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 57	91.63 1	86.34 1	97.97 1	94.77 3	66.57 120	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	63.30 123	84.80 35	87.77 10	86.18 2	96.26 2	96.06 1	90.32 1	84.49 72	68.08 91	97.05 2	96.93 1

MTAPA		83.19 22	83.87 22	81.13 34	91.16 3	78.16 12	84.87 33	80.63 130	72.08 41	84.93 59	90.79 48	74.65 49	84.42 75	80.98 5	94.75 32	80.82 208

mPP-MVS		84.01 14	84.39 15	82.88 7	90.65 4	81.38 4	87.08 13	82.79 87	72.41 39	85.11 58	90.85 47	76.65 31	84.89 66	79.30 20	94.63 36	82.35 178

MP-MVS		83.19 22	83.54 27	82.14 20	90.54 5	79.00 9	86.42 25	83.59 77	71.31 44	81.26 103	90.96 42	74.57 50	84.69 70	78.41 25	94.78 31	82.74 168
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMVS	70.70 6	81.70 36	83.15 35	77.36 79	90.35 6	82.82 3	82.15 59	79.22 156	74.08 24	87.16 32	91.97 21	84.80 2	76.97 200	64.98 122	93.61 63	72.28 313
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 51	82.86 40	73.07 136	89.93 7	39.21 328	77.15 115	81.28 114	79.74 6	90.87 5	92.73 12	75.03 46	84.93 65	63.83 135	95.19 19	95.07 3

DTE-MVSNet		80.35 52	82.89 39	72.74 150	89.84 8	37.34 348	77.16 114	81.81 104	80.45 4	90.92 4	92.95 8	74.57 50	86.12 31	63.65 136	94.68 35	94.76 6

PEN-MVS		80.46 50	82.91 38	73.11 135	89.83 9	39.02 331	77.06 117	82.61 92	80.04 5	90.60 7	92.85 10	74.93 47	85.21 60	63.15 143	95.15 21	95.09 2

region2R		83.54 18	83.86 23	82.58 15	89.82 10	77.53 18	87.06 16	84.23 67	70.19 53	83.86 73	90.72 52	75.20 43	86.27 23	79.41 18	94.25 53	83.95 128

ACMMPR		83.62 16	83.93 21	82.69 12	89.78 11	77.51 22	87.01 17	84.19 68	70.23 51	84.49 66	90.67 53	75.15 44	86.37 20	79.58 14	94.26 52	84.18 123

MSP-MVS		80.49 49	79.67 62	82.96 6	89.70 12	77.46 23	87.16 12	85.10 43	64.94 93	81.05 106	88.38 117	57.10 217	87.10 9	79.75 11	83.87 233	84.31 120
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 58	81.65 49	72.98 139	89.66 13	39.06 330	76.76 118	80.46 134	78.91 9	90.32 8	91.70 29	68.49 96	84.89 66	63.40 140	95.12 22	95.01 4

PGM-MVS		83.07 25	83.25 34	82.54 16	89.57 14	77.21 24	82.04 61	85.40 36	67.96 64	84.91 62	90.88 45	75.59 39	86.57 16	78.16 26	94.71 34	83.82 130

WR-MVS_H		80.22 54	82.17 45	74.39 113	89.46 15	42.69 301	78.24 101	82.24 96	78.21 13	89.57 10	92.10 19	68.05 101	85.59 50	66.04 115	95.62 10	94.88 5

XVS		83.51 19	83.73 24	82.85 9	89.43 16	77.61 16	86.80 20	84.66 56	72.71 32	82.87 83	90.39 65	73.86 55	86.31 21	78.84 23	94.03 56	84.64 103

X-MVStestdata		76.81 81	74.79 103	82.85 9	89.43 16	77.61 16	86.80 20	84.66 56	72.71 32	82.87 83	9.95 418	73.86 55	86.31 21	78.84 23	94.03 56	84.64 103

CP-MVS		84.12 12	84.55 14	82.80 11	89.42 18	79.74 6	88.19 5	84.43 61	71.96 43	84.70 64	90.56 55	77.12 28	86.18 28	79.24 21	95.36 13	82.49 176

ACMMP_NAP		82.33 31	83.28 32	79.46 51	89.28 19	69.09 78	83.62 46	84.98 45	64.77 94	83.97 72	91.02 41	75.53 42	85.93 38	82.00 3	94.36 48	83.35 149

UniMVSNet_ETH3D		76.74 82	79.02 65	69.92 193	89.27 20	43.81 288	74.47 153	71.70 235	72.33 40	85.50 53	93.65 4	77.98 23	76.88 203	54.60 219	91.64 88	89.08 31

HFP-MVS		83.39 21	84.03 20	81.48 27	89.25 21	75.69 28	87.01 17	84.27 64	70.23 51	84.47 67	90.43 60	76.79 29	85.94 36	79.58 14	94.23 54	82.82 165

ACMMP		84.22 10	84.84 12	82.35 18	89.23 22	76.66 26	87.65 7	85.89 26	71.03 47	85.85 45	90.58 54	78.77 18	85.78 44	79.37 19	95.17 20	84.62 105
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 38	81.76 47	80.76 38	89.20 23	78.75 10	86.48 24	82.03 100	68.80 58	80.92 108	88.52 113	72.00 68	82.39 103	74.80 47	93.04 70	81.14 198

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

ZNCC-MVS		83.12 24	83.68 25	81.45 28	89.14 25	73.28 46	86.32 26	85.97 25	67.39 65	84.02 71	90.39 65	74.73 48	86.46 17	80.73 7	94.43 43	84.60 108

HPM-MVS++		79.89 55	79.80 61	80.18 43	89.02 26	78.44 11	83.49 49	80.18 140	64.71 95	78.11 140	88.39 116	65.46 131	83.14 92	77.64 33	91.20 98	78.94 241

GST-MVS		82.79 28	83.27 33	81.34 31	88.99 27	73.29 45	85.94 28	85.13 41	68.58 62	84.14 70	90.21 75	73.37 59	86.41 18	79.09 22	93.98 59	84.30 122

TSAR-MVS + MP.		79.05 61	78.81 66	79.74 46	88.94 28	67.52 87	86.61 22	81.38 112	51.71 231	77.15 151	91.42 36	65.49 130	87.20 7	79.44 17	87.17 188	84.51 114
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
UA-Net		81.56 37	82.28 44	79.40 52	88.91 29	69.16 76	84.67 36	80.01 143	75.34 19	79.80 119	94.91 2	69.79 88	80.25 144	72.63 66	94.46 39	88.78 41

SMA-MVS		82.12 32	82.68 42	80.43 40	88.90 30	69.52 69	85.12 32	84.76 50	63.53 106	84.23 69	91.47 34	72.02 67	87.16 8	79.74 13	94.36 48	84.61 106
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MP-MVS-pluss		82.54 30	83.46 29	79.76 45	88.88 31	68.44 80	81.57 64	86.33 19	63.17 112	85.38 55	91.26 37	76.33 33	84.67 71	83.30 2	94.96 26	86.17 68
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 14	79.37 15	84.79 69	74.51 51	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 16	73.69 27	86.17 40	91.70 29	78.23 21	85.20 61	79.45 16	94.91 28	88.15 46

SR-MVS		84.51 9	85.27 8	82.25 19	88.52 34	77.71 15	86.81 19	85.25 40	77.42 17	86.15 41	90.24 73	81.69 5	85.94 36	77.77 30	93.58 64	83.09 156

新几何1				69.99 191	88.37 35	71.34 55		62.08 313	43.85 305	74.99 193	86.11 171	52.85 241	70.57 273	50.99 247	83.23 242	68.05 351

HPM-MVS		84.12 12	84.63 13	82.60 14	88.21 36	74.40 35	85.24 31	87.21 14	70.69 50	85.14 57	90.42 61	78.99 17	86.62 15	80.83 6	94.93 27	86.79 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMM	69.25 9	82.11 33	83.31 31	78.49 66	88.17 37	73.96 38	83.11 53	84.52 60	66.40 73	87.45 26	89.16 96	81.02 8	80.52 140	74.27 54	95.73 8	80.98 204
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 38	69.15 77	67.85 243	59.59 323	41.06 329	73.05 226	85.72 179	48.03 273			80.65 270	66.92 356

XVG-ACMP-BASELINE		80.54 48	81.06 52	78.98 59	87.01 39	72.91 47	80.23 80	85.56 31	66.56 72	85.64 48	89.57 85	69.12 92	80.55 139	72.51 68	93.37 66	83.48 142

save fliter					87.00 40	67.23 90	79.24 89	77.94 182	56.65 169

LPG-MVS_test		83.47 20	84.33 16	80.90 36	87.00 40	70.41 64	82.04 61	86.35 17	69.77 55	87.75 19	91.13 38	81.83 3	86.20 26	77.13 39	95.96 6	86.08 69

LGP-MVS_train				80.90 36	87.00 40	70.41 64		86.35 17	69.77 55	87.75 19	91.13 38	81.83 3	86.20 26	77.13 39	95.96 6	86.08 69

EGC-MVSNET		64.77 241	61.17 275	75.60 100	86.90 43	74.47 34	84.04 39	68.62 272	0.60 420	1.13 422	91.61 32	65.32 133	74.15 236	64.01 129	88.28 162	78.17 251

OPM-MVS		80.99 45	81.63 50	79.07 56	86.86 44	69.39 72	79.41 88	84.00 73	65.64 77	85.54 52	89.28 89	76.32 34	83.47 87	74.03 56	93.57 65	84.35 119
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepC-MVS	72.44 4	81.00 44	80.83 54	81.50 26	86.70 45	70.03 68	82.06 60	87.00 15	59.89 136	80.91 109	90.53 56	72.19 64	88.56 2	73.67 59	94.52 38	85.92 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMP	69.50 8	82.64 29	83.38 30	80.40 41	86.50 46	69.44 71	82.30 58	86.08 24	66.80 69	86.70 34	89.99 78	81.64 6	85.95 35	74.35 53	96.11 4	85.81 75
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 56	79.99 59	78.49 66	86.46 47	74.79 33	77.15 115	85.39 37	66.73 70	80.39 115	88.85 105	74.43 53	78.33 181	74.73 49	85.79 204	82.35 178

VDDNet		71.60 155	73.13 134	67.02 239	86.29 48	41.11 311	69.97 211	66.50 281	68.72 60	74.74 196	91.70 29	59.90 185	75.81 211	48.58 269	91.72 86	84.15 125

test_0728_SECOND				76.57 87	86.20 49	60.57 152	83.77 44	85.49 32					85.90 40	75.86 42	94.39 44	83.25 151

SR-MVS-dyc-post		84.75 7	85.26 9	83.21 4	86.19 50	79.18 7	87.23 9	86.27 20	77.51 14	87.65 22	90.73 50	79.20 16	85.58 51	78.11 27	94.46 39	84.89 93

RE-MVS-def			85.50 7		86.19 50	79.18 7	87.23 9	86.27 20	77.51 14	87.65 22	90.73 50	81.38 7		78.11 27	94.46 39	84.89 93

DVP-MVS		81.15 41	83.12 36	75.24 105	86.16 52	60.78 149	83.77 44	80.58 132	72.48 37	85.83 46	90.41 62	78.57 19	85.69 47	75.86 42	94.39 44	79.24 237
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	60.78 149	83.81 43	85.10 43	72.48 37	85.27 56	89.96 79	78.57 19

SED-MVS		81.78 35	83.48 28	76.67 85	86.12 54	61.06 143	83.62 46	84.72 52	72.61 35	87.38 28	89.70 83	77.48 26	85.89 42	75.29 45	94.39 44	83.08 157

IU-MVS					86.12 54	60.90 147		80.38 136	45.49 293	81.31 102				75.64 44	94.39 44	84.65 102

test_241102_ONE					86.12 54	61.06 143		84.72 52	72.64 34	87.38 28	89.47 86	77.48 26	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 28	78.70 10	88.94 13	91.88 24	79.74 12	86.05 32	79.90 9	95.21 16	82.72 169

our_new_method		84.97 4	85.93 4	82.10 21	86.11 57	77.53 18	87.08 13	85.81 28	78.70 10	88.94 13	91.88 24	79.74 12	86.05 32	79.90 9	95.21 16	82.72 169

XVG-OURS		79.51 57	79.82 60	78.58 65	86.11 57	74.96 32	76.33 127	84.95 47	66.89 67	82.75 86	88.99 102	66.82 113	78.37 179	74.80 47	90.76 118	82.40 177

test_part2					85.90 60	66.44 95				84.61 65

原ACMM1				73.90 120	85.90 60	65.15 110		81.67 106	50.97 243	74.25 207	86.16 168	61.60 164	83.54 85	56.75 194	91.08 105	73.00 303

testdata				64.13 260	85.87 62	63.34 122		61.80 316	47.83 274	76.42 177	86.60 155	48.83 267	62.31 334	54.46 221	81.26 263	66.74 360

