High-res multi-view results

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

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

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

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

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

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

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

LCM-MVSNet		86.90 1	88.67 1	81.57 21	91.50 1	63.30 120	84.80 32	87.77 9	86.18 1	96.26 1	96.06 1	90.32 1	84.49 69	68.08 89	97.05 1	96.93 1

MTAPA		83.19 18	83.87 18	81.13 30	91.16 2	78.16 11	84.87 30	80.63 123	72.08 36	84.93 56	90.79 45	74.65 46	84.42 72	80.98 4	94.75 28	80.82 203

mPP-MVS		84.01 10	84.39 11	82.88 6	90.65 3	81.38 3	87.08 12	82.79 82	72.41 34	85.11 55	90.85 44	76.65 28	84.89 63	79.30 16	94.63 33	82.35 175

MP-MVS		83.19 18	83.54 23	82.14 19	90.54 4	79.00 8	86.42 22	83.59 73	71.31 39	81.26 102	90.96 39	74.57 47	84.69 67	78.41 21	94.78 27	82.74 167
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMVS	70.70 6	81.70 32	83.15 31	77.36 77	90.35 5	82.82 2	82.15 54	79.22 150	74.08 20	87.16 28	91.97 19	84.80 2	76.97 196	64.98 121	93.61 60	72.28 300
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 47	82.86 36	73.07 135	89.93 6	39.21 317	77.15 111	81.28 107	79.74 5	90.87 4	92.73 11	75.03 43	84.93 62	63.83 133	95.19 15	95.07 3

DTE-MVSNet		80.35 48	82.89 35	72.74 148	89.84 7	37.34 337	77.16 110	81.81 97	80.45 3	90.92 3	92.95 7	74.57 47	86.12 29	63.65 134	94.68 31	94.76 6

PEN-MVS		80.46 46	82.91 34	73.11 133	89.83 8	39.02 320	77.06 113	82.61 86	80.04 4	90.60 6	92.85 9	74.93 44	85.21 57	63.15 141	95.15 17	95.09 2

region2R		83.54 14	83.86 19	82.58 14	89.82 9	77.53 16	87.06 13	84.23 63	70.19 48	83.86 71	90.72 49	75.20 40	86.27 21	79.41 14	94.25 50	83.95 128

ACMMPR		83.62 12	83.93 17	82.69 11	89.78 10	77.51 18	87.01 14	84.19 64	70.23 46	84.49 63	90.67 50	75.15 41	86.37 18	79.58 10	94.26 49	84.18 124

MSP-MVS		80.49 45	79.67 58	82.96 5	89.70 11	77.46 19	87.16 11	85.10 39	64.94 89	81.05 105	88.38 114	57.10 210	87.10 8	79.75 7	83.87 228	84.31 121
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 54	81.65 45	72.98 138	89.66 12	39.06 319	76.76 114	80.46 127	78.91 7	90.32 7	91.70 25	68.49 91	84.89 63	63.40 138	95.12 18	95.01 4

PGM-MVS		83.07 21	83.25 30	82.54 15	89.57 13	77.21 20	82.04 56	85.40 33	67.96 59	84.91 59	90.88 42	75.59 36	86.57 14	78.16 22	94.71 30	83.82 130

WR-MVS_H		80.22 50	82.17 41	74.39 111	89.46 14	42.69 294	78.24 97	82.24 89	78.21 9	89.57 9	92.10 18	68.05 96	85.59 48	66.04 113	95.62 9	94.88 5

XVS		83.51 15	83.73 20	82.85 8	89.43 15	77.61 14	86.80 17	84.66 52	72.71 27	82.87 82	90.39 62	73.86 52	86.31 19	78.84 19	94.03 53	84.64 104

X-MVStestdata		76.81 77	74.79 100	82.85 8	89.43 15	77.61 14	86.80 17	84.66 52	72.71 27	82.87 82	9.95 398	73.86 52	86.31 19	78.84 19	94.03 53	84.64 104

CP-MVS		84.12 8	84.55 10	82.80 10	89.42 17	79.74 5	88.19 5	84.43 57	71.96 38	84.70 61	90.56 52	77.12 25	86.18 26	79.24 17	95.36 12	82.49 173

ACMMP_NAP		82.33 27	83.28 28	79.46 49	89.28 18	69.09 74	83.62 42	84.98 41	64.77 90	83.97 69	91.02 38	75.53 39	85.93 37	82.00 2	94.36 45	83.35 148

UniMVSNet_ETH3D		76.74 78	79.02 61	69.92 191	89.27 19	43.81 282	74.47 149	71.70 229	72.33 35	85.50 50	93.65 3	77.98 21	76.88 199	54.60 212	91.64 86	89.08 32

HFP-MVS		83.39 17	84.03 16	81.48 23	89.25 20	75.69 24	87.01 14	84.27 60	70.23 46	84.47 64	90.43 57	76.79 26	85.94 35	79.58 10	94.23 51	82.82 164

ACMMP		84.22 6	84.84 8	82.35 17	89.23 21	76.66 22	87.65 6	85.89 25	71.03 42	85.85 42	90.58 51	78.77 16	85.78 42	79.37 15	95.17 16	84.62 106
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 34	81.76 43	80.76 34	89.20 22	78.75 9	86.48 21	82.03 93	68.80 53	80.92 107	88.52 110	72.00 63	82.39 100	74.80 44	93.04 68	81.14 193

FOURS1					89.19 23	77.84 12	91.64 1	89.11 2	84.05 2	91.57 2

ZNCC-MVS		83.12 20	83.68 21	81.45 24	89.14 24	73.28 42	86.32 23	85.97 24	67.39 60	84.02 68	90.39 62	74.73 45	86.46 15	80.73 6	94.43 40	84.60 109

HPM-MVS++		79.89 51	79.80 57	80.18 39	89.02 25	78.44 10	83.49 45	80.18 134	64.71 91	78.11 136	88.39 113	65.46 125	83.14 89	77.64 29	91.20 97	78.94 235

GST-MVS		82.79 24	83.27 29	81.34 27	88.99 26	73.29 41	85.94 25	85.13 37	68.58 57	84.14 67	90.21 73	73.37 56	86.41 16	79.09 18	93.98 56	84.30 123

TSAR-MVS + MP.		79.05 57	78.81 62	79.74 42	88.94 27	67.52 83	86.61 19	81.38 105	51.71 222	77.15 147	91.42 32	65.49 124	87.20 6	79.44 13	87.17 184	84.51 116
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 33	82.28 40	79.40 50	88.91 28	69.16 72	84.67 33	80.01 137	75.34 15	79.80 118	94.91 2	69.79 83	80.25 141	72.63 63	94.46 36	88.78 42

SMA-MVS		82.12 28	82.68 38	80.43 36	88.90 29	69.52 65	85.12 29	84.76 46	63.53 102	84.23 66	91.47 30	72.02 62	87.16 7	79.74 9	94.36 45	84.61 107
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 26	83.46 25	79.76 41	88.88 30	68.44 76	81.57 59	86.33 18	63.17 108	85.38 52	91.26 33	76.33 30	84.67 68	83.30 1	94.96 22	86.17 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 4	88.84 4	90.72 1	78.27 8	87.95 14	92.53 13	79.37 13	84.79 66	74.51 48	96.15 2	92.88 7

HPM-MVS_fast		84.59 4	85.10 6	83.06 4	88.60 32	75.83 23	86.27 24	86.89 15	73.69 23	86.17 37	91.70 25	78.23 19	85.20 58	79.45 12	94.91 24	88.15 47

SR-MVS		84.51 5	85.27 4	82.25 18	88.52 33	77.71 13	86.81 16	85.25 36	77.42 13	86.15 38	90.24 71	81.69 5	85.94 35	77.77 26	93.58 61	83.09 155

新几何1				69.99 189	88.37 34	71.34 51		62.08 304	43.85 293	74.99 184	86.11 164	52.85 232	70.57 267	50.99 239	83.23 237	68.05 334

HPM-MVS		84.12 8	84.63 9	82.60 13	88.21 35	74.40 31	85.24 28	87.21 13	70.69 45	85.14 54	90.42 58	78.99 15	86.62 13	80.83 5	94.93 23	86.79 63
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 29	83.31 27	78.49 64	88.17 36	73.96 34	83.11 49	84.52 56	66.40 69	87.45 22	89.16 94	81.02 8	80.52 137	74.27 51	95.73 7	80.98 199
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 37	69.15 73	67.85 235	59.59 314	41.06 315	73.05 216	85.72 172	48.03 265			80.65 264	66.92 339

XVG-ACMP-BASELINE		80.54 44	81.06 48	78.98 57	87.01 38	72.91 43	80.23 75	85.56 28	66.56 68	85.64 45	89.57 83	69.12 87	80.55 136	72.51 65	93.37 63	83.48 141

save fliter					87.00 39	67.23 86	79.24 85	77.94 176	56.65 163

LPG-MVS_test		83.47 16	84.33 12	80.90 32	87.00 39	70.41 60	82.04 56	86.35 16	69.77 50	87.75 15	91.13 34	81.83 3	86.20 24	77.13 35	95.96 5	86.08 71

LGP-MVS_train				80.90 32	87.00 39	70.41 60		86.35 16	69.77 50	87.75 15	91.13 34	81.83 3	86.20 24	77.13 35	95.96 5	86.08 71

EGC-MVSNET		64.77 233	61.17 266	75.60 98	86.90 42	74.47 30	84.04 35	68.62 263	0.60 400	1.13 402	91.61 28	65.32 127	74.15 230	64.01 128	88.28 160	78.17 245

OPM-MVS		80.99 41	81.63 46	79.07 54	86.86 43	69.39 68	79.41 84	84.00 69	65.64 73	85.54 49	89.28 87	76.32 31	83.47 84	74.03 52	93.57 62	84.35 120
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepC-MVS	72.44 4	81.00 40	80.83 50	81.50 22	86.70 44	70.03 64	82.06 55	87.00 14	59.89 130	80.91 108	90.53 53	72.19 60	88.56 1	73.67 55	94.52 35	85.92 75
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 25	83.38 26	80.40 37	86.50 45	69.44 67	82.30 53	86.08 23	66.80 65	86.70 30	89.99 76	81.64 6	85.95 34	74.35 50	96.11 3	85.81 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 52	79.99 55	78.49 64	86.46 46	74.79 29	77.15 111	85.39 34	66.73 66	80.39 114	88.85 103	74.43 50	78.33 177	74.73 46	85.79 200	82.35 175

VDDNet		71.60 149	73.13 128	67.02 234	86.29 47	41.11 304	69.97 205	66.50 272	68.72 55	74.74 187	91.70 25	59.90 178	75.81 207	48.58 260	91.72 84	84.15 125

test_0728_SECOND				76.57 85	86.20 48	60.57 151	83.77 40	85.49 29					85.90 38	75.86 39	94.39 41	83.25 150

SR-MVS-dyc-post		84.75 3	85.26 5	83.21 3	86.19 49	79.18 6	87.23 8	86.27 19	77.51 10	87.65 18	90.73 47	79.20 14	85.58 49	78.11 23	94.46 36	84.89 95

RE-MVS-def			85.50 3		86.19 49	79.18 6	87.23 8	86.27 19	77.51 10	87.65 18	90.73 47	81.38 7		78.11 23	94.46 36	84.89 95

DVP-MVS		81.15 37	83.12 32	75.24 103	86.16 51	60.78 148	83.77 40	80.58 125	72.48 32	85.83 43	90.41 59	78.57 17	85.69 45	75.86 39	94.39 41	79.24 231
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 51	60.78 148	83.81 39	85.10 39	72.48 32	85.27 53	89.96 77	78.57 17

SED-MVS		81.78 31	83.48 24	76.67 83	86.12 53	61.06 141	83.62 42	84.72 48	72.61 30	87.38 24	89.70 81	77.48 23	85.89 40	75.29 42	94.39 41	83.08 156

IU-MVS					86.12 53	60.90 145		80.38 129	45.49 282	81.31 101				75.64 41	94.39 41	84.65 103

test_241102_ONE					86.12 53	61.06 141		84.72 48	72.64 29	87.38 24	89.47 84	77.48 23	85.74 44

