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	66.57 122	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	63.30 124	84.80 35	87.77 10	86.18 2	96.26 2	96.06 1	90.32 1	84.49 72	68.08 96	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	80.63 132	72.08 41	84.93 59	90.79 48	74.65 49	84.42 75	80.98 5	94.75 32	80.82 213

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	82.35 183

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	82.74 173
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	79.22 158	74.08 24	87.16 32	91.97 21	84.80 2	76.97 202	64.98 127	93.61 63	72.28 319
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 51	82.86 40	73.07 138	89.93 7	39.21 334	77.15 115	81.28 115	79.74 6	90.87 5	92.73 12	75.03 46	84.93 65	63.83 141	95.19 19	95.07 3

DTE-MVSNet		80.35 52	82.89 39	72.74 153	89.84 8	37.34 354	77.16 114	81.81 105	80.45 4	90.92 4	92.95 8	74.57 50	86.12 31	63.65 142	94.68 35	94.76 6

PEN-MVS		80.46 50	82.91 38	73.11 137	89.83 9	39.02 337	77.06 117	82.61 93	80.04 5	90.60 7	92.85 10	74.93 47	85.21 60	63.15 149	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	83.95 132

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	84.18 127

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	57.10 219	87.10 9	79.75 11	83.87 235	84.31 124
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	72.98 141	89.66 13	39.06 336	76.76 118	80.46 136	78.91 9	90.32 8	91.70 29	68.49 96	84.89 66	63.40 146	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	83.82 134

WR-MVS_H		80.22 54	82.17 45	74.39 115	89.46 15	42.69 307	78.24 101	82.24 97	78.21 13	89.57 10	92.10 19	68.05 101	85.59 50	66.04 120	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	84.64 107

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	9.95 424	73.86 55	86.31 21	78.84 23	94.03 56	84.64 107

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	82.49 181

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	83.35 153

UniMVSNet_ETH3D		76.74 82	79.02 65	69.92 198	89.27 20	43.81 294	74.47 154	71.70 239	72.33 40	85.50 53	93.65 4	77.98 23	76.88 205	54.60 225	91.64 88	89.08 32

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	82.82 170

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	84.62 109
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	82.03 101	68.80 58	80.92 108	88.52 113	72.00 68	82.39 105	74.80 48	93.04 70	81.14 203

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	84.60 112

HPM-MVS++		79.89 55	79.80 61	80.18 43	89.02 26	78.44 11	83.49 49	80.18 142	64.71 95	78.11 140	88.39 116	65.46 133	83.14 93	77.64 33	91.20 98	78.94 246

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	84.30 126

TSAR-MVS + MP.		79.05 61	78.81 66	79.74 46	88.94 28	67.52 87	86.61 22	81.38 113	51.71 233	77.15 151	91.42 36	65.49 132	87.20 7	79.44 17	87.17 189	84.51 118
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	80.01 145	75.34 19	79.80 119	94.91 2	69.79 88	80.25 146	72.63 67	94.46 39	88.78 42

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	84.61 110
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 71
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 52	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 48

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	83.09 160

新几何1				69.99 196	88.37 35	71.34 55		62.08 319	43.85 311	74.99 194	86.11 171	52.85 245	70.57 277	50.99 253	83.23 244	68.05 357

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 64
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	80.52 142	74.27 55	95.73 8	80.98 209
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 38	69.15 77	67.85 247	59.59 329	41.06 335	73.05 230	85.72 179	48.03 277			80.65 274	66.92 362

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	80.55 141	72.51 69	93.37 66	83.48 146

save fliter					87.00 40	67.23 90	79.24 89	77.94 184	56.65 171

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 72

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 72

EGC-MVSNET		64.77 247	61.17 281	75.60 102	86.90 43	74.47 34	84.04 39	68.62 278	0.60 426	1.13 428	91.61 32	65.32 135	74.15 238	64.01 135	88.28 162	78.17 256

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 88	74.03 57	93.57 65	84.35 123
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 60	94.52 38	85.92 77
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 54	96.11 4	85.81 78
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	78.33 183	74.73 50	85.79 206	82.35 183

VDDNet		71.60 157	73.13 135	67.02 245	86.29 48	41.11 317	69.97 215	66.50 287	68.72 60	74.74 197	91.70 29	59.90 187	75.81 213	48.58 275	91.72 86	84.15 129

test_0728_SECOND				76.57 87	86.20 49	60.57 153	83.77 44	85.49 32					85.90 40	75.86 42	94.39 44	83.25 155

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 97

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 97

DVP-MVS		81.15 41	83.12 36	75.24 107	86.16 52	60.78 150	83.77 44	80.58 134	72.48 37	85.83 46	90.41 62	78.57 19	85.69 47	75.86 42	94.39 44	79.24 242
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	60.78 150	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	61.06 144	83.62 46	84.72 52	72.61 35	87.38 28	89.70 83	77.48 26	85.89 42	75.29 46	94.39 44	83.08 161

IU-MVS					86.12 54	60.90 148		80.38 138	45.49 299	81.31 102				75.64 45	94.39 44	84.65 106

test_241102_ONE					86.12 54	61.06 144		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	82.72 174

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	82.72 174

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	66.82 115	78.37 181	74.80 48	90.76 118	82.40 182

test_part2					85.90 60	66.44 95				84.61 65

原ACMM1				73.90 122	85.90 60	65.15 110		81.67 107	50.97 245	74.25 210	86.16 168	61.60 166	83.54 85	56.75 200	91.08 105	73.00 309

testdata				64.13 266	85.87 62	63.34 123		61.80 322	47.83 280	76.42 177	86.60 155	48.83 271	62.31 340	54.46 227	81.26 266	66.74 366

CNVR-MVS		78.49 68	78.59 70	78.16 70	85.86 63	67.40 88	78.12 104	81.50 109	63.92 100	77.51 148	86.56 156	68.43 98	84.82 68	73.83 58	91.61 90	82.26 187

test_one_0601					85.84 64	61.45 138		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	80.99 124	65.77 76	75.55 185	86.25 165	67.42 108	85.42 52	70.10 82	90.88 113	81.81 194

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	82.10 189

TEST9					85.47 67	69.32 74	76.42 123	78.69 169	53.73 215	76.97 153	86.74 146	66.84 114	81.10 127

train_agg		76.38 84	76.55 88	75.86 98	85.47 67	69.32 74	76.42 123	78.69 169	54.00 210	76.97 153	86.74 146	66.60 120	81.10 127	72.50 70	91.56 91	77.15 271

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 51	94.02 58	82.62 178
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	60.83 179	86.15 29	71.09 75	90.94 107	84.82 102

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 88
Skip Steuart: Steuart Systems R&D Blog.
test_8					85.09 73	67.89 83	76.26 128	78.66 171	54.00 210	76.89 157	86.72 148	66.60 120	80.89 137

WR-MVS		71.20 161	72.48 148	67.36 240	84.98 74	35.70 364	64.43 296	68.66 277	65.05 90	81.49 100	86.43 160	57.57 214	76.48 209	50.36 258	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	79.59 152	53.48 217	86.29 39	92.43 16	62.39 158	80.25 146	67.90 101	90.61 119	87.77 50

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	81.53 119	81.53 4	92.15 84	88.91 38
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	76.50 200	51.98 231	87.40 27	91.86 26	76.09 36	78.53 173	68.58 91	90.20 124	86.69 66

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	83.47 147
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	70.59 182	84.66 79	62.40 128	78.65 94	84.24 66	60.55 131	77.71 146	81.98 235	63.12 149	77.64 197	62.95 150	88.14 164	71.73 324

jajsoiax		78.51 67	78.16 75	79.59 49	84.65 80	73.83 41	80.42 74	76.12 202	51.33 241	87.19 31	91.51 33	73.79 57	78.44 177	68.27 94	90.13 128	86.49 69

TranMVSNet+NR-MVSNet		76.13 85	77.66 79	71.56 171	84.61 81	42.57 309	70.98 202	78.29 178	68.67 61	83.04 79	89.26 90	72.99 61	80.75 138	55.58 216	95.47 11	91.35 12

旧先验1					84.55 82	60.36 155		63.69 310			87.05 137	54.65 235			83.34 243	69.66 345

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 68	92.95 71	81.14 203
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		78.62 172		76.95 155			80.34 144

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	56.66 170	80.27 116	85.31 182	68.56 95	87.03 12	67.39 107	91.26 96	83.50 143

CDPH-MVS		77.33 77	77.06 85	78.14 71	84.21 87	63.98 119	76.07 131	83.45 78	54.20 205	77.68 147	87.18 132	69.98 85	85.37 53	68.01 98	92.72 76	85.08 94

plane_prior6					84.18 88	65.31 107						60.83 179

114514_t		73.40 121	73.33 132	73.64 126	84.15 89	57.11 180	78.20 102	80.02 144	43.76 314	72.55 235	86.07 173	64.00 145	83.35 91	60.14 174	91.03 106	80.45 224

ZD-MVS					83.91 90	69.36 73		81.09 121	58.91 146	82.73 87	89.11 97	75.77 38	86.63 14	72.73 66	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	56.73 169	74.88 196	85.32 181	65.54 131	87.79 3	65.61 124	91.14 101	83.35 153
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	71.61 162	77.48 77	83.89 92	72.89 48	70.47 209	71.12 256	54.28 201	77.89 141	83.41 210	49.04 268	80.98 132	63.62 143	90.77 117	78.58 250

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	80.97 133	77.70 32	92.32 82	80.62 221
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	72.93 145	83.50 94	45.79 279	69.47 221	80.14 143	65.22 86	81.74 97	87.08 134	61.82 164	81.07 129	56.21 207	94.98 24	91.93 9

NR-MVSNet		73.62 116	74.05 116	72.33 163	83.50 94	43.71 295	65.65 279	77.32 191	64.32 97	75.59 184	87.08 134	62.45 157	81.34 121	54.90 220	95.63 9	91.93 9

test_0402		78.17 72	79.48 63	74.24 117	83.50 94	59.15 164	72.52 172	74.60 217	75.34 19	88.69 17	91.81 27	75.06 45	82.37 106	65.10 125	88.68 158	81.20 201

OPU-MVS				78.65 64	83.44 97	66.85 93	83.62 46				86.12 170	66.82 115	86.01 34	61.72 157	89.79 136	83.08 161

NP-MVS					83.34 98	63.07 126					85.97 174

DVP-MVS++		81.24 39	82.74 41	76.76 84	83.14 99	60.90 148	91.64 1	85.49 32	74.03 25	84.93 59	90.38 67	66.82 115	85.90 40	77.43 34	90.78 115	83.49 144

MSC_two_6792asdad				79.02 57	83.14 99	67.03 91		80.75 126					86.24 24	77.27 37	94.85 29	83.78 136

No_MVS				79.02 57	83.14 99	67.03 91		80.75 126					86.24 24	77.27 37	94.85 29	83.78 136

UniMVSNet (Re)		75.00 102	75.48 99	73.56 129	83.14 99	47.92 255	70.41 211	81.04 123	63.67 104	79.54 121	86.37 161	62.83 152	81.82 115	57.10 199	95.25 15	90.94 16

hse-mvs2		72.32 148	70.66 175	77.31 81	83.10 103	71.77 51	69.19 227	71.45 245	54.28 201	77.89 141	78.26 291	49.04 268	79.23 160	63.62 143	89.13 152	80.92 210