CNVR-MVS		78.49 68	78.59 70	78.16 70	85.86 63	67.40 88	78.12 104	81.50 108	63.92 100	77.51 148	86.56 156	68.43 98	84.82 68	73.83 57	91.61 90	82.26 182

test_one_0601					85.84 64	61.45 137		85.63 30	75.27 21	85.62 51	90.38 67	76.72 30

NCCC		78.25 71	78.04 76	78.89 61	85.61 65	69.45 70	79.80 85	80.99 123	65.77 76	75.55 185	86.25 165	67.42 106	85.42 52	70.10 78	90.88 113	81.81 189

reproduce_model		84.87 6	85.80 6	82.05 23	85.52 66	78.14 13	87.69 6	85.36 38	79.26 7	89.12 12	92.10 19	77.52 25	85.92 39	80.47 8	95.20 18	82.10 184

TEST9					85.47 67	69.32 74	76.42 123	78.69 167	53.73 213	76.97 153	86.74 146	66.84 112	81.10 125

train_agg		76.38 84	76.55 88	75.86 97	85.47 67	69.32 74	76.42 123	78.69 167	54.00 208	76.97 153	86.74 146	66.60 118	81.10 125	72.50 69	91.56 91	77.15 265

DPE-MVS		82.00 34	83.02 37	78.95 60	85.36 69	67.25 89	82.91 54	84.98 45	73.52 28	85.43 54	90.03 77	76.37 32	86.97 13	74.56 50	94.02 58	82.62 173
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HQP_MVS		78.77 64	78.78 68	78.72 62	85.18 70	65.18 108	82.74 55	85.49 32	65.45 80	78.23 137	89.11 97	60.83 177	86.15 29	71.09 73	90.94 107	84.82 98

plane_prior7					85.18 70	66.21 98

SteuartSystems-ACMMP		83.07 25	83.64 26	81.35 30	85.14 72	71.00 58	85.53 29	84.78 49	70.91 48	85.64 48	90.41 62	75.55 41	87.69 5	79.75 11	95.08 23	85.36 84
Skip Steuart: Steuart Systems R&D Blog.
test_8					85.09 73	67.89 83	76.26 128	78.66 169	54.00 208	76.89 157	86.72 148	66.60 118	80.89 135

WR-MVS		71.20 158	72.48 147	67.36 234	84.98 74	35.70 358	64.43 290	68.66 271	65.05 90	81.49 100	86.43 160	57.57 212	76.48 207	50.36 252	93.32 68	89.90 22

PS-MVSNAJss		77.54 75	77.35 82	78.13 72	84.88 75	66.37 96	78.55 96	79.59 150	53.48 215	86.29 39	92.43 16	62.39 156	80.25 144	67.90 96	90.61 119	87.77 48

LTVRE_ROB	75.46 1	84.22 10	84.98 11	81.94 24	84.82 76	75.40 29	91.60 3	87.80 8	73.52 28	88.90 15	93.06 7	71.39 73	81.53 117	81.53 4	92.15 84	88.91 37
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 62	78.67 69	79.72 47	84.81 77	73.93 39	80.65 71	76.50 197	51.98 229	87.40 27	91.86 26	76.09 36	78.53 171	68.58 86	90.20 124	86.69 64

APDe-MVS		82.88 27	84.14 18	79.08 55	84.80 78	66.72 94	86.54 23	85.11 42	72.00 42	86.65 35	91.75 28	78.20 22	87.04 11	77.93 29	94.32 51	83.47 143
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSLP-MVS++		74.48 109	75.78 95	70.59 177	84.66 79	62.40 127	78.65 94	84.24 66	60.55 131	77.71 146	81.98 229	63.12 147	77.64 195	62.95 144	88.14 164	71.73 318

jajsoiax		78.51 67	78.16 75	79.59 49	84.65 80	73.83 41	80.42 74	76.12 199	51.33 239	87.19 31	91.51 33	73.79 57	78.44 175	68.27 89	90.13 128	86.49 66

TranMVSNet+NR-MVSNet		76.13 85	77.66 79	71.56 168	84.61 81	42.57 303	70.98 198	78.29 176	68.67 61	83.04 79	89.26 90	72.99 61	80.75 136	55.58 210	95.47 11	91.35 12

旧先验1					84.55 82	60.36 154		63.69 304			87.05 137	54.65 231			83.34 241	69.66 339

APD-MVS		81.13 42	81.73 48	79.36 53	84.47 83	70.53 63	83.85 42	83.70 75	69.43 57	83.67 75	88.96 103	75.89 37	86.41 18	72.62 67	92.95 71	81.14 198
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
plane_prior1					84.46 84

agg_prior					84.44 85	66.02 101		78.62 170		76.95 155			80.34 142

DeepPCF-MVS	71.07 5	78.48 69	77.14 84	82.52 17	84.39 86	77.04 25	76.35 125	84.05 71	56.66 168	80.27 116	85.31 182	68.56 95	87.03 12	67.39 102	91.26 96	83.50 139

CDPH-MVS		77.33 77	77.06 85	78.14 71	84.21 87	63.98 118	76.07 131	83.45 78	54.20 203	77.68 147	87.18 132	69.98 85	85.37 53	68.01 93	92.72 76	85.08 90

plane_prior6					84.18 88	65.31 107						60.83 177

114514_t		73.40 121	73.33 131	73.64 124	84.15 89	57.11 176	78.20 102	80.02 142	43.76 308	72.55 231	86.07 173	64.00 143	83.35 90	60.14 168	91.03 106	80.45 219

ZD-MVS					83.91 90	69.36 73		81.09 120	58.91 146	82.73 87	89.11 97	75.77 38	86.63 14	72.73 65	92.93 72

DeepC-MVS_fast	69.89 7	77.17 78	76.33 90	79.70 48	83.90 91	67.94 82	80.06 83	83.75 74	56.73 167	74.88 195	85.32 181	65.54 129	87.79 3	65.61 119	91.14 101	83.35 149
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
h-mvs33		73.08 128	71.61 160	77.48 77	83.89 92	72.89 48	70.47 205	71.12 252	54.28 199	77.89 141	83.41 206	49.04 264	80.98 130	63.62 137	90.77 117	78.58 245

SD-MVS		80.28 53	81.55 51	76.47 90	83.57 93	67.83 84	83.39 51	85.35 39	64.42 96	86.14 42	87.07 136	74.02 54	80.97 131	77.70 32	92.32 82	80.62 216
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		74.91 104	75.57 98	72.93 143	83.50 94	45.79 273	69.47 217	80.14 141	65.22 86	81.74 97	87.08 134	61.82 162	81.07 127	56.21 201	94.98 24	91.93 9

NR-MVSNet		73.62 116	74.05 115	72.33 160	83.50 94	43.71 289	65.65 275	77.32 189	64.32 97	75.59 184	87.08 134	62.45 155	81.34 119	54.90 214	95.63 9	91.93 9

test_0402		78.17 72	79.48 63	74.24 115	83.50 94	59.15 163	72.52 168	74.60 214	75.34 19	88.69 17	91.81 27	75.06 45	82.37 104	65.10 120	88.68 158	81.20 196

OPU-MVS				78.65 64	83.44 97	66.85 93	83.62 46				86.12 170	66.82 113	86.01 34	61.72 151	89.79 136	83.08 157

NP-MVS					83.34 98	63.07 125					85.97 174

DVP-MVS++		81.24 39	82.74 41	76.76 84	83.14 99	60.90 147	91.64 1	85.49 32	74.03 25	84.93 59	90.38 67	66.82 113	85.90 40	77.43 34	90.78 115	83.49 140

MSC_two_6792asdad				79.02 57	83.14 99	67.03 91		80.75 125					86.24 24	77.27 37	94.85 29	83.78 132

No_MVS				79.02 57	83.14 99	67.03 91		80.75 125					86.24 24	77.27 37	94.85 29	83.78 132

UniMVSNet (Re)		75.00 102	75.48 99	73.56 127	83.14 99	47.92 249	70.41 207	81.04 122	63.67 104	79.54 121	86.37 161	62.83 150	81.82 113	57.10 193	95.25 15	90.94 16

hse-mvs2		72.32 147	70.66 173	77.31 81	83.10 103	71.77 51	69.19 223	71.45 241	54.28 199	77.89 141	78.26 285	49.04 264	79.23 158	63.62 137	89.13 152	80.92 205

UniMVSNet_NR-MVSNet		74.90 105	75.65 96	72.64 153	83.04 104	45.79 273	69.26 221	78.81 162	66.66 71	81.74 97	86.88 141	63.26 146	81.07 127	56.21 201	94.98 24	91.05 14

HyFIR lowres test		63.01 261	60.47 282	70.61 176	83.04 104	54.10 196	59.93 324	72.24 234	33.67 380	69.00 276	75.63 307	38.69 325	76.93 201	36.60 355	75.45 322	80.81 210

COLMAP_ROB	72.78 3	83.75 15	84.11 19	82.68 13	82.97 106	74.39 36	87.18 11	88.18 7	78.98 8	86.11 43	91.47 34	79.70 14	85.76 45	66.91 110	95.46 12	87.89 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		70.22 169	67.88 207	77.22 82	82.96 107	71.61 52	69.08 224	71.39 242	49.17 263	71.70 241	78.07 290	37.62 333	79.21 159	61.81 148	89.15 150	80.82 208

DP-MVS Recon		73.57 118	72.69 143	76.23 93	82.85 108	63.39 121	74.32 154	82.96 85	57.75 154	70.35 259	81.98 229	64.34 142	84.41 76	49.69 256	89.95 131	80.89 206

APD-MVS_3200maxsize		83.57 17	84.33 16	81.31 32	82.83 109	73.53 44	85.50 30	87.45 13	74.11 23	86.45 38	90.52 58	80.02 10	84.48 73	77.73 31	94.34 50	85.93 73

PVSNet_Blended_VisFu		70.04 171	68.88 189	73.53 128	82.71 110	63.62 120	74.81 144	81.95 102	48.53 268	67.16 302	79.18 274	51.42 250	78.38 178	54.39 223	79.72 285	78.60 244

DPM-MVS		69.98 173	69.22 185	72.26 161	82.69 111	58.82 167	70.53 204	81.23 116	47.79 275	64.16 320	80.21 253	51.32 251	83.12 93	60.14 168	84.95 220	74.83 285

EG-PatchMatch MVS		70.70 164	70.88 169	70.16 187	82.64 112	58.80 168	71.48 188	73.64 219	54.98 184	76.55 170	81.77 232	61.10 174	78.94 164	54.87 215	80.84 267	72.74 308

HQP-NCC					82.37 113		77.32 111		59.08 140	71.58 243

ACMP_Plane					82.37 113		77.32 111		59.08 140	71.58 243

HQP-MVS		75.24 97	75.01 102	75.94 95	82.37 113	58.80 168	77.32 111	84.12 69	59.08 140	71.58 243	85.96 175	58.09 204	85.30 55	67.38 104	89.16 148	83.73 135

test12				76.51 88	82.28 116	60.94 146		81.64 107		73.60 217		64.88 137	85.19 62		90.42 122	83.38 147

TAMVS		65.31 234	63.75 253	69.97 192	82.23 117	59.76 158	66.78 262	63.37 307	45.20 297	69.79 269	79.37 270	47.42 276	72.17 256	34.48 368	85.15 215	77.99 256

test_prior				75.27 104	82.15 118	59.85 157		84.33 63					83.39 89			82.58 174

SF-MVS		80.72 47	81.80 46	77.48 77	82.03 119	64.40 115	83.41 50	88.46 6	65.28 85	84.29 68	89.18 94	73.73 58	83.22 91	76.01 41	93.77 61	84.81 100

AdaColmap		74.22 110	74.56 106	73.20 132	81.95 120	60.97 145	79.43 86	80.90 124	65.57 78	72.54 232	81.76 233	70.98 78	85.26 57	47.88 278	90.00 129	73.37 299

PAPM_NR		73.91 112	74.16 113	73.16 133	81.90 121	53.50 201	81.28 66	81.40 111	66.17 74	73.30 223	83.31 212	59.96 184	83.10 94	58.45 183	81.66 260	82.87 163

DP-MVS		78.44 70	79.29 64	75.90 96	81.86 122	65.33 106	79.05 91	84.63 58	74.83 22	80.41 114	86.27 163	71.68 69	83.45 88	62.45 147	92.40 79	78.92 242