XVG-OURS		79.51 53	79.82 56	78.58 63	86.11 56	74.96 28	76.33 123	84.95 43	66.89 63	82.75 85	88.99 99	66.82 108	78.37 175	74.80 44	90.76 117	82.40 174

test_part2					85.90 57	66.44 91				84.61 62

原ACMM1				73.90 118	85.90 57	65.15 106		81.67 99	50.97 234	74.25 198	86.16 161	61.60 157	83.54 82	56.75 189	91.08 104	73.00 290

testdata				64.13 255	85.87 59	63.34 119		61.80 307	47.83 264	76.42 170	86.60 148	48.83 259	62.31 324	54.46 214	81.26 258	66.74 343

CNVR-MVS		78.49 64	78.59 66	78.16 68	85.86 60	67.40 84	78.12 100	81.50 101	63.92 96	77.51 144	86.56 149	68.43 93	84.82 65	73.83 53	91.61 88	82.26 179

test_one_0601					85.84 61	61.45 134		85.63 27	75.27 17	85.62 48	90.38 64	76.72 27

NCCC		78.25 67	78.04 71	78.89 59	85.61 62	69.45 66	79.80 81	80.99 116	65.77 72	75.55 177	86.25 158	67.42 101	85.42 50	70.10 76	90.88 112	81.81 185

TEST9					85.47 63	69.32 70	76.42 119	78.69 161	53.73 203	76.97 149	86.74 139	66.84 107	81.10 122

train_agg		76.38 80	76.55 83	75.86 95	85.47 63	69.32 70	76.42 119	78.69 161	54.00 198	76.97 149	86.74 139	66.60 113	81.10 122	72.50 66	91.56 90	77.15 258

DPE-MVS		82.00 30	83.02 33	78.95 58	85.36 65	67.25 85	82.91 50	84.98 41	73.52 24	85.43 51	90.03 75	76.37 29	86.97 11	74.56 47	94.02 55	82.62 170
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HQP_MVS		78.77 60	78.78 64	78.72 60	85.18 66	65.18 104	82.74 51	85.49 29	65.45 76	78.23 133	89.11 95	60.83 170	86.15 27	71.09 71	90.94 106	84.82 99

plane_prior7					85.18 66	66.21 94

SteuartSystems-ACMMP		83.07 21	83.64 22	81.35 26	85.14 68	71.00 54	85.53 26	84.78 45	70.91 43	85.64 45	90.41 59	75.55 38	87.69 4	79.75 7	95.08 19	85.36 85
Skip Steuart: Steuart Systems R&D Blog.
test_8					85.09 69	67.89 79	76.26 124	78.66 163	54.00 198	76.89 153	86.72 141	66.60 113	80.89 132

bld_raw_dy_0_64		72.85 134	72.76 136	73.09 134	85.08 70	64.80 108	78.72 90	64.22 293	51.92 220	83.13 77	90.26 70	39.21 313	69.91 272	70.73 73	91.60 89	84.56 111

WR-MVS		71.20 152	72.48 141	67.36 229	84.98 71	35.70 347	64.43 282	68.66 262	65.05 86	81.49 99	86.43 153	57.57 206	76.48 203	50.36 244	93.32 65	89.90 23

PS-MVSNAJss		77.54 71	77.35 77	78.13 70	84.88 72	66.37 92	78.55 93	79.59 144	53.48 206	86.29 36	92.43 15	62.39 149	80.25 141	67.90 94	90.61 118	87.77 49

LTVRE_ROB	75.46 1	84.22 6	84.98 7	81.94 20	84.82 73	75.40 25	91.60 3	87.80 7	73.52 24	88.90 11	93.06 6	71.39 68	81.53 114	81.53 3	92.15 82	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 58	78.67 65	79.72 43	84.81 74	73.93 35	80.65 65	76.50 191	51.98 219	87.40 23	91.86 21	76.09 33	78.53 167	68.58 84	90.20 123	86.69 66

APDe-MVS		82.88 23	84.14 14	79.08 53	84.80 75	66.72 90	86.54 20	85.11 38	72.00 37	86.65 31	91.75 24	78.20 20	87.04 9	77.93 25	94.32 48	83.47 142
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSLP-MVS++		74.48 105	75.78 92	70.59 175	84.66 76	62.40 124	78.65 91	84.24 62	60.55 125	77.71 142	81.98 221	63.12 140	77.64 191	62.95 142	88.14 162	71.73 305

jajsoiax		78.51 63	78.16 70	79.59 47	84.65 77	73.83 37	80.42 69	76.12 193	51.33 230	87.19 27	91.51 29	73.79 54	78.44 171	68.27 87	90.13 127	86.49 68

TranMVSNet+NR-MVSNet		76.13 82	77.66 74	71.56 166	84.61 78	42.57 296	70.98 192	78.29 170	68.67 56	83.04 78	89.26 88	72.99 58	80.75 133	55.58 204	95.47 10	91.35 13

旧先验1					84.55 79	60.36 153		63.69 296			87.05 131	54.65 223			83.34 236	69.66 323

APD-MVS		81.13 38	81.73 44	79.36 51	84.47 80	70.53 59	83.85 38	83.70 71	69.43 52	83.67 73	88.96 100	75.89 34	86.41 16	72.62 64	92.95 69	81.14 193
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
plane_prior1					84.46 81

agg_prior					84.44 82	66.02 97		78.62 164		76.95 151			80.34 139

DeepPCF-MVS	71.07 5	78.48 65	77.14 79	82.52 16	84.39 83	77.04 21	76.35 121	84.05 67	56.66 162	80.27 115	85.31 175	68.56 90	87.03 10	67.39 100	91.26 95	83.50 138

CDPH-MVS		77.33 73	77.06 80	78.14 69	84.21 84	63.98 115	76.07 127	83.45 74	54.20 193	77.68 143	87.18 126	69.98 80	85.37 51	68.01 91	92.72 74	85.08 92

plane_prior6					84.18 85	65.31 103						60.83 170

114514_t		73.40 116	73.33 125	73.64 123	84.15 86	57.11 175	78.20 98	80.02 136	43.76 296	72.55 222	86.07 166	64.00 136	83.35 87	60.14 166	91.03 105	80.45 214

ZD-MVS					83.91 87	69.36 69		81.09 113	58.91 140	82.73 86	89.11 95	75.77 35	86.63 12	72.73 62	92.93 70

DeepC-MVS_fast	69.89 7	77.17 75	76.33 86	79.70 44	83.90 88	67.94 78	80.06 79	83.75 70	56.73 161	74.88 186	85.32 174	65.54 123	87.79 2	65.61 117	91.14 100	83.35 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
h-mvs33		73.08 123	71.61 154	77.48 74	83.89 89	72.89 44	70.47 199	71.12 245	54.28 189	77.89 137	83.41 197	49.04 256	80.98 127	63.62 135	90.77 116	78.58 239

SD-MVS		80.28 49	81.55 47	76.47 88	83.57 90	67.83 80	83.39 47	85.35 35	64.42 92	86.14 39	87.07 130	74.02 51	80.97 128	77.70 28	92.32 80	80.62 211
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		74.91 100	75.57 95	72.93 142	83.50 91	45.79 268	69.47 211	80.14 135	65.22 82	81.74 96	87.08 128	61.82 155	81.07 124	56.21 196	94.98 20	91.93 8

NR-MVSNet		73.62 112	74.05 110	72.33 159	83.50 91	43.71 283	65.65 267	77.32 183	64.32 93	75.59 176	87.08 128	62.45 148	81.34 116	54.90 207	95.63 8	91.93 8

test_0402		78.17 69	79.48 59	74.24 113	83.50 91	59.15 162	72.52 163	74.60 208	75.34 15	88.69 13	91.81 22	75.06 42	82.37 101	65.10 119	88.68 157	81.20 191

OPU-MVS				78.65 62	83.44 94	66.85 89	83.62 42				86.12 163	66.82 108	86.01 31	61.72 149	89.79 135	83.08 156

NP-MVS					83.34 95	63.07 122					85.97 167

DVP-MVS++		81.24 35	82.74 37	76.76 82	83.14 96	60.90 145	91.64 1	85.49 29	74.03 21	84.93 56	90.38 64	66.82 108	85.90 38	77.43 30	90.78 114	83.49 139

MSC_two_6792asdad				79.02 55	83.14 96	67.03 87		80.75 118					86.24 22	77.27 33	94.85 25	83.78 132

No_MVS				79.02 55	83.14 96	67.03 87		80.75 118					86.24 22	77.27 33	94.85 25	83.78 132

RRT_MVS		78.18 68	77.69 73	79.66 46	83.14 96	61.34 136	83.29 48	80.34 132	57.43 154	86.65 31	91.79 23	50.52 245	86.01 31	71.36 70	94.65 32	91.62 11

UniMVSNet (Re)		75.00 98	75.48 96	73.56 125	83.14 96	47.92 245	70.41 201	81.04 115	63.67 100	79.54 120	86.37 154	62.83 143	81.82 110	57.10 188	95.25 14	90.94 17

hse-mvs2		72.32 143	70.66 166	77.31 79	83.10 101	71.77 47	69.19 216	71.45 235	54.28 189	77.89 137	78.26 275	49.04 256	79.23 155	63.62 135	89.13 151	80.92 200

UniMVSNet_NR-MVSNet		74.90 101	75.65 93	72.64 151	83.04 102	45.79 268	69.26 214	78.81 156	66.66 67	81.74 96	86.88 134	63.26 139	81.07 124	56.21 196	94.98 20	91.05 15

HyFIR lowres test		63.01 253	60.47 273	70.61 174	83.04 102	54.10 194	59.93 315	72.24 228	33.67 361	69.00 267	75.63 296	38.69 316	76.93 197	36.60 342	75.45 308	80.81 205

COLMAP_ROB	72.78 3	83.75 11	84.11 15	82.68 12	82.97 104	74.39 32	87.18 10	88.18 6	78.98 6	86.11 40	91.47 30	79.70 12	85.76 43	66.91 108	95.46 11	87.89 48
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		70.22 162	67.88 198	77.22 80	82.96 105	71.61 48	69.08 217	71.39 236	49.17 253	71.70 232	78.07 280	37.62 324	79.21 156	61.81 146	89.15 149	80.82 203

DP-MVS Recon		73.57 113	72.69 137	76.23 91	82.85 106	63.39 118	74.32 150	82.96 80	57.75 148	70.35 250	81.98 221	64.34 135	84.41 73	49.69 248	89.95 130	80.89 201

APD-MVS_3200maxsize		83.57 13	84.33 12	81.31 28	82.83 107	73.53 40	85.50 27	87.45 12	74.11 19	86.45 35	90.52 55	80.02 10	84.48 70	77.73 27	94.34 47	85.93 74

PVSNet_Blended_VisFu		70.04 164	68.88 181	73.53 126	82.71 108	63.62 117	74.81 140	81.95 95	48.53 258	67.16 292	79.18 264	51.42 241	78.38 174	54.39 216	79.72 276	78.60 238

DPM-MVS		69.98 166	69.22 177	72.26 160	82.69 109	58.82 166	70.53 198	81.23 109	47.79 265	64.16 309	80.21 244	51.32 242	83.12 90	60.14 166	84.95 215	74.83 276

EG-PatchMatch MVS		70.70 158	70.88 163	70.16 185	82.64 110	58.80 167	71.48 182	73.64 212	54.98 177	76.55 164	81.77 224	61.10 167	78.94 161	54.87 208	80.84 262	72.74 295

HQP-NCC					82.37 111		77.32 107		59.08 134	71.58 234

ACMP_Plane					82.37 111		77.32 107		59.08 134	71.58 234

HQP-MVS		75.24 93	75.01 99	75.94 93	82.37 111	58.80 167	77.32 107	84.12 65	59.08 134	71.58 234	85.96 168	58.09 197	85.30 53	67.38 102	89.16 147	83.73 135

test12				76.51 86	82.28 114	60.94 144		81.64 100		73.60 207		64.88 130	85.19 59		90.42 121	83.38 146