UniMVSNet_NR-MVSNet		74.90 105	75.65 96	72.64 156	83.04 104	45.79 279	69.26 225	78.81 164	66.66 71	81.74 97	86.88 141	63.26 148	81.07 129	56.21 207	94.98 24	91.05 14

HyFIR lowres test		63.01 267	60.47 288	70.61 181	83.04 104	54.10 202	59.93 330	72.24 238	33.67 386	69.00 282	75.63 313	38.69 329	76.93 203	36.60 361	75.45 328	80.81 215

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	66.91 115	95.46 12	87.89 49
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		70.22 173	67.88 213	77.22 82	82.96 107	71.61 52	69.08 228	71.39 246	49.17 266	71.70 246	78.07 296	37.62 337	79.21 161	61.81 154	89.15 150	80.82 213

DP-MVS Recon		73.57 118	72.69 144	76.23 93	82.85 108	63.39 122	74.32 155	82.96 85	57.75 154	70.35 265	81.98 235	64.34 144	84.41 76	49.69 262	89.95 131	80.89 211

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 76

PVSNet_Blended_VisFu		70.04 175	68.88 193	73.53 130	82.71 110	63.62 121	74.81 144	81.95 103	48.53 272	67.16 308	79.18 280	51.42 254	78.38 180	54.39 229	79.72 291	78.60 249

DPM-MVS		69.98 177	69.22 189	72.26 164	82.69 111	58.82 168	70.53 208	81.23 117	47.79 281	64.16 326	80.21 259	51.32 255	83.12 94	60.14 174	84.95 222	74.83 291

EG-PatchMatch MVS		70.70 168	70.88 171	70.16 192	82.64 112	58.80 169	71.48 192	73.64 222	54.98 186	76.55 170	81.77 238	61.10 176	78.94 166	54.87 221	80.84 270	72.74 314

HQP-NCC					82.37 113		77.32 111		59.08 140	71.58 248

ACMP_Plane					82.37 113		77.32 111		59.08 140	71.58 248

HQP-MVS		75.24 97	75.01 102	75.94 96	82.37 113	58.80 169	77.32 111	84.12 69	59.08 140	71.58 248	85.96 175	58.09 206	85.30 55	67.38 109	89.16 148	83.73 139

test12				76.51 88	82.28 116	60.94 147		81.64 108		73.60 221		64.88 139	85.19 62		90.42 122	83.38 151

TAMVS		65.31 240	63.75 259	69.97 197	82.23 117	59.76 159	66.78 266	63.37 313	45.20 303	69.79 275	79.37 276	47.42 280	72.17 259	34.48 374	85.15 217	77.99 261

test_prior				75.27 106	82.15 118	59.85 158		84.33 63					83.39 90			82.58 179

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 92	76.01 41	93.77 61	84.81 104

AdaColmap		74.22 110	74.56 106	73.20 134	81.95 120	60.97 146	79.43 86	80.90 125	65.57 78	72.54 236	81.76 239	70.98 78	85.26 57	47.88 284	90.00 129	73.37 305

PAPM_NR		73.91 112	74.16 113	73.16 135	81.90 121	53.50 207	81.28 66	81.40 112	66.17 74	73.30 227	83.31 216	59.96 186	83.10 95	58.45 189	81.66 262	82.87 168

DP-MVS		78.44 70	79.29 64	75.90 97	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 89	62.45 153	92.40 79	78.92 247

F-COLMAP		75.29 95	73.99 117	79.18 54	81.73 123	71.90 50	81.86 63	82.98 84	59.86 137	72.27 239	84.00 202	64.56 142	83.07 96	51.48 247	87.19 188	82.56 180

SixPastTwentyTwo		75.77 87	76.34 89	74.06 120	81.69 124	54.84 196	76.47 120	75.49 209	64.10 99	87.73 21	92.24 18	50.45 259	81.30 123	67.41 105	91.46 93	86.04 74

Vis-MVSNet		74.85 108	74.56 106	75.72 99	81.63 125	64.64 113	76.35 125	79.06 160	62.85 115	73.33 226	88.41 115	62.54 156	79.59 157	63.94 140	82.92 245	82.94 165
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	75.72 207	52.27 226	87.37 30	92.25 17	68.04 102	80.56 139	72.28 72	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	59.80 190	87.58 6	73.06 63	91.34 95	89.01 34

tt0805		76.12 86	78.43 72	69.20 209	81.32 128	41.37 315	76.72 119	77.64 187	63.78 103	82.06 91	87.88 126	79.78 11	79.05 163	64.33 133	92.40 79	87.17 61

MCST-MVS		73.42 120	73.34 131	73.63 127	81.28 129	59.17 163	74.80 146	83.13 83	45.50 297	72.84 231	83.78 207	65.15 137	80.99 131	64.54 130	89.09 154	80.73 217

MIMVSNet1		66.57 229	69.23 188	58.59 321	81.26 130	37.73 351	64.06 299	57.62 333	57.02 163	78.40 136	90.75 49	62.65 153	58.10 358	41.77 325	89.58 140	79.95 231

ACMH+	66.64 10	81.20 40	82.48 43	77.35 80	81.16 131	62.39 129	80.51 72	87.80 8	73.02 30	87.57 24	91.08 40	80.28 9	82.44 104	64.82 129	96.10 5	87.21 58

MVSMamba_PlusPlus		76.88 80	78.21 74	72.88 148	80.83 132	48.71 243	83.28 52	82.79 87	72.78 31	79.17 126	91.94 22	56.47 226	83.95 78	70.51 81	86.15 201	85.99 75

MVS_111021_HR		72.98 135	72.97 141	72.99 140	80.82 133	65.47 104	68.81 232	72.77 231	57.67 156	75.76 182	82.38 230	71.01 77	77.17 200	61.38 159	86.15 201	76.32 279

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	81.75 117	73.75 59	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	67.95 100	92.44 78	89.60 24

OurMVSNet-221017-0		78.57 66	78.53 71	78.67 63	80.48 136	64.16 116	80.24 79	82.06 100	61.89 121	88.77 16	93.32 5	57.15 217	82.60 103	70.08 83	92.80 73	89.25 28

CDS-MVSNet		64.33 255	62.66 272	69.35 206	80.44 137	58.28 174	65.26 284	65.66 293	44.36 309	67.30 307	75.54 314	43.27 299	71.77 265	37.68 351	84.44 230	78.01 260
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	62.01 16	71.79 155	70.28 178	76.33 91	80.31 138	68.63 79	78.18 103	81.24 116	54.57 196	67.09 309	80.63 253	59.44 191	81.74 118	46.91 291	84.17 232	78.63 248
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	78.74 168	71.22 45	72.40 238	88.70 107	60.51 181	87.70 4	77.40 36	89.13 152	85.48 87

CHOSEN 1792x2688		58.09 308	56.30 319	63.45 275	79.95 140	50.93 221	54.07 371	65.59 294	28.56 402	61.53 345	74.33 327	41.09 313	66.52 319	33.91 377	67.69 386	72.92 310

K. test v3		73.67 115	73.61 125	73.87 123	79.78 141	55.62 193	74.69 150	62.04 321	66.16 75	84.76 63	93.23 6	49.47 264	80.97 133	65.66 123	86.67 197	85.02 96

VPNet		65.58 238	67.56 216	59.65 313	79.72 142	30.17 394	60.27 327	62.14 317	54.19 206	71.24 257	86.63 153	58.80 198	67.62 303	44.17 311	90.87 114	81.18 202

ACMH	63.62 14	77.50 76	80.11 58	69.68 200	79.61 143	56.28 184	78.81 93	83.62 76	63.41 110	87.14 33	90.23 74	76.11 35	73.32 244	67.58 102	94.44 42	79.44 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 152	79.60 144	56.83 183		57.37 336		83.80 74	89.01 101	47.45 279	78.74 170	64.39 132	86.49 200	82.69 176

MVS_111021_LR		72.10 151	71.82 157	72.95 142	79.53 145	73.90 40	70.45 210	66.64 286	56.87 164	76.81 161	81.76 239	68.78 93	71.76 266	61.81 154	83.74 237	73.18 307

Test_1112_low_res		58.78 304	58.69 300	59.04 319	79.41 146	38.13 347	57.62 346	66.98 285	34.74 379	59.62 361	77.56 300	42.92 302	63.65 335	38.66 343	70.73 367	75.35 288

CSCG		74.12 111	74.39 108	73.33 132	79.35 147	61.66 136	77.45 110	81.98 102	62.47 119	79.06 128	80.19 261	61.83 163	78.79 169	59.83 178	87.35 179	79.54 239

MVP-Stereo		61.56 282	59.22 295	68.58 226	79.28 148	60.44 154	69.20 226	71.57 241	43.58 317	56.42 377	78.37 290	39.57 324	76.46 210	34.86 373	60.16 403	68.86 353
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 171	71.34 167	67.85 234	79.26 149	40.42 328	74.67 151	75.15 213	58.41 148	68.74 293	88.14 124	56.08 229	83.69 82	59.90 177	81.71 261	79.43 241

IS-MVSNet		75.10 99	75.42 100	74.15 119	79.23 150	48.05 253	79.43 86	78.04 182	70.09 54	79.17 126	88.02 125	53.04 244	83.60 83	58.05 192	93.76 62	90.79 18

FC-MVSNet-test		73.32 123	74.78 104	68.93 219	79.21 151	36.57 356	71.82 189	79.54 154	57.63 159	82.57 88	90.38 67	59.38 193	78.99 165	57.91 193	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	78.43 178	55.60 213	90.90 111	85.81 78

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	78.43 178	55.60 213	90.90 111	85.81 78

xiu_mvs_v1_base_debu		67.87 211	67.07 224	70.26 188	79.13 154	61.90 133	67.34 254	71.25 251	47.98 277	67.70 301	74.19 331	61.31 169	72.62 251	56.51 202	78.26 305	76.27 280

xiu_mvs_v1_base		67.87 211	67.07 224	70.26 188	79.13 154	61.90 133	67.34 254	71.25 251	47.98 277	67.70 301	74.19 331	61.31 169	72.62 251	56.51 202	78.26 305	76.27 280

xiu_mvs_v1_base_debi		67.87 211	67.07 224	70.26 188	79.13 154	61.90 133	67.34 254	71.25 251	47.98 277	67.70 301	74.19 331	61.31 169	72.62 251	56.51 202	78.26 305	76.27 280

VDD-MVS		70.81 167	71.44 166	68.91 220	79.07 157	46.51 273	67.82 248	70.83 260	61.23 124	74.07 214	88.69 108	59.86 188	75.62 216	51.11 251	90.28 123	84.61 110

test1111		64.62 248	65.19 244	62.93 283	79.01 158	29.91 395	65.45 282	54.41 357	54.09 208	71.47 255	88.48 114	37.02 339	74.29 236	46.83 293	89.94 132	84.58 113

TSAR-MVS + GP.		73.08 128	71.60 163	77.54 76	78.99 159	70.73 61	74.96 141	69.38 271	60.73 130	74.39 207	78.44 289	57.72 213	82.78 100	60.16 172	89.60 138	79.11 244

test2506		61.23 284	60.85 285	62.38 288	78.80 160	27.88 403	67.33 257	37.42 419	54.23 203	67.55 304	88.68 109	17.87 422	74.39 234	46.33 297	89.41 144	84.86 100

