High-res multi-view results

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

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

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

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

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

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

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

LCM-MVSNet		86.90 1	88.67 1	81.57 24	91.50 1	63.30 125	84.80 34	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 71	68.08 98	97.05 1	96.93 1

zzz-MVS		83.01 25	83.63 25	81.13 34	91.16 2	78.16 14	82.72 57	80.63 130	72.08 39	84.93 58	90.79 46	74.65 49	84.42 74	80.98 4	94.75 29	80.82 205

MTAPA		83.19 20	83.87 20	81.13 34	91.16 2	78.16 14	84.87 32	80.63 130	72.08 39	84.93 58	90.79 46	74.65 49	84.42 74	80.98 4	94.75 29	80.82 205

mPP-MVS		84.01 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 13	82.79 92	72.41 36	85.11 57	90.85 45	76.65 30	84.89 65	79.30 17	94.63 35	82.35 180

MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 23	83.59 83	71.31 43	81.26 108	90.96 42	74.57 51	84.69 69	78.41 22	94.78 28	82.74 171
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 34	77.36 87	90.35 6	82.82 2	82.15 59	79.22 156	74.08 22	87.16 30	91.97 20	84.80 2	76.97 208	64.98 125	93.61 66	72.28 291
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 52	82.86 39	73.07 147	89.93 7	39.21 306	77.15 120	81.28 116	79.74 6	90.87 4	92.73 11	75.03 45	84.93 64	63.83 136	95.19 16	95.07 3

DTE-MVSNet		80.35 53	82.89 38	72.74 158	89.84 8	37.34 323	77.16 119	81.81 106	80.45 4	90.92 3	92.95 7	74.57 51	86.12 29	63.65 137	94.68 33	94.76 6

PEN-MVS		80.46 51	82.91 37	73.11 145	89.83 9	39.02 309	77.06 122	82.61 96	80.04 5	90.60 6	92.85 9	74.93 47	85.21 59	63.15 144	95.15 18	95.09 2

region2R		83.54 16	83.86 21	82.58 16	89.82 10	77.53 20	87.06 14	84.23 72	70.19 51	83.86 77	90.72 51	75.20 42	86.27 21	79.41 15	94.25 53	83.95 133

ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 22	87.01 15	84.19 73	70.23 49	84.49 67	90.67 52	75.15 43	86.37 18	79.58 11	94.26 52	84.18 129

MSP-MVS		80.49 50	79.67 65	82.96 7	89.70 12	77.46 23	87.16 12	85.10 45	64.94 90	81.05 110	88.38 115	57.10 207	87.10 7	79.75 8	83.87 232	84.31 126
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 60	81.65 50	72.98 150	89.66 13	39.06 308	76.76 123	80.46 135	78.91 8	90.32 7	91.70 26	68.49 98	84.89 65	63.40 141	95.12 19	95.01 4

PGM-MVS		83.07 23	83.25 33	82.54 18	89.57 14	77.21 24	82.04 61	85.40 38	67.96 62	84.91 62	90.88 43	75.59 38	86.57 14	78.16 23	94.71 32	83.82 134

WR-MVS_H		80.22 56	82.17 45	74.39 123	89.46 15	42.69 284	78.24 107	82.24 99	78.21 10	89.57 9	92.10 18	68.05 103	85.59 49	66.04 116	95.62 9	94.88 5

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 18	86.80 18	84.66 57	72.71 29	82.87 87	90.39 66	73.86 56	86.31 19	78.84 20	94.03 58	84.64 108

X-MVStestdata		76.81 87	74.79 108	82.85 10	89.43 16	77.61 18	86.80 18	84.66 57	72.71 29	82.87 87	9.95 376	73.86 56	86.31 19	78.84 20	94.03 58	84.64 108

CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 5	84.43 64	71.96 42	84.70 64	90.56 55	77.12 26	86.18 26	79.24 18	95.36 13	82.49 177

ACMMP_NAP		82.33 31	83.28 31	79.46 55	89.28 19	69.09 80	83.62 45	84.98 47	64.77 91	83.97 75	91.02 41	75.53 41	85.93 38	82.00 2	94.36 48	83.35 152