TAMVS		65.31 226	63.75 245	69.97 190	82.23 115	59.76 157	66.78 254	63.37 298	45.20 286	69.79 259	79.37 260	47.42 268	72.17 250	34.48 355	85.15 210	77.99 250

test_prior				75.27 102	82.15 116	59.85 156		84.33 59					83.39 86			82.58 171

SF-MVS		80.72 43	81.80 42	77.48 74	82.03 117	64.40 112	83.41 46	88.46 5	65.28 81	84.29 65	89.18 92	73.73 55	83.22 88	76.01 38	93.77 58	84.81 101

AdaColmap		74.22 106	74.56 102	73.20 130	81.95 118	60.97 143	79.43 82	80.90 117	65.57 74	72.54 223	81.76 225	70.98 73	85.26 54	47.88 269	90.00 128	73.37 287

PAPM_NR		73.91 108	74.16 109	73.16 131	81.90 119	53.50 198	81.28 60	81.40 104	66.17 70	73.30 213	83.31 203	59.96 177	83.10 91	58.45 180	81.66 255	82.87 162

DP-MVS		78.44 66	79.29 60	75.90 94	81.86 120	65.33 102	79.05 87	84.63 54	74.83 18	80.41 113	86.27 156	71.68 64	83.45 85	62.45 145	92.40 77	78.92 236

F-COLMAP		75.29 91	73.99 111	79.18 52	81.73 121	71.90 46	81.86 58	82.98 79	59.86 131	72.27 226	84.00 190	64.56 133	83.07 92	51.48 234	87.19 183	82.56 172

SixPastTwentyTwo		75.77 84	76.34 85	74.06 116	81.69 122	54.84 188	76.47 116	75.49 200	64.10 95	87.73 17	92.24 17	50.45 247	81.30 118	67.41 98	91.46 92	86.04 73

Vis-MVSNet		74.85 104	74.56 102	75.72 96	81.63 123	64.64 110	76.35 121	79.06 152	62.85 110	73.33 212	88.41 112	62.54 147	79.59 152	63.94 132	82.92 238	82.94 160
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_djsdf		78.88 59	78.27 69	80.70 35	81.42 124	71.24 52	83.98 36	75.72 198	52.27 214	87.37 26	92.25 16	68.04 97	80.56 134	72.28 67	91.15 99	90.32 22

3Dnovator+	73.19 2	81.08 39	80.48 51	82.87 7	81.41 125	72.03 45	84.38 34	86.23 22	77.28 14	80.65 111	90.18 74	59.80 181	87.58 5	73.06 59	91.34 94	89.01 34

tt0805		76.12 83	78.43 68	69.20 201	81.32 126	41.37 302	76.72 115	77.64 179	63.78 99	82.06 90	87.88 123	79.78 11	79.05 158	64.33 126	92.40 77	87.17 60

MCST-MVS		73.42 115	73.34 124	73.63 124	81.28 127	59.17 161	74.80 142	83.13 78	45.50 280	72.84 218	83.78 194	65.15 128	80.99 126	64.54 123	89.09 153	80.73 207

MIMVSNet1		66.57 216	69.23 176	58.59 305	81.26 128	37.73 334	64.06 285	57.62 318	57.02 157	78.40 132	90.75 46	62.65 144	58.10 339	41.77 308	89.58 140	79.95 220

ACMH+	66.64 10	81.20 36	82.48 39	77.35 78	81.16 129	62.39 125	80.51 67	87.80 7	73.02 26	87.57 20	91.08 36	80.28 9	82.44 99	64.82 122	96.10 4	87.21 57

MVS_111021_HR		72.98 130	72.97 133	72.99 137	80.82 130	65.47 100	68.81 221	72.77 221	57.67 150	75.76 174	82.38 218	71.01 72	77.17 194	61.38 151	86.15 196	76.32 264

9.14			80.22 53		80.68 131		80.35 72	87.69 10	59.90 129	83.00 79	88.20 117	74.57 47	81.75 112	73.75 54	93.78 57

OMC-MVS		79.41 55	78.79 63	81.28 29	80.62 132	70.71 58	80.91 63	84.76 46	62.54 112	81.77 94	86.65 145	71.46 66	83.53 83	67.95 93	92.44 76	89.60 24

OurMVSNet-221017-0		78.57 62	78.53 67	78.67 61	80.48 133	64.16 113	80.24 74	82.06 92	61.89 116	88.77 12	93.32 4	57.15 208	82.60 98	70.08 77	92.80 71	89.25 28

CDS-MVSNet		64.33 241	62.66 257	69.35 198	80.44 134	58.28 171	65.26 272	65.66 278	44.36 291	67.30 291	75.54 297	43.27 286	71.77 256	37.68 334	84.44 223	78.01 249
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		77.20 74	76.37 84	79.69 45	80.34 135	61.52 133	80.58 66	82.12 91	53.54 205	83.93 70	91.03 37	49.49 251	85.97 33	73.26 57	93.08 67	91.59 12

PLC	62.01 16	71.79 148	70.28 168	76.33 89	80.31 136	68.63 75	78.18 99	81.24 108	54.57 186	67.09 293	80.63 238	59.44 182	81.74 113	46.91 276	84.17 225	78.63 237
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		58.09 292	56.30 303	63.45 264	79.95 137	50.93 212	54.07 350	65.59 279	28.56 377	61.53 326	74.33 309	41.09 300	66.52 307	33.91 358	67.69 362	72.92 291

K. test v3		73.67 111	73.61 119	73.87 119	79.78 138	55.62 186	74.69 146	62.04 306	66.16 71	84.76 60	93.23 5	49.47 252	80.97 128	65.66 116	86.67 191	85.02 94

VPNet		65.58 224	67.56 201	59.65 298	79.72 139	30.17 374	60.27 313	62.14 302	54.19 194	71.24 242	86.63 146	58.80 189	67.62 291	44.17 295	90.87 113	81.18 192

ACMH	63.62 14	77.50 72	80.11 54	69.68 193	79.61 140	56.28 179	78.81 89	83.62 72	63.41 106	87.14 29	90.23 72	76.11 32	73.32 236	67.58 95	94.44 39	79.44 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 147	79.60 141	56.83 178		57.37 321		83.80 72	89.01 98	47.45 267	78.74 165	64.39 125	86.49 194	82.69 168

MVS_111021_LR		72.10 145	71.82 150	72.95 139	79.53 142	73.90 36	70.45 200	66.64 271	56.87 158	76.81 157	81.76 225	68.78 88	71.76 257	61.81 146	83.74 230	73.18 289

Test_1112_low_res		58.78 288	58.69 285	59.04 303	79.41 143	38.13 330	57.62 329	66.98 270	34.74 354	59.62 342	77.56 284	42.92 289	63.65 319	38.66 326	70.73 344	75.35 273

CSCG		74.12 107	74.39 104	73.33 128	79.35 144	61.66 132	77.45 106	81.98 94	62.47 114	79.06 125	80.19 246	61.83 154	78.79 164	59.83 170	87.35 176	79.54 228

MVS_0304		76.32 81	75.96 91	77.42 76	79.33 145	60.86 147	80.18 76	74.88 205	66.93 62	69.11 265	88.95 101	57.84 204	86.12 29	76.63 37	89.77 136	85.28 86

MVP-Stereo		61.56 267	59.22 280	68.58 216	79.28 146	60.44 152	69.20 215	71.57 231	43.58 299	56.42 356	78.37 274	39.57 311	76.46 204	34.86 354	60.16 379	68.86 331
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 161	71.34 159	67.85 224	79.26 147	40.42 312	74.67 147	75.15 204	58.41 142	68.74 277	88.14 121	56.08 219	83.69 80	59.90 169	81.71 254	79.43 230

IS-MVSNet		75.10 95	75.42 97	74.15 115	79.23 148	48.05 243	79.43 82	78.04 174	70.09 49	79.17 124	88.02 122	53.04 231	83.60 81	58.05 183	93.76 59	90.79 19

FC-MVSNet-test		73.32 118	74.78 101	68.93 209	79.21 149	36.57 339	71.82 179	79.54 146	57.63 153	82.57 87	90.38 64	59.38 184	78.99 160	57.91 184	94.56 34	91.23 14

AllTest		77.66 70	77.43 75	78.35 66	79.19 150	70.81 55	78.60 92	88.64 3	65.37 79	80.09 116	88.17 118	70.33 76	78.43 172	55.60 201	90.90 110	85.81 76

TestCases				78.35 66	79.19 150	70.81 55		88.64 3	65.37 79	80.09 116	88.17 118	70.33 76	78.43 172	55.60 201	90.90 110	85.81 76

xiu_mvs_v1_base_debu		67.87 197	67.07 208	70.26 181	79.13 152	61.90 129	67.34 242	71.25 241	47.98 261	67.70 285	74.19 313	61.31 160	72.62 243	56.51 191	78.26 289	76.27 265

xiu_mvs_v1_base		67.87 197	67.07 208	70.26 181	79.13 152	61.90 129	67.34 242	71.25 241	47.98 261	67.70 285	74.19 313	61.31 160	72.62 243	56.51 191	78.26 289	76.27 265

xiu_mvs_v1_base_debi		67.87 197	67.07 208	70.26 181	79.13 152	61.90 129	67.34 242	71.25 241	47.98 261	67.70 285	74.19 313	61.31 160	72.62 243	56.51 191	78.26 289	76.27 265

VDD-MVS		70.81 157	71.44 158	68.91 210	79.07 155	46.51 262	67.82 236	70.83 249	61.23 119	74.07 202	88.69 106	59.86 179	75.62 210	51.11 237	90.28 122	84.61 107

test1111		64.62 234	65.19 230	62.93 271	79.01 156	29.91 375	65.45 270	54.41 341	54.09 196	71.47 241	88.48 111	37.02 326	74.29 228	46.83 278	89.94 131	84.58 110

TSAR-MVS + GP.		73.08 123	71.60 155	77.54 73	78.99 157	70.73 57	74.96 137	69.38 257	60.73 124	74.39 196	78.44 273	57.72 205	82.78 95	60.16 165	89.60 138	79.11 233

test2506		61.23 269	60.85 270	62.38 276	78.80 158	27.88 383	67.33 245	37.42 395	54.23 191	67.55 288	88.68 107	17.87 399	74.39 226	46.33 281	89.41 143	84.86 97

ECVR-MVS		64.82 231	65.22 229	63.60 261	78.80 158	31.14 371	66.97 250	56.47 332	54.23 191	69.94 256	88.68 107	37.23 325	74.81 221	45.28 291	89.41 143	84.86 97

FIs		72.56 139	73.80 114	68.84 212	78.74 160	37.74 333	71.02 191	79.83 139	56.12 166	80.88 110	89.45 85	58.18 193	78.28 178	56.63 190	93.36 64	90.51 21

v7n		79.37 56	80.41 52	76.28 90	78.67 161	55.81 183	79.22 86	82.51 88	70.72 44	87.54 21	92.44 14	68.00 98	81.34 116	72.84 61	91.72 84	91.69 10

LS3D		80.99 41	80.85 49	81.41 25	78.37 162	71.37 50	87.45 7	85.87 26	77.48 12	81.98 91	89.95 78	69.14 86	85.26 54	66.15 110	91.24 96	87.61 52

CNLPA		73.44 114	73.03 131	74.66 105	78.27 163	75.29 26	75.99 128	78.49 165	65.39 78	75.67 175	83.22 209	61.23 163	66.77 305	53.70 223	85.33 206	81.92 184

EPP-MVSNet		73.86 110	73.38 122	75.31 101	78.19 164	53.35 200	80.45 68	77.32 183	65.11 85	76.47 168	86.80 135	49.47 252	83.77 77	53.89 221	92.72 74	88.81 41

PCF-MVS	63.80 13	72.70 137	71.69 151	75.72 96	78.10 165	60.01 155	73.04 160	81.50 101	45.34 284	79.66 119	84.35 186	65.15 128	82.65 97	48.70 258	89.38 146	84.50 117
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		73.14 121	73.77 116	71.26 170	78.09 166	52.64 203	74.32 150	79.56 145	56.32 165	76.35 171	83.36 202	70.76 74	77.96 185	63.32 139	81.84 249	83.18 153