ECVR-MVS		64.82 245	65.22 243	63.60 272	78.80 160	31.14 389	66.97 262	56.47 347	54.23 203	69.94 273	88.68 109	37.23 338	74.81 229	45.28 307	89.41 144	84.86 100

FIs		72.56 144	73.80 120	68.84 222	78.74 162	37.74 350	71.02 201	79.83 147	56.12 174	80.88 111	89.45 87	58.18 202	78.28 184	56.63 201	93.36 67	90.51 20

v7n		79.37 60	80.41 56	76.28 92	78.67 163	55.81 189	79.22 90	82.51 95	70.72 49	87.54 25	92.44 15	68.00 103	81.34 121	72.84 65	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	66.15 117	91.24 97	87.61 53

CNLPA		73.44 119	73.03 139	74.66 109	78.27 165	75.29 30	75.99 132	78.49 173	65.39 82	75.67 183	83.22 222	61.23 172	66.77 317	53.70 236	85.33 213	81.92 193

EPP-MVSNet		73.86 114	73.38 128	75.31 105	78.19 166	53.35 209	80.45 73	77.32 191	65.11 89	76.47 175	86.80 142	49.47 264	83.77 81	53.89 234	92.72 76	88.81 41

PCF-MVS	63.80 13	72.70 142	71.69 158	75.72 99	78.10 167	60.01 157	73.04 169	81.50 109	45.34 302	79.66 120	84.35 197	65.15 137	82.65 102	48.70 273	89.38 147	84.50 119
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		73.14 126	73.77 122	71.26 176	78.09 168	52.64 212	74.32 155	79.56 153	56.32 173	76.35 178	83.36 215	70.76 79	77.96 191	63.32 147	81.84 256	83.18 158

LFMVS		67.06 224	67.89 212	64.56 263	78.02 169	38.25 345	70.81 206	59.60 328	65.18 87	71.06 259	86.56 156	43.85 296	75.22 221	46.35 296	89.63 137	80.21 229

anonymousdsp		78.60 65	77.80 77	81.00 35	78.01 170	74.34 37	80.09 81	76.12 202	50.51 250	89.19 11	90.88 45	71.45 72	77.78 195	73.38 61	90.60 120	90.90 17

BH-untuned		69.39 187	69.46 183	69.18 210	77.96 171	56.88 181	68.47 242	77.53 188	56.77 167	77.79 144	79.63 270	60.30 184	80.20 149	46.04 299	80.65 274	70.47 337

1112_ss		59.48 298	58.99 298	60.96 304	77.84 172	42.39 310	61.42 317	68.45 279	37.96 360	59.93 358	67.46 383	45.11 289	65.07 328	40.89 331	71.81 359	75.41 286

PS-MVSNAJ		64.27 256	63.73 260	65.90 256	77.82 173	51.42 217	63.33 306	72.33 236	45.09 305	61.60 344	68.04 380	62.39 158	73.95 240	49.07 269	73.87 344	72.34 317

ambc				70.10 194	77.74 174	50.21 228	74.28 158	77.93 185		79.26 124	88.29 119	54.11 240	79.77 153	64.43 131	91.10 104	80.30 227

xiu_mvs_v2_base		64.43 253	63.96 257	65.85 257	77.72 175	51.32 218	63.63 303	72.31 237	45.06 306	61.70 343	69.66 366	62.56 154	73.93 241	49.06 270	73.91 343	72.31 318

Anonymous20231211		75.54 92	77.19 83	70.59 182	77.67 176	45.70 282	74.73 148	80.19 141	68.80 58	82.95 82	92.91 9	66.26 124	76.76 207	58.41 190	92.77 74	89.30 27

FMVSNet1		71.06 162	72.48 148	66.81 246	77.65 177	40.68 324	71.96 183	73.03 226	61.14 125	79.45 123	90.36 70	60.44 182	75.20 223	50.20 259	88.05 166	84.54 114

FPMVS		59.43 299	60.07 290	57.51 327	77.62 178	71.52 53	62.33 313	50.92 375	57.40 160	69.40 279	80.00 265	39.14 327	61.92 342	37.47 354	66.36 388	39.09 418

balanced_conf03		73.59 117	74.06 115	72.17 166	77.48 179	47.72 260	81.43 65	82.20 98	54.38 198	79.19 125	87.68 128	54.41 237	83.57 84	63.98 137	85.78 207	85.22 89

testing3		58.28 307	58.38 304	58.00 325	77.45 180	26.12 412	60.78 323	43.00 405	56.02 175	70.18 268	75.76 311	13.27 430	67.24 309	48.02 282	80.89 268	80.65 220

Effi-MVS+-dtu		75.43 94	72.28 152	84.91 3	77.05 181	83.58 2	78.47 97	77.70 186	57.68 155	74.89 195	78.13 295	64.80 140	84.26 77	56.46 205	85.32 214	86.88 63

CLD-MVS		72.88 139	72.36 151	74.43 114	77.03 182	54.30 200	68.77 235	83.43 79	52.12 228	76.79 162	74.44 326	69.54 90	83.91 79	55.88 210	93.25 69	85.09 93
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 92	60.39 132	74.15 211	83.30 217	69.65 89	82.07 112	69.27 88	86.75 196	87.36 56

SPE-MVS-test		74.89 106	74.23 112	76.86 83	77.01 184	62.94 127	78.98 92	84.61 59	58.62 147	70.17 269	80.80 250	66.74 119	81.96 113	61.74 156	89.40 146	85.69 84

Baseline_NR-MVSNet		70.62 169	73.19 133	62.92 284	76.97 185	34.44 372	68.84 230	70.88 259	60.25 133	79.50 122	90.53 56	61.82 164	69.11 290	54.67 224	95.27 14	85.22 89

ITE_SJBPF				80.35 42	76.94 186	73.60 42		80.48 135	66.87 68	83.64 76	86.18 166	70.25 83	79.90 152	61.12 163	88.95 156	87.56 54

SSC-MVS		61.79 280	66.08 234	48.89 373	76.91 187	10.00 430	53.56 373	47.37 391	68.20 63	76.56 169	89.21 92	54.13 239	57.59 359	54.75 222	74.07 342	79.08 245

jason		64.47 252	62.84 270	69.34 207	76.91 187	59.20 160	67.15 259	65.67 292	35.29 375	65.16 319	76.74 307	44.67 291	70.68 275	54.74 223	79.28 294	78.14 257
jason: jason.
ETV-MVS		72.72 141	72.16 154	74.38 116	76.90 189	55.95 186	73.34 166	84.67 55	62.04 120	72.19 242	70.81 353	65.90 128	85.24 59	58.64 187	84.96 221	81.95 192

Anonymous20240529		72.56 144	73.79 121	68.86 221	76.89 190	45.21 285	68.80 234	77.25 193	67.16 66	76.89 157	90.44 59	65.95 127	74.19 237	50.75 254	90.00 129	87.18 60

EC-MVSNet		77.08 79	77.39 81	76.14 95	76.86 191	56.87 182	80.32 78	87.52 12	63.45 108	74.66 201	84.52 194	69.87 87	84.94 64	69.76 85	89.59 139	86.60 67

PM-MVS		64.49 251	63.61 261	67.14 244	76.68 192	75.15 31	68.49 241	42.85 406	51.17 244	77.85 143	80.51 254	45.76 283	66.31 320	52.83 242	76.35 319	59.96 395

mvsmamba		68.87 195	67.30 222	73.57 128	76.58 193	53.70 206	84.43 37	74.25 219	45.38 301	76.63 165	84.55 193	35.85 344	85.27 56	49.54 265	78.49 302	81.75 196

TransMVSNet (Re)		69.62 182	71.63 161	63.57 273	76.51 194	35.93 362	65.75 278	71.29 250	61.05 126	75.02 193	89.90 81	65.88 129	70.41 281	49.79 261	89.48 142	84.38 122

GDP-MVS		70.84 166	69.24 187	75.62 101	76.44 195	55.65 191	74.62 153	82.78 89	49.63 260	72.10 243	83.79 206	31.86 366	82.84 99	64.93 128	87.01 191	88.39 47

BH-RMVSNet		68.69 201	68.20 208	70.14 193	76.40 196	53.90 205	64.62 293	73.48 223	58.01 151	73.91 218	81.78 237	59.09 195	78.22 185	48.59 274	77.96 309	78.31 253

PHI-MVS		74.92 103	74.36 110	76.61 86	76.40 196	62.32 130	80.38 75	83.15 82	54.16 207	73.23 228	80.75 251	62.19 161	83.86 80	68.02 97	90.92 110	83.65 140

UGNet		70.20 174	69.05 190	73.65 125	76.24 198	63.64 120	75.87 134	72.53 234	61.48 123	60.93 352	86.14 169	52.37 247	77.12 201	50.67 255	85.21 215	80.17 230
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		58.68 305	57.72 309	61.57 294	76.21 199	73.59 43	61.83 314	49.00 385	47.30 285	61.08 348	68.97 371	50.16 260	59.01 351	36.06 368	68.84 379	52.10 405

VPA-MVSNet		68.71 200	70.37 177	63.72 271	76.13 200	38.06 348	64.10 298	71.48 244	56.60 172	74.10 213	88.31 118	64.78 141	69.72 284	47.69 286	90.15 126	83.37 152

WB-MVS		60.04 294	64.19 255	47.59 376	76.09 201	10.22 429	52.44 378	46.74 393	65.17 88	74.07 214	87.48 129	53.48 242	55.28 365	49.36 267	72.84 350	77.28 267

PAPM		61.79 280	60.37 289	66.05 254	76.09 201	41.87 312	69.30 224	76.79 199	40.64 343	53.80 390	79.62 271	44.38 293	82.92 98	29.64 395	73.11 349	73.36 306

BH-w/o		64.81 246	64.29 254	66.36 251	76.08 203	54.71 197	65.61 280	75.23 212	50.10 256	71.05 260	71.86 347	54.33 238	79.02 164	38.20 348	76.14 321	65.36 372

dcpmvs_2		71.02 164	72.65 145	66.16 253	76.06 204	50.49 224	71.97 182	79.36 155	50.34 251	82.81 85	83.63 208	64.38 143	67.27 308	61.54 158	83.71 239	80.71 219

pmmvs6		71.82 154	73.66 123	66.31 252	75.94 205	42.01 311	66.99 261	72.53 234	63.45 108	76.43 176	92.78 11	72.95 62	69.69 285	51.41 249	90.46 121	87.22 57

CANet		73.00 133	71.84 156	76.48 89	75.82 206	61.28 140	74.81 144	80.37 139	63.17 112	62.43 342	80.50 255	61.10 176	85.16 63	64.00 136	84.34 231	83.01 164

pmmvs-eth3d		64.41 254	63.27 266	67.82 236	75.81 207	60.18 156	69.49 220	62.05 320	38.81 355	74.13 212	82.23 232	43.76 297	68.65 294	42.53 318	80.63 276	74.63 293

TR-MVS		64.59 249	63.54 262	67.73 237	75.75 208	50.83 222	63.39 305	70.29 264	49.33 264	71.55 252	74.55 324	50.94 256	78.46 176	40.43 333	75.69 324	73.89 302

