		48.76 400	47.25 409	53.29 382	55.90 437	40.54 357	47.34 429	54.99 386	31.41 433	50.48 436	72.06 378	31.23 405	54.26 398	25.93 441	55.93 447	65.07 411

ADS-MVSNet		44.62 413	45.58 412	41.73 432	55.90 437	20.83 456	47.34 429	39.94 452	31.41 433	50.48 436	72.06 378	31.23 405	39.31 450	25.93 441	55.93 447	65.07 411

ttmdpeth		56.40 349	55.45 360	59.25 345	55.63 439	40.69 353	58.94 365	49.72 414	36.22 406	65.39 345	86.97 144	23.16 441	56.69 392	42.30 350	80.74 303	80.36 251

test0.0.03 1		47.72 403	48.31 405	45.93 418	55.53 440	29.39 429	46.40 433	41.21 450	43.41 354	55.81 414	67.65 418	29.22 420	43.77 440	25.73 444	69.87 408	64.62 415

UnsupCasMVSNet_eth		52.26 381	53.29 376	49.16 405	55.08 441	33.67 408	50.03 420	58.79 363	37.67 398	63.43 370	74.75 356	41.82 341	45.83 424	38.59 376	59.42 441	67.98 394

pmmvs5		52.49 380	52.58 380	52.21 386	54.99 442	32.38 413	55.45 392	53.84 392	32.15 428	55.49 416	74.81 354	38.08 365	57.37 390	34.02 409	74.40 373	66.88 399

DSMNet-mixed		43.18 419	44.66 418	38.75 436	54.75 443	28.88 432	57.06 378	27.42 461	13.47 459	47.27 446	77.67 333	38.83 361	39.29 451	25.32 446	60.12 440	48.08 445

MDA-MVSNet_test_wron		52.57 379	53.49 375	49.81 399	54.24 444	36.47 388	40.48 445	46.58 429	38.13 393	75.47 199	73.32 371	41.05 348	43.85 439	40.98 361	71.20 399	69.10 387

YYNet1		52.58 378	53.50 373	49.85 398	54.15 445	36.45 389	40.53 444	46.55 430	38.09 394	75.52 197	73.31 372	41.08 347	43.88 438	41.10 359	71.14 400	69.21 385

EPMVS		45.74 407	46.53 410	43.39 429	54.14 446	22.33 454	55.02 394	35.00 457	34.69 416	51.09 434	70.20 394	25.92 430	42.04 444	37.19 387	55.50 449	65.78 405

test_cas_vis1_n_1920		50.90 390	50.92 394	50.83 394	54.12 447	47.80 281	51.44 415	54.61 387	26.95 443	63.95 358	60.85 439	37.86 369	44.97 432	45.53 334	62.97 432	59.72 432

test_fmvs3		56.78 347	55.99 356	59.12 347	53.96 448	48.09 276	58.76 367	66.22 320	27.54 440	76.66 172	68.69 413	25.32 434	51.31 405	53.42 268	73.38 382	77.97 290

test_fmvs1_n		52.70 377	52.01 384	54.76 372	53.83 449	50.36 245	55.80 389	65.90 322	24.96 449	65.39 345	60.64 441	27.69 423	48.46 416	45.88 332	67.99 418	65.46 407

KD-MVS_2432*1600		52.05 383	51.58 387	53.44 380	52.11 450	31.20 419	44.88 437	64.83 334	41.53 365	64.37 352	70.03 398	15.61 462	64.20 359	36.25 395	74.61 370	64.93 413

miper_refine_blended		52.05 383	51.58 387	53.44 380	52.11 450	31.20 419	44.88 437	64.83 334	41.53 365	64.37 352	70.03 398	15.61 462	64.20 359	36.25 395	74.61 370	64.93 413

test_fmvs2		54.80 361	54.11 371	56.88 363	51.76 452	49.95 252	56.70 380	65.80 323	26.22 445	69.42 303	65.25 427	31.82 400	49.98 411	49.63 294	70.36 404	70.71 371