LFMVS		67.06 211	67.89 197	64.56 252	78.02 167	38.25 328	70.81 196	59.60 313	65.18 83	71.06 244	86.56 149	43.85 283	75.22 214	46.35 280	89.63 137	80.21 218

anonymousdsp		78.60 61	77.80 72	81.00 31	78.01 168	74.34 33	80.09 77	76.12 193	50.51 240	89.19 10	90.88 42	71.45 67	77.78 189	73.38 56	90.60 119	90.90 18

BH-untuned		69.39 176	69.46 172	69.18 202	77.96 169	56.88 176	68.47 230	77.53 180	56.77 160	77.79 140	79.63 255	60.30 175	80.20 144	46.04 283	80.65 264	70.47 316

1112_ss		59.48 283	58.99 283	60.96 290	77.84 170	42.39 297	61.42 303	68.45 264	37.96 338	59.93 339	67.46 360	45.11 276	65.07 312	40.89 314	71.81 337	75.41 271

PS-MVSNAJ		64.27 242	63.73 246	65.90 245	77.82 171	51.42 209	63.33 292	72.33 226	45.09 288	61.60 325	68.04 358	62.39 149	73.95 232	49.07 254	73.87 324	72.34 298

ambc				70.10 187	77.74 172	50.21 219	74.28 152	77.93 177		79.26 123	88.29 116	54.11 227	79.77 148	64.43 124	91.10 103	80.30 216

xiu_mvs_v2_base		64.43 239	63.96 243	65.85 246	77.72 173	51.32 210	63.63 289	72.31 227	45.06 289	61.70 324	69.66 345	62.56 145	73.93 233	49.06 255	73.91 323	72.31 299

Anonymous20231211		75.54 89	77.19 78	70.59 175	77.67 174	45.70 271	74.73 144	80.19 133	68.80 53	82.95 81	92.91 8	66.26 116	76.76 201	58.41 181	92.77 72	89.30 27

FMVSNet1		71.06 153	72.48 141	66.81 235	77.65 175	40.68 308	71.96 173	73.03 216	61.14 120	79.45 122	90.36 67	60.44 173	75.20 215	50.20 245	88.05 164	84.54 112

FPMVS		59.43 284	60.07 275	57.51 311	77.62 176	71.52 49	62.33 299	50.92 356	57.40 155	69.40 263	80.00 250	39.14 314	61.92 326	37.47 337	66.36 364	39.09 394

testing3		58.28 291	58.38 289	58.00 309	77.45 177	26.12 388	60.78 309	43.00 381	56.02 167	70.18 253	75.76 294	13.27 406	67.24 297	48.02 267	80.89 260	80.65 210

Effi-MVS+-dtu		75.43 90	72.28 145	84.91 2	77.05 178	83.58 1	78.47 94	77.70 178	57.68 149	74.89 185	78.13 279	64.80 131	84.26 74	56.46 194	85.32 207	86.88 62

CLD-MVS		72.88 133	72.36 144	74.43 110	77.03 179	54.30 192	68.77 224	83.43 75	52.12 216	76.79 158	74.44 308	69.54 85	83.91 75	55.88 199	93.25 66	85.09 91
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 79	76.00 89	78.06 71	77.02 180	64.77 109	80.78 64	82.66 85	60.39 126	74.15 199	83.30 204	69.65 84	82.07 107	69.27 82	86.75 190	87.36 55

CS-MVS-test		74.89 102	74.23 108	76.86 81	77.01 181	62.94 123	78.98 88	84.61 55	58.62 141	70.17 254	80.80 235	66.74 112	81.96 108	61.74 148	89.40 145	85.69 81

Baseline_NR-MVSNet		70.62 159	73.19 126	62.92 272	76.97 182	34.44 355	68.84 219	70.88 248	60.25 127	79.50 121	90.53 53	61.82 155	69.11 278	54.67 211	95.27 13	85.22 87

ITE_SJBPF				80.35 38	76.94 183	73.60 38		80.48 126	66.87 64	83.64 74	86.18 159	70.25 78	79.90 147	61.12 156	88.95 155	87.56 53

SSC-MVS		61.79 265	66.08 219	48.89 350	76.91 184	10.00 404	53.56 352	47.37 370	68.20 58	76.56 163	89.21 90	54.13 226	57.59 340	54.75 209	74.07 322	79.08 234

jason		64.47 238	62.84 255	69.34 199	76.91 184	59.20 158	67.15 247	65.67 277	35.29 351	65.16 302	76.74 290	44.67 278	70.68 265	54.74 210	79.28 279	78.14 246
jason: jason.
ETV-MVS		72.72 136	72.16 147	74.38 112	76.90 186	55.95 180	73.34 158	84.67 51	62.04 115	72.19 229	70.81 334	65.90 120	85.24 56	58.64 178	84.96 214	81.95 183

Anonymous20240529		72.56 139	73.79 115	68.86 211	76.89 187	45.21 273	68.80 223	77.25 185	67.16 61	76.89 153	90.44 56	65.95 119	74.19 229	50.75 240	90.00 128	87.18 59

EC-MVSNet		77.08 76	77.39 76	76.14 92	76.86 188	56.87 177	80.32 73	87.52 11	63.45 104	74.66 191	84.52 183	69.87 82	84.94 61	69.76 79	89.59 139	86.60 67

PM-MVS		64.49 237	63.61 247	67.14 233	76.68 189	75.15 27	68.49 229	42.85 382	51.17 233	77.85 139	80.51 239	45.76 270	66.31 308	52.83 229	76.35 300	59.96 371

TransMVSNet (Re)		69.62 171	71.63 153	63.57 262	76.51 190	35.93 345	65.75 266	71.29 240	61.05 121	75.02 183	89.90 79	65.88 121	70.41 271	49.79 247	89.48 141	84.38 119

BH-RMVSNet		68.69 186	68.20 194	70.14 186	76.40 191	53.90 197	64.62 279	73.48 214	58.01 145	73.91 206	81.78 223	59.09 186	78.22 179	48.59 259	77.96 293	78.31 242

PHI-MVS		74.92 99	74.36 106	76.61 84	76.40 191	62.32 126	80.38 70	83.15 77	54.16 195	73.23 214	80.75 236	62.19 152	83.86 76	68.02 90	90.92 109	83.65 136

UGNet		70.20 163	69.05 178	73.65 122	76.24 193	63.64 116	75.87 131	72.53 224	61.48 118	60.93 333	86.14 162	52.37 234	77.12 195	50.67 241	85.21 208	80.17 219
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		58.68 289	57.72 293	61.57 282	76.21 194	73.59 39	61.83 300	49.00 364	47.30 269	61.08 329	68.97 350	50.16 248	59.01 333	36.06 349	68.84 355	52.10 381

VPA-MVSNet		68.71 185	70.37 167	63.72 260	76.13 195	38.06 331	64.10 284	71.48 234	56.60 164	74.10 201	88.31 115	64.78 132	69.72 273	47.69 271	90.15 125	83.37 147

WB-MVS		60.04 279	64.19 241	47.59 352	76.09 196	10.22 403	52.44 357	46.74 371	65.17 84	74.07 202	87.48 125	53.48 229	55.28 343	49.36 252	72.84 329	77.28 255

PAPM		61.79 265	60.37 274	66.05 243	76.09 196	41.87 299	69.30 213	76.79 190	40.64 323	53.80 368	79.62 256	44.38 280	82.92 94	29.64 374	73.11 328	73.36 288

BH-w/o		64.81 232	64.29 240	66.36 240	76.08 198	54.71 189	65.61 268	75.23 203	50.10 245	71.05 245	71.86 329	54.33 225	79.02 159	38.20 331	76.14 302	65.36 349

dcpmvs_2		71.02 155	72.65 138	66.16 242	76.06 199	50.49 215	71.97 172	79.36 147	50.34 241	82.81 84	83.63 195	64.38 134	67.27 296	61.54 150	83.71 232	80.71 209

pmmvs6		71.82 147	73.66 117	66.31 241	75.94 200	42.01 298	66.99 249	72.53 224	63.45 104	76.43 169	92.78 10	72.95 59	69.69 274	51.41 235	90.46 120	87.22 56

CANet		73.00 128	71.84 149	76.48 87	75.82 201	61.28 137	74.81 140	80.37 130	63.17 108	62.43 323	80.50 240	61.10 167	85.16 60	64.00 129	84.34 224	83.01 159

pmmvs-eth3d		64.41 240	63.27 251	67.82 226	75.81 202	60.18 154	69.49 210	62.05 305	38.81 334	74.13 200	82.23 219	43.76 284	68.65 282	42.53 302	80.63 266	74.63 277

TR-MVS		64.59 235	63.54 248	67.73 227	75.75 203	50.83 213	63.39 291	70.29 252	49.33 251	71.55 238	74.55 306	50.94 243	78.46 170	40.43 316	75.69 304	73.89 284

tttt0517		69.46 174	67.79 200	74.46 107	75.34 204	52.72 202	75.05 136	63.27 299	54.69 183	78.87 127	84.37 185	26.63 377	81.15 120	63.95 130	87.93 168	89.51 25

cascas		64.59 235	62.77 256	70.05 188	75.27 205	50.02 221	61.79 301	71.61 230	42.46 306	63.68 316	68.89 353	49.33 254	80.35 138	47.82 270	84.05 227	79.78 223

API-MVS		70.97 156	71.51 157	69.37 196	75.20 206	55.94 181	80.99 61	76.84 188	62.48 113	71.24 242	77.51 285	61.51 159	80.96 131	52.04 230	85.76 201	71.22 310

EIA-MVS		68.59 188	67.16 207	72.90 143	75.18 207	55.64 185	69.39 212	81.29 106	52.44 213	64.53 305	70.69 335	60.33 174	82.30 103	54.27 218	76.31 301	80.75 206

PAPR		69.20 178	68.66 187	70.82 172	75.15 208	47.77 248	75.31 134	81.11 111	49.62 250	66.33 295	79.27 261	61.53 158	82.96 93	48.12 266	81.50 257	81.74 187

MVSFormer		69.93 167	69.03 179	72.63 152	74.93 209	59.19 159	83.98 36	75.72 198	52.27 214	63.53 319	76.74 290	43.19 287	80.56 134	72.28 67	78.67 285	78.14 246

lupinMVS		63.36 248	61.49 264	68.97 207	74.93 209	59.19 159	65.80 265	64.52 290	34.68 356	63.53 319	74.25 311	43.19 287	70.62 266	53.88 222	78.67 285	77.10 259

nrg030		74.87 103	75.99 90	71.52 167	74.90 211	49.88 228	74.10 154	82.58 87	54.55 187	83.50 75	89.21 90	71.51 65	75.74 209	61.24 152	92.34 79	88.94 37

TAPA-MVS	65.27 12	75.16 94	74.29 107	77.77 72	74.86 212	68.08 77	77.89 101	84.04 68	55.15 176	76.19 173	83.39 198	66.91 106	80.11 145	60.04 168	90.14 126	85.13 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RPSCF		75.76 85	74.37 105	79.93 40	74.81 213	77.53 16	77.53 105	79.30 149	59.44 133	78.88 126	89.80 80	71.26 69	73.09 238	57.45 185	80.89 260	89.17 31

EI-MVSNet-Vis-set		72.78 135	71.87 148	75.54 99	74.77 214	59.02 165	72.24 165	71.56 232	63.92 96	78.59 128	71.59 330	66.22 117	78.60 166	67.58 95	80.32 267	89.00 35

v1240		73.06 125	73.14 127	72.84 145	74.74 215	47.27 256	71.88 178	81.11 111	51.80 221	82.28 89	84.21 187	56.22 218	82.34 102	68.82 83	87.17 184	88.91 38

v1921920		72.96 131	72.98 132	72.89 144	74.67 216	47.58 251	71.92 176	80.69 120	51.70 223	81.69 98	83.89 192	56.58 215	82.25 104	68.34 86	87.36 175	88.82 40

EI-MVSNet-UG-set		72.63 138	71.68 152	75.47 100	74.67 216	58.64 170	72.02 170	71.50 233	63.53 102	78.58 130	71.39 333	65.98 118	78.53 167	67.30 105	80.18 269	89.23 29