F-COLMAP		75.29 95	73.99 116	79.18 54	81.73 123	71.90 50	81.86 63	82.98 84	59.86 137	72.27 235	84.00 199	64.56 140	83.07 95	51.48 241	87.19 187	82.56 175

SixPastTwentyTwo		75.77 87	76.34 89	74.06 118	81.69 124	54.84 190	76.47 120	75.49 206	64.10 99	87.73 21	92.24 18	50.45 255	81.30 121	67.41 100	91.46 93	86.04 71

Vis-MVSNet		74.85 108	74.56 106	75.72 98	81.63 125	64.64 113	76.35 125	79.06 158	62.85 115	73.33 222	88.41 115	62.54 154	79.59 155	63.94 134	82.92 243	82.94 161
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_djsdf		78.88 63	78.27 73	80.70 39	81.42 126	71.24 56	83.98 40	75.72 204	52.27 224	87.37 30	92.25 17	68.04 102	80.56 137	72.28 70	91.15 100	90.32 21

3Dnovator+	73.19 2	81.08 43	80.48 55	82.87 8	81.41 127	72.03 49	84.38 38	86.23 23	77.28 18	80.65 112	90.18 76	59.80 188	87.58 6	73.06 62	91.34 95	89.01 33

tt0805		76.12 86	78.43 72	69.20 203	81.32 128	41.37 309	76.72 119	77.64 185	63.78 103	82.06 91	87.88 126	79.78 11	79.05 161	64.33 127	92.40 79	87.17 59

MCST-MVS		73.42 120	73.34 130	73.63 125	81.28 129	59.17 162	74.80 146	83.13 83	45.50 291	72.84 227	83.78 203	65.15 135	80.99 129	64.54 124	89.09 154	80.73 212

MIMVSNet1		66.57 223	69.23 184	58.59 315	81.26 130	37.73 345	64.06 293	57.62 327	57.02 162	78.40 136	90.75 49	62.65 151	58.10 352	41.77 319	89.58 140	79.95 226

ACMH+	66.64 10	81.20 40	82.48 43	77.35 80	81.16 131	62.39 128	80.51 72	87.80 8	73.02 30	87.57 24	91.08 40	80.28 9	82.44 102	64.82 123	96.10 5	87.21 56

MVSMamba_PlusPlus		76.88 80	78.21 74	72.88 146	80.83 132	48.71 237	83.28 52	82.79 87	72.78 31	79.17 126	91.94 22	56.47 224	83.95 78	70.51 77	86.15 199	85.99 72

MVS_111021_HR		72.98 135	72.97 140	72.99 138	80.82 133	65.47 104	68.81 228	72.77 227	57.67 156	75.76 182	82.38 225	71.01 77	77.17 198	61.38 153	86.15 199	76.32 273

9.14			80.22 57		80.68 134		80.35 77	87.69 11	59.90 135	83.00 80	88.20 120	74.57 50	81.75 115	73.75 58	93.78 60

OMC-MVS		79.41 59	78.79 67	81.28 33	80.62 135	70.71 62	80.91 69	84.76 50	62.54 117	81.77 95	86.65 152	71.46 71	83.53 86	67.95 95	92.44 78	89.60 23

OurMVSNet-221017-0		78.57 66	78.53 71	78.67 63	80.48 136	64.16 116	80.24 79	82.06 99	61.89 121	88.77 16	93.32 5	57.15 215	82.60 101	70.08 79	92.80 73	89.25 27

CDS-MVSNet		64.33 249	62.66 266	69.35 200	80.44 137	58.28 172	65.26 280	65.66 287	44.36 303	67.30 301	75.54 308	43.27 295	71.77 262	37.68 345	84.44 228	78.01 255
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	62.01 16	71.79 153	70.28 176	76.33 91	80.31 138	68.63 79	78.18 103	81.24 115	54.57 194	67.09 303	80.63 247	59.44 189	81.74 116	46.91 285	84.17 230	78.63 243
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MM		78.15 73	77.68 78	79.55 50	80.10 139	65.47 104	80.94 68	78.74 166	71.22 45	72.40 234	88.70 107	60.51 179	87.70 4	77.40 36	89.13 152	85.48 83

CHOSEN 1792x2688		58.09 302	56.30 313	63.45 269	79.95 140	50.93 215	54.07 365	65.59 288	28.56 396	61.53 339	74.33 321	41.09 309	66.52 313	33.91 371	67.69 380	72.92 304

K. test v3		73.67 115	73.61 124	73.87 121	79.78 141	55.62 188	74.69 150	62.04 315	66.16 75	84.76 63	93.23 6	49.47 260	80.97 131	65.66 118	86.67 195	85.02 92

VPNet		65.58 232	67.56 210	59.65 307	79.72 142	30.17 388	60.27 321	62.14 311	54.19 204	71.24 251	86.63 153	58.80 196	67.62 297	44.17 305	90.87 114	81.18 197

ACMH	63.62 14	77.50 76	80.11 58	69.68 195	79.61 143	56.28 180	78.81 93	83.62 76	63.41 110	87.14 33	90.23 74	76.11 35	73.32 242	67.58 97	94.44 42	79.44 235
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 149	79.60 144	56.83 179		57.37 330		83.80 74	89.01 101	47.45 275	78.74 168	64.39 126	86.49 198	82.69 171

MVS_111021_LR		72.10 150	71.82 156	72.95 140	79.53 145	73.90 40	70.45 206	66.64 280	56.87 163	76.81 161	81.76 233	68.78 93	71.76 263	61.81 148	83.74 235	73.18 301

Test_1112_low_res		58.78 298	58.69 294	59.04 313	79.41 146	38.13 341	57.62 340	66.98 279	34.74 373	59.62 355	77.56 294	42.92 298	63.65 329	38.66 337	70.73 361	75.35 282

CSCG		74.12 111	74.39 108	73.33 130	79.35 147	61.66 135	77.45 110	81.98 101	62.47 119	79.06 128	80.19 255	61.83 161	78.79 167	59.83 172	87.35 178	79.54 234

MVP-Stereo		61.56 276	59.22 289	68.58 220	79.28 148	60.44 153	69.20 222	71.57 237	43.58 311	56.42 371	78.37 284	39.57 320	76.46 208	34.86 367	60.16 397	68.86 347
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 167	71.34 165	67.85 228	79.26 149	40.42 322	74.67 151	75.15 210	58.41 148	68.74 287	88.14 124	56.08 227	83.69 82	59.90 171	81.71 259	79.43 236

IS-MVSNet		75.10 99	75.42 100	74.15 117	79.23 150	48.05 247	79.43 86	78.04 180	70.09 54	79.17 126	88.02 125	53.04 240	83.60 83	58.05 186	93.76 62	90.79 18

FC-MVSNet-test		73.32 123	74.78 104	68.93 213	79.21 151	36.57 350	71.82 185	79.54 152	57.63 159	82.57 88	90.38 67	59.38 191	78.99 163	57.91 187	94.56 37	91.23 13

AllTest		77.66 74	77.43 80	78.35 68	79.19 152	70.81 59	78.60 95	88.64 4	65.37 83	80.09 117	88.17 121	70.33 81	78.43 176	55.60 207	90.90 111	85.81 75

TestCases				78.35 68	79.19 152	70.81 59		88.64 4	65.37 83	80.09 117	88.17 121	70.33 81	78.43 176	55.60 207	90.90 111	85.81 75

xiu_mvs_v1_base_debu		67.87 205	67.07 218	70.26 183	79.13 154	61.90 132	67.34 250	71.25 247	47.98 271	67.70 295	74.19 325	61.31 167	72.62 249	56.51 196	78.26 299	76.27 274

xiu_mvs_v1_base		67.87 205	67.07 218	70.26 183	79.13 154	61.90 132	67.34 250	71.25 247	47.98 271	67.70 295	74.19 325	61.31 167	72.62 249	56.51 196	78.26 299	76.27 274

xiu_mvs_v1_base_debi		67.87 205	67.07 218	70.26 183	79.13 154	61.90 132	67.34 250	71.25 247	47.98 271	67.70 295	74.19 325	61.31 167	72.62 249	56.51 196	78.26 299	76.27 274

VDD-MVS		70.81 163	71.44 164	68.91 214	79.07 157	46.51 267	67.82 244	70.83 256	61.23 124	74.07 211	88.69 108	59.86 186	75.62 214	51.11 245	90.28 123	84.61 106

test1111		64.62 242	65.19 238	62.93 277	79.01 158	29.91 389	65.45 278	54.41 351	54.09 206	71.47 250	88.48 114	37.02 335	74.29 234	46.83 287	89.94 132	84.58 109

TSAR-MVS + GP.		73.08 128	71.60 161	77.54 76	78.99 159	70.73 61	74.96 141	69.38 265	60.73 130	74.39 205	78.44 283	57.72 211	82.78 98	60.16 166	89.60 138	79.11 239

test2506		61.23 278	60.85 279	62.38 282	78.80 160	27.88 397	67.33 253	37.42 413	54.23 201	67.55 298	88.68 109	17.87 416	74.39 232	46.33 291	89.41 144	84.86 96

ECVR-MVS		64.82 239	65.22 237	63.60 266	78.80 160	31.14 383	66.97 258	56.47 341	54.23 201	69.94 267	88.68 109	37.23 334	74.81 227	45.28 301	89.41 144	84.86 96

FIs		72.56 143	73.80 119	68.84 216	78.74 162	37.74 344	71.02 197	79.83 145	56.12 172	80.88 111	89.45 87	58.18 200	78.28 182	56.63 195	93.36 67	90.51 20

v7n		79.37 60	80.41 56	76.28 92	78.67 163	55.81 185	79.22 90	82.51 94	70.72 49	87.54 25	92.44 15	68.00 103	81.34 119	72.84 64	91.72 86	91.69 11

LS3D		80.99 45	80.85 53	81.41 29	78.37 164	71.37 54	87.45 8	85.87 27	77.48 16	81.98 92	89.95 80	69.14 91	85.26 57	66.15 112	91.24 97	87.61 51

CNLPA		73.44 119	73.03 138	74.66 107	78.27 165	75.29 30	75.99 132	78.49 171	65.39 82	75.67 183	83.22 218	61.23 170	66.77 311	53.70 230	85.33 211	81.92 188

EPP-MVSNet		73.86 114	73.38 127	75.31 103	78.19 166	53.35 203	80.45 73	77.32 189	65.11 89	76.47 175	86.80 142	49.47 260	83.77 81	53.89 228	92.72 76	88.81 40

PCF-MVS	63.80 13	72.70 141	71.69 157	75.72 98	78.10 167	60.01 156	73.04 165	81.50 108	45.34 296	79.66 120	84.35 195	65.15 135	82.65 100	48.70 267	89.38 147	84.50 115
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		73.14 126	73.77 121	71.26 172	78.09 168	52.64 206	74.32 154	79.56 151	56.32 171	76.35 178	83.36 211	70.76 79	77.96 189	63.32 141	81.84 254	83.18 154

LFMVS		67.06 218	67.89 206	64.56 257	78.02 169	38.25 339	70.81 202	59.60 322	65.18 87	71.06 253	86.56 156	43.85 292	75.22 219	46.35 290	89.63 137	80.21 224

anonymousdsp		78.60 65	77.80 77	81.00 35	78.01 170	74.34 37	80.09 81	76.12 199	50.51 248	89.19 11	90.88 45	71.45 72	77.78 193	73.38 60	90.60 120	90.90 17

BH-untuned		69.39 183	69.46 180	69.18 204	77.96 171	56.88 177	68.47 238	77.53 186	56.77 165	77.79 144	79.63 264	60.30 182	80.20 147	46.04 293	80.65 270	70.47 331

1112_ss		59.48 292	58.99 292	60.96 298	77.84 172	42.39 304	61.42 311	68.45 273	37.96 354	59.93 352	67.46 377	45.11 285	65.07 322	40.89 325	71.81 353	75.41 280

PS-MVSNAJ		64.27 250	63.73 254	65.90 250	77.82 173	51.42 211	63.33 300	72.33 232	45.09 299	61.60 338	68.04 374	62.39 156	73.95 238	49.07 263	73.87 338	72.34 311

ambc				70.10 189	77.74 174	50.21 222	74.28 156	77.93 183		79.26 124	88.29 119	54.11 236	79.77 151	64.43 125	91.10 104	80.30 222

xiu_mvs_v2_base		64.43 247	63.96 251	65.85 251	77.72 175	51.32 212	63.63 297	72.31 233	45.06 300	61.70 337	69.66 360	62.56 152	73.93 239	49.06 264	73.91 337	72.31 312

Anonymous20231211		75.54 92	77.19 83	70.59 177	77.67 176	45.70 276	74.73 148	80.19 139	68.80 58	82.95 82	92.91 9	66.26 122	76.76 205	58.41 184	92.77 74	89.30 26