MVS_0304		75.45 93	74.66 105	77.83 74	75.58 209	61.53 137	78.29 99	77.18 194	63.15 114	69.97 272	87.20 131	57.54 215	87.05 10	74.05 56	88.96 155	84.89 97

tttt0517		69.46 185	67.79 215	74.46 111	75.34 210	52.72 211	75.05 140	63.27 314	54.69 192	78.87 130	84.37 196	26.63 393	81.15 125	63.95 138	87.93 171	89.51 25

cascas		64.59 249	62.77 271	70.05 195	75.27 211	50.02 230	61.79 315	71.61 240	42.46 325	63.68 333	68.89 374	49.33 266	80.35 143	47.82 285	84.05 234	79.78 234

API-MVS		70.97 165	71.51 165	69.37 204	75.20 212	55.94 187	80.99 67	76.84 197	62.48 118	71.24 257	77.51 301	61.51 168	80.96 136	52.04 243	85.76 208	71.22 330

EIA-MVS		68.59 202	67.16 223	72.90 146	75.18 213	55.64 192	69.39 222	81.29 114	52.44 225	64.53 322	70.69 354	60.33 183	82.30 108	54.27 231	76.31 320	80.75 216

PAPR		69.20 189	68.66 199	70.82 179	75.15 214	47.77 258	75.31 137	81.11 119	49.62 262	66.33 311	79.27 277	61.53 167	82.96 97	48.12 281	81.50 265	81.74 197

MVSFormer		69.93 178	69.03 191	72.63 157	74.93 215	59.19 161	83.98 40	75.72 207	52.27 226	63.53 336	76.74 307	43.19 300	80.56 139	72.28 72	78.67 300	78.14 257

lupinMVS		63.36 262	61.49 279	68.97 217	74.93 215	59.19 161	65.80 277	64.52 305	34.68 381	63.53 336	74.25 329	43.19 300	70.62 276	53.88 235	78.67 300	77.10 272

nrg030		74.87 107	75.99 94	71.52 172	74.90 217	49.88 237	74.10 160	82.58 94	54.55 197	83.50 77	89.21 92	71.51 70	75.74 215	61.24 160	92.34 81	88.94 37

TAPA-MVS	65.27 12	75.16 98	74.29 111	77.77 75	74.86 218	68.08 81	77.89 105	84.04 72	55.15 185	76.19 180	83.39 211	66.91 113	80.11 150	60.04 176	90.14 127	85.13 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RPSCF		75.76 88	74.37 109	79.93 44	74.81 219	77.53 18	77.53 109	79.30 157	59.44 139	78.88 129	89.80 82	71.26 74	73.09 246	57.45 195	80.89 268	89.17 31

EI-MVSNet-Vis-set		72.78 140	71.87 155	75.54 103	74.77 220	59.02 167	72.24 175	71.56 242	63.92 100	78.59 132	71.59 348	66.22 125	78.60 172	67.58 102	80.32 279	89.00 35

v1240		73.06 130	73.14 134	72.84 150	74.74 221	47.27 268	71.88 188	81.11 119	51.80 232	82.28 90	84.21 198	56.22 228	82.34 107	68.82 90	87.17 189	88.91 38

v1921920		72.96 137	72.98 140	72.89 147	74.67 222	47.58 262	71.92 186	80.69 128	51.70 234	81.69 99	83.89 204	56.58 224	82.25 109	68.34 93	87.36 178	88.82 40

EI-MVSNet-UG-set		72.63 143	71.68 159	75.47 104	74.67 222	58.64 172	72.02 180	71.50 243	63.53 106	78.58 134	71.39 352	65.98 126	78.53 173	67.30 112	80.18 282	89.23 29

Fast-Effi-MVS+		68.81 197	68.30 203	70.35 187	74.66 224	48.61 246	66.06 272	78.32 176	50.62 249	71.48 254	75.54 314	68.75 94	79.59 157	50.55 257	78.73 299	82.86 169

v1192		73.40 121	73.42 126	73.32 133	74.65 225	48.67 245	72.21 176	81.73 106	52.76 222	81.85 93	84.56 192	57.12 218	82.24 110	68.58 91	87.33 181	89.06 33

v144192		72.99 134	73.06 138	72.77 151	74.58 226	47.48 263	71.90 187	80.44 137	51.57 235	81.46 101	84.11 201	58.04 210	82.12 111	67.98 99	87.47 176	88.70 43

MAR-MVS		67.72 214	66.16 233	72.40 161	74.45 227	64.99 111	74.87 142	77.50 189	48.67 271	65.78 315	68.58 378	57.01 221	77.79 194	46.68 294	81.92 253	74.42 298
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	74.16 118	74.44 228	48.69 244	75.84 135	82.93 86	59.02 144	85.92 44	89.17 95	58.56 200	82.74 101	70.73 77	89.14 151	91.05 14

sasdasda		72.29 149	73.38 128	69.04 213	74.23 229	47.37 265	73.93 162	83.18 80	54.36 199	76.61 167	81.64 241	72.03 65	75.34 219	57.12 197	87.28 183	84.40 120

canonicalmvs		72.29 149	73.38 128	69.04 213	74.23 229	47.37 265	73.93 162	83.18 80	54.36 199	76.61 167	81.64 241	72.03 65	75.34 219	57.12 197	87.28 183	84.40 120

Anonymous202405211		66.02 235	66.89 228	63.43 276	74.22 231	38.14 346	59.00 335	66.13 289	63.33 111	69.76 276	85.95 176	51.88 249	70.50 278	44.23 310	87.52 174	81.64 198

Effi-MVS+		72.10 151	72.28 152	71.58 170	74.21 232	50.33 226	74.72 149	82.73 90	62.62 116	70.77 261	76.83 306	69.96 86	80.97 133	60.20 170	78.43 303	83.45 149

FE-MVS		68.29 207	66.96 227	72.26 164	74.16 233	54.24 201	77.55 108	73.42 225	57.65 158	72.66 233	84.91 186	32.02 365	81.49 120	48.43 277	81.85 255	81.04 205

v1144		73.29 124	73.39 127	73.01 139	74.12 234	48.11 251	72.01 181	81.08 122	53.83 214	81.77 95	84.68 187	58.07 209	81.91 114	68.10 95	86.86 192	88.99 36

BP-MVS1		71.60 157	70.06 179	76.20 94	74.07 235	55.22 194	74.29 157	73.44 224	57.29 161	73.87 219	84.65 189	32.57 358	83.49 87	72.43 71	87.94 170	89.89 23

FA-MVS(test-final)		71.27 160	71.06 169	71.92 168	73.96 236	52.32 214	76.45 122	76.12 202	59.07 143	74.04 216	86.18 166	52.18 248	79.43 159	59.75 180	81.76 257	84.03 130

EI-MVSNet		69.61 183	69.01 192	71.41 174	73.94 237	49.90 233	71.31 197	71.32 248	58.22 149	75.40 190	70.44 355	58.16 203	75.85 211	62.51 151	79.81 288	88.48 44

CVMVSNet		59.21 300	58.44 303	61.51 295	73.94 237	47.76 259	71.31 197	64.56 304	26.91 408	60.34 354	70.44 355	36.24 343	67.65 302	53.57 237	68.66 380	69.12 351

IterMVS-LS		73.01 132	73.12 136	72.66 155	73.79 239	49.90 233	71.63 191	78.44 174	58.22 149	80.51 113	86.63 153	58.15 204	79.62 155	62.51 151	88.20 163	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UWE-MVS		52.94 341	52.70 344	53.65 345	73.56 240	27.49 404	57.30 349	49.57 382	38.56 357	62.79 340	71.42 351	19.49 417	60.41 345	24.33 415	77.33 314	73.06 308

alignmvs		70.54 170	71.00 170	69.15 211	73.50 241	48.04 254	69.85 218	79.62 149	53.94 213	76.54 171	82.00 233	59.00 196	74.68 230	57.32 196	87.21 187	84.72 105

Fast-Effi-MVS+-dtu		70.00 176	68.74 197	73.77 124	73.47 242	64.53 114	71.36 195	78.14 181	55.81 179	68.84 291	74.71 323	65.36 134	75.75 214	52.00 244	79.00 296	81.03 206

v8		75.07 100	75.64 97	73.35 131	73.42 243	47.46 264	75.20 138	81.45 111	60.05 134	85.64 48	89.26 90	58.08 208	81.80 116	69.71 87	87.97 169	90.79 18

tfpnnormal		66.48 230	67.93 211	62.16 290	73.40 244	36.65 355	63.45 304	64.99 299	55.97 176	72.82 232	87.80 127	57.06 220	69.10 291	48.31 279	87.54 173	80.72 218

IterMVS-SCA-FT		67.68 215	66.07 235	72.49 159	73.34 245	58.20 175	63.80 301	65.55 295	48.10 276	76.91 156	82.64 227	45.20 287	78.84 167	61.20 161	77.89 311	80.44 225

VNet		64.01 259	65.15 247	60.57 307	73.28 246	35.61 365	57.60 347	67.08 284	54.61 194	66.76 310	83.37 213	56.28 227	66.87 313	42.19 321	85.20 216	79.23 243

MGCFI-Net		71.70 156	73.10 137	67.49 238	73.23 247	43.08 303	72.06 179	82.43 96	54.58 195	75.97 181	82.00 233	72.42 63	75.22 221	57.84 194	87.34 180	84.18 127

3Dnovator	65.95 11	71.50 159	71.22 168	72.34 162	73.16 248	63.09 125	78.37 98	78.32 176	57.67 156	72.22 241	84.61 191	54.77 233	78.47 175	60.82 166	81.07 267	75.45 285

GBi-Net		68.30 205	68.79 194	66.81 246	73.14 249	40.68 324	71.96 183	73.03 226	54.81 187	74.72 198	90.36 70	48.63 274	75.20 223	47.12 288	85.37 210	84.54 114

test1		68.30 205	68.79 194	66.81 246	73.14 249	40.68 324	71.96 183	73.03 226	54.81 187	74.72 198	90.36 70	48.63 274	75.20 223	47.12 288	85.37 210	84.54 114

FMVSNet2		67.48 217	68.21 207	65.29 258	73.14 249	38.94 338	68.81 232	71.21 255	54.81 187	76.73 163	86.48 158	48.63 274	74.60 231	47.98 283	86.11 204	82.35 183

thisisatest0530		67.05 225	65.16 245	72.73 154	73.10 252	50.55 223	71.26 199	63.91 309	50.22 254	74.46 206	80.75 251	26.81 392	80.25 146	59.43 182	86.50 199	87.37 55

pm-mvs1		68.40 203	69.85 182	64.04 269	73.10 252	39.94 331	64.61 294	70.50 262	55.52 181	73.97 217	89.33 88	63.91 146	68.38 296	49.68 263	88.02 167	83.81 135

pmmvs4		60.78 288	59.04 297	66.00 255	73.06 254	57.67 177	64.53 295	60.22 326	36.91 368	65.96 312	77.27 302	39.66 323	68.54 295	38.87 341	74.89 332	71.80 323

SDMVSNet		66.36 232	67.85 214	61.88 292	73.04 255	46.14 278	58.54 340	71.36 247	51.42 238	68.93 287	82.72 225	65.62 130	62.22 341	54.41 228	84.67 223	77.28 267

sd_testset		63.55 260	65.38 241	58.07 324	73.04 255	38.83 340	57.41 348	65.44 296	51.42 238	68.93 287	82.72 225	63.76 147	58.11 357	41.05 329	84.67 223	77.28 267

v2v482		72.55 146	72.58 146	72.43 160	72.92 257	46.72 271	71.41 194	79.13 159	55.27 183	81.17 105	85.25 183	55.41 232	81.13 126	67.25 113	85.46 209	89.43 26

casdiffmvs_mvg		75.26 96	76.18 92	72.52 158	72.87 258	49.47 238	72.94 170	84.71 54	59.49 138	80.90 110	88.81 106	70.07 84	79.71 154	67.40 106	88.39 161	88.40 46
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		71.98 153	71.68 159	72.88 148	72.84 259	64.15 117	73.48 164	77.11 195	48.97 269	71.31 256	84.18 199	67.98 104	71.60 270	68.86 89	80.43 278	82.89 166