Fast-Effi-MVS+		68.81 183	68.30 190	70.35 180	74.66 218	48.61 236	66.06 260	78.32 168	50.62 238	71.48 240	75.54 297	68.75 89	79.59 152	50.55 243	78.73 284	82.86 163

v1192		73.40 116	73.42 120	73.32 129	74.65 219	48.67 235	72.21 166	81.73 98	52.76 211	81.85 92	84.56 182	57.12 209	82.24 105	68.58 84	87.33 177	89.06 33

v144192		72.99 129	73.06 130	72.77 146	74.58 220	47.48 252	71.90 177	80.44 128	51.57 224	81.46 100	84.11 189	58.04 201	82.12 106	67.98 92	87.47 173	88.70 43

MAR-MVS		67.72 200	66.16 218	72.40 157	74.45 221	64.99 107	74.87 138	77.50 181	48.67 257	65.78 299	68.58 357	57.01 212	77.79 188	46.68 279	81.92 246	74.42 280
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 86	76.20 87	74.16 114	74.44 222	48.69 234	75.84 132	82.93 81	59.02 138	85.92 41	89.17 93	58.56 191	82.74 96	70.73 73	89.14 150	91.05 15

canonicalmvs		72.29 144	73.38 122	69.04 204	74.23 223	47.37 254	73.93 156	83.18 76	54.36 188	76.61 162	81.64 227	72.03 61	75.34 213	57.12 187	87.28 179	84.40 118

Anonymous202405211		66.02 221	66.89 213	63.43 265	74.22 224	38.14 329	59.00 319	66.13 274	63.33 107	69.76 260	85.95 169	51.88 236	70.50 268	44.23 294	87.52 171	81.64 188

Effi-MVS+		72.10 145	72.28 145	71.58 165	74.21 225	50.33 217	74.72 145	82.73 83	62.62 111	70.77 246	76.83 289	69.96 81	80.97 128	60.20 163	78.43 287	83.45 144

FE-MVS		68.29 193	66.96 212	72.26 160	74.16 226	54.24 193	77.55 104	73.42 215	57.65 152	72.66 220	84.91 179	32.02 350	81.49 115	48.43 262	81.85 248	81.04 195

v1144		73.29 119	73.39 121	73.01 136	74.12 227	48.11 241	72.01 171	81.08 114	53.83 202	81.77 94	84.68 180	58.07 200	81.91 109	68.10 88	86.86 186	88.99 36

FA-MVS(test-final)		71.27 151	71.06 161	71.92 163	73.96 228	52.32 206	76.45 118	76.12 193	59.07 137	74.04 204	86.18 159	52.18 235	79.43 154	59.75 172	81.76 250	84.03 126

EI-MVSNet		69.61 172	69.01 180	71.41 169	73.94 229	49.90 224	71.31 187	71.32 238	58.22 143	75.40 181	70.44 336	58.16 194	75.85 205	62.51 143	79.81 273	88.48 44

CVMVSNet		59.21 285	58.44 288	61.51 283	73.94 229	47.76 249	71.31 187	64.56 289	26.91 383	60.34 335	70.44 336	36.24 330	67.65 290	53.57 224	68.66 356	69.12 329

IterMVS-LS		73.01 127	73.12 129	72.66 150	73.79 231	49.90 224	71.63 181	78.44 166	58.22 143	80.51 112	86.63 146	58.15 195	79.62 150	62.51 143	88.20 161	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
iter_conf_final		68.69 186	67.00 211	73.76 121	73.68 232	52.33 205	75.96 129	73.54 213	50.56 239	69.90 257	82.85 210	24.76 386	83.73 78	65.40 118	86.33 195	85.22 87

alignmvs		70.54 160	71.00 162	69.15 203	73.50 233	48.04 244	69.85 208	79.62 141	53.94 201	76.54 165	82.00 220	59.00 187	74.68 222	57.32 186	87.21 182	84.72 102

Fast-Effi-MVS+-dtu		70.00 165	68.74 185	73.77 120	73.47 234	64.53 111	71.36 185	78.14 173	55.81 171	68.84 275	74.71 305	65.36 126	75.75 208	52.00 231	79.00 281	81.03 196

v8		75.07 96	75.64 94	73.35 127	73.42 235	47.46 253	75.20 135	81.45 103	60.05 128	85.64 45	89.26 88	58.08 199	81.80 111	69.71 81	87.97 167	90.79 19

tfpnnormal		66.48 217	67.93 196	62.16 278	73.40 236	36.65 338	63.45 290	64.99 284	55.97 168	72.82 219	87.80 124	57.06 211	69.10 279	48.31 264	87.54 170	80.72 208

IterMVS-SCA-FT		67.68 201	66.07 220	72.49 155	73.34 237	58.20 172	63.80 287	65.55 280	48.10 260	76.91 152	82.64 215	45.20 274	78.84 162	61.20 153	77.89 294	80.44 215

VNet		64.01 245	65.15 233	60.57 292	73.28 238	35.61 348	57.60 330	67.08 269	54.61 185	66.76 294	83.37 200	56.28 217	66.87 301	42.19 304	85.20 209	79.23 232

3Dnovator	65.95 11	71.50 150	71.22 160	72.34 158	73.16 239	63.09 121	78.37 95	78.32 168	57.67 150	72.22 228	84.61 181	54.77 221	78.47 169	60.82 159	81.07 259	75.45 270

GBi-Net		68.30 191	68.79 182	66.81 235	73.14 240	40.68 308	71.96 173	73.03 216	54.81 178	74.72 188	90.36 67	48.63 262	75.20 215	47.12 273	85.37 203	84.54 112

test1		68.30 191	68.79 182	66.81 235	73.14 240	40.68 308	71.96 173	73.03 216	54.81 178	74.72 188	90.36 67	48.63 262	75.20 215	47.12 273	85.37 203	84.54 112

FMVSNet2		67.48 203	68.21 193	65.29 247	73.14 240	38.94 321	68.81 221	71.21 244	54.81 178	76.73 159	86.48 151	48.63 262	74.60 223	47.98 268	86.11 198	82.35 175

thisisatest0530		67.05 212	65.16 231	72.73 149	73.10 243	50.55 214	71.26 189	63.91 295	50.22 243	74.46 195	80.75 236	26.81 376	80.25 141	59.43 174	86.50 193	87.37 54

pm-mvs1		68.40 189	69.85 171	64.04 258	73.10 243	39.94 314	64.61 280	70.50 250	55.52 173	73.97 205	89.33 86	63.91 137	68.38 284	49.68 249	88.02 165	83.81 131

pmmvs4		60.78 273	59.04 282	66.00 244	73.06 245	57.67 174	64.53 281	60.22 311	36.91 345	65.96 296	77.27 286	39.66 310	68.54 283	38.87 324	74.89 312	71.80 304

SDMVSNet		66.36 219	67.85 199	61.88 280	73.04 246	46.14 267	58.54 323	71.36 237	51.42 227	68.93 271	82.72 213	65.62 122	62.22 325	54.41 215	84.67 216	77.28 255

sd_testset		63.55 246	65.38 227	58.07 308	73.04 246	38.83 323	57.41 331	65.44 281	51.42 227	68.93 271	82.72 213	63.76 138	58.11 338	41.05 312	84.67 216	77.28 255

v2v482		72.55 141	72.58 139	72.43 156	72.92 248	46.72 260	71.41 184	79.13 151	55.27 174	81.17 104	85.25 176	55.41 220	81.13 121	67.25 106	85.46 202	89.43 26

casdiffmvs_mvg		75.26 92	76.18 88	72.52 153	72.87 249	49.47 229	72.94 161	84.71 50	59.49 132	80.90 109	88.81 104	70.07 79	79.71 149	67.40 99	88.39 159	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
MIMVSNet		54.39 309	56.12 305	49.20 346	72.57 250	30.91 372	59.98 314	48.43 366	41.66 309	55.94 358	83.86 193	41.19 299	50.42 351	26.05 384	75.38 309	66.27 344

Patchmatch-RL test		59.95 280	59.12 281	62.44 275	72.46 251	54.61 191	59.63 316	47.51 369	41.05 316	74.58 193	74.30 310	31.06 359	65.31 310	51.61 233	79.85 272	67.39 336

CL-MVSNet_self_test		62.44 260	63.40 249	59.55 299	72.34 252	32.38 364	56.39 335	64.84 286	51.21 232	67.46 289	81.01 233	50.75 244	63.51 320	38.47 329	88.12 163	82.75 166

Vis-MVSNet (Re-imp)		62.74 257	63.21 252	61.34 286	72.19 253	31.56 368	67.31 246	53.87 342	53.60 204	69.88 258	83.37 200	40.52 304	70.98 264	41.40 310	86.78 189	81.48 190

thres100view900		61.17 270	61.09 267	61.39 285	72.14 254	35.01 351	65.42 271	56.99 326	55.23 175	70.71 247	79.90 251	32.07 348	72.09 251	35.61 350	81.73 251	77.08 260

ab-mvs		64.11 243	65.13 234	61.05 288	71.99 255	38.03 332	67.59 237	68.79 261	49.08 255	65.32 301	86.26 157	58.02 202	66.85 303	39.33 320	79.79 275	78.27 243

thres600view7		61.82 264	61.38 265	63.12 267	71.81 256	34.93 352	64.64 278	56.99 326	54.78 182	70.33 251	79.74 253	32.07 348	72.42 248	38.61 327	83.46 235	82.02 181

QAPM		69.18 179	69.26 175	68.94 208	71.61 257	52.58 204	80.37 71	78.79 159	49.63 249	73.51 208	85.14 177	53.66 228	79.12 157	55.11 206	75.54 306	75.11 275

WB-MVSnew		53.94 315	54.76 313	51.49 335	71.53 258	28.05 381	58.22 326	50.36 359	37.94 339	59.16 343	70.17 341	49.21 255	51.94 348	24.49 391	71.80 338	74.47 279

baseline		73.10 122	73.96 112	70.51 177	71.46 259	46.39 265	72.08 169	84.40 58	55.95 169	76.62 161	86.46 152	67.20 102	78.03 184	64.22 127	87.27 180	87.11 61

casdiffmvs		73.06 125	73.84 113	70.72 173	71.32 260	46.71 261	70.93 193	84.26 61	55.62 172	77.46 145	87.10 127	67.09 104	77.81 187	63.95 130	86.83 188	87.64 51
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		72.36 142	72.49 140	71.96 162	71.29 261	64.06 114	72.79 162	81.82 96	40.23 325	81.25 103	81.04 232	70.62 75	68.69 281	69.74 80	83.60 234	83.14 154

Anonymous20231206		54.13 310	55.82 307	49.04 349	70.89 262	35.96 344	51.73 358	50.87 357	34.86 352	62.49 322	79.22 262	42.52 293	44.29 377	27.95 381	81.88 247	66.88 340

fmvsm_s_conf0.1_n_a		67.37 207	66.36 216	70.37 179	70.86 263	61.17 139	74.00 155	57.18 325	40.77 320	68.83 276	80.88 234	63.11 141	67.61 292	66.94 107	74.72 313	82.33 178

tfpn200view9		60.35 277	59.97 276	61.51 283	70.78 264	35.35 349	63.27 293	57.47 319	53.00 209	68.31 280	77.09 287	32.45 345	72.09 251	35.61 350	81.73 251	77.08 260

thres400		60.77 274	59.97 276	63.15 266	70.78 264	35.35 349	63.27 293	57.47 319	53.00 209	68.31 280	77.09 287	32.45 345	72.09 251	35.61 350	81.73 251	82.02 181

MSDG		67.47 205	67.48 204	67.46 228	70.70 266	54.69 190	66.90 252	78.17 171	60.88 123	70.41 249	74.76 303	61.22 165	73.18 237	47.38 272	76.87 298	74.49 278

test_yl		65.11 227	65.09 235	65.18 248	70.59 267	40.86 306	63.22 295	72.79 219	57.91 146	68.88 273	79.07 267	42.85 290	74.89 219	45.50 288	84.97 211	79.81 221

DCV-MVSNet		65.11 227	65.09 235	65.18 248	70.59 267	40.86 306	63.22 295	72.79 219	57.91 146	68.88 273	79.07 267	42.85 290	74.89 219	45.50 288	84.97 211	79.81 221

test_fmvsm_n_1920		69.63 170	68.45 188	73.16 131	70.56 269	65.86 98	70.26 202	78.35 167	37.69 340	74.29 197	78.89 269	61.10 167	68.10 287	65.87 115	79.07 280	85.53 83