FMVSNet1		71.06 159	72.48 147	66.81 240	77.65 177	40.68 318	71.96 179	73.03 222	61.14 125	79.45 123	90.36 70	60.44 180	75.20 221	50.20 253	88.05 166	84.54 110

FPMVS		59.43 293	60.07 284	57.51 321	77.62 178	71.52 53	62.33 307	50.92 369	57.40 160	69.40 273	80.00 259	39.14 323	61.92 336	37.47 348	66.36 382	39.09 412

balanced_conf03		73.59 117	74.06 114	72.17 163	77.48 179	47.72 254	81.43 65	82.20 97	54.38 196	79.19 125	87.68 128	54.41 233	83.57 84	63.98 131	85.78 205	85.22 85

testing3		58.28 301	58.38 298	58.00 319	77.45 180	26.12 406	60.78 317	43.00 399	56.02 173	70.18 262	75.76 305	13.27 424	67.24 303	48.02 276	80.89 265	80.65 215

Effi-MVS+-dtu		75.43 94	72.28 151	84.91 3	77.05 181	83.58 2	78.47 97	77.70 184	57.68 155	74.89 194	78.13 289	64.80 138	84.26 77	56.46 199	85.32 212	86.88 61

CLD-MVS		72.88 138	72.36 150	74.43 112	77.03 182	54.30 194	68.77 231	83.43 79	52.12 226	76.79 162	74.44 320	69.54 90	83.91 79	55.88 204	93.25 69	85.09 89
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 83	76.00 93	78.06 73	77.02 183	64.77 112	80.78 70	82.66 91	60.39 132	74.15 208	83.30 213	69.65 89	82.07 110	69.27 84	86.75 194	87.36 54

SPE-MVS-test		74.89 106	74.23 112	76.86 83	77.01 184	62.94 126	78.98 92	84.61 59	58.62 147	70.17 263	80.80 244	66.74 117	81.96 111	61.74 150	89.40 146	85.69 80

Baseline_NR-MVSNet		70.62 165	73.19 132	62.92 278	76.97 185	34.44 366	68.84 226	70.88 255	60.25 133	79.50 122	90.53 56	61.82 162	69.11 284	54.67 218	95.27 14	85.22 85

ITE_SJBPF				80.35 42	76.94 186	73.60 42		80.48 133	66.87 68	83.64 76	86.18 166	70.25 83	79.90 150	61.12 157	88.95 156	87.56 52

SSC-MVS		61.79 274	66.08 228	48.89 367	76.91 187	10.00 424	53.56 367	47.37 385	68.20 63	76.56 169	89.21 92	54.13 235	57.59 353	54.75 216	74.07 336	79.08 240

jason		64.47 246	62.84 264	69.34 201	76.91 187	59.20 159	67.15 255	65.67 286	35.29 369	65.16 313	76.74 301	44.67 287	70.68 271	54.74 217	79.28 288	78.14 252
jason: jason.
ETV-MVS		72.72 140	72.16 153	74.38 114	76.90 189	55.95 182	73.34 163	84.67 55	62.04 120	72.19 238	70.81 347	65.90 126	85.24 59	58.64 181	84.96 219	81.95 187

Anonymous20240529		72.56 143	73.79 120	68.86 215	76.89 190	45.21 279	68.80 230	77.25 191	67.16 66	76.89 157	90.44 59	65.95 125	74.19 235	50.75 248	90.00 129	87.18 58

EC-MVSNet		77.08 79	77.39 81	76.14 94	76.86 191	56.87 178	80.32 78	87.52 12	63.45 108	74.66 200	84.52 192	69.87 87	84.94 64	69.76 81	89.59 139	86.60 65

PM-MVS		64.49 245	63.61 255	67.14 238	76.68 192	75.15 31	68.49 237	42.85 400	51.17 242	77.85 143	80.51 248	45.76 279	66.31 314	52.83 236	76.35 313	59.96 389

mvsmamba		68.87 189	67.30 216	73.57 126	76.58 193	53.70 200	84.43 37	74.25 216	45.38 295	76.63 165	84.55 191	35.85 340	85.27 56	49.54 259	78.49 296	81.75 191

TransMVSNet (Re)		69.62 178	71.63 159	63.57 267	76.51 194	35.93 356	65.75 274	71.29 246	61.05 126	75.02 192	89.90 81	65.88 127	70.41 277	49.79 255	89.48 142	84.38 118

BH-RMVSNet		68.69 195	68.20 202	70.14 188	76.40 195	53.90 199	64.62 287	73.48 220	58.01 151	73.91 215	81.78 231	59.09 193	78.22 183	48.59 268	77.96 303	78.31 248

PHI-MVS		74.92 103	74.36 110	76.61 86	76.40 195	62.32 129	80.38 75	83.15 82	54.16 205	73.23 224	80.75 245	62.19 159	83.86 80	68.02 92	90.92 110	83.65 136

UGNet		70.20 170	69.05 186	73.65 123	76.24 197	63.64 119	75.87 134	72.53 230	61.48 123	60.93 346	86.14 169	52.37 243	77.12 199	50.67 249	85.21 213	80.17 225
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		58.68 299	57.72 303	61.57 288	76.21 198	73.59 43	61.83 308	49.00 379	47.30 279	61.08 342	68.97 365	50.16 256	59.01 345	36.06 362	68.84 373	52.10 399

VPA-MVSNet		68.71 194	70.37 175	63.72 265	76.13 199	38.06 342	64.10 292	71.48 240	56.60 170	74.10 210	88.31 118	64.78 139	69.72 279	47.69 280	90.15 126	83.37 148

WB-MVS		60.04 288	64.19 249	47.59 370	76.09 200	10.22 423	52.44 372	46.74 387	65.17 88	74.07 211	87.48 129	53.48 238	55.28 359	49.36 261	72.84 344	77.28 261

PAPM		61.79 274	60.37 283	66.05 248	76.09 200	41.87 306	69.30 220	76.79 196	40.64 337	53.80 384	79.62 265	44.38 289	82.92 97	29.64 389	73.11 343	73.36 300

BH-w/o		64.81 240	64.29 248	66.36 245	76.08 202	54.71 191	65.61 276	75.23 209	50.10 254	71.05 254	71.86 341	54.33 234	79.02 162	38.20 342	76.14 315	65.36 366

dcpmvs_2		71.02 161	72.65 144	66.16 247	76.06 203	50.49 218	71.97 178	79.36 153	50.34 249	82.81 85	83.63 204	64.38 141	67.27 302	61.54 152	83.71 237	80.71 214

pmmvs6		71.82 152	73.66 122	66.31 246	75.94 204	42.01 305	66.99 257	72.53 230	63.45 108	76.43 176	92.78 11	72.95 62	69.69 280	51.41 243	90.46 121	87.22 55

CANet		73.00 133	71.84 155	76.48 89	75.82 205	61.28 139	74.81 144	80.37 137	63.17 112	62.43 336	80.50 249	61.10 174	85.16 63	64.00 130	84.34 229	83.01 160

pmmvs-eth3d		64.41 248	63.27 260	67.82 230	75.81 206	60.18 155	69.49 216	62.05 314	38.81 349	74.13 209	82.23 226	43.76 293	68.65 288	42.53 312	80.63 272	74.63 287

TR-MVS		64.59 243	63.54 256	67.73 231	75.75 207	50.83 216	63.39 299	70.29 260	49.33 261	71.55 247	74.55 318	50.94 252	78.46 174	40.43 327	75.69 318	73.89 296

MVS_0304		75.45 93	74.66 105	77.83 74	75.58 208	61.53 136	78.29 99	77.18 192	63.15 114	69.97 266	87.20 131	57.54 213	87.05 10	74.05 55	88.96 155	84.89 93

tttt0517		69.46 181	67.79 209	74.46 109	75.34 209	52.72 205	75.05 140	63.27 308	54.69 190	78.87 130	84.37 194	26.63 387	81.15 123	63.95 132	87.93 170	89.51 24

cascas		64.59 243	62.77 265	70.05 190	75.27 210	50.02 224	61.79 309	71.61 236	42.46 319	63.68 327	68.89 368	49.33 262	80.35 141	47.82 279	84.05 232	79.78 229

API-MVS		70.97 162	71.51 163	69.37 198	75.20 211	55.94 183	80.99 67	76.84 194	62.48 118	71.24 251	77.51 295	61.51 166	80.96 134	52.04 237	85.76 206	71.22 324

EIA-MVS		68.59 196	67.16 217	72.90 144	75.18 212	55.64 187	69.39 218	81.29 113	52.44 223	64.53 316	70.69 348	60.33 181	82.30 106	54.27 225	76.31 314	80.75 211

PAPR		69.20 185	68.66 195	70.82 174	75.15 213	47.77 252	75.31 137	81.11 118	49.62 259	66.33 305	79.27 271	61.53 165	82.96 96	48.12 275	81.50 262	81.74 192

MVSFormer		69.93 174	69.03 187	72.63 154	74.93 214	59.19 160	83.98 40	75.72 204	52.27 224	63.53 330	76.74 301	43.19 296	80.56 137	72.28 70	78.67 294	78.14 252

lupinMVS		63.36 256	61.49 273	68.97 211	74.93 214	59.19 160	65.80 273	64.52 299	34.68 375	63.53 330	74.25 323	43.19 296	70.62 272	53.88 229	78.67 294	77.10 266

nrg030		74.87 107	75.99 94	71.52 169	74.90 216	49.88 231	74.10 158	82.58 93	54.55 195	83.50 77	89.21 92	71.51 70	75.74 213	61.24 154	92.34 81	88.94 36

TAPA-MVS	65.27 12	75.16 98	74.29 111	77.77 75	74.86 217	68.08 81	77.89 105	84.04 72	55.15 183	76.19 180	83.39 207	66.91 111	80.11 148	60.04 170	90.14 127	85.13 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RPSCF		75.76 88	74.37 109	79.93 44	74.81 218	77.53 18	77.53 109	79.30 155	59.44 139	78.88 129	89.80 82	71.26 74	73.09 244	57.45 189	80.89 265	89.17 30

EI-MVSNet-Vis-set		72.78 139	71.87 154	75.54 101	74.77 219	59.02 166	72.24 171	71.56 238	63.92 100	78.59 132	71.59 342	66.22 123	78.60 170	67.58 97	80.32 273	89.00 34

v1240		73.06 130	73.14 133	72.84 147	74.74 220	47.27 262	71.88 184	81.11 118	51.80 230	82.28 90	84.21 196	56.22 226	82.34 105	68.82 85	87.17 188	88.91 37

v1921920		72.96 136	72.98 139	72.89 145	74.67 221	47.58 256	71.92 182	80.69 127	51.70 232	81.69 99	83.89 201	56.58 222	82.25 107	68.34 88	87.36 177	88.82 39

EI-MVSNet-UG-set		72.63 142	71.68 158	75.47 102	74.67 221	58.64 171	72.02 176	71.50 239	63.53 106	78.58 134	71.39 346	65.98 124	78.53 171	67.30 107	80.18 276	89.23 28

Fast-Effi-MVS+		68.81 191	68.30 198	70.35 182	74.66 223	48.61 240	66.06 268	78.32 174	50.62 247	71.48 249	75.54 308	68.75 94	79.59 155	50.55 251	78.73 293	82.86 164

v1192		73.40 121	73.42 125	73.32 131	74.65 224	48.67 239	72.21 172	81.73 105	52.76 220	81.85 93	84.56 190	57.12 216	82.24 108	68.58 86	87.33 180	89.06 32

v144192		72.99 134	73.06 137	72.77 148	74.58 225	47.48 257	71.90 183	80.44 135	51.57 233	81.46 101	84.11 198	58.04 208	82.12 109	67.98 94	87.47 175	88.70 42

MAR-MVS		67.72 208	66.16 227	72.40 158	74.45 226	64.99 111	74.87 142	77.50 187	48.67 267	65.78 309	68.58 372	57.01 219	77.79 192	46.68 288	81.92 251	74.42 292
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 89	76.20 91	74.16 116	74.44 227	48.69 238	75.84 135	82.93 86	59.02 144	85.92 44	89.17 95	58.56 198	82.74 99	70.73 75	89.14 151	91.05 14

sasdasda		72.29 148	73.38 127	69.04 207	74.23 228	47.37 259	73.93 160	83.18 80	54.36 197	76.61 167	81.64 235	72.03 65	75.34 217	57.12 191	87.28 182	84.40 116

canonicalmvs		72.29 148	73.38 127	69.04 207	74.23 228	47.37 259	73.93 160	83.18 80	54.36 197	76.61 167	81.64 235	72.03 65	75.34 217	57.12 191	87.28 182	84.40 116