MIMVSNet		54.39 329	56.12 321	49.20 369	72.57 260	30.91 390	59.98 329	48.43 387	41.66 329	55.94 379	83.86 205	41.19 312	50.42 375	26.05 406	75.38 329	66.27 367

Patchmatch-RL test		59.95 295	59.12 296	62.44 287	72.46 261	54.61 199	59.63 331	47.51 390	41.05 336	74.58 203	74.30 328	31.06 375	65.31 326	51.61 246	79.85 287	67.39 359

CL-MVSNet_self_test		62.44 275	63.40 264	59.55 314	72.34 262	32.38 381	56.39 353	64.84 301	51.21 243	67.46 305	81.01 248	50.75 257	63.51 336	38.47 346	88.12 165	82.75 172

Vis-MVSNet (Re-imp)		62.74 272	63.21 267	61.34 300	72.19 263	31.56 386	67.31 258	53.87 359	53.60 216	69.88 274	83.37 213	40.52 317	70.98 274	41.40 327	86.78 195	81.48 200

thres100view900		61.17 285	61.09 282	61.39 298	72.14 264	35.01 368	65.42 283	56.99 341	55.23 184	70.71 262	79.90 266	32.07 363	72.09 260	35.61 369	81.73 258	77.08 273

ab-mvs		64.11 257	65.13 248	61.05 302	71.99 265	38.03 349	67.59 249	68.79 276	49.08 268	65.32 318	86.26 164	58.02 211	66.85 315	39.33 337	79.79 290	78.27 254

RRT-MVS		70.33 172	70.73 173	69.14 212	71.93 266	45.24 284	75.10 139	75.08 214	60.85 129	78.62 131	87.36 130	49.54 263	78.64 171	60.16 172	77.90 310	83.55 142

thres600view7		61.82 279	61.38 280	63.12 279	71.81 267	34.93 369	64.64 292	56.99 341	54.78 191	70.33 266	79.74 268	32.07 363	72.42 256	38.61 344	83.46 242	82.02 190

QAPM		69.18 190	69.26 186	68.94 218	71.61 268	52.58 213	80.37 76	78.79 167	49.63 260	73.51 222	85.14 184	53.66 241	79.12 162	55.11 218	75.54 326	75.11 290

WB-MVSnew		53.94 335	54.76 332	51.49 357	71.53 269	28.05 401	58.22 343	50.36 378	37.94 361	59.16 362	70.17 360	49.21 267	51.94 372	24.49 413	71.80 360	74.47 297

baseline		73.10 127	73.96 118	70.51 184	71.46 270	46.39 276	72.08 178	84.40 62	55.95 177	76.62 166	86.46 159	67.20 109	78.03 190	64.22 134	87.27 185	87.11 62

fmvsm_s_conf0.5_n_3		72.97 136	74.13 114	69.47 203	71.40 271	58.36 173	73.07 168	80.64 131	56.86 165	75.49 188	84.67 188	67.86 106	72.33 258	75.68 44	81.54 264	77.73 264

casdiffmvs		73.06 130	73.84 119	70.72 180	71.32 272	46.71 272	70.93 203	84.26 65	55.62 180	77.46 149	87.10 133	67.09 111	77.81 193	63.95 138	86.83 194	87.64 52
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		72.36 147	72.49 147	71.96 167	71.29 273	64.06 118	72.79 171	81.82 104	40.23 345	81.25 104	81.04 247	70.62 80	68.69 293	69.74 86	83.60 241	83.14 159

Anonymous20231206		54.13 330	55.82 323	49.04 372	70.89 274	35.96 361	51.73 380	50.87 376	34.86 376	62.49 341	79.22 278	42.52 306	44.29 401	27.95 402	81.88 254	66.88 363

fmvsm_s_conf0.1_n_a		67.37 221	66.36 231	70.37 186	70.86 275	61.17 142	74.00 161	57.18 340	40.77 340	68.83 292	80.88 249	63.11 150	67.61 304	66.94 114	74.72 333	82.33 186

tfpn200view9		60.35 292	59.97 291	61.51 295	70.78 276	35.35 366	63.27 307	57.47 334	53.00 220	68.31 296	77.09 304	32.45 360	72.09 260	35.61 369	81.73 258	77.08 273

thres400		60.77 289	59.97 291	63.15 278	70.78 276	35.35 366	63.27 307	57.47 334	53.00 220	68.31 296	77.09 304	32.45 360	72.09 260	35.61 369	81.73 258	82.02 190

MSDG		67.47 219	67.48 219	67.46 239	70.70 278	54.69 198	66.90 264	78.17 179	60.88 128	70.41 264	74.76 321	61.22 174	73.18 245	47.38 287	76.87 316	74.49 296

testing91		55.74 319	55.29 329	57.08 328	70.63 279	30.85 391	54.94 366	56.31 350	50.34 251	57.08 370	70.10 362	24.50 403	65.86 321	36.98 359	76.75 317	74.53 295

test_yl		65.11 241	65.09 249	65.18 259	70.59 280	40.86 320	63.22 309	72.79 229	57.91 152	68.88 289	79.07 283	42.85 303	74.89 227	45.50 304	84.97 218	79.81 232

DCV-MVSNet		65.11 241	65.09 249	65.18 259	70.59 280	40.86 320	63.22 309	72.79 229	57.91 152	68.88 289	79.07 283	42.85 303	74.89 227	45.50 304	84.97 218	79.81 232

test_fmvsm_n_1920		69.63 181	68.45 200	73.16 135	70.56 282	65.86 102	70.26 212	78.35 175	37.69 362	74.29 209	78.89 285	61.10 176	68.10 299	65.87 122	79.07 295	85.53 86

OpenMVS	62.51 15	68.76 198	68.75 196	68.78 223	70.56 282	53.91 204	78.29 99	77.35 190	48.85 270	70.22 267	83.52 209	52.65 246	76.93 203	55.31 217	81.99 252	75.49 284

DELS-MVS		68.83 196	68.31 202	70.38 185	70.55 284	48.31 247	63.78 302	82.13 99	54.00 210	68.96 284	75.17 319	58.95 197	80.06 151	58.55 188	82.74 247	82.76 171
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		53.37 337	52.50 347	55.98 336	70.51 285	29.68 396	56.20 356	51.85 372	46.19 291	56.76 374	68.94 372	19.18 418	65.39 325	25.87 409	76.98 315	72.87 311

testing11		53.13 339	52.26 349	55.75 337	70.44 286	31.73 385	54.75 367	52.40 370	44.81 307	52.36 395	68.40 379	21.83 411	65.74 324	32.64 383	72.73 351	69.78 343

LCM-MVSNet-Re		69.10 192	71.57 164	61.70 293	70.37 287	34.30 374	61.45 316	79.62 149	56.81 166	89.59 9	88.16 123	68.44 97	72.94 247	42.30 319	87.33 181	77.85 263

UBG		49.18 364	49.35 368	48.66 374	70.36 288	26.56 409	50.53 384	45.61 396	37.43 364	53.37 391	65.97 388	23.03 409	54.20 369	26.29 404	71.54 361	65.20 374

patch_mono-2		62.73 273	64.08 256	58.68 320	70.36 288	55.87 188	60.84 322	64.11 308	41.23 333	64.04 327	78.22 292	60.00 185	48.80 380	54.17 232	83.71 239	71.37 327

ETVMVS		50.32 359	49.87 367	51.68 355	70.30 290	26.66 407	52.33 379	43.93 401	43.54 318	54.91 384	67.95 381	20.01 416	60.17 347	22.47 417	73.40 346	68.22 354

SCA		58.57 306	58.04 307	60.17 310	70.17 291	41.07 318	65.19 285	53.38 365	43.34 322	61.00 351	73.48 335	45.20 287	69.38 288	40.34 334	70.31 370	70.05 340

WBMVS		53.38 336	54.14 336	51.11 359	70.16 292	26.66 407	50.52 385	51.64 374	39.32 349	63.08 339	77.16 303	23.53 406	55.56 363	31.99 384	79.88 286	71.11 333

ET-MVSNet_ETH3D		63.32 263	60.69 287	71.20 177	70.15 293	55.66 190	65.02 288	64.32 306	43.28 323	68.99 283	72.05 346	25.46 399	78.19 188	54.16 233	82.80 246	79.74 235

testing99		55.16 325	54.56 334	56.98 330	70.13 294	30.58 393	54.55 369	54.11 358	49.53 263	56.76 374	70.14 361	22.76 410	65.79 323	36.99 358	76.04 322	74.57 294

APD_test1		75.04 101	75.38 101	74.02 121	69.89 295	70.15 66	76.46 121	79.71 148	65.50 79	82.99 81	88.60 112	66.94 112	72.35 257	59.77 179	88.54 159	79.56 236

PVSNet_BlendedMVS		65.38 239	64.30 253	68.61 225	69.81 296	49.36 239	65.60 281	78.96 161	45.50 297	59.98 355	78.61 287	51.82 250	78.20 186	44.30 308	84.11 233	78.27 254

PVSNet_Blended		62.90 269	61.64 276	66.69 249	69.81 296	49.36 239	61.23 319	78.96 161	42.04 326	59.98 355	68.86 375	51.82 250	78.20 186	44.30 308	77.77 312	72.52 315

OpenMVS_ROB	54.93 17	63.23 265	63.28 265	63.07 280	69.81 296	45.34 283	68.52 240	67.14 283	43.74 315	70.61 263	79.22 278	47.90 278	72.66 250	48.75 272	73.84 345	71.21 331

test_fmvsmconf0.01_n		73.91 112	73.64 124	74.71 108	69.79 299	66.25 97	75.90 133	79.90 146	46.03 293	76.48 174	85.02 185	67.96 105	73.97 239	74.47 53	87.22 186	83.90 133

fmvsm_s_conf0.5_n_a		67.00 226	65.95 238	70.17 191	69.72 300	61.16 143	73.34 166	56.83 343	40.96 337	68.36 295	80.08 264	62.84 151	67.57 305	66.90 116	74.50 337	81.78 195

FMVSNet3		65.00 244	65.16 245	64.52 264	69.47 301	37.56 353	66.63 267	70.38 263	51.55 236	74.72 198	83.27 218	37.89 335	74.44 233	47.12 288	85.37 210	81.57 199

MS-PatchMatch		55.59 321	54.89 331	57.68 326	69.18 302	49.05 242	61.00 321	62.93 315	35.98 372	58.36 365	68.93 373	36.71 341	66.59 318	37.62 353	63.30 395	57.39 401

baseline1		57.82 310	58.36 305	56.19 334	69.17 303	30.76 392	62.94 311	55.21 352	46.04 292	63.83 331	78.47 288	41.20 311	63.68 334	39.44 336	68.99 378	74.13 299

v148		69.38 188	69.39 184	69.36 205	69.14 304	44.56 289	68.83 231	72.70 232	54.79 190	78.59 132	84.12 200	54.69 234	76.74 208	59.40 183	82.20 250	86.79 64

test_fmvsmconf0.1_n		73.26 125	72.82 143	74.56 110	69.10 305	66.18 99	74.65 152	79.34 156	45.58 296	75.54 186	83.91 203	67.19 110	73.88 242	73.26 62	86.86 192	83.63 141

fmvsm_s_conf0.1_n		66.60 228	65.54 239	69.77 199	68.99 306	59.15 164	72.12 177	56.74 345	40.72 342	68.25 298	80.14 263	61.18 175	66.92 311	67.34 111	74.40 338	83.23 157