OpenMVS	62.51 15	68.76 184	68.75 184	68.78 213	70.56 269	53.91 196	78.29 96	77.35 182	48.85 256	70.22 252	83.52 196	52.65 233	76.93 197	55.31 205	81.99 245	75.49 269

DELS-MVS		68.83 182	68.31 189	70.38 178	70.55 271	48.31 237	63.78 288	82.13 90	54.00 198	68.96 269	75.17 301	58.95 188	80.06 146	58.55 179	82.74 240	82.76 165
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		53.37 316	52.50 325	55.98 317	70.51 272	29.68 376	56.20 338	51.85 354	46.19 275	56.76 354	68.94 351	19.18 396	65.39 309	25.87 387	76.98 297	72.87 292

LCM-MVSNet-Re		69.10 180	71.57 156	61.70 281	70.37 273	34.30 357	61.45 302	79.62 141	56.81 159	89.59 8	88.16 120	68.44 92	72.94 239	42.30 303	87.33 177	77.85 252

patch_mono-2		62.73 258	64.08 242	58.68 304	70.36 274	55.87 182	60.84 308	64.11 294	41.23 313	64.04 310	78.22 276	60.00 176	48.80 356	54.17 219	83.71 232	71.37 307

SCA		58.57 290	58.04 291	60.17 295	70.17 275	41.07 305	65.19 273	53.38 348	43.34 303	61.00 332	73.48 317	45.20 274	69.38 276	40.34 317	70.31 347	70.05 319

ET-MVSNet_ETH3D		63.32 249	60.69 272	71.20 171	70.15 276	55.66 184	65.02 275	64.32 291	43.28 304	68.99 268	72.05 328	25.46 383	78.19 182	54.16 220	82.80 239	79.74 224

APD_test1		75.04 97	75.38 98	74.02 117	69.89 277	70.15 62	76.46 117	79.71 140	65.50 75	82.99 80	88.60 109	66.94 105	72.35 249	59.77 171	88.54 158	79.56 225

iter_conf05		67.34 208	65.62 224	72.50 154	69.82 278	47.06 258	72.19 167	76.86 187	45.32 285	72.86 217	82.85 210	20.53 393	83.73 78	61.13 155	89.02 154	86.70 65

PVSNet_BlendedMVS		65.38 225	64.30 239	68.61 215	69.81 279	49.36 230	65.60 269	78.96 153	45.50 280	59.98 336	78.61 271	51.82 237	78.20 180	44.30 292	84.11 226	78.27 243

PVSNet_Blended		62.90 255	61.64 261	66.69 238	69.81 279	49.36 230	61.23 305	78.96 153	42.04 307	59.98 336	68.86 354	51.82 237	78.20 180	44.30 292	77.77 295	72.52 296

OpenMVS_ROB	54.93 17	63.23 251	63.28 250	63.07 268	69.81 279	45.34 272	68.52 228	67.14 268	43.74 297	70.61 248	79.22 262	47.90 266	72.66 242	48.75 257	73.84 325	71.21 311

test_fmvsmconf0.01_n		73.91 108	73.64 118	74.71 104	69.79 282	66.25 93	75.90 130	79.90 138	46.03 277	76.48 167	85.02 178	67.96 99	73.97 231	74.47 49	87.22 181	83.90 129

fmvsm_s_conf0.5_n_a		67.00 213	65.95 223	70.17 184	69.72 283	61.16 140	73.34 158	56.83 328	40.96 317	68.36 279	80.08 249	62.84 142	67.57 293	66.90 109	74.50 317	81.78 186

FMVSNet3		65.00 230	65.16 231	64.52 253	69.47 284	37.56 336	66.63 255	70.38 251	51.55 225	74.72 188	83.27 205	37.89 322	74.44 225	47.12 273	85.37 203	81.57 189

MS-PatchMatch		55.59 303	54.89 312	57.68 310	69.18 285	49.05 233	61.00 307	62.93 300	35.98 348	58.36 346	68.93 352	36.71 328	66.59 306	37.62 336	63.30 371	57.39 377

baseline1		57.82 294	58.36 290	56.19 315	69.17 286	30.76 373	62.94 297	55.21 336	46.04 276	63.83 314	78.47 272	41.20 298	63.68 318	39.44 319	68.99 354	74.13 281

v148		69.38 177	69.39 173	69.36 197	69.14 287	44.56 277	68.83 220	72.70 222	54.79 181	78.59 128	84.12 188	54.69 222	76.74 202	59.40 175	82.20 243	86.79 63

test_fmvsmconf0.1_n		73.26 120	72.82 135	74.56 106	69.10 288	66.18 95	74.65 148	79.34 148	45.58 279	75.54 178	83.91 191	67.19 103	73.88 234	73.26 57	86.86 186	83.63 137

fmvsm_s_conf0.1_n		66.60 215	65.54 225	69.77 192	68.99 289	59.15 162	72.12 168	56.74 330	40.72 322	68.25 282	80.14 248	61.18 166	66.92 299	67.34 104	74.40 318	83.23 152

Syy-MVS		54.13 310	55.45 310	50.18 340	68.77 290	23.59 392	55.02 345	44.55 376	43.80 294	58.05 348	64.07 369	46.22 269	58.83 334	46.16 282	72.36 332	68.12 332

myMVS_eth3d		50.36 335	50.52 340	49.88 341	68.77 290	22.69 394	55.02 345	44.55 376	43.80 294	58.05 348	64.07 369	14.16 405	58.83 334	33.90 359	72.36 332	68.12 332

test_fmvsmconf_n		72.91 132	72.40 143	74.46 107	68.62 292	66.12 96	74.21 153	78.80 158	45.64 278	74.62 192	83.25 206	66.80 111	73.86 235	72.97 60	86.66 192	83.39 145

CANet_DTU		64.04 244	63.83 244	64.66 251	68.39 293	42.97 292	73.45 157	74.50 209	52.05 218	54.78 363	75.44 300	43.99 282	70.42 270	53.49 225	78.41 288	80.59 212

EU-MVSNet		60.82 272	60.80 271	60.86 291	68.37 294	41.16 303	72.27 164	68.27 265	26.96 381	69.08 266	75.71 295	32.09 347	67.44 294	55.59 203	78.90 282	73.97 282

PVSNet	43.83 21	51.56 329	51.17 332	52.73 329	68.34 295	38.27 327	48.22 366	53.56 346	36.41 346	54.29 366	64.94 368	34.60 334	54.20 347	30.34 369	69.87 350	65.71 347

EPNet		69.10 180	67.32 205	74.46 107	68.33 296	61.27 138	77.56 103	63.57 297	60.95 122	56.62 355	82.75 212	51.53 240	81.24 119	54.36 217	90.20 123	80.88 202
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n		66.34 220	65.27 228	69.57 195	68.20 297	59.14 164	71.66 180	56.48 331	40.92 318	67.78 284	79.46 257	61.23 163	66.90 300	67.39 100	74.32 321	82.66 169

IB-MVS	49.67 18	59.69 282	56.96 298	67.90 223	68.19 298	50.30 218	61.42 303	65.18 283	47.57 267	55.83 359	67.15 364	23.77 389	79.60 151	43.56 298	79.97 271	73.79 285
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		60.62 275	59.97 276	62.58 274	68.13 299	47.28 255	68.59 226	73.96 211	32.19 365	59.94 338	68.86 354	50.48 246	77.64 191	41.85 307	75.74 303	62.83 360

eth_miper_zixun_eth		69.42 175	68.73 186	71.50 168	67.99 300	46.42 263	67.58 238	78.81 156	50.72 237	78.13 135	80.34 243	50.15 249	80.34 139	60.18 164	84.65 218	87.74 50

TinyColmap		67.98 196	69.28 174	64.08 256	67.98 301	46.82 259	70.04 203	75.26 202	53.05 208	77.36 146	86.79 136	59.39 183	72.59 246	45.64 286	88.01 166	72.83 293

EPNet_dtu		58.93 287	58.52 286	60.16 296	67.91 302	47.70 250	69.97 205	58.02 317	49.73 248	47.28 384	73.02 322	38.14 318	62.34 323	36.57 343	85.99 199	70.43 317
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 295	57.02 297	59.17 300	67.89 303	34.93 352	58.91 321	57.25 323	50.24 242	64.01 311	71.46 332	32.49 344	71.39 261	31.31 366	79.57 277	71.19 312

our_test_3		56.46 298	56.51 301	56.30 314	67.70 304	39.66 316	55.36 344	52.34 353	40.57 324	63.85 313	69.91 344	40.04 307	58.22 337	43.49 299	75.29 311	71.03 314

ppachtmachnet_test		60.26 278	59.61 279	62.20 277	67.70 304	44.33 279	58.18 327	60.96 309	40.75 321	65.80 298	72.57 324	41.23 297	63.92 317	46.87 277	82.42 242	78.33 241

MVS_Test		69.84 168	70.71 165	67.24 230	67.49 306	43.25 290	69.87 207	81.22 110	52.69 212	71.57 237	86.68 142	62.09 153	74.51 224	66.05 112	78.74 283	83.96 127

fmvsm_l_conf0.5_n		67.48 203	66.88 214	69.28 200	67.41 307	62.04 127	70.69 197	69.85 254	39.46 328	69.59 261	81.09 231	58.15 195	68.73 280	67.51 97	78.16 292	77.07 262

thisisatest0515		60.48 276	57.86 292	68.34 218	67.25 308	46.42 263	60.58 311	62.14 302	40.82 319	63.58 318	69.12 348	26.28 379	78.34 176	48.83 256	82.13 244	80.26 217

V42		71.06 153	70.83 164	71.72 164	67.25 308	47.14 257	65.94 261	80.35 131	51.35 229	83.40 76	83.23 207	59.25 185	78.80 163	65.91 114	80.81 263	89.23 29

fmvsm_l_conf0.5_n_a		66.66 214	65.97 222	68.72 214	67.09 310	61.38 135	70.03 204	69.15 259	38.59 335	68.41 278	80.36 242	56.56 216	68.32 285	66.10 111	77.45 296	76.46 263

GA-MVS		62.91 254	61.66 260	66.66 239	67.09 310	44.49 278	61.18 306	69.36 258	51.33 230	69.33 264	74.47 307	36.83 327	74.94 218	50.60 242	74.72 313	80.57 213

testf1		75.66 87	76.57 81	72.95 139	67.07 312	67.62 81	76.10 125	80.68 121	64.95 87	86.58 33	90.94 40	71.20 70	71.68 259	60.46 161	91.13 101	79.56 225

APD_test2		75.66 87	76.57 81	72.95 139	67.07 312	67.62 81	76.10 125	80.68 121	64.95 87	86.58 33	90.94 40	71.20 70	71.68 259	60.46 161	91.13 101	79.56 225

HY-MVS	49.31 19	57.96 293	57.59 294	59.10 302	66.85 314	36.17 342	65.13 274	65.39 282	39.24 331	54.69 365	78.14 278	44.28 281	67.18 298	33.75 360	70.79 343	73.95 283

CR-MVSNet		58.96 286	58.49 287	60.36 294	66.37 315	48.24 239	70.93 193	56.40 333	32.87 364	61.35 327	86.66 143	33.19 339	63.22 321	48.50 261	70.17 348	69.62 324

RPMNet		65.77 223	65.08 237	67.84 225	66.37 315	48.24 239	70.93 193	86.27 19	54.66 184	61.35 327	86.77 138	33.29 338	85.67 47	55.93 198	70.17 348	69.62 324

IterMVS		63.12 252	62.48 258	65.02 250	66.34 317	52.86 201	63.81 286	62.25 301	46.57 273	71.51 239	80.40 241	44.60 279	66.82 304	51.38 236	75.47 307	75.38 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		69.82 169	69.89 170	69.61 194	66.24 318	43.48 286	68.12 233	79.61 143	51.43 226	77.72 141	80.18 247	54.61 224	78.15 183	63.62 135	87.50 172	87.20 58

tpm2		56.12 299	54.64 314	60.55 293	66.24 318	36.01 343	68.14 232	56.77 329	33.60 362	58.25 347	75.52 299	30.25 365	74.33 227	33.27 361	69.76 352	71.32 308