Anonymous202405211		66.02 229	66.89 222	63.43 270	74.22 230	38.14 340	59.00 329	66.13 283	63.33 111	69.76 270	85.95 176	51.88 245	70.50 274	44.23 304	87.52 173	81.64 193

Effi-MVS+		72.10 150	72.28 151	71.58 167	74.21 231	50.33 220	74.72 149	82.73 89	62.62 116	70.77 255	76.83 300	69.96 86	80.97 131	60.20 164	78.43 297	83.45 145

FE-MVS		68.29 201	66.96 221	72.26 161	74.16 232	54.24 195	77.55 108	73.42 221	57.65 158	72.66 229	84.91 186	32.02 360	81.49 118	48.43 271	81.85 253	81.04 200

v1144		73.29 124	73.39 126	73.01 137	74.12 233	48.11 245	72.01 177	81.08 121	53.83 212	81.77 95	84.68 187	58.07 207	81.91 112	68.10 90	86.86 190	88.99 35

FA-MVS(test-final)		71.27 157	71.06 167	71.92 165	73.96 234	52.32 208	76.45 122	76.12 199	59.07 143	74.04 213	86.18 166	52.18 244	79.43 157	59.75 174	81.76 255	84.03 126

EI-MVSNet		69.61 179	69.01 188	71.41 171	73.94 235	49.90 227	71.31 193	71.32 244	58.22 149	75.40 189	70.44 349	58.16 201	75.85 209	62.51 145	79.81 282	88.48 43

CVMVSNet		59.21 294	58.44 297	61.51 289	73.94 235	47.76 253	71.31 193	64.56 298	26.91 402	60.34 348	70.44 349	36.24 339	67.65 296	53.57 231	68.66 374	69.12 345

IterMVS-LS		73.01 132	73.12 135	72.66 152	73.79 237	49.90 227	71.63 187	78.44 172	58.22 149	80.51 113	86.63 153	58.15 202	79.62 153	62.51 145	88.20 163	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UWE-MVS		52.94 335	52.70 338	53.65 339	73.56 238	27.49 398	57.30 343	49.57 376	38.56 351	62.79 334	71.42 345	19.49 411	60.41 339	24.33 409	77.33 308	73.06 302

alignmvs		70.54 166	71.00 168	69.15 205	73.50 239	48.04 248	69.85 214	79.62 147	53.94 211	76.54 171	82.00 227	59.00 194	74.68 228	57.32 190	87.21 186	84.72 101

Fast-Effi-MVS+-dtu		70.00 172	68.74 193	73.77 122	73.47 240	64.53 114	71.36 191	78.14 179	55.81 177	68.84 285	74.71 317	65.36 132	75.75 212	52.00 238	79.00 290	81.03 201

v8		75.07 100	75.64 97	73.35 129	73.42 241	47.46 258	75.20 138	81.45 110	60.05 134	85.64 48	89.26 90	58.08 206	81.80 114	69.71 83	87.97 169	90.79 18

tfpnnormal		66.48 224	67.93 205	62.16 284	73.40 242	36.65 349	63.45 298	64.99 293	55.97 174	72.82 228	87.80 127	57.06 218	69.10 285	48.31 273	87.54 172	80.72 213

IterMVS-SCA-FT		67.68 209	66.07 229	72.49 156	73.34 243	58.20 173	63.80 295	65.55 289	48.10 270	76.91 156	82.64 222	45.20 283	78.84 165	61.20 155	77.89 305	80.44 220

VNet		64.01 253	65.15 241	60.57 301	73.28 244	35.61 359	57.60 341	67.08 278	54.61 192	66.76 304	83.37 209	56.28 225	66.87 307	42.19 315	85.20 214	79.23 238

MGCFI-Net		71.70 154	73.10 136	67.49 232	73.23 245	43.08 297	72.06 175	82.43 95	54.58 193	75.97 181	82.00 227	72.42 63	75.22 219	57.84 188	87.34 179	84.18 123

3Dnovator	65.95 11	71.50 156	71.22 166	72.34 159	73.16 246	63.09 124	78.37 98	78.32 174	57.67 156	72.22 237	84.61 189	54.77 229	78.47 173	60.82 160	81.07 264	75.45 279

GBi-Net		68.30 199	68.79 190	66.81 240	73.14 247	40.68 318	71.96 179	73.03 222	54.81 185	74.72 197	90.36 70	48.63 270	75.20 221	47.12 282	85.37 208	84.54 110

test1		68.30 199	68.79 190	66.81 240	73.14 247	40.68 318	71.96 179	73.03 222	54.81 185	74.72 197	90.36 70	48.63 270	75.20 221	47.12 282	85.37 208	84.54 110

FMVSNet2		67.48 211	68.21 201	65.29 252	73.14 247	38.94 332	68.81 228	71.21 251	54.81 185	76.73 163	86.48 158	48.63 270	74.60 229	47.98 277	86.11 202	82.35 178

thisisatest0530		67.05 219	65.16 239	72.73 151	73.10 250	50.55 217	71.26 195	63.91 303	50.22 252	74.46 204	80.75 245	26.81 386	80.25 144	59.43 176	86.50 197	87.37 53

pm-mvs1		68.40 197	69.85 179	64.04 263	73.10 250	39.94 325	64.61 288	70.50 258	55.52 179	73.97 214	89.33 88	63.91 144	68.38 290	49.68 257	88.02 167	83.81 131

pmmvs4		60.78 282	59.04 291	66.00 249	73.06 252	57.67 175	64.53 289	60.22 320	36.91 362	65.96 306	77.27 296	39.66 319	68.54 289	38.87 335	74.89 326	71.80 317

SDMVSNet		66.36 226	67.85 208	61.88 286	73.04 253	46.14 272	58.54 334	71.36 243	51.42 236	68.93 281	82.72 220	65.62 128	62.22 335	54.41 222	84.67 221	77.28 261

sd_testset		63.55 254	65.38 235	58.07 318	73.04 253	38.83 334	57.41 342	65.44 290	51.42 236	68.93 281	82.72 220	63.76 145	58.11 351	41.05 323	84.67 221	77.28 261

v2v482		72.55 145	72.58 145	72.43 157	72.92 255	46.72 265	71.41 190	79.13 157	55.27 181	81.17 105	85.25 183	55.41 228	81.13 124	67.25 108	85.46 207	89.43 25

casdiffmvs_mvg		75.26 96	76.18 92	72.52 155	72.87 256	49.47 232	72.94 166	84.71 54	59.49 138	80.90 110	88.81 106	70.07 84	79.71 152	67.40 101	88.39 161	88.40 45
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MIMVSNet		54.39 323	56.12 315	49.20 363	72.57 257	30.91 384	59.98 323	48.43 381	41.66 323	55.94 373	83.86 202	41.19 308	50.42 369	26.05 400	75.38 323	66.27 361

Patchmatch-RL test		59.95 289	59.12 290	62.44 281	72.46 258	54.61 193	59.63 325	47.51 384	41.05 330	74.58 202	74.30 322	31.06 369	65.31 320	51.61 240	79.85 281	67.39 353

CL-MVSNet_self_test		62.44 269	63.40 258	59.55 308	72.34 259	32.38 375	56.39 347	64.84 295	51.21 241	67.46 299	81.01 242	50.75 253	63.51 330	38.47 340	88.12 165	82.75 167

Vis-MVSNet (Re-imp)		62.74 266	63.21 261	61.34 294	72.19 260	31.56 380	67.31 254	53.87 353	53.60 214	69.88 268	83.37 209	40.52 313	70.98 270	41.40 321	86.78 193	81.48 195

thres100view900		61.17 279	61.09 276	61.39 292	72.14 261	35.01 362	65.42 279	56.99 335	55.23 182	70.71 256	79.90 260	32.07 358	72.09 257	35.61 363	81.73 256	77.08 267

ab-mvs		64.11 251	65.13 242	61.05 296	71.99 262	38.03 343	67.59 245	68.79 270	49.08 265	65.32 312	86.26 164	58.02 209	66.85 309	39.33 331	79.79 284	78.27 249

RRT-MVS		70.33 168	70.73 171	69.14 206	71.93 263	45.24 278	75.10 139	75.08 211	60.85 129	78.62 131	87.36 130	49.54 259	78.64 169	60.16 166	77.90 304	83.55 138

thres600view7		61.82 273	61.38 274	63.12 273	71.81 264	34.93 363	64.64 286	56.99 335	54.78 189	70.33 260	79.74 262	32.07 358	72.42 254	38.61 338	83.46 240	82.02 185

QAPM		69.18 186	69.26 183	68.94 212	71.61 265	52.58 207	80.37 76	78.79 165	49.63 258	73.51 218	85.14 184	53.66 237	79.12 160	55.11 212	75.54 320	75.11 284

WB-MVSnew		53.94 329	54.76 326	51.49 351	71.53 266	28.05 395	58.22 337	50.36 372	37.94 355	59.16 356	70.17 354	49.21 263	51.94 366	24.49 407	71.80 354	74.47 291

baseline		73.10 127	73.96 117	70.51 179	71.46 267	46.39 270	72.08 174	84.40 62	55.95 175	76.62 166	86.46 159	67.20 107	78.03 188	64.22 128	87.27 184	87.11 60

casdiffmvs		73.06 130	73.84 118	70.72 175	71.32 268	46.71 266	70.93 199	84.26 65	55.62 178	77.46 149	87.10 133	67.09 109	77.81 191	63.95 132	86.83 192	87.64 50
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		72.36 146	72.49 146	71.96 164	71.29 269	64.06 117	72.79 167	81.82 103	40.23 339	81.25 104	81.04 241	70.62 80	68.69 287	69.74 82	83.60 239	83.14 155

Anonymous20231206		54.13 324	55.82 317	49.04 366	70.89 270	35.96 355	51.73 374	50.87 370	34.86 370	62.49 335	79.22 272	42.52 302	44.29 395	27.95 396	81.88 252	66.88 357

fmvsm_s_conf0.1_n_a		67.37 215	66.36 225	70.37 181	70.86 271	61.17 141	74.00 159	57.18 334	40.77 334	68.83 286	80.88 243	63.11 148	67.61 298	66.94 109	74.72 327	82.33 181

tfpn200view9		60.35 286	59.97 285	61.51 289	70.78 272	35.35 360	63.27 301	57.47 328	53.00 218	68.31 290	77.09 298	32.45 355	72.09 257	35.61 363	81.73 256	77.08 267

thres400		60.77 283	59.97 285	63.15 272	70.78 272	35.35 360	63.27 301	57.47 328	53.00 218	68.31 290	77.09 298	32.45 355	72.09 257	35.61 363	81.73 256	82.02 185

MSDG		67.47 213	67.48 213	67.46 233	70.70 274	54.69 192	66.90 260	78.17 177	60.88 128	70.41 258	74.76 315	61.22 172	73.18 243	47.38 281	76.87 310	74.49 290

testing91		55.74 313	55.29 323	57.08 322	70.63 275	30.85 385	54.94 360	56.31 344	50.34 249	57.08 364	70.10 356	24.50 397	65.86 315	36.98 353	76.75 311	74.53 289

test_yl		65.11 235	65.09 243	65.18 253	70.59 276	40.86 314	63.22 303	72.79 225	57.91 152	68.88 283	79.07 277	42.85 299	74.89 225	45.50 298	84.97 216	79.81 227

DCV-MVSNet		65.11 235	65.09 243	65.18 253	70.59 276	40.86 314	63.22 303	72.79 225	57.91 152	68.88 283	79.07 277	42.85 299	74.89 225	45.50 298	84.97 216	79.81 227

test_fmvsm_n_1920		69.63 177	68.45 196	73.16 133	70.56 278	65.86 102	70.26 208	78.35 173	37.69 356	74.29 206	78.89 279	61.10 174	68.10 293	65.87 117	79.07 289	85.53 82

OpenMVS	62.51 15	68.76 192	68.75 192	68.78 217	70.56 278	53.91 198	78.29 99	77.35 188	48.85 266	70.22 261	83.52 205	52.65 242	76.93 201	55.31 211	81.99 250	75.49 278

DELS-MVS		68.83 190	68.31 197	70.38 180	70.55 280	48.31 241	63.78 296	82.13 98	54.00 208	68.96 278	75.17 313	58.95 195	80.06 149	58.55 182	82.74 245	82.76 166
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		53.37 331	52.50 341	55.98 330	70.51 281	29.68 390	56.20 350	51.85 366	46.19 285	56.76 368	68.94 366	19.18 412	65.39 319	25.87 403	76.98 309	72.87 305

testing11		53.13 333	52.26 343	55.75 331	70.44 282	31.73 379	54.75 361	52.40 364	44.81 301	52.36 389	68.40 373	21.83 405	65.74 318	32.64 377	72.73 345	69.78 337