Syy-MVS		54.13 330	55.45 326	50.18 363	68.77 307	23.59 416	55.02 363	44.55 399	43.80 312	58.05 367	64.07 393	46.22 282	58.83 352	46.16 298	72.36 354	68.12 355

myMVS_eth3d		50.36 358	50.52 363	49.88 364	68.77 307	22.69 418	55.02 363	44.55 399	43.80 312	58.05 367	64.07 393	14.16 428	58.83 352	33.90 378	72.36 354	68.12 355

test_fmvsmconf_n		72.91 138	72.40 150	74.46 111	68.62 309	66.12 100	74.21 159	78.80 166	45.64 295	74.62 202	83.25 219	66.80 118	73.86 243	72.97 64	86.66 198	83.39 150

CANet_DTU		64.04 258	63.83 258	64.66 262	68.39 310	42.97 305	73.45 165	74.50 218	52.05 230	54.78 385	75.44 317	43.99 295	70.42 280	53.49 238	78.41 304	80.59 222

EU-MVSNet		60.82 287	60.80 286	60.86 305	68.37 311	41.16 316	72.27 174	68.27 280	26.96 406	69.08 281	75.71 312	32.09 362	67.44 306	55.59 215	78.90 297	73.97 300

PVSNet	43.83 21	51.56 352	51.17 355	52.73 350	68.34 312	38.27 344	48.22 390	53.56 363	36.41 369	54.29 388	64.94 392	34.60 348	54.20 369	30.34 390	69.87 373	65.71 370

EPNet		69.10 192	67.32 220	74.46 111	68.33 313	61.27 141	77.56 107	63.57 311	60.95 127	56.62 376	82.75 224	51.53 253	81.24 124	54.36 230	90.20 124	80.88 212
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.1_n_2		69.14 191	68.42 201	71.28 175	68.30 314	57.60 178	65.06 287	69.91 266	48.24 273	74.56 204	82.84 223	55.55 231	69.73 283	70.66 79	80.69 273	86.52 68

fmvsm_s_conf0.5_n		66.34 234	65.27 242	69.57 202	68.20 315	59.14 166	71.66 190	56.48 346	40.92 338	67.78 300	79.46 272	61.23 172	66.90 312	67.39 107	74.32 341	82.66 177

IB-MVS	49.67 18	59.69 297	56.96 314	67.90 233	68.19 316	50.30 227	61.42 317	65.18 298	47.57 283	55.83 380	67.15 387	23.77 405	79.60 156	43.56 314	79.97 284	73.79 303
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		60.62 290	59.97 291	62.58 286	68.13 317	47.28 267	68.59 238	73.96 221	32.19 390	59.94 357	68.86 375	50.48 258	77.64 197	41.85 324	75.74 323	62.83 384

eth_miper_zixun_eth		69.42 186	68.73 198	71.50 173	67.99 318	46.42 274	67.58 250	78.81 164	50.72 248	78.13 139	80.34 258	50.15 261	80.34 144	60.18 171	84.65 225	87.74 51

TinyColmap		67.98 210	69.28 185	64.08 267	67.98 319	46.82 270	70.04 213	75.26 211	53.05 219	77.36 150	86.79 143	59.39 192	72.59 254	45.64 302	88.01 168	72.83 312

EPNet_dtu		58.93 303	58.52 301	60.16 311	67.91 320	47.70 261	69.97 215	58.02 332	49.73 259	47.28 409	73.02 340	38.14 331	62.34 339	36.57 362	85.99 205	70.43 338
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 311	57.02 313	59.17 316	67.89 321	34.93 369	58.91 338	57.25 338	50.24 253	64.01 328	71.46 350	32.49 359	71.39 271	31.31 387	79.57 292	71.19 332

fmvsm_s_conf0.5_n_2		68.93 194	68.23 206	71.02 178	67.78 322	57.58 179	64.74 290	69.56 270	48.16 275	74.38 208	82.32 231	56.00 230	69.68 286	70.65 80	80.52 277	85.80 82

our_test_3		56.46 314	56.51 317	56.30 333	67.70 323	39.66 333	55.36 362	52.34 371	40.57 344	63.85 330	69.91 365	40.04 320	58.22 356	43.49 315	75.29 331	71.03 335

ppachtmachnet_test		60.26 293	59.61 294	62.20 289	67.70 323	44.33 291	58.18 344	60.96 324	40.75 341	65.80 314	72.57 342	41.23 310	63.92 333	46.87 292	82.42 249	78.33 252

MVS_Test		69.84 179	70.71 174	67.24 241	67.49 325	43.25 302	69.87 217	81.22 118	52.69 223	71.57 251	86.68 149	62.09 162	74.51 232	66.05 119	78.74 298	83.96 131

fmvsm_l_conf0.5_n		67.48 217	66.88 229	69.28 208	67.41 326	62.04 131	70.69 207	69.85 267	39.46 348	69.59 277	81.09 246	58.15 204	68.73 292	67.51 104	78.16 308	77.07 275

thisisatest0515		60.48 291	57.86 308	68.34 228	67.25 327	46.42 274	60.58 325	62.14 317	40.82 339	63.58 335	69.12 369	26.28 395	78.34 182	48.83 271	82.13 251	80.26 228

V42		71.06 162	70.83 172	71.72 169	67.25 327	47.14 269	65.94 273	80.35 140	51.35 240	83.40 78	83.23 220	59.25 194	78.80 168	65.91 121	80.81 271	89.23 29

fmvsm_l_conf0.5_n_a		66.66 227	65.97 237	68.72 224	67.09 329	61.38 139	70.03 214	69.15 274	38.59 356	68.41 294	80.36 257	56.56 225	68.32 297	66.10 118	77.45 313	76.46 277

GA-MVS		62.91 268	61.66 275	66.66 250	67.09 329	44.49 290	61.18 320	69.36 272	51.33 241	69.33 280	74.47 325	36.83 340	74.94 226	50.60 256	74.72 333	80.57 223

testf1		75.66 90	76.57 86	72.95 142	67.07 331	67.62 85	76.10 129	80.68 129	64.95 91	86.58 36	90.94 43	71.20 75	71.68 268	60.46 168	91.13 102	79.56 236

APD_test2		75.66 90	76.57 86	72.95 142	67.07 331	67.62 85	76.10 129	80.68 129	64.95 91	86.58 36	90.94 43	71.20 75	71.68 268	60.46 168	91.13 102	79.56 236

mmtdpeth		68.76 198	70.55 176	63.40 277	67.06 333	56.26 185	68.73 237	71.22 254	55.47 182	70.09 270	88.64 111	65.29 136	56.89 361	58.94 186	89.50 141	77.04 276

HY-MVS	49.31 19	57.96 309	57.59 310	59.10 318	66.85 334	36.17 359	65.13 286	65.39 297	39.24 352	54.69 387	78.14 294	44.28 294	67.18 310	33.75 379	70.79 366	73.95 301

CR-MVSNet		58.96 301	58.49 302	60.36 309	66.37 335	48.24 249	70.93 203	56.40 348	32.87 389	61.35 346	86.66 150	33.19 353	63.22 337	48.50 276	70.17 371	69.62 346

RPMNet		65.77 237	65.08 251	67.84 235	66.37 335	48.24 249	70.93 203	86.27 20	54.66 193	61.35 346	86.77 145	33.29 352	85.67 49	55.93 209	70.17 371	69.62 346

IterMVS		63.12 266	62.48 273	65.02 261	66.34 337	52.86 210	63.81 300	62.25 316	46.57 289	71.51 253	80.40 256	44.60 292	66.82 316	51.38 250	75.47 327	75.38 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		69.82 180	69.89 181	69.61 201	66.24 338	43.48 298	68.12 245	79.61 151	51.43 237	77.72 145	80.18 262	54.61 236	78.15 189	63.62 143	87.50 175	87.20 59

tpm2		56.12 316	54.64 333	60.55 308	66.24 338	36.01 360	68.14 244	56.77 344	33.60 387	58.25 366	75.52 316	30.25 381	74.33 235	33.27 380	69.76 375	71.32 328

Anonymous20240521		63.55 260	66.07 235	55.99 335	66.18 340	44.04 293	68.77 235	68.80 275	46.99 286	72.57 234	85.84 177	39.87 321	50.22 376	53.40 241	92.23 83	73.71 304

Patchmtry		60.91 286	63.01 269	54.62 342	66.10 341	26.27 411	67.47 252	56.40 348	54.05 209	72.04 244	86.66 150	33.19 353	60.17 347	43.69 312	87.45 177	77.42 265

FMVSNet5		55.08 326	55.54 325	53.71 344	65.80 342	33.50 378	56.22 355	52.50 369	43.72 316	61.06 349	83.38 212	25.46 399	54.87 366	30.11 392	81.64 263	72.75 313

1314		59.83 296	58.86 299	62.74 285	65.71 343	44.78 288	68.59 238	72.63 233	33.54 388	61.05 350	67.29 386	43.62 298	71.26 272	49.49 266	67.84 385	72.19 320

MonoMVSNet		62.75 271	63.42 263	60.73 306	65.60 344	40.77 322	72.49 173	70.56 261	52.49 224	75.07 192	79.42 274	39.52 325	69.97 282	46.59 295	69.06 377	71.44 326

MDTV_nov1_ep13			54.05 338		65.54 345	29.30 398	59.00 335	55.22 351	35.96 373	52.44 393	75.98 310	30.77 378	59.62 349	38.21 347	73.33 348

baseline2		55.57 322	52.74 343	64.05 268	65.26 346	44.11 292	62.38 312	54.43 356	39.03 353	51.21 398	67.35 385	33.66 351	72.45 255	37.14 356	64.22 393	75.60 283

USDC		62.80 270	63.10 268	61.89 291	65.19 347	43.30 301	67.42 253	74.20 220	35.80 374	72.25 240	84.48 195	45.67 284	71.95 264	37.95 350	84.97 218	70.42 339

tpm		50.60 356	52.42 348	45.14 387	65.18 348	26.29 410	60.30 326	43.50 402	37.41 365	57.01 371	79.09 282	30.20 383	42.32 406	32.77 382	66.36 388	66.81 365

PatchmatchNet		54.60 328	54.27 335	55.59 338	65.17 349	39.08 335	66.92 263	51.80 373	39.89 346	58.39 364	73.12 339	31.69 369	58.33 355	43.01 317	58.38 409	69.38 349
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_ehance_all_eth		68.36 204	68.16 209	68.98 216	65.14 350	43.34 300	67.07 260	78.92 163	49.11 267	76.21 179	77.72 298	53.48 242	77.92 192	61.16 162	84.59 227	85.68 85

cl____		68.26 209	68.26 204	68.29 229	64.98 351	43.67 296	65.89 274	74.67 215	50.04 257	76.86 159	82.42 229	48.74 272	75.38 217	60.92 165	89.81 134	85.80 82