Anonymous20240521		63.55 246	66.07 220	55.99 316	66.18 320	44.04 281	68.77 224	68.80 260	46.99 270	72.57 221	85.84 170	39.87 308	50.22 352	53.40 228	92.23 81	73.71 286

Patchmtry		60.91 271	63.01 254	54.62 322	66.10 321	26.27 387	67.47 240	56.40 333	54.05 197	72.04 230	86.66 143	33.19 339	60.17 330	43.69 296	87.45 174	77.42 253

FMVSNet5		55.08 306	55.54 309	53.71 324	65.80 322	33.50 361	56.22 337	52.50 352	43.72 298	61.06 330	83.38 199	25.46 383	54.87 344	30.11 371	81.64 256	72.75 294

1314		59.83 281	58.86 284	62.74 273	65.71 323	44.78 276	68.59 226	72.63 223	33.54 363	61.05 331	67.29 363	43.62 285	71.26 262	49.49 251	67.84 361	72.19 301

MDTV_nov1_ep13			54.05 317		65.54 324	29.30 378	59.00 319	55.22 335	35.96 349	52.44 370	75.98 293	30.77 362	59.62 331	38.21 330	73.33 327

baseline2		55.57 304	52.74 322	64.05 257	65.26 325	44.11 280	62.38 298	54.43 340	39.03 332	51.21 374	67.35 362	33.66 337	72.45 247	37.14 339	64.22 369	75.60 268

USDC		62.80 256	63.10 253	61.89 279	65.19 326	43.30 289	67.42 241	74.20 210	35.80 350	72.25 227	84.48 184	45.67 271	71.95 255	37.95 333	84.97 211	70.42 318

tpm		50.60 333	52.42 326	45.14 363	65.18 327	26.29 386	60.30 312	43.50 378	37.41 342	57.01 351	79.09 266	30.20 367	42.32 382	32.77 363	66.36 364	66.81 342

PatchmatchNet		54.60 308	54.27 315	55.59 318	65.17 328	39.08 318	66.92 251	51.80 355	39.89 326	58.39 345	73.12 321	31.69 353	58.33 336	43.01 301	58.38 385	69.38 327
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_ehance_all_eth		68.36 190	68.16 195	68.98 206	65.14 329	43.34 288	67.07 248	78.92 155	49.11 254	76.21 172	77.72 282	53.48 229	77.92 186	61.16 154	84.59 220	85.68 82

cl____		68.26 195	68.26 191	68.29 219	64.98 330	43.67 284	65.89 262	74.67 206	50.04 246	76.86 155	82.42 217	48.74 260	75.38 211	60.92 158	89.81 133	85.80 80

DIV-MVS_self_test		68.27 194	68.26 191	68.29 219	64.98 330	43.67 284	65.89 262	74.67 206	50.04 246	76.86 155	82.43 216	48.74 260	75.38 211	60.94 157	89.81 133	85.81 76

tpm cat1		54.02 313	52.63 323	58.19 307	64.85 332	39.86 315	66.26 259	57.28 322	32.16 366	56.90 352	70.39 338	32.75 343	65.30 311	34.29 356	58.79 382	69.41 326

XXY-MVS		55.19 305	57.40 296	48.56 351	64.45 333	34.84 354	51.54 359	53.59 344	38.99 333	63.79 315	79.43 258	56.59 214	45.57 367	36.92 341	71.29 340	65.25 350

PatchT		53.35 317	56.47 302	43.99 368	64.19 334	17.46 399	59.15 317	43.10 380	52.11 217	54.74 364	86.95 132	29.97 368	49.98 353	43.62 297	74.40 318	64.53 357

D2MVS		62.58 259	61.05 268	67.20 231	63.85 335	47.92 245	56.29 336	69.58 256	39.32 329	70.07 255	78.19 277	34.93 333	72.68 241	53.44 226	83.74 230	81.00 198

mvs_anonymous		65.08 229	65.49 226	63.83 259	63.79 336	37.60 335	66.52 257	69.82 255	43.44 300	73.46 210	86.08 165	58.79 190	71.75 258	51.90 232	75.63 305	82.15 180

CostFormer		57.35 296	56.14 304	60.97 289	63.76 337	38.43 325	67.50 239	60.22 311	37.14 344	59.12 344	76.34 292	32.78 342	71.99 254	39.12 323	69.27 353	72.47 297

Gipuma		69.55 173	72.83 134	59.70 297	63.63 338	53.97 195	80.08 78	75.93 196	64.24 94	73.49 209	88.93 102	57.89 203	62.46 322	59.75 172	91.55 91	62.67 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
cl22		67.14 209	66.51 215	69.03 205	63.20 339	43.46 287	66.88 253	76.25 192	49.22 252	74.48 194	77.88 281	45.49 273	77.40 193	60.64 160	84.59 220	86.24 69

gg-mvs-nofinetune		55.75 301	56.75 300	52.72 330	62.87 340	28.04 382	68.92 218	41.36 390	71.09 41	50.80 376	92.63 12	20.74 392	66.86 302	29.97 372	72.41 331	63.25 359

gm-plane-assit					62.51 341	33.91 359			37.25 343		62.71 374		72.74 240	38.70 325

MVS-HIRNet		45.53 348	47.29 348	40.24 374	62.29 342	26.82 385	56.02 340	37.41 396	29.74 376	43.69 394	81.27 228	33.96 335	55.48 342	24.46 392	56.79 386	38.43 395

diffmvs		67.42 206	67.50 203	67.20 231	62.26 343	45.21 273	64.87 276	77.04 186	48.21 259	71.74 231	79.70 254	58.40 192	71.17 263	64.99 120	80.27 268	85.22 87
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		41.62 359	39.89 364	46.80 356	61.81 344	51.59 207	33.56 392	35.74 397	27.48 380	37.64 398	53.53 388	23.24 390	42.09 383	27.39 382	58.64 383	46.72 387

KD-MVS_self_test		66.38 218	67.51 202	62.97 270	61.76 345	34.39 356	58.11 328	75.30 201	50.84 236	77.12 148	85.42 173	56.84 213	69.44 275	51.07 238	91.16 98	85.08 92

MDA-MVSNet-bldmvs		62.34 261	61.73 259	64.16 254	61.64 346	49.90 224	48.11 367	57.24 324	53.31 207	80.95 106	79.39 259	49.00 258	61.55 327	45.92 284	80.05 270	81.03 196

miper_enhance_ethall		65.86 222	65.05 238	68.28 221	61.62 347	42.62 295	64.74 277	77.97 175	42.52 305	73.42 211	72.79 323	49.66 250	77.68 190	58.12 182	84.59 220	84.54 112

WTY-MVS		49.39 339	50.31 342	46.62 357	61.22 348	32.00 367	46.61 372	49.77 361	33.87 359	54.12 367	69.55 347	41.96 294	45.40 369	31.28 367	64.42 368	62.47 364

CMPMVS	48.73 20	61.54 268	60.89 269	63.52 263	61.08 349	51.55 208	68.07 234	68.00 266	33.88 358	65.87 297	81.25 229	37.91 321	67.71 289	49.32 253	82.60 241	71.31 309
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 334	50.69 339	49.64 344	60.76 350	41.87 299	53.18 353	45.48 374	43.41 301	49.41 381	60.47 382	29.22 371	44.73 374	42.09 305	72.14 335	62.33 366

test-mter		48.56 341	48.20 346	49.64 344	60.76 350	41.87 299	53.18 353	45.48 374	31.91 370	49.41 381	60.47 382	18.34 397	44.73 374	42.09 305	72.14 335	62.33 366

GG-mvs-BLEND				52.24 331	60.64 352	29.21 379	69.73 209	42.41 383		45.47 387	52.33 391	20.43 394	68.16 286	25.52 389	65.42 366	59.36 373

tpmvs		55.84 300	55.45 310	57.01 312	60.33 353	33.20 362	65.89 262	59.29 315	47.52 268	56.04 357	73.60 316	31.05 360	68.06 288	40.64 315	64.64 367	69.77 322

miper_lstm_enhance		61.97 262	61.63 262	62.98 269	60.04 354	45.74 270	47.53 369	70.95 246	44.04 292	73.06 215	78.84 270	39.72 309	60.33 329	55.82 200	84.64 219	82.88 161

dmvs_re		49.91 338	50.77 338	47.34 353	59.98 355	38.86 322	53.18 353	53.58 345	39.75 327	55.06 362	61.58 378	36.42 329	44.40 376	29.15 379	68.23 357	58.75 374

PVSNet_0	36.71 22	41.12 360	40.78 363	42.14 370	59.97 356	40.13 313	40.97 381	42.24 387	30.81 374	44.86 390	49.41 394	40.70 303	45.12 371	23.15 393	34.96 397	41.16 393

dmvs_testset		45.26 349	47.51 347	38.49 377	59.96 357	14.71 401	58.50 324	43.39 379	41.30 312	51.79 373	56.48 386	39.44 312	49.91 355	21.42 395	55.35 391	50.85 382

new-patchmatchnet		52.89 319	55.76 308	44.26 367	59.94 358	6.31 405	37.36 389	50.76 358	41.10 314	64.28 308	79.82 252	44.77 277	48.43 360	36.24 346	87.61 169	78.03 248

test20.03		55.74 302	57.51 295	50.42 339	59.89 359	32.09 366	50.63 361	49.01 363	50.11 244	65.07 303	83.23 207	45.61 272	48.11 361	30.22 370	83.82 229	71.07 313

MVSTER		63.29 250	61.60 263	68.36 217	59.77 360	46.21 266	60.62 310	71.32 238	41.83 308	75.40 181	79.12 265	30.25 365	75.85 205	56.30 195	79.81 273	83.03 158

N_pmnet		52.06 325	51.11 333	54.92 319	59.64 361	71.03 53	37.42 388	61.62 308	33.68 360	57.12 350	72.10 325	37.94 320	31.03 395	29.13 380	71.35 339	62.70 361

test_vis1_n_1920		52.96 318	53.50 318	51.32 336	59.15 362	44.90 275	56.13 339	64.29 292	30.56 375	59.87 340	60.68 380	40.16 306	47.47 362	48.25 265	62.46 373	61.58 368

JIA-IIPM		54.03 312	51.62 328	61.25 287	59.14 363	55.21 187	59.10 318	47.72 367	50.85 235	50.31 380	85.81 171	20.10 395	63.97 316	36.16 347	55.41 390	64.55 356

LF4IMVS		67.50 202	67.31 206	68.08 222	58.86 364	61.93 128	71.43 183	75.90 197	44.67 290	72.42 224	80.20 245	57.16 207	70.44 269	58.99 177	86.12 197	71.88 303

UnsupCasMVSNet_bld		50.01 337	51.03 335	46.95 354	58.61 365	32.64 363	48.31 365	53.27 349	34.27 357	60.47 334	71.53 331	41.40 296	47.07 364	30.68 368	60.78 378	61.13 369

dp		44.09 355	44.88 357	41.72 373	58.53 366	23.18 393	54.70 348	42.38 385	34.80 353	44.25 392	65.61 366	24.48 388	44.80 373	29.77 373	49.42 393	57.18 378

testgi		54.00 314	56.86 299	45.45 361	58.20 367	25.81 389	49.05 363	49.50 362	45.43 283	67.84 283	81.17 230	51.81 239	43.20 381	29.30 375	79.41 278	67.34 338

wuyk23d		61.97 262	66.25 217	49.12 348	58.19 368	60.77 150	66.32 258	52.97 350	55.93 170	90.62 5	86.91 133	73.07 57	35.98 393	20.63 397	91.63 87	50.62 383

ANet_high		67.08 210	69.94 169	58.51 306	57.55 369	27.09 384	58.43 325	76.80 189	63.56 101	82.40 88	91.93 20	59.82 180	64.98 313	50.10 246	88.86 156	83.46 143