LCM-MVSNet-Re		69.10 187	71.57 162	61.70 287	70.37 283	34.30 368	61.45 310	79.62 147	56.81 164	89.59 9	88.16 123	68.44 97	72.94 245	42.30 313	87.33 180	77.85 258

UBG		49.18 358	49.35 362	48.66 368	70.36 284	26.56 403	50.53 378	45.61 390	37.43 358	53.37 385	65.97 382	23.03 403	54.20 363	26.29 398	71.54 355	65.20 368

patch_mono-2		62.73 267	64.08 250	58.68 314	70.36 284	55.87 184	60.84 316	64.11 302	41.23 327	64.04 321	78.22 286	60.00 183	48.80 374	54.17 226	83.71 237	71.37 321

ETVMVS		50.32 353	49.87 361	51.68 349	70.30 286	26.66 401	52.33 373	43.93 395	43.54 312	54.91 378	67.95 375	20.01 410	60.17 341	22.47 411	73.40 340	68.22 348

SCA		58.57 300	58.04 301	60.17 304	70.17 287	41.07 312	65.19 281	53.38 359	43.34 316	61.00 345	73.48 329	45.20 283	69.38 282	40.34 328	70.31 364	70.05 334

WBMVS		53.38 330	54.14 330	51.11 353	70.16 288	26.66 401	50.52 379	51.64 368	39.32 343	63.08 333	77.16 297	23.53 400	55.56 357	31.99 378	79.88 280	71.11 327

ET-MVSNet_ETH3D		63.32 257	60.69 281	71.20 173	70.15 289	55.66 186	65.02 283	64.32 300	43.28 317	68.99 277	72.05 340	25.46 393	78.19 186	54.16 227	82.80 244	79.74 230

testing99		55.16 319	54.56 328	56.98 324	70.13 290	30.58 387	54.55 363	54.11 352	49.53 260	56.76 368	70.14 355	22.76 404	65.79 317	36.99 352	76.04 316	74.57 288

APD_test1		75.04 101	75.38 101	74.02 119	69.89 291	70.15 66	76.46 121	79.71 146	65.50 79	82.99 81	88.60 112	66.94 110	72.35 255	59.77 173	88.54 159	79.56 231

PVSNet_BlendedMVS		65.38 233	64.30 247	68.61 219	69.81 292	49.36 233	65.60 277	78.96 159	45.50 291	59.98 349	78.61 281	51.82 246	78.20 184	44.30 302	84.11 231	78.27 249

PVSNet_Blended		62.90 263	61.64 270	66.69 243	69.81 292	49.36 233	61.23 313	78.96 159	42.04 320	59.98 349	68.86 369	51.82 246	78.20 184	44.30 302	77.77 306	72.52 309

OpenMVS_ROB	54.93 17	63.23 259	63.28 259	63.07 274	69.81 292	45.34 277	68.52 236	67.14 277	43.74 309	70.61 257	79.22 272	47.90 274	72.66 248	48.75 266	73.84 339	71.21 325

test_fmvsmconf0.01_n		73.91 112	73.64 123	74.71 106	69.79 295	66.25 97	75.90 133	79.90 144	46.03 287	76.48 174	85.02 185	67.96 104	73.97 237	74.47 52	87.22 185	83.90 129

fmvsm_s_conf0.5_n_a		67.00 220	65.95 232	70.17 186	69.72 296	61.16 142	73.34 163	56.83 337	40.96 331	68.36 289	80.08 258	62.84 149	67.57 299	66.90 111	74.50 331	81.78 190

FMVSNet3		65.00 238	65.16 239	64.52 258	69.47 297	37.56 347	66.63 263	70.38 259	51.55 234	74.72 197	83.27 214	37.89 331	74.44 231	47.12 282	85.37 208	81.57 194

MS-PatchMatch		55.59 315	54.89 325	57.68 320	69.18 298	49.05 236	61.00 315	62.93 309	35.98 366	58.36 359	68.93 367	36.71 337	66.59 312	37.62 347	63.30 389	57.39 395

baseline1		57.82 304	58.36 299	56.19 328	69.17 299	30.76 386	62.94 305	55.21 346	46.04 286	63.83 325	78.47 282	41.20 307	63.68 328	39.44 330	68.99 372	74.13 293

v148		69.38 184	69.39 181	69.36 199	69.14 300	44.56 283	68.83 227	72.70 228	54.79 188	78.59 132	84.12 197	54.69 230	76.74 206	59.40 177	82.20 248	86.79 62

test_fmvsmconf0.1_n		73.26 125	72.82 142	74.56 108	69.10 301	66.18 99	74.65 152	79.34 154	45.58 290	75.54 186	83.91 200	67.19 108	73.88 240	73.26 61	86.86 190	83.63 137

fmvsm_s_conf0.1_n		66.60 222	65.54 233	69.77 194	68.99 302	59.15 163	72.12 173	56.74 339	40.72 336	68.25 292	80.14 257	61.18 173	66.92 305	67.34 106	74.40 332	83.23 153

Syy-MVS		54.13 324	55.45 320	50.18 357	68.77 303	23.59 410	55.02 357	44.55 393	43.80 306	58.05 361	64.07 387	46.22 278	58.83 346	46.16 292	72.36 348	68.12 349

myMVS_eth3d		50.36 352	50.52 357	49.88 358	68.77 303	22.69 412	55.02 357	44.55 393	43.80 306	58.05 361	64.07 387	14.16 422	58.83 346	33.90 372	72.36 348	68.12 349

test_fmvsmconf_n		72.91 137	72.40 149	74.46 109	68.62 305	66.12 100	74.21 157	78.80 164	45.64 289	74.62 201	83.25 215	66.80 116	73.86 241	72.97 63	86.66 196	83.39 146

CANet_DTU		64.04 252	63.83 252	64.66 256	68.39 306	42.97 299	73.45 162	74.50 215	52.05 228	54.78 379	75.44 311	43.99 291	70.42 276	53.49 232	78.41 298	80.59 217

EU-MVSNet		60.82 281	60.80 280	60.86 299	68.37 307	41.16 310	72.27 170	68.27 274	26.96 400	69.08 275	75.71 306	32.09 357	67.44 300	55.59 209	78.90 291	73.97 294

PVSNet	43.83 21	51.56 346	51.17 349	52.73 344	68.34 308	38.27 338	48.22 384	53.56 357	36.41 363	54.29 382	64.94 386	34.60 344	54.20 363	30.34 384	69.87 367	65.71 364

EPNet		69.10 187	67.32 214	74.46 109	68.33 309	61.27 140	77.56 107	63.57 305	60.95 127	56.62 370	82.75 219	51.53 249	81.24 122	54.36 224	90.20 124	80.88 207
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n		66.34 228	65.27 236	69.57 197	68.20 310	59.14 165	71.66 186	56.48 340	40.92 332	67.78 294	79.46 266	61.23 170	66.90 306	67.39 102	74.32 335	82.66 172

IB-MVS	49.67 18	59.69 291	56.96 308	67.90 227	68.19 311	50.30 221	61.42 311	65.18 292	47.57 277	55.83 374	67.15 381	23.77 399	79.60 154	43.56 308	79.97 278	73.79 297
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		60.62 284	59.97 285	62.58 280	68.13 312	47.28 261	68.59 234	73.96 218	32.19 384	59.94 351	68.86 369	50.48 254	77.64 195	41.85 318	75.74 317	62.83 378

eth_miper_zixun_eth		69.42 182	68.73 194	71.50 170	67.99 313	46.42 268	67.58 246	78.81 162	50.72 246	78.13 139	80.34 252	50.15 257	80.34 142	60.18 165	84.65 223	87.74 49

TinyColmap		67.98 204	69.28 182	64.08 261	67.98 314	46.82 264	70.04 209	75.26 208	53.05 217	77.36 150	86.79 143	59.39 190	72.59 252	45.64 296	88.01 168	72.83 306

EPNet_dtu		58.93 297	58.52 295	60.16 305	67.91 315	47.70 255	69.97 211	58.02 326	49.73 257	47.28 403	73.02 334	38.14 327	62.34 333	36.57 356	85.99 203	70.43 332
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 305	57.02 307	59.17 310	67.89 316	34.93 363	58.91 332	57.25 332	50.24 251	64.01 322	71.46 344	32.49 354	71.39 267	31.31 381	79.57 286	71.19 326

our_test_3		56.46 308	56.51 311	56.30 327	67.70 317	39.66 327	55.36 356	52.34 365	40.57 338	63.85 324	69.91 359	40.04 316	58.22 350	43.49 309	75.29 325	71.03 329

ppachtmachnet_test		60.26 287	59.61 288	62.20 283	67.70 317	44.33 285	58.18 338	60.96 318	40.75 335	65.80 308	72.57 336	41.23 306	63.92 327	46.87 286	82.42 247	78.33 247

MVS_Test		69.84 175	70.71 172	67.24 235	67.49 319	43.25 296	69.87 213	81.22 117	52.69 221	71.57 246	86.68 149	62.09 160	74.51 230	66.05 114	78.74 292	83.96 127

fmvsm_l_conf0.5_n		67.48 211	66.88 223	69.28 202	67.41 320	62.04 130	70.69 203	69.85 262	39.46 342	69.59 271	81.09 240	58.15 202	68.73 286	67.51 99	78.16 302	77.07 269

thisisatest0515		60.48 285	57.86 302	68.34 222	67.25 321	46.42 268	60.58 319	62.14 311	40.82 333	63.58 329	69.12 363	26.28 389	78.34 180	48.83 265	82.13 249	80.26 223

V42		71.06 159	70.83 170	71.72 166	67.25 321	47.14 263	65.94 269	80.35 138	51.35 238	83.40 78	83.23 216	59.25 192	78.80 166	65.91 116	80.81 268	89.23 28

fmvsm_l_conf0.5_n_a		66.66 221	65.97 231	68.72 218	67.09 323	61.38 138	70.03 210	69.15 268	38.59 350	68.41 288	80.36 251	56.56 223	68.32 291	66.10 113	77.45 307	76.46 271

GA-MVS		62.91 262	61.66 269	66.66 244	67.09 323	44.49 284	61.18 314	69.36 266	51.33 239	69.33 274	74.47 319	36.83 336	74.94 224	50.60 250	74.72 327	80.57 218

testf1		75.66 90	76.57 86	72.95 140	67.07 325	67.62 85	76.10 129	80.68 128	64.95 91	86.58 36	90.94 43	71.20 75	71.68 265	60.46 162	91.13 102	79.56 231

APD_test2		75.66 90	76.57 86	72.95 140	67.07 325	67.62 85	76.10 129	80.68 128	64.95 91	86.58 36	90.94 43	71.20 75	71.68 265	60.46 162	91.13 102	79.56 231

mmtdpeth		68.76 192	70.55 174	63.40 271	67.06 327	56.26 181	68.73 233	71.22 250	55.47 180	70.09 264	88.64 111	65.29 134	56.89 355	58.94 180	89.50 141	77.04 270

HY-MVS	49.31 19	57.96 303	57.59 304	59.10 312	66.85 328	36.17 353	65.13 282	65.39 291	39.24 346	54.69 381	78.14 288	44.28 290	67.18 304	33.75 373	70.79 360	73.95 295

CR-MVSNet		58.96 295	58.49 296	60.36 303	66.37 329	48.24 243	70.93 199	56.40 342	32.87 383	61.35 340	86.66 150	33.19 349	63.22 331	48.50 270	70.17 365	69.62 340

RPMNet		65.77 231	65.08 245	67.84 229	66.37 329	48.24 243	70.93 199	86.27 20	54.66 191	61.35 340	86.77 145	33.29 348	85.67 49	55.93 203	70.17 365	69.62 340

IterMVS		63.12 260	62.48 267	65.02 255	66.34 331	52.86 204	63.81 294	62.25 310	46.57 283	71.51 248	80.40 250	44.60 288	66.82 310	51.38 244	75.47 321	75.38 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		69.82 176	69.89 178	69.61 196	66.24 332	43.48 292	68.12 241	79.61 149	51.43 235	77.72 145	80.18 256	54.61 232	78.15 187	63.62 137	87.50 174	87.20 57

tpm2		56.12 310	54.64 327	60.55 302	66.24 332	36.01 354	68.14 240	56.77 338	33.60 381	58.25 360	75.52 310	30.25 375	74.33 233	33.27 374	69.76 369	71.32 322

Anonymous20240521		63.55 254	66.07 229	55.99 329	66.18 334	44.04 287	68.77 231	68.80 269	46.99 280	72.57 230	85.84 177	39.87 317	50.22 370	53.40 235	92.23 83	73.71 298