UniMVSNet_ETH3D		76.74 88	79.02 68	69.92 196	89.27 20	43.81 272	74.47 154	71.70 233	72.33 37	85.50 50	93.65 3	77.98 22	76.88 211	54.60 211	91.64 97	89.08 35

HFP-MVS		83.39 19	84.03 18	81.48 26	89.25 21	75.69 28	87.01 15	84.27 68	70.23 49	84.47 68	90.43 60	76.79 27	85.94 35	79.58 11	94.23 54	82.82 167

#test#		82.40 30	82.71 41	81.48 26	89.25 21	75.69 28	84.47 36	84.27 68	64.45 94	84.47 68	90.43 60	76.79 27	85.94 35	76.01 39	94.23 54	82.82 167

ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 26	87.65 6	85.89 30	71.03 45	85.85 42	90.58 54	78.77 17	85.78 43	79.37 16	95.17 17	84.62 110
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 39	89.20 24	78.75 12	86.48 22	82.03 103	68.80 56	80.92 113	88.52 111	72.00 68	82.39 111	74.80 47	93.04 77	81.14 196

FOURS1					89.19 25	77.84 16	91.64 1	89.11 2	84.05 2	91.57 2

ZNCC-MVS		83.12 22	83.68 23	81.45 28	89.14 26	73.28 47	86.32 24	85.97 29	67.39 64	84.02 74	90.39 66	74.73 48	86.46 15	80.73 7	94.43 43	84.60 113

HPM-MVS++		79.89 57	79.80 63	80.18 44	89.02 27	78.44 13	83.49 49	80.18 142	64.71 93	78.11 145	88.39 114	65.46 130	83.14 98	77.64 32	91.20 109	78.94 233

GST-MVS		82.79 27	83.27 32	81.34 31	88.99 28	73.29 46	85.94 26	85.13 43	68.58 60	84.14 73	90.21 78	73.37 61	86.41 16	79.09 19	93.98 61	84.30 128

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 48	88.94 29	67.52 87	86.61 20	81.38 114	51.71 229	77.15 156	91.42 35	65.49 129	87.20 5	79.44 14	87.17 190	84.51 120
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 56	88.91 30	69.16 78	84.67 35	80.01 145	75.34 17	79.80 125	94.91 2	69.79 87	80.25 154	72.63 66	94.46 39	88.78 45

SMA-MVS		82.12 32	82.68 42	80.43 41	88.90 31	69.52 71	85.12 30	84.76 52	63.53 104	84.23 72	91.47 32	72.02 67	87.16 6	79.74 10	94.36 48	84.61 111
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 29	83.46 28	79.76 47	88.88 32	68.44 82	81.57 64	86.33 21	63.17 110	85.38 52	91.26 36	76.33 32	84.67 70	83.30 1	94.96 23	86.17 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		86.32 2	86.33 2	86.29 1	88.64 33	81.19 5	88.84 4	90.72 1	78.27 9	87.95 15	92.53 13	79.37 13	84.79 68	74.51 51	96.15 2	92.88 7

HPM-MVS_fast		84.59 6	85.10 8	83.06 6	88.60 34	75.83 27	86.27 25	86.89 18	73.69 25	86.17 37	91.70 26	78.23 20	85.20 60	79.45 13	94.91 25	88.15 49

SR-MVS		84.51 7	85.27 6	82.25 21	88.52 35	77.71 17	86.81 17	85.25 41	77.42 15	86.15 38	90.24 76	81.69 5	85.94 35	77.77 29	93.58 68	83.09 158

新几何1				69.99 194	88.37 36	71.34 57		62.08 299	43.85 295	74.99 192	86.11 168	52.85 229	70.57 275	50.99 237	83.23 241	68.05 323

1121		69.23 185	68.26 198	72.12 170	88.36 37	71.40 55	68.59 225	62.06 300	43.80 296	74.75 195	86.18 163	52.92 228	76.85 212	54.47 212	83.27 240	68.12 322

test1172		84.85 4	85.39 5	83.21 3	88.34 38	80.50 6	85.12 30	85.22 42	81.06 3	87.20 28	90.28 74	79.20 14	85.58 50	78.04 27	94.08 57	83.55 142