Patchmatch-test		47.93 342	49.96 343	41.84 371	57.42 370	24.26 391	48.75 364	41.49 389	39.30 330	56.79 353	73.48 317	30.48 364	33.87 394	29.29 376	72.61 330	67.39 336

test_vis1_n		51.27 331	50.41 341	53.83 323	56.99 371	50.01 222	56.75 333	60.53 310	25.68 385	59.74 341	57.86 385	29.40 370	47.41 363	43.10 300	63.66 370	64.08 358

new_pmnet		37.55 363	39.80 365	30.79 379	56.83 372	16.46 400	39.35 385	30.65 399	25.59 386	45.26 388	61.60 377	24.54 387	28.02 398	21.60 394	52.80 392	47.90 386

pmmvs3		46.71 345	45.09 355	51.55 334	56.76 373	48.25 238	55.78 342	39.53 394	24.13 390	50.35 379	63.40 371	15.90 402	51.08 350	29.29 376	70.69 345	55.33 380

sss		47.59 344	48.32 344	45.40 362	56.73 374	33.96 358	45.17 375	48.51 365	32.11 369	52.37 371	65.79 365	40.39 305	41.91 385	31.85 364	61.97 375	60.35 370

tpmrst		50.15 336	51.38 331	46.45 358	56.05 375	24.77 390	64.40 283	49.98 360	36.14 347	53.32 369	69.59 346	35.16 332	48.69 357	39.24 321	58.51 384	65.89 345

TESTMET0.1,1		45.17 350	44.93 356	45.89 360	56.02 376	38.31 326	53.18 353	41.94 388	27.85 378	44.86 390	56.47 387	17.93 398	41.50 386	38.08 332	68.06 358	57.85 375

ADS-MVSNet2		48.76 340	47.25 349	53.29 328	55.90 377	40.54 311	47.34 370	54.99 338	31.41 372	50.48 377	72.06 326	31.23 356	54.26 346	25.93 385	55.93 387	65.07 351

ADS-MVSNet		44.62 353	45.58 352	41.73 372	55.90 377	20.83 397	47.34 370	39.94 393	31.41 372	50.48 377	72.06 326	31.23 356	39.31 389	25.93 385	55.93 387	65.07 351

test0.0.03 1		47.72 343	48.31 345	45.93 359	55.53 379	29.39 377	46.40 373	41.21 391	43.41 301	55.81 360	67.65 359	29.22 371	43.77 380	25.73 388	69.87 350	64.62 355

UnsupCasMVSNet_eth		52.26 324	53.29 321	49.16 347	55.08 380	33.67 360	50.03 362	58.79 316	37.67 341	63.43 321	74.75 304	41.82 295	45.83 366	38.59 328	59.42 381	67.98 335

pmmvs5		52.49 323	52.58 324	52.21 332	54.99 381	32.38 364	55.45 343	53.84 343	32.15 367	55.49 361	74.81 302	38.08 319	57.37 341	34.02 357	74.40 318	66.88 340

DSMNet-mixed		43.18 358	44.66 358	38.75 376	54.75 382	28.88 380	57.06 332	27.42 401	13.47 397	47.27 385	77.67 283	38.83 315	39.29 390	25.32 390	60.12 380	48.08 385

MDA-MVSNet_test_wron		52.57 322	53.49 320	49.81 343	54.24 383	36.47 340	40.48 383	46.58 372	38.13 336	75.47 180	73.32 319	41.05 302	43.85 379	40.98 313	71.20 341	69.10 330

YYNet1		52.58 321	53.50 318	49.85 342	54.15 384	36.45 341	40.53 382	46.55 373	38.09 337	75.52 179	73.31 320	41.08 301	43.88 378	41.10 311	71.14 342	69.21 328

EPMVS		45.74 347	46.53 350	43.39 369	54.14 385	22.33 396	55.02 345	35.00 398	34.69 355	51.09 375	70.20 340	25.92 381	42.04 384	37.19 338	55.50 389	65.78 346

test_cas_vis1_n_1920		50.90 332	50.92 336	50.83 338	54.12 386	47.80 247	51.44 360	54.61 339	26.95 382	63.95 312	60.85 379	37.86 323	44.97 372	45.53 287	62.97 372	59.72 372

test_fmvs3		56.78 297	55.99 306	59.12 301	53.96 387	48.09 242	58.76 322	66.22 273	27.54 379	76.66 160	68.69 356	25.32 385	51.31 349	53.42 227	73.38 326	77.97 251

test_fmvs1_n		52.70 320	52.01 327	54.76 320	53.83 388	50.36 216	55.80 341	65.90 275	24.96 387	65.39 300	60.64 381	27.69 374	48.46 358	45.88 285	67.99 359	65.46 348

KD-MVS_2432*1600		52.05 326	51.58 329	53.44 326	52.11 389	31.20 369	44.88 376	64.83 287	41.53 310	64.37 306	70.03 342	15.61 403	64.20 314	36.25 344	74.61 315	64.93 353

miper_refine_blended		52.05 326	51.58 329	53.44 326	52.11 389	31.20 369	44.88 376	64.83 287	41.53 310	64.37 306	70.03 342	15.61 403	64.20 314	36.25 344	74.61 315	64.93 353

test_fmvs2		54.80 307	54.11 316	56.88 313	51.76 391	49.95 223	56.70 334	65.80 276	26.22 384	69.42 262	65.25 367	31.82 351	49.98 353	49.63 250	70.36 346	70.71 315

E-PMN		45.17 350	45.36 353	44.60 365	50.07 392	42.75 293	38.66 386	42.29 386	46.39 274	39.55 395	51.15 392	26.00 380	45.37 370	37.68 334	76.41 299	45.69 389

PMMVS		44.69 352	43.95 360	46.92 355	50.05 393	53.47 199	48.08 368	42.40 384	22.36 393	44.01 393	53.05 390	42.60 292	45.49 368	31.69 365	61.36 377	41.79 392

test_fmvs1		51.51 330	50.86 337	53.48 325	49.72 394	49.35 232	54.11 349	64.96 285	24.64 389	63.66 317	59.61 384	28.33 373	48.45 359	45.38 290	67.30 363	62.66 363

EMVS		44.61 354	44.45 359	45.10 364	48.91 395	43.00 291	37.92 387	41.10 392	46.75 272	38.00 397	48.43 395	26.42 378	46.27 365	37.11 340	75.38 309	46.03 388

mvsany_test3		43.76 357	41.01 361	52.01 333	48.09 396	57.74 173	42.47 380	23.85 404	23.30 392	64.80 304	62.17 376	27.12 375	40.59 387	29.17 378	48.11 394	57.69 376

mvsany_test1		37.88 361	35.74 366	44.28 366	47.28 397	49.90 224	36.54 390	24.37 403	19.56 396	45.76 386	53.46 389	32.99 341	37.97 392	26.17 383	35.52 396	44.99 391

test_vis3_rt		51.94 328	51.04 334	54.65 321	46.32 398	50.13 220	44.34 378	78.17 171	23.62 391	68.95 270	62.81 373	21.41 391	38.52 391	41.49 309	72.22 334	75.30 274

test_vis1_rt		46.70 346	45.24 354	51.06 337	44.58 399	51.04 211	39.91 384	67.56 267	21.84 395	51.94 372	50.79 393	33.83 336	39.77 388	35.25 353	61.50 376	62.38 365

MVE	27.91 23	36.69 364	35.64 367	39.84 375	43.37 400	35.85 346	19.49 394	24.61 402	24.68 388	39.05 396	62.63 375	38.67 317	27.10 399	21.04 396	47.25 395	56.56 379
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 362	40.87 362	28.36 380	42.41 401	5.35 406	24.61 393	27.75 400	32.15 367	47.85 383	70.27 339	35.85 331	29.51 397	19.08 398	67.85 360	50.22 384

test_f		43.79 356	45.63 351	38.24 378	42.29 402	38.58 324	34.76 391	47.68 368	22.22 394	67.34 290	63.15 372	31.82 351	30.60 396	39.19 322	62.28 374	45.53 390

DeepMVS_CX				11.83 382	15.51 403	13.86 402		11.25 407	5.76 398	20.85 400	26.46 397	17.06 401	9.22 401	9.69 401	13.82 400	12.42 397

test_method		19.26 365	19.12 369	19.71 381	9.09 404	1.91 408	7.79 396	53.44 347	1.42 399	10.27 401	35.80 396	17.42 400	25.11 400	12.44 399	24.38 399	32.10 396

tmp_tt		11.98 367	14.73 370	3.72 383	2.28 405	4.62 407	19.44 395	14.50 406	0.47 401	21.55 399	9.58 399	25.78 382	4.57 402	11.61 400	27.37 398	1.96 398

test123		4.43 370	5.78 373	0.39 385	0.97 406	0.28 409	46.33 374	0.45 408	0.31 402	0.62 403	1.50 402	0.61 408	0.11 404	0.56 402	0.63 401	0.77 400

testmvs		4.06 371	5.28 374	0.41 384	0.64 407	0.16 410	42.54 379	0.31 409	0.26 403	0.50 404	1.40 403	0.77 407	0.17 403	0.56 402	0.55 402	0.90 399

test_blank		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

eth-test2					0.00 408

eth-test					0.00 408

uanet_test		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

DCPMVS		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

cdsmvs_eth3d_5k		17.71 366	23.62 368	0.00 386	0.00 408	0.00 411	0.00 397	70.17 253	0.00 404	0.00 405	74.25 311	68.16 95	0.00 405	0.00 404	0.00 403	0.00 401

pcd_1.5k_mvsjas		5.20 369	6.93 372	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	62.39 149	0.00 405	0.00 404	0.00 403	0.00 401

sosnet-low-res		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

sosnet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uncertanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

Regformer		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

ab-mvs-re		5.62 368	7.50 371	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	67.46 360	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

uanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 411	0.00 397	0.00 410	0.00 404	0.00 405	0.00 404	0.00 409	0.00 405	0.00 404	0.00 403	0.00 401

MM				79.55 48		65.47 100	80.94 62	78.74 160	71.22 40	72.40 225	88.70 105	60.51 172	87.70 3	77.40 32	89.13 151	85.48 84

WAC-MVS						22.69 394								36.10 348

PC_three_1452									46.98 271	81.83 93	86.28 155	66.55 115	84.47 71	63.31 140	90.78 114	83.49 139

test_241102_TWO								84.80 44	72.61 30	84.93 56	89.70 81	77.73 22	85.89 40	75.29 42	94.22 52	83.25 150

test_0728_THIRD									74.03 21	85.83 43	90.41 59	75.58 37	85.69 45	77.43 30	94.74 29	84.31 121

GSMVS																70.05 319

sam_mvs1												31.41 354				70.05 319

sam_mvs												31.21 358

MTGPA								80.63 123

test_post1							66.63 255				2.08 400	30.66 363	59.33 332	40.34 317

test_post											1.99 401	30.91 361	54.76 345

patchmatchnet-post											68.99 349	31.32 355	69.38 276

MTMP							84.83 31	19.26 405

test9_res														72.12 69	91.37 93	77.40 254

agg_prior2														70.70 75	90.93 108	78.55 240

test_prior4						70.14 63	77.57 102

test_prior2							75.57 133		58.92 139	76.53 166	86.78 137	67.83 100		69.81 78	92.76 73

旧先验2							71.17 190		45.11 287	78.54 131			61.28 328	59.19 176

新几何2							71.33 186

无先验							74.82 139	70.94 247	47.75 266				76.85 200	54.47 213		72.09 302

原ACMM2							74.78 143

testdata2													67.30 295	48.34 263

segment_acmp												68.30 94

testdata1							68.34 231		57.24 156

plane_prior5								85.49 29					86.15 27	71.09 71	90.94 106	84.82 99

plane_prior4											89.11 95

plane_prior3						65.67 99			63.82 98	78.23 133

plane_prior2							82.74 51		65.45 76

plane_prior						65.18 104	80.06 79		61.88 117						89.91 132

n2								0.00 410

nn								0.00 410

door-mid								55.02 337

test11								82.71 84

door								52.91 351

HQP5-MVS						58.80 167

BP-MVS														67.38 102

HQP4-MVS										71.59 233			85.31 52			83.74 134

HQP3-MVS								84.12 65							89.16 147

HQP2-MVS												58.09 197

MDTV_nov1_ep13_2view						18.41 398	53.74 351		31.57 371	44.89 389		29.90 369		32.93 362		71.48 306

ACMMP++_ref															89.47 142

ACMMP++															91.96 83

Test By Simon												62.56 145

High-resolution multi-view benchmark