Patchmtry		60.91 280	63.01 263	54.62 336	66.10 335	26.27 405	67.47 248	56.40 342	54.05 207	72.04 239	86.66 150	33.19 349	60.17 341	43.69 306	87.45 176	77.42 259

FMVSNet5		55.08 320	55.54 319	53.71 338	65.80 336	33.50 372	56.22 349	52.50 363	43.72 310	61.06 343	83.38 208	25.46 393	54.87 360	30.11 386	81.64 261	72.75 307

1314		59.83 290	58.86 293	62.74 279	65.71 337	44.78 282	68.59 234	72.63 229	33.54 382	61.05 344	67.29 380	43.62 294	71.26 268	49.49 260	67.84 379	72.19 314

MonoMVSNet		62.75 265	63.42 257	60.73 300	65.60 338	40.77 316	72.49 169	70.56 257	52.49 222	75.07 191	79.42 268	39.52 321	69.97 278	46.59 289	69.06 371	71.44 320

MDTV_nov1_ep13			54.05 332		65.54 339	29.30 392	59.00 329	55.22 345	35.96 367	52.44 387	75.98 304	30.77 372	59.62 343	38.21 341	73.33 342

baseline2		55.57 316	52.74 337	64.05 262	65.26 340	44.11 286	62.38 306	54.43 350	39.03 347	51.21 392	67.35 379	33.66 347	72.45 253	37.14 350	64.22 387	75.60 277

USDC		62.80 264	63.10 262	61.89 285	65.19 341	43.30 295	67.42 249	74.20 217	35.80 368	72.25 236	84.48 193	45.67 280	71.95 261	37.95 344	84.97 216	70.42 333

tpm		50.60 350	52.42 342	45.14 381	65.18 342	26.29 404	60.30 320	43.50 396	37.41 359	57.01 365	79.09 276	30.20 377	42.32 400	32.77 376	66.36 382	66.81 359

PatchmatchNet		54.60 322	54.27 329	55.59 332	65.17 343	39.08 329	66.92 259	51.80 367	39.89 340	58.39 358	73.12 333	31.69 363	58.33 349	43.01 311	58.38 403	69.38 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_ehance_all_eth		68.36 198	68.16 203	68.98 210	65.14 344	43.34 294	67.07 256	78.92 161	49.11 264	76.21 179	77.72 292	53.48 238	77.92 190	61.16 156	84.59 225	85.68 81

cl____		68.26 203	68.26 199	68.29 223	64.98 345	43.67 290	65.89 270	74.67 212	50.04 255	76.86 159	82.42 224	48.74 268	75.38 215	60.92 159	89.81 134	85.80 79

DIV-MVS_self_test		68.27 202	68.26 199	68.29 223	64.98 345	43.67 290	65.89 270	74.67 212	50.04 255	76.86 159	82.43 223	48.74 268	75.38 215	60.94 158	89.81 134	85.81 75

tpm cat1		54.02 327	52.63 339	58.19 317	64.85 347	39.86 326	66.26 267	57.28 331	32.16 385	56.90 366	70.39 351	32.75 353	65.30 321	34.29 369	58.79 400	69.41 342

XXY-MVS		55.19 318	57.40 306	48.56 369	64.45 348	34.84 365	51.54 375	53.59 355	38.99 348	63.79 326	79.43 267	56.59 221	45.57 385	36.92 354	71.29 357	65.25 367

PatchT		53.35 332	56.47 312	43.99 386	64.19 349	17.46 417	59.15 326	43.10 398	52.11 227	54.74 380	86.95 139	29.97 378	49.98 371	43.62 307	74.40 332	64.53 375

D2MVS		62.58 268	61.05 277	67.20 236	63.85 350	47.92 249	56.29 348	69.58 264	39.32 343	70.07 265	78.19 287	34.93 343	72.68 247	53.44 233	83.74 235	81.00 203

mvs_anonymous		65.08 237	65.49 234	63.83 264	63.79 351	37.60 346	66.52 265	69.82 263	43.44 313	73.46 220	86.08 172	58.79 197	71.75 264	51.90 239	75.63 319	82.15 183

CostFormer		57.35 306	56.14 314	60.97 297	63.76 352	38.43 336	67.50 247	60.22 320	37.14 361	59.12 357	76.34 303	32.78 352	71.99 260	39.12 334	69.27 370	72.47 310

Gipuma		69.55 180	72.83 141	59.70 306	63.63 353	53.97 197	80.08 82	75.93 202	64.24 98	73.49 219	88.93 104	57.89 210	62.46 332	59.75 174	91.55 92	62.67 380
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
cl22		67.14 216	66.51 224	69.03 209	63.20 354	43.46 293	66.88 261	76.25 198	49.22 262	74.48 203	77.88 291	45.49 282	77.40 197	60.64 161	84.59 225	86.24 67

gg-mvs-nofinetune		55.75 312	56.75 310	52.72 345	62.87 355	28.04 396	68.92 225	41.36 408	71.09 46	50.80 394	92.63 13	20.74 407	66.86 308	29.97 387	72.41 347	63.25 377

gm-plane-assit					62.51 356	33.91 370			37.25 360		62.71 392		72.74 246	38.70 336

mvs5depth		66.35 227	67.98 204	61.47 291	62.43 357	51.05 213	69.38 219	69.24 267	56.74 166	73.62 216	89.06 100	46.96 277	58.63 348	55.87 205	88.49 160	74.73 286

MVS-HIRNet		45.53 367	47.29 367	40.24 392	62.29 358	26.82 400	56.02 352	37.41 414	29.74 395	43.69 413	81.27 237	33.96 345	55.48 358	24.46 408	56.79 404	38.43 413

diffmvs		67.42 214	67.50 212	67.20 236	62.26 359	45.21 279	64.87 284	77.04 193	48.21 269	71.74 240	79.70 263	58.40 199	71.17 269	64.99 121	80.27 274	85.22 85
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		41.62 378	39.89 383	46.80 374	61.81 360	51.59 209	33.56 412	35.74 415	27.48 399	37.64 417	53.53 406	23.24 401	42.09 401	27.39 397	58.64 401	46.72 405

KD-MVS_self_test		66.38 225	67.51 211	62.97 276	61.76 361	34.39 367	58.11 339	75.30 207	50.84 245	77.12 152	85.42 180	56.84 220	69.44 281	51.07 246	91.16 99	85.08 90

MDA-MVSNet-bldmvs		62.34 270	61.73 268	64.16 259	61.64 362	49.90 227	48.11 385	57.24 333	53.31 216	80.95 107	79.39 269	49.00 266	61.55 337	45.92 294	80.05 277	81.03 201

miper_enhance_ethall		65.86 230	65.05 246	68.28 225	61.62 363	42.62 302	64.74 285	77.97 181	42.52 318	73.42 221	72.79 335	49.66 258	77.68 194	58.12 185	84.59 225	84.54 110

WTY-MVS		49.39 357	50.31 359	46.62 375	61.22 364	32.00 378	46.61 390	49.77 374	33.87 378	54.12 383	69.55 362	41.96 303	45.40 387	31.28 382	64.42 386	62.47 382

CMPMVS	48.73 20	61.54 277	60.89 278	63.52 268	61.08 365	51.55 210	68.07 242	68.00 275	33.88 377	65.87 307	81.25 238	37.91 330	67.71 295	49.32 262	82.60 246	71.31 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 351	50.69 356	49.64 361	60.76 366	41.87 306	53.18 368	45.48 391	43.41 314	49.41 399	60.47 400	29.22 381	44.73 392	42.09 316	72.14 351	62.33 384

test-mter		48.56 360	48.20 365	49.64 361	60.76 366	41.87 306	53.18 368	45.48 391	31.91 389	49.41 399	60.47 400	18.34 413	44.73 392	42.09 316	72.14 351	62.33 384

GG-mvs-BLEND				52.24 346	60.64 368	29.21 393	69.73 215	42.41 401		45.47 406	52.33 409	20.43 408	68.16 292	25.52 405	65.42 384	59.36 391

tpmvs		55.84 311	55.45 320	57.01 323	60.33 369	33.20 373	65.89 270	59.29 324	47.52 278	56.04 372	73.60 328	31.05 370	68.06 294	40.64 326	64.64 385	69.77 338

miper_lstm_enhance		61.97 271	61.63 271	62.98 275	60.04 370	45.74 275	47.53 387	70.95 253	44.04 304	73.06 225	78.84 280	39.72 318	60.33 340	55.82 206	84.64 224	82.88 162

dmvs_re		49.91 356	50.77 355	47.34 371	59.98 371	38.86 333	53.18 368	53.58 356	39.75 341	55.06 377	61.58 396	36.42 338	44.40 394	29.15 394	68.23 375	58.75 392

PVSNet_0	36.71 22	41.12 379	40.78 382	42.14 388	59.97 372	40.13 323	40.97 401	42.24 405	30.81 393	44.86 409	49.41 412	40.70 312	45.12 389	23.15 410	34.96 415	41.16 411

dmvs_testset		45.26 368	47.51 366	38.49 395	59.96 373	14.71 419	58.50 335	43.39 397	41.30 326	51.79 391	56.48 404	39.44 322	49.91 373	21.42 413	55.35 409	50.85 400

new-patchmatchnet		52.89 336	55.76 318	44.26 385	59.94 374	6.31 425	37.36 409	50.76 371	41.10 328	64.28 319	79.82 261	44.77 286	48.43 378	36.24 359	87.61 171	78.03 254

test20.03		55.74 313	57.51 305	50.42 356	59.89 375	32.09 377	50.63 377	49.01 378	50.11 253	65.07 314	83.23 216	45.61 281	48.11 379	30.22 385	83.82 234	71.07 328

MVSTER		63.29 258	61.60 272	68.36 221	59.77 376	46.21 271	60.62 318	71.32 244	41.83 322	75.40 189	79.12 275	30.25 375	75.85 209	56.30 200	79.81 282	83.03 159

reproduce_monomvs		58.94 296	58.14 300	61.35 293	59.70 377	40.98 313	60.24 322	63.51 306	45.85 288	68.95 279	75.31 312	18.27 414	65.82 316	51.47 242	79.97 278	77.26 264

N_pmnet		52.06 342	51.11 350	54.92 333	59.64 378	71.03 57	37.42 408	61.62 317	33.68 379	57.12 363	72.10 337	37.94 329	31.03 414	29.13 395	71.35 356	62.70 379

test_vis1_n_1920		52.96 334	53.50 333	51.32 352	59.15 379	44.90 281	56.13 351	64.29 301	30.56 394	59.87 353	60.68 398	40.16 315	47.47 380	48.25 274	62.46 391	61.58 386

JIA-IIPM		54.03 326	51.62 345	61.25 295	59.14 380	55.21 189	59.10 328	47.72 382	50.85 244	50.31 398	85.81 178	20.10 409	63.97 326	36.16 360	55.41 408	64.55 374

LF4IMVS		67.50 210	67.31 215	68.08 226	58.86 381	61.93 131	71.43 189	75.90 203	44.67 302	72.42 233	80.20 254	57.16 214	70.44 275	58.99 179	86.12 201	71.88 316

UnsupCasMVSNet_bld		50.01 355	51.03 352	46.95 372	58.61 382	32.64 374	48.31 383	53.27 360	34.27 376	60.47 347	71.53 343	41.40 305	47.07 382	30.68 383	60.78 396	61.13 387

dongtai		31.66 384	32.98 387	27.71 399	58.58 383	12.61 421	45.02 394	14.24 425	41.90 321	47.93 401	43.91 414	10.65 425	41.81 404	14.06 417	20.53 418	28.72 415

dp		44.09 374	44.88 376	41.72 391	58.53 384	23.18 411	54.70 362	42.38 403	34.80 372	44.25 411	65.61 384	24.48 398	44.80 391	29.77 388	49.42 411	57.18 396

testgi		54.00 328	56.86 309	45.45 379	58.20 385	25.81 407	49.05 381	49.50 377	45.43 294	67.84 293	81.17 239	51.81 248	43.20 399	29.30 390	79.41 287	67.34 355

wuyk23d		61.97 271	66.25 226	49.12 365	58.19 386	60.77 151	66.32 266	52.97 361	55.93 176	90.62 6	86.91 140	73.07 60	35.98 412	20.63 415	91.63 89	50.62 401

ANet_high		67.08 217	69.94 177	58.51 316	57.55 387	27.09 399	58.43 336	76.80 195	63.56 105	82.40 89	91.93 23	59.82 187	64.98 323	50.10 254	88.86 157	83.46 144