HPM-MVS		84.12 10	84.63 11	82.60 15	88.21 39	74.40 36	85.24 29	87.21 16	70.69 48	85.14 56	90.42 62	78.99 16	86.62 13	80.83 6	94.93 24	86.79 66
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 30	78.49 70	88.17 40	73.96 39	83.11 53	84.52 63	66.40 74	87.45 23	89.16 99	81.02 9	80.52 149	74.27 53	95.73 7	80.98 201
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 41	69.15 79	67.85 235	59.59 309	41.06 315	73.05 221	85.72 176	48.03 261			80.65 269	66.92 328

XVG-ACMP-BASELINE		80.54 49	81.06 53	78.98 63	87.01 42	72.91 48	80.23 81	85.56 33	66.56 73	85.64 45	89.57 88	69.12 92	80.55 148	72.51 68	93.37 70	83.48 146

xxxxxxxxxxxxxcwj		80.31 54	80.94 54	78.42 72	87.00 43	67.23 90	79.24 91	88.61 5	56.65 167	84.29 70	89.18 96	73.73 59	83.22 96	76.01 39	93.77 63	84.81 104

save fliter					87.00 43	67.23 90	79.24 91	77.94 179	56.65 167

LPG-MVS_test		83.47 18	84.33 14	80.90 37	87.00 43	70.41 66	82.04 61	86.35 19	69.77 53	87.75 16	91.13 37	81.83 3	86.20 24	77.13 37	95.96 5	86.08 74

LGP-MVS_train				80.90 37	87.00 43	70.41 66		86.35 19	69.77 53	87.75 16	91.13 37	81.83 3	86.20 24	77.13 37	95.96 5	86.08 74

EGC-MVSNET		64.77 233	61.17 264	75.60 110	86.90 47	74.47 35	84.04 38	68.62 264	0.60 378	1.13 380	91.61 29	65.32 132	74.15 244	64.01 131	88.28 167	78.17 243

OPM-MVS		80.99 46	81.63 51	79.07 60	86.86 48	69.39 74	79.41 90	84.00 79	65.64 78	85.54 49	89.28 92	76.32 33	83.47 91	74.03 55	93.57 69	84.35 125
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
abl_6		84.92 3	85.70 3	82.57 17	86.72 49	79.27 8	87.56 7	86.08 27	77.48 13	88.12 14	91.53 30	81.18 8	84.31 77	78.12 24	94.47 38	84.15 130

DeepC-MVS	72.44 4	81.00 45	80.83 56	81.50 25	86.70 50	70.03 69	82.06 60	87.00 17	59.89 133	80.91 114	90.53 56	72.19 65	88.56 1	73.67 58	94.52 37	85.92 79
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 28	83.38 29	80.40 42	86.50 51	69.44 73	82.30 58	86.08 27	66.80 69	86.70 32	89.99 81	81.64 6	85.95 34	74.35 52	96.11 3	85.81 80
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 58	79.99 61	78.49 70	86.46 52	74.79 34	77.15 120	85.39 39	66.73 70	80.39 120	88.85 107	74.43 54	78.33 189	74.73 49	85.79 204	82.35 180

VDDNet		71.60 158	73.13 138	67.02 234	86.29 53	41.11 293	69.97 204	66.50 272	68.72 58	74.74 196	91.70 26	59.90 177	75.81 221	48.58 256	91.72 95	84.15 130

test_0728_SECOND				76.57 96	86.20 54	60.57 153	83.77 43	85.49 34					85.90 39	75.86 42	94.39 44	83.25 154

SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 55	79.18 9	87.23 9	86.27 22	77.51 11	87.65 19	90.73 49	79.20 14	85.58 50	78.11 25	94.46 39	84.89 98

RE-MVS-def			85.50 4		86.19 55	79.18 9	87.23 9	86.27 22	77.51 11	87.65 19	90.73 49	81.38 7		78.11 25	94.46 39	84.89 98

DVP-MVS		81.15 42	83.12 35	75.24 116	86.16 57	60.78 150	83.77 43	80.58 133	72.48 34	85.83 43	90.41 63	78.57 18	85.69 46	75.86 42	94.39 44	79.24 230
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 57	60.78 150	83.81 42	85.10 45	72.48 34	85.27 55	89.96 82	78.57 18