E-PMN		45.17 410	45.36 413	44.60 424	50.07 453	42.75 335	38.66 448	42.29 445	46.39 318	39.55 456	51.15 452	26.00 429	45.37 430	37.68 383	76.41 353	45.69 449

PMMVS		44.69 412	43.95 421	46.92 414	50.05 454	53.47 226	48.08 427	42.40 443	22.36 455	44.01 454	53.05 450	42.60 338	45.49 427	31.69 419	61.36 437	41.79 452

test_fmvs1		51.51 387	50.86 395	53.48 379	49.72 455	49.35 261	54.11 401	64.96 332	24.64 451	63.66 365	59.61 444	28.33 422	48.45 417	45.38 337	67.30 422	62.66 423

EMVS		44.61 414	44.45 419	45.10 423	48.91 456	43.00 333	37.92 449	41.10 451	46.75 316	38.00 458	48.43 455	26.42 427	46.27 423	37.11 389	75.38 364	46.03 448

mvsany_test3		43.76 418	41.01 422	52.01 387	48.09 457	57.74 189	42.47 441	23.85 464	23.30 454	64.80 350	62.17 436	27.12 424	40.59 448	29.17 433	48.11 454	57.69 436

mvsany_test1		37.88 422	35.74 427	44.28 425	47.28 458	49.90 253	36.54 452	24.37 463	19.56 458	45.76 447	53.46 449	32.99 388	37.97 453	26.17 439	35.52 456	44.99 451

test_vis3_rt		51.94 385	51.04 392	54.65 373	46.32 459	50.13 249	44.34 439	78.17 189	23.62 453	68.95 310	62.81 433	21.41 447	38.52 452	41.49 357	72.22 391	75.30 321

test_vis1_rt		46.70 406	45.24 414	51.06 393	44.58 460	51.04 240	39.91 446	67.56 313	21.84 457	51.94 431	50.79 453	33.83 383	39.77 449	35.25 405	61.50 436	62.38 425

MVStest1		55.38 357	54.97 364	56.58 364	43.72 461	40.07 361	59.13 361	47.09 427	34.83 413	76.53 180	84.65 198	13.55 465	53.30 402	55.04 246	80.23 312	76.38 310

MVE	27.91 23	36.69 425	35.64 428	39.84 435	43.37 462	35.85 394	19.49 456	24.61 462	24.68 450	39.05 457	62.63 435	38.67 363	27.10 460	21.04 455	47.25 455	56.56 439
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 423	40.87 423	28.36 440	42.41 463	5.35 468	24.61 455	27.75 460	32.15 428	47.85 444	70.27 393	35.85 377	29.51 458	19.08 457	67.85 419	50.22 444

test_f		43.79 417	45.63 411	38.24 438	42.29 464	38.58 372	34.76 453	47.68 424	22.22 456	67.34 335	63.15 432	31.82 400	30.60 457	39.19 370	62.28 434	45.53 450

kuosan		22.02 427	23.52 431	17.54 443	41.56 465	11.24 464	41.99 442	13.39 468	26.13 446	28.87 460	30.75 458	9.72 468	21.94 462	4.77 463	14.49 461	19.43 458

DeepMVS_CX				11.83 444	15.51 466	13.86 462		11.25 469	5.76 460	20.85 462	26.46 459	17.06 460	9.22 463	9.69 462	13.82 462	12.42 459

test_method		19.26 428	19.12 432	19.71 442	9.09 467	1.91 470	7.79 458	53.44 396	1.42 461	10.27 463	35.80 457	17.42 459	25.11 461	12.44 460	24.38 459	32.10 456

tmp_tt		11.98 430	14.73 433	3.72 445	2.28 468	4.62 469	19.44 457	14.50 466	0.47 463	21.55 461	9.58 461	25.78 431	4.57 464	11.61 461	27.37 458	1.96 460