DIV-MVS_self_test		68.27 208	68.26 204	68.29 229	64.98 351	43.67 296	65.89 274	74.67 215	50.04 257	76.86 159	82.43 228	48.74 272	75.38 217	60.94 164	89.81 134	85.81 78

tpm cat1		54.02 333	52.63 345	58.19 323	64.85 353	39.86 332	66.26 271	57.28 337	32.16 391	56.90 372	70.39 357	32.75 357	65.30 327	34.29 375	58.79 406	69.41 348

XXY-MVS		55.19 324	57.40 312	48.56 375	64.45 354	34.84 371	51.54 381	53.59 361	38.99 354	63.79 332	79.43 273	56.59 223	45.57 391	36.92 360	71.29 363	65.25 373

PatchT		53.35 338	56.47 318	43.99 392	64.19 355	17.46 423	59.15 332	43.10 404	52.11 229	54.74 386	86.95 139	29.97 384	49.98 377	43.62 313	74.40 338	64.53 381

D2MVS		62.58 274	61.05 283	67.20 242	63.85 356	47.92 255	56.29 354	69.58 269	39.32 349	70.07 271	78.19 293	34.93 347	72.68 249	53.44 239	83.74 237	81.00 208

mvs_anonymous		65.08 243	65.49 240	63.83 270	63.79 357	37.60 352	66.52 269	69.82 268	43.44 319	73.46 224	86.08 172	58.79 199	71.75 267	51.90 245	75.63 325	82.15 188

CostFormer		57.35 312	56.14 320	60.97 303	63.76 358	38.43 342	67.50 251	60.22 326	37.14 367	59.12 363	76.34 309	32.78 356	71.99 263	39.12 340	69.27 376	72.47 316

Gipuma		69.55 184	72.83 142	59.70 312	63.63 359	53.97 203	80.08 82	75.93 205	64.24 98	73.49 223	88.93 104	57.89 212	62.46 338	59.75 180	91.55 92	62.67 386
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
cl22		67.14 222	66.51 230	69.03 215	63.20 360	43.46 299	66.88 265	76.25 201	49.22 265	74.48 205	77.88 297	45.49 286	77.40 199	60.64 167	84.59 227	86.24 70

gg-mvs-nofinetune		55.75 318	56.75 316	52.72 351	62.87 361	28.04 402	68.92 229	41.36 414	71.09 46	50.80 400	92.63 13	20.74 413	66.86 314	29.97 393	72.41 353	63.25 383

gm-plane-assit					62.51 362	33.91 376			37.25 366		62.71 398		72.74 248	38.70 342

mvs5depth		66.35 233	67.98 210	61.47 297	62.43 363	51.05 219	69.38 223	69.24 273	56.74 168	73.62 220	89.06 100	46.96 281	58.63 354	55.87 211	88.49 160	74.73 292

MVS-HIRNet		45.53 373	47.29 373	40.24 398	62.29 364	26.82 406	56.02 358	37.41 420	29.74 401	43.69 419	81.27 243	33.96 349	55.48 364	24.46 414	56.79 410	38.43 419

diffmvs		67.42 220	67.50 218	67.20 242	62.26 365	45.21 285	64.87 289	77.04 196	48.21 274	71.74 245	79.70 269	58.40 201	71.17 273	64.99 126	80.27 280	85.22 89
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		41.62 384	39.89 389	46.80 380	61.81 366	51.59 215	33.56 418	35.74 421	27.48 405	37.64 423	53.53 412	23.24 407	42.09 407	27.39 403	58.64 407	46.72 411

KD-MVS_self_test		66.38 231	67.51 217	62.97 282	61.76 367	34.39 373	58.11 345	75.30 210	50.84 247	77.12 152	85.42 180	56.84 222	69.44 287	51.07 252	91.16 99	85.08 94

MDA-MVSNet-bldmvs		62.34 276	61.73 274	64.16 265	61.64 368	49.90 233	48.11 391	57.24 339	53.31 218	80.95 107	79.39 275	49.00 270	61.55 343	45.92 300	80.05 283	81.03 206

miper_enhance_ethall		65.86 236	65.05 252	68.28 231	61.62 369	42.62 308	64.74 290	77.97 183	42.52 324	73.42 225	72.79 341	49.66 262	77.68 196	58.12 191	84.59 227	84.54 114

WTY-MVS		49.39 363	50.31 365	46.62 381	61.22 370	32.00 384	46.61 396	49.77 380	33.87 384	54.12 389	69.55 368	41.96 307	45.40 393	31.28 388	64.42 392	62.47 388

CMPMVS	48.73 20	61.54 283	60.89 284	63.52 274	61.08 371	51.55 216	68.07 246	68.00 281	33.88 383	65.87 313	81.25 244	37.91 334	67.71 301	49.32 268	82.60 248	71.31 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 357	50.69 362	49.64 367	60.76 372	41.87 312	53.18 374	45.48 397	43.41 320	49.41 405	60.47 406	29.22 387	44.73 398	42.09 322	72.14 357	62.33 390

test-mter		48.56 366	48.20 371	49.64 367	60.76 372	41.87 312	53.18 374	45.48 397	31.91 395	49.41 405	60.47 406	18.34 419	44.73 398	42.09 322	72.14 357	62.33 390

GG-mvs-BLEND				52.24 352	60.64 374	29.21 399	69.73 219	42.41 407		45.47 412	52.33 415	20.43 414	68.16 298	25.52 411	65.42 390	59.36 397

tpmvs		55.84 317	55.45 326	57.01 329	60.33 375	33.20 379	65.89 274	59.29 330	47.52 284	56.04 378	73.60 334	31.05 376	68.06 300	40.64 332	64.64 391	69.77 344

miper_lstm_enhance		61.97 277	61.63 277	62.98 281	60.04 376	45.74 281	47.53 393	70.95 257	44.04 310	73.06 229	78.84 286	39.72 322	60.33 346	55.82 212	84.64 226	82.88 167

dmvs_re		49.91 362	50.77 361	47.34 377	59.98 377	38.86 339	53.18 374	53.58 362	39.75 347	55.06 383	61.58 402	36.42 342	44.40 400	29.15 400	68.23 381	58.75 398

PVSNet_0	36.71 22	41.12 385	40.78 388	42.14 394	59.97 378	40.13 329	40.97 407	42.24 411	30.81 399	44.86 415	49.41 418	40.70 316	45.12 395	23.15 416	34.96 421	41.16 417

dmvs_testset		45.26 374	47.51 372	38.49 401	59.96 379	14.71 425	58.50 341	43.39 403	41.30 332	51.79 397	56.48 410	39.44 326	49.91 379	21.42 419	55.35 415	50.85 406

new-patchmatchnet		52.89 342	55.76 324	44.26 391	59.94 380	6.31 431	37.36 415	50.76 377	41.10 334	64.28 325	79.82 267	44.77 290	48.43 384	36.24 365	87.61 172	78.03 259

test20.03		55.74 319	57.51 311	50.42 362	59.89 381	32.09 383	50.63 383	49.01 384	50.11 255	65.07 320	83.23 220	45.61 285	48.11 385	30.22 391	83.82 236	71.07 334

MVSTER		63.29 264	61.60 278	68.36 227	59.77 382	46.21 277	60.62 324	71.32 248	41.83 328	75.40 190	79.12 281	30.25 381	75.85 211	56.30 206	79.81 288	83.03 163

reproduce_monomvs		58.94 302	58.14 306	61.35 299	59.70 383	40.98 319	60.24 328	63.51 312	45.85 294	68.95 285	75.31 318	18.27 420	65.82 322	51.47 248	79.97 284	77.26 270

N_pmnet		52.06 348	51.11 356	54.92 339	59.64 384	71.03 57	37.42 414	61.62 323	33.68 385	57.12 369	72.10 343	37.94 333	31.03 420	29.13 401	71.35 362	62.70 385

test_vis1_n_1920		52.96 340	53.50 339	51.32 358	59.15 385	44.90 287	56.13 357	64.29 307	30.56 400	59.87 359	60.68 404	40.16 319	47.47 386	48.25 280	62.46 397	61.58 392

JIA-IIPM		54.03 332	51.62 351	61.25 301	59.14 386	55.21 195	59.10 334	47.72 388	50.85 246	50.31 404	85.81 178	20.10 415	63.97 332	36.16 366	55.41 414	64.55 380

LF4IMVS		67.50 216	67.31 221	68.08 232	58.86 387	61.93 132	71.43 193	75.90 206	44.67 308	72.42 237	80.20 260	57.16 216	70.44 279	58.99 185	86.12 203	71.88 322

UnsupCasMVSNet_bld		50.01 361	51.03 358	46.95 378	58.61 388	32.64 380	48.31 389	53.27 366	34.27 382	60.47 353	71.53 349	41.40 309	47.07 388	30.68 389	60.78 402	61.13 393

dongtai		31.66 390	32.98 393	27.71 405	58.58 389	12.61 427	45.02 400	14.24 431	41.90 327	47.93 407	43.91 420	10.65 431	41.81 410	14.06 423	20.53 424	28.72 421

dp		44.09 380	44.88 382	41.72 397	58.53 390	23.18 417	54.70 368	42.38 409	34.80 378	44.25 417	65.61 390	24.48 404	44.80 397	29.77 394	49.42 417	57.18 402

testgi		54.00 334	56.86 315	45.45 385	58.20 391	25.81 413	49.05 387	49.50 383	45.43 300	67.84 299	81.17 245	51.81 252	43.20 405	29.30 396	79.41 293	67.34 361

wuyk23d		61.97 277	66.25 232	49.12 371	58.19 392	60.77 152	66.32 270	52.97 367	55.93 178	90.62 6	86.91 140	73.07 60	35.98 418	20.63 421	91.63 89	50.62 407

ANet_high		67.08 223	69.94 180	58.51 322	57.55 393	27.09 405	58.43 342	76.80 198	63.56 105	82.40 89	91.93 23	59.82 189	64.98 329	50.10 260	88.86 157	83.46 148

Patchmatch-test		47.93 367	49.96 366	41.84 395	57.42 394	24.26 415	48.75 388	41.49 413	39.30 351	56.79 373	73.48 335	30.48 380	33.87 419	29.29 397	72.61 352	67.39 359

test_vis1_n		51.27 354	50.41 364	53.83 343	56.99 395	50.01 231	56.75 351	60.53 325	25.68 411	59.74 360	57.86 409	29.40 386	47.41 387	43.10 316	63.66 394	64.08 382

new_pmnet		37.55 388	39.80 390	30.79 403	56.83 396	16.46 424	39.35 411	30.65 423	25.59 412	45.26 413	61.60 401	24.54 402	28.02 423	21.60 418	52.80 416	47.90 410

pmmvs3		46.71 370	45.09 380	51.55 356	56.76 397	48.25 248	55.78 360	39.53 418	24.13 416	50.35 403	63.40 395	15.90 425	51.08 374	29.29 397	70.69 368	55.33 404

sss		47.59 369	48.32 369	45.40 386	56.73 398	33.96 375	45.17 399	48.51 386	32.11 394	52.37 394	65.79 389	40.39 318	41.91 409	31.85 385	61.97 399	60.35 394

tpmrst		50.15 360	51.38 354	46.45 382	56.05 399	24.77 414	64.40 297	49.98 379	36.14 371	53.32 392	69.59 367	35.16 346	48.69 381	39.24 338	58.51 408	65.89 368