SED-MVS		81.78 35	83.48 27	76.67 94	86.12 59	61.06 144	83.62 45	84.72 54	72.61 32	87.38 25	89.70 86	77.48 24	85.89 41	75.29 45	94.39 44	83.08 159

IU-MVS					86.12 59	60.90 148		80.38 137	45.49 284	81.31 107				75.64 44	94.39 44	84.65 107

test_241102_ONE					86.12 59	61.06 144		84.72 54	72.64 31	87.38 25	89.47 89	77.48 24	85.74 45

XVG-OURS		79.51 59	79.82 62	78.58 69	86.11 62	74.96 33	76.33 129	84.95 49	66.89 66	82.75 91	88.99 104	66.82 114	78.37 187	74.80 47	90.76 127	82.40 179

test_part2					85.90 63	66.44 97				84.61 66

原ACMM1				73.90 130	85.90 63	65.15 109		81.67 108	50.97 239	74.25 205	86.16 165	61.60 160	83.54 89	56.75 188	91.08 114	73.00 282

testdata				64.13 255	85.87 65	63.34 124		61.80 303	47.83 270	76.42 179	86.60 152	48.83 255	62.31 323	54.46 214	81.26 261	66.74 332

CNVR-MVS		78.49 71	78.59 74	78.16 76	85.86 66	67.40 88	78.12 110	81.50 110	63.92 99	77.51 153	86.56 153	68.43 100	84.82 67	73.83 56	91.61 99	82.26 183

test_one_0601					85.84 67	61.45 140		85.63 32	75.27 19	85.62 48	90.38 68	76.72 29

NCCC		78.25 75	78.04 79	78.89 65	85.61 68	69.45 72	79.80 87	80.99 125	65.77 77	75.55 186	86.25 162	67.42 108	85.42 52	70.10 83	90.88 122	81.81 189

TEST9					85.47 69	69.32 76	76.42 125	78.69 165	53.73 209	76.97 159	86.74 143	66.84 113	81.10 133

train_agg		76.38 91	76.55 91	75.86 107	85.47 69	69.32 76	76.42 125	78.69 165	54.00 204	76.97 159	86.74 143	66.60 118	81.10 133	72.50 69	91.56 101	77.15 252

DPE-MVS		82.00 34	83.02 36	78.95 64	85.36 71	67.25 89	82.91 54	84.98 47	73.52 26	85.43 51	90.03 80	76.37 31	86.97 10	74.56 50	94.02 60	82.62 173
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HQP_MVS		78.77 67	78.78 71	78.72 66	85.18 72	65.18 107	82.74 55	85.49 34	65.45 80	78.23 142	89.11 100	60.83 170	86.15 27	71.09 77	90.94 116	84.82 102

plane_prior7					85.18 72	66.21 99

SteuartSystems-ACMMP		83.07 23	83.64 24	81.35 30	85.14 74	71.00 60	85.53 27	84.78 51	70.91 46	85.64 45	90.41 63	75.55 40	87.69 3	79.75 8	95.08 20	85.36 88
Skip Steuart: Steuart Systems R&D Blog.
test_8					85.09 75	67.89 85	76.26 130	78.66 167	54.00 204	76.89 164	86.72 145	66.60 118	80.89 144

bld_raw_dy_0_64		72.85 144	72.76 145	73.09 146	85.08 76	64.80 111	78.72 98	64.22 287	51.92 227	83.13 83	90.26 75	39.21 307	69.91 280	70.73 79	91.60 100	84.56 115

WR-MVS		71.20 160	72.48 149	67.36 229	84.98 77	35.70 333	64.43 282	68.66 263	65.05 89	81.49 105	86.43 157	57.57 203	76.48 216	50.36 242	93.32 72	89.90 24

PS-MVSNAJss		77.54 80	77.35 86	78.13 78	84.88 78	66.37 98	78.55 101	79.59 150	53.48 212	86.29 36	92.43 15	62.39 152	80.25 154	67.90 104	90.61 128	87.77 52

LTVRE_ROB	75.46 1	84.22 8	84.98 9	81.94 23	84.82 79	75.40 30	91.60 3	87.80 8	73.52 26	88.90 11	93.06 6	71.39 75	81.53 126	81.53 3	92.15 93	88.91 41
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
mvs_tets		78.93 65	78.67 73	79.72 49	84.81 80	73.93 40	80.65 70	76.50 195	51.98 226	87.40 24	91.86 22	76.09 35	78.53 179	68.58 93	90.20 133	86.69 69