test123		4.43 433	5.78 436	0.39 447	0.97 469	0.28 471	46.33 434	0.45 470	0.31 464	0.62 465	1.50 464	0.61 470	0.11 466	0.56 464	0.63 463	0.77 462

testmvs		4.06 434	5.28 437	0.41 446	0.64 470	0.16 472	42.54 440	0.31 471	0.26 465	0.50 466	1.40 465	0.77 469	0.17 465	0.56 464	0.55 464	0.90 461

mmdepth		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

monomultidepth		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

test_blank		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

eth-test2					0.00 471

eth-test					0.00 471

uanet_test		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

DCPMVS		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

cdsmvs_eth3d_5k		17.71 429	23.62 430	0.00 448	0.00 471	0.00 473	0.00 459	70.17 290	0.00 466	0.00 467	74.25 363	68.16 104	0.00 467	0.00 466	0.00 465	0.00 463

pcd_1.5k_mvsjas		5.20 432	6.93 435	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	62.39 166	0.00 467	0.00 466	0.00 465	0.00 463

sosnet-low-res		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

sosnet		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

uncertanet		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

Regformer		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

ab-mvs-re		5.62 431	7.50 434	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	67.46 419	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

uanet		0.00 435	0.00 438	0.00 448	0.00 471	0.00 473	0.00 459	0.00 472	0.00 466	0.00 467	0.00 466	0.00 471	0.00 467	0.00 466	0.00 465	0.00 463

WAC-MVS						22.69 451								36.10 399

PC_three_1452									46.98 315	81.83 95	86.28 168	66.55 127	84.47 74	63.31 159	90.78 120	83.49 158

test_241102_TWO								84.80 49	72.61 36	84.93 60	89.70 87	77.73 25	85.89 42	75.29 47	94.22 57	83.25 169

test_0728_THIRD									74.03 25	85.83 47	90.41 66	75.58 41	85.69 47	77.43 35	94.74 35	84.31 137

GSMVS																70.05 375

sam_mvs1												31.41 403				70.05 375

sam_mvs												31.21 407

MTGPA								80.63 137

test_post1							66.63 286				2.08 462	30.66 412	59.33 380	40.34 365

test_post											1.99 463	30.91 410	54.76 397

patchmatchnet-post											68.99 406	31.32 404	69.38 311

MTMP							84.83 34	19.26 465

test9_res														72.12 79	91.37 98	77.40 294

agg_prior2														70.70 87	90.93 114	78.55 277

test_prior4						70.14 67	77.57 109

test_prior2							75.57 141		58.92 153	76.53 180	86.78 150	67.83 111		69.81 94	92.76 77

旧先验2							71.17 211		45.11 337	78.54 138			61.28 373	59.19 200

新几何2							71.33 207

无先验							74.82 148	70.94 283	47.75 309				76.85 215	54.47 253		72.09 356

原ACMM2							74.78 152

testdata2													67.30 333	48.34 309

segment_acmp												68.30 103

testdata1							68.34 262		57.24 172

plane_prior5								85.49 33					86.15 29	71.09 82	90.94 112	84.82 114

plane_prior4											89.11 101

plane_prior3						65.67 106			63.82 108	78.23 141

plane_prior2							82.74 56		65.45 86

plane_prior						65.18 111	80.06 84		61.88 128						89.91 139

n2								0.00 472

nn								0.00 472

door-mid								55.02 385

test11								82.71 95

door								52.91 400

HQP5-MVS						58.80 178

BP-MVS														67.38 119

HQP4-MVS										71.59 271			85.31 54			83.74 152

HQP3-MVS								84.12 73							89.16 154

HQP2-MVS												58.09 229

MDTV_nov1_ep13_2view						18.41 457	53.74 403		31.57 432	44.89 450		29.90 418		32.93 414		71.48 360

ACMMP++_ref															89.47 149

ACMMP++															91.96 88

Test By Simon												62.56 162

High-resolution multi-view benchmark