Patchmatch-test		47.93 361	49.96 360	41.84 389	57.42 388	24.26 409	48.75 382	41.49 407	39.30 345	56.79 367	73.48 329	30.48 374	33.87 413	29.29 391	72.61 346	67.39 353

test_vis1_n		51.27 348	50.41 358	53.83 337	56.99 389	50.01 225	56.75 345	60.53 319	25.68 405	59.74 354	57.86 403	29.40 380	47.41 381	43.10 310	63.66 388	64.08 376

new_pmnet		37.55 382	39.80 384	30.79 397	56.83 390	16.46 418	39.35 405	30.65 417	25.59 406	45.26 407	61.60 395	24.54 396	28.02 417	21.60 412	52.80 410	47.90 404

pmmvs3		46.71 364	45.09 374	51.55 350	56.76 391	48.25 242	55.78 354	39.53 412	24.13 410	50.35 397	63.40 389	15.90 419	51.08 368	29.29 391	70.69 362	55.33 398

sss		47.59 363	48.32 363	45.40 380	56.73 392	33.96 369	45.17 393	48.51 380	32.11 388	52.37 388	65.79 383	40.39 314	41.91 403	31.85 379	61.97 393	60.35 388

tpmrst		50.15 354	51.38 348	46.45 376	56.05 393	24.77 408	64.40 291	49.98 373	36.14 365	53.32 386	69.59 361	35.16 342	48.69 375	39.24 332	58.51 402	65.89 362

TESTMET0.1,1		45.17 369	44.93 375	45.89 378	56.02 394	38.31 337	53.18 368	41.94 406	27.85 397	44.86 409	56.47 405	17.93 415	41.50 405	38.08 343	68.06 376	57.85 393

ADS-MVSNet2		48.76 359	47.25 368	53.29 343	55.90 395	40.54 321	47.34 388	54.99 348	31.41 391	50.48 395	72.06 338	31.23 366	54.26 362	25.93 401	55.93 405	65.07 369

ADS-MVSNet		44.62 372	45.58 371	41.73 390	55.90 395	20.83 415	47.34 388	39.94 411	31.41 391	50.48 395	72.06 338	31.23 366	39.31 408	25.93 401	55.93 405	65.07 369

ttmdpeth		56.40 309	55.45 320	59.25 309	55.63 397	40.69 317	58.94 331	49.72 375	36.22 364	65.39 310	86.97 138	23.16 402	56.69 356	42.30 313	80.74 269	80.36 221

test0.0.03 1		47.72 362	48.31 364	45.93 377	55.53 398	29.39 391	46.40 391	41.21 409	43.41 314	55.81 375	67.65 376	29.22 381	43.77 398	25.73 404	69.87 367	64.62 373

UnsupCasMVSNet_eth		52.26 341	53.29 336	49.16 364	55.08 399	33.67 371	50.03 380	58.79 325	37.67 357	63.43 332	74.75 316	41.82 304	45.83 384	38.59 339	59.42 399	67.98 352

pmmvs5		52.49 340	52.58 340	52.21 347	54.99 400	32.38 375	55.45 355	53.84 354	32.15 386	55.49 376	74.81 314	38.08 328	57.37 354	34.02 370	74.40 332	66.88 357

DSMNet-mixed		43.18 377	44.66 377	38.75 394	54.75 401	28.88 394	57.06 344	27.42 419	13.47 417	47.27 404	77.67 293	38.83 324	39.29 409	25.32 406	60.12 398	48.08 403

MDA-MVSNet_test_wron		52.57 339	53.49 335	49.81 360	54.24 402	36.47 351	40.48 403	46.58 388	38.13 352	75.47 188	73.32 331	41.05 311	43.85 397	40.98 324	71.20 358	69.10 346

YYNet1		52.58 338	53.50 333	49.85 359	54.15 403	36.45 352	40.53 402	46.55 389	38.09 353	75.52 187	73.31 332	41.08 310	43.88 396	41.10 322	71.14 359	69.21 344

EPMVS		45.74 366	46.53 369	43.39 387	54.14 404	22.33 414	55.02 357	35.00 416	34.69 374	51.09 393	70.20 353	25.92 391	42.04 402	37.19 349	55.50 407	65.78 363

test_cas_vis1_n_1920		50.90 349	50.92 353	50.83 355	54.12 405	47.80 251	51.44 376	54.61 349	26.95 401	63.95 323	60.85 397	37.86 332	44.97 390	45.53 297	62.97 390	59.72 390

test_fmvs3		56.78 307	55.99 316	59.12 311	53.96 406	48.09 246	58.76 333	66.22 282	27.54 398	76.66 164	68.69 371	25.32 395	51.31 367	53.42 234	73.38 341	77.97 257

test_fmvs1_n		52.70 337	52.01 344	54.76 334	53.83 407	50.36 219	55.80 353	65.90 284	24.96 407	65.39 310	60.64 399	27.69 384	48.46 376	45.88 295	67.99 377	65.46 365

KD-MVS_2432*1600		52.05 343	51.58 346	53.44 341	52.11 408	31.20 381	44.88 395	64.83 296	41.53 324	64.37 317	70.03 357	15.61 420	64.20 324	36.25 357	74.61 329	64.93 371

miper_refine_blended		52.05 343	51.58 346	53.44 341	52.11 408	31.20 381	44.88 395	64.83 296	41.53 324	64.37 317	70.03 357	15.61 420	64.20 324	36.25 357	74.61 329	64.93 371

test_fmvs2		54.80 321	54.11 331	56.88 325	51.76 410	49.95 226	56.70 346	65.80 285	26.22 403	69.42 272	65.25 385	31.82 361	49.98 371	49.63 258	70.36 363	70.71 330

E-PMN		45.17 369	45.36 372	44.60 383	50.07 411	42.75 300	38.66 406	42.29 404	46.39 284	39.55 414	51.15 410	26.00 390	45.37 388	37.68 345	76.41 312	45.69 407

PMMVS		44.69 371	43.95 379	46.92 373	50.05 412	53.47 202	48.08 386	42.40 402	22.36 413	44.01 412	53.05 408	42.60 301	45.49 386	31.69 380	61.36 395	41.79 410

test_fmvs1		51.51 347	50.86 354	53.48 340	49.72 413	49.35 235	54.11 364	64.96 294	24.64 409	63.66 328	59.61 402	28.33 383	48.45 377	45.38 300	67.30 381	62.66 381

EMVS		44.61 373	44.45 378	45.10 382	48.91 414	43.00 298	37.92 407	41.10 410	46.75 282	38.00 416	48.43 413	26.42 388	46.27 383	37.11 351	75.38 323	46.03 406

mvsany_test3		43.76 376	41.01 380	52.01 348	48.09 415	57.74 174	42.47 399	23.85 422	23.30 412	64.80 315	62.17 394	27.12 385	40.59 406	29.17 393	48.11 412	57.69 394

mvsany_test1		37.88 380	35.74 385	44.28 384	47.28 416	49.90 227	36.54 410	24.37 421	19.56 416	45.76 405	53.46 407	32.99 351	37.97 411	26.17 399	35.52 414	44.99 409

test_vis3_rt		51.94 345	51.04 351	54.65 335	46.32 417	50.13 223	44.34 397	78.17 177	23.62 411	68.95 279	62.81 391	21.41 406	38.52 410	41.49 320	72.22 350	75.30 283

test_vis1_rt		46.70 365	45.24 373	51.06 354	44.58 418	51.04 214	39.91 404	67.56 276	21.84 415	51.94 390	50.79 411	33.83 346	39.77 407	35.25 366	61.50 394	62.38 383

MVStest1		55.38 317	54.97 324	56.58 326	43.72 419	40.07 324	59.13 327	47.09 386	34.83 371	76.53 172	84.65 188	13.55 423	53.30 365	55.04 213	80.23 275	76.38 272

MVE	27.91 23	36.69 383	35.64 386	39.84 393	43.37 420	35.85 357	19.49 414	24.61 420	24.68 408	39.05 415	62.63 393	38.67 326	27.10 418	21.04 414	47.25 413	56.56 397
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 381	40.87 381	28.36 398	42.41 421	5.35 426	24.61 413	27.75 418	32.15 386	47.85 402	70.27 352	35.85 340	29.51 416	19.08 416	67.85 378	50.22 402

test_f		43.79 375	45.63 370	38.24 396	42.29 422	38.58 335	34.76 411	47.68 383	22.22 414	67.34 300	63.15 390	31.82 361	30.60 415	39.19 333	62.28 392	45.53 408

kuosan		22.02 385	23.52 389	17.54 401	41.56 423	11.24 422	41.99 400	13.39 426	26.13 404	28.87 418	30.75 416	9.72 426	21.94 420	4.77 421	14.49 419	19.43 416

DeepMVS_CX				11.83 402	15.51 424	13.86 420		11.25 427	5.76 418	20.85 420	26.46 417	17.06 418	9.22 421	9.69 420	13.82 420	12.42 417

test_method		19.26 386	19.12 390	19.71 400	9.09 425	1.91 428	7.79 416	53.44 358	1.42 419	10.27 421	35.80 415	17.42 417	25.11 419	12.44 418	24.38 417	32.10 414

tmp_tt		11.98 388	14.73 391	3.72 403	2.28 426	4.62 427	19.44 415	14.50 424	0.47 421	21.55 419	9.58 419	25.78 392	4.57 422	11.61 419	27.37 416	1.96 418

test123		4.43 391	5.78 394	0.39 405	0.97 427	0.28 429	46.33 392	0.45 428	0.31 422	0.62 423	1.50 422	0.61 428	0.11 424	0.56 422	0.63 421	0.77 420

testmvs		4.06 392	5.28 395	0.41 404	0.64 428	0.16 430	42.54 398	0.31 429	0.26 423	0.50 424	1.40 423	0.77 427	0.17 423	0.56 422	0.55 422	0.90 419

mmdepth		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

monomultidepth		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

test_blank		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

eth-test2					0.00 429

eth-test					0.00 429

uanet_test		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

DCPMVS		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

cdsmvs_eth3d_5k		17.71 387	23.62 388	0.00 406	0.00 429	0.00 431	0.00 417	70.17 261	0.00 424	0.00 425	74.25 323	68.16 100	0.00 425	0.00 424	0.00 423	0.00 421

pcd_1.5k_mvsjas		5.20 390	6.93 393	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	62.39 156	0.00 425	0.00 424	0.00 423	0.00 421

sosnet-low-res		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

sosnet		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uncertanet		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

Regformer		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

ab-mvs-re		5.62 389	7.50 392	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	67.46 377	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uanet		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

WAC-MVS						22.69 412								36.10 361

PC_three_1452									46.98 281	81.83 94	86.28 162	66.55 121	84.47 74	63.31 142	90.78 115	83.49 140

test_241102_TWO								84.80 48	72.61 35	84.93 59	89.70 83	77.73 24	85.89 42	75.29 45	94.22 55	83.25 151

test_0728_THIRD									74.03 25	85.83 46	90.41 62	75.58 40	85.69 47	77.43 34	94.74 33	84.31 120

GSMVS																70.05 334

sam_mvs1												31.41 364				70.05 334

sam_mvs												31.21 368

MTGPA								80.63 130

test_post1							66.63 263				2.08 420	30.66 373	59.33 344	40.34 328

test_post											1.99 421	30.91 371	54.76 361

patchmatchnet-post											68.99 364	31.32 365	69.38 282

MTMP							84.83 34	19.26 423

test9_res														72.12 72	91.37 94	77.40 260

agg_prior2														70.70 76	90.93 109	78.55 246

test_prior4						70.14 67	77.57 106

test_prior2							75.57 136		58.92 145	76.53 172	86.78 144	67.83 105		69.81 80	92.76 75

旧先验2							71.17 196		45.11 298	78.54 135			61.28 338	59.19 178

新几何2							71.33 192

无先验							74.82 143	70.94 254	47.75 276				76.85 204	54.47 220		72.09 315

原ACMM2							74.78 147

testdata2													67.30 301	48.34 272

segment_acmp												68.30 99

testdata1							68.34 239		57.24 161

plane_prior5								85.49 32					86.15 29	71.09 73	90.94 107	84.82 98

plane_prior4											89.11 97

plane_prior3						65.67 103			63.82 102	78.23 137

plane_prior2							82.74 55		65.45 80

plane_prior						65.18 108	80.06 83		61.88 122						89.91 133

n2								0.00 430

nn								0.00 430

door-mid								55.02 347

test11								82.71 90

door								52.91 362

HQP5-MVS						58.80 168

BP-MVS														67.38 104

HQP4-MVS										71.59 242			85.31 54			83.74 134

HQP3-MVS								84.12 69							89.16 148

HQP2-MVS												58.09 204

MDTV_nov1_ep13_2view						18.41 416	53.74 366		31.57 390	44.89 408		29.90 379		32.93 375		71.48 319

ACMMP++_ref															89.47 143

ACMMP++															91.96 85

Test By Simon												62.56 152

High-resolution multi-view benchmark