TESTMET0.1,1		45.17 375	44.93 381	45.89 384	56.02 400	38.31 343	53.18 374	41.94 412	27.85 403	44.86 415	56.47 411	17.93 421	41.50 411	38.08 349	68.06 382	57.85 399

ADS-MVSNet2		48.76 365	47.25 374	53.29 349	55.90 401	40.54 327	47.34 394	54.99 354	31.41 397	50.48 401	72.06 344	31.23 372	54.26 368	25.93 407	55.93 411	65.07 375

ADS-MVSNet		44.62 378	45.58 377	41.73 396	55.90 401	20.83 421	47.34 394	39.94 417	31.41 397	50.48 401	72.06 344	31.23 372	39.31 414	25.93 407	55.93 411	65.07 375

ttmdpeth		56.40 315	55.45 326	59.25 315	55.63 403	40.69 323	58.94 337	49.72 381	36.22 370	65.39 316	86.97 138	23.16 408	56.69 362	42.30 319	80.74 272	80.36 226

test0.0.03 1		47.72 368	48.31 370	45.93 383	55.53 404	29.39 397	46.40 397	41.21 415	43.41 320	55.81 381	67.65 382	29.22 387	43.77 404	25.73 410	69.87 373	64.62 379

UnsupCasMVSNet_eth		52.26 347	53.29 342	49.16 370	55.08 405	33.67 377	50.03 386	58.79 331	37.67 363	63.43 338	74.75 322	41.82 308	45.83 390	38.59 345	59.42 405	67.98 358

pmmvs5		52.49 346	52.58 346	52.21 353	54.99 406	32.38 381	55.45 361	53.84 360	32.15 392	55.49 382	74.81 320	38.08 332	57.37 360	34.02 376	74.40 338	66.88 363

DSMNet-mixed		43.18 383	44.66 383	38.75 400	54.75 407	28.88 400	57.06 350	27.42 425	13.47 423	47.27 410	77.67 299	38.83 328	39.29 415	25.32 412	60.12 404	48.08 409

MDA-MVSNet_test_wron		52.57 345	53.49 341	49.81 366	54.24 408	36.47 357	40.48 409	46.58 394	38.13 358	75.47 189	73.32 337	41.05 315	43.85 403	40.98 330	71.20 364	69.10 352

YYNet1		52.58 344	53.50 339	49.85 365	54.15 409	36.45 358	40.53 408	46.55 395	38.09 359	75.52 187	73.31 338	41.08 314	43.88 402	41.10 328	71.14 365	69.21 350

EPMVS		45.74 372	46.53 375	43.39 393	54.14 410	22.33 420	55.02 363	35.00 422	34.69 380	51.09 399	70.20 359	25.92 397	42.04 408	37.19 355	55.50 413	65.78 369

test_cas_vis1_n_1920		50.90 355	50.92 359	50.83 361	54.12 411	47.80 257	51.44 382	54.61 355	26.95 407	63.95 329	60.85 403	37.86 336	44.97 396	45.53 303	62.97 396	59.72 396

test_fmvs3		56.78 313	55.99 322	59.12 317	53.96 412	48.09 252	58.76 339	66.22 288	27.54 404	76.66 164	68.69 377	25.32 401	51.31 373	53.42 240	73.38 347	77.97 262

test_fmvs1_n		52.70 343	52.01 350	54.76 340	53.83 413	50.36 225	55.80 359	65.90 290	24.96 413	65.39 316	60.64 405	27.69 390	48.46 382	45.88 301	67.99 383	65.46 371

KD-MVS_2432*1600		52.05 349	51.58 352	53.44 347	52.11 414	31.20 387	44.88 401	64.83 302	41.53 330	64.37 323	70.03 363	15.61 426	64.20 330	36.25 363	74.61 335	64.93 377

miper_refine_blended		52.05 349	51.58 352	53.44 347	52.11 414	31.20 387	44.88 401	64.83 302	41.53 330	64.37 323	70.03 363	15.61 426	64.20 330	36.25 363	74.61 335	64.93 377

test_fmvs2		54.80 327	54.11 337	56.88 331	51.76 416	49.95 232	56.70 352	65.80 291	26.22 409	69.42 278	65.25 391	31.82 367	49.98 377	49.63 264	70.36 369	70.71 336

E-PMN		45.17 375	45.36 378	44.60 389	50.07 417	42.75 306	38.66 412	42.29 410	46.39 290	39.55 420	51.15 416	26.00 396	45.37 394	37.68 351	76.41 318	45.69 413

PMMVS		44.69 377	43.95 385	46.92 379	50.05 418	53.47 208	48.08 392	42.40 408	22.36 419	44.01 418	53.05 414	42.60 305	45.49 392	31.69 386	61.36 401	41.79 416

test_fmvs1		51.51 353	50.86 360	53.48 346	49.72 419	49.35 241	54.11 370	64.96 300	24.64 415	63.66 334	59.61 408	28.33 389	48.45 383	45.38 306	67.30 387	62.66 387

EMVS		44.61 379	44.45 384	45.10 388	48.91 420	43.00 304	37.92 413	41.10 416	46.75 288	38.00 422	48.43 419	26.42 394	46.27 389	37.11 357	75.38 329	46.03 412

mvsany_test3		43.76 382	41.01 386	52.01 354	48.09 421	57.74 176	42.47 405	23.85 428	23.30 418	64.80 321	62.17 400	27.12 391	40.59 412	29.17 399	48.11 418	57.69 400

mvsany_test1		37.88 386	35.74 391	44.28 390	47.28 422	49.90 233	36.54 416	24.37 427	19.56 422	45.76 411	53.46 413	32.99 355	37.97 417	26.17 405	35.52 420	44.99 415

test_vis3_rt		51.94 351	51.04 357	54.65 341	46.32 423	50.13 229	44.34 403	78.17 179	23.62 417	68.95 285	62.81 397	21.41 412	38.52 416	41.49 326	72.22 356	75.30 289

test_vis1_rt		46.70 371	45.24 379	51.06 360	44.58 424	51.04 220	39.91 410	67.56 282	21.84 421	51.94 396	50.79 417	33.83 350	39.77 413	35.25 372	61.50 400	62.38 389

MVStest1		55.38 323	54.97 330	56.58 332	43.72 425	40.07 330	59.13 333	47.09 392	34.83 377	76.53 172	84.65 189	13.55 429	53.30 371	55.04 219	80.23 281	76.38 278

MVE	27.91 23	36.69 389	35.64 392	39.84 399	43.37 426	35.85 363	19.49 420	24.61 426	24.68 414	39.05 421	62.63 399	38.67 330	27.10 424	21.04 420	47.25 419	56.56 403
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 387	40.87 387	28.36 404	42.41 427	5.35 432	24.61 419	27.75 424	32.15 392	47.85 408	70.27 358	35.85 344	29.51 422	19.08 422	67.85 384	50.22 408

test_f		43.79 381	45.63 376	38.24 402	42.29 428	38.58 341	34.76 417	47.68 389	22.22 420	67.34 306	63.15 396	31.82 367	30.60 421	39.19 339	62.28 398	45.53 414

kuosan		22.02 391	23.52 395	17.54 407	41.56 429	11.24 428	41.99 406	13.39 432	26.13 410	28.87 424	30.75 422	9.72 432	21.94 426	4.77 427	14.49 425	19.43 422

DeepMVS_CX				11.83 408	15.51 430	13.86 426		11.25 433	5.76 424	20.85 426	26.46 423	17.06 424	9.22 427	9.69 426	13.82 426	12.42 423

test_method		19.26 392	19.12 396	19.71 406	9.09 431	1.91 434	7.79 422	53.44 364	1.42 425	10.27 427	35.80 421	17.42 423	25.11 425	12.44 424	24.38 423	32.10 420

tmp_tt		11.98 394	14.73 397	3.72 409	2.28 432	4.62 433	19.44 421	14.50 430	0.47 427	21.55 425	9.58 425	25.78 398	4.57 428	11.61 425	27.37 422	1.96 424

test123		4.43 397	5.78 400	0.39 411	0.97 433	0.28 435	46.33 398	0.45 434	0.31 428	0.62 429	1.50 428	0.61 434	0.11 430	0.56 428	0.63 427	0.77 426

testmvs		4.06 398	5.28 401	0.41 410	0.64 434	0.16 436	42.54 404	0.31 435	0.26 429	0.50 430	1.40 429	0.77 433	0.17 429	0.56 428	0.55 428	0.90 425

mmdepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

monomultidepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

test_blank		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

eth-test2					0.00 435

eth-test					0.00 435

uanet_test		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

DCPMVS		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

cdsmvs_eth3d_5k		17.71 393	23.62 394	0.00 412	0.00 435	0.00 437	0.00 423	70.17 265	0.00 430	0.00 431	74.25 329	68.16 100	0.00 431	0.00 430	0.00 429	0.00 427

pcd_1.5k_mvsjas		5.20 396	6.93 399	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	62.39 158	0.00 431	0.00 430	0.00 429	0.00 427

sosnet-low-res		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uncertanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

Regformer		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

ab-mvs-re		5.62 395	7.50 398	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	67.46 383	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

WAC-MVS						22.69 418								36.10 367

PC_three_1452									46.98 287	81.83 94	86.28 162	66.55 123	84.47 74	63.31 148	90.78 115	83.49 144

test_241102_TWO								84.80 48	72.61 35	84.93 59	89.70 83	77.73 24	85.89 42	75.29 46	94.22 55	83.25 155

test_0728_THIRD									74.03 25	85.83 46	90.41 62	75.58 40	85.69 47	77.43 34	94.74 33	84.31 124

GSMVS																70.05 340

sam_mvs1												31.41 370				70.05 340

sam_mvs												31.21 374

MTGPA								80.63 132

test_post1							66.63 267				2.08 426	30.66 379	59.33 350	40.34 334

test_post											1.99 427	30.91 377	54.76 367

patchmatchnet-post											68.99 370	31.32 371	69.38 288

MTMP							84.83 34	19.26 429

test9_res														72.12 74	91.37 94	77.40 266

agg_prior2														70.70 78	90.93 109	78.55 251

test_prior4						70.14 67	77.57 106

test_prior2							75.57 136		58.92 145	76.53 172	86.78 144	67.83 107		69.81 84	92.76 75

旧先验2							71.17 200		45.11 304	78.54 135			61.28 344	59.19 184

新几何2							71.33 196

无先验							74.82 143	70.94 258	47.75 282				76.85 206	54.47 226		72.09 321

原ACMM2							74.78 147

testdata2													67.30 307	48.34 278

segment_acmp												68.30 99

testdata1							68.34 243		57.24 162

plane_prior5								85.49 32					86.15 29	71.09 75	90.94 107	84.82 102

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								0.00 436

nn								0.00 436

door-mid								55.02 353

test11								82.71 91

door								52.91 368

HQP5-MVS						58.80 169

BP-MVS														67.38 109

HQP4-MVS										71.59 247			85.31 54			83.74 138

HQP3-MVS								84.12 69							89.16 148

HQP2-MVS												58.09 206

MDTV_nov1_ep13_2view						18.41 422	53.74 372		31.57 396	44.89 414		29.90 385		32.93 381		71.48 325

ACMMP++_ref															89.47 143

ACMMP++															91.96 85

Test By Simon												62.56 154

High-resolution multi-view benchmark