APDe-MVS		82.88 26	84.14 16	79.08 59	84.80 81	66.72 95	86.54 21	85.11 44	72.00 41	86.65 33	91.75 25	78.20 21	87.04 8	77.93 28	94.32 51	83.47 147

MSLP-MVS++		74.48 115	75.78 100	70.59 182	84.66 82	62.40 130	78.65 99	84.24 71	60.55 128	77.71 151	81.98 220	63.12 145	77.64 203	62.95 145	88.14 169	71.73 296

jajsoiax		78.51 70	78.16 78	79.59 54	84.65 83	73.83 42	80.42 74	76.12 197	51.33 235	87.19 29	91.51 31	73.79 58	78.44 183	68.27 96	90.13 138	86.49 71

TranMVSNet+NR-MVSNet		76.13 93	77.66 83	71.56 173	84.61 84	42.57 286	70.98 194	78.29 174	68.67 59	83.04 84	89.26 93	72.99 63	80.75 145	55.58 204	95.47 10	91.35 14

旧先验1					84.55 85	60.36 155		63.69 290			87.05 133	54.65 220			83.34 239	69.66 313

APD-MVS		81.13 43	81.73 48	79.36 57	84.47 86	70.53 65	83.85 41	83.70 81	69.43 55	83.67 79	88.96 105	75.89 36	86.41 16	72.62 67	92.95 78	81.14 196
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
plane_prior1					84.46 87

agg_prior1		75.89 94	76.41 92	74.31 125	84.44 88	66.02 101	76.12 131	78.62 168	54.40 193	76.95 161	86.85 137	66.44 121	80.34 151	72.45 70	91.42 104	76.57 256

agg_prior					84.44 88	66.02 101		78.62 168		76.95 161			80.34 151

DeepPCF-MVS	71.07 5	78.48 72	77.14 89	82.52 19	84.39 90	77.04 25	76.35 127	84.05 77	56.66 166	80.27 121	85.31 179	68.56 97	87.03 9	67.39 108	91.26 107	83.50 143

CDPH-MVS		77.33 82	77.06 90	78.14 77	84.21 91	63.98 120	76.07 132	83.45 84	54.20 199	77.68 152	87.18 128	69.98 84	85.37 53	68.01 100	92.72 86	85.08 95

plane_prior6					84.18 92	65.31 106						60.83 170

114514_t		73.40 127	73.33 135	73.64 135	84.15 93	57.11 176	78.20 108	80.02 144	43.76 297	72.55 226	86.07 170	64.00 142	83.35 95	60.14 167	91.03 115	80.45 216

testtj		81.19 41	81.70 49	79.67 52	83.95 94	69.77 70	83.58 48	84.63 59	72.13 38	82.85 89	88.36 116	75.00 46	86.79 11	71.99 75	92.84 80	82.44 178

ZD-MVS					83.91 95	69.36 75		81.09 122	58.91 143	82.73 92	89.11 100	75.77 37	86.63 12	72.73 65	92.93 79

DeepC-MVS_fast	69.89 7	77.17 84	76.33 95	79.70 50	83.90 96	67.94 84	80.06 85	83.75 80	56.73 165	74.88 194	85.32 178	65.54 128	87.79 2	65.61 120	91.14 112	83.35 152
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
h-mvs33		73.08 133	71.61 162	77.48 84	83.89 97	72.89 49	70.47 200	71.12 248	54.28 195	77.89 146	83.41 199	49.04 252	80.98 138	63.62 138	90.77 126	78.58 237

SD-MVS		80.28 55	81.55 52	76.47 99	83.57 98	67.83 86	83.39 51	85.35 40	64.42 95	86.14 39	87.07 132	74.02 55	80.97 139	77.70 31	92.32 91	80.62 213
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		74.91 109	75.57 104	72.93 152	83.50 99	45.79 258	69.47 210	80.14 143	65.22 86	81.74 102	87.08 130	61.82 158	81.07 135	56.21 196	94.98 21	91.93 8

NR-MVSNet		73.62 123	74.05 119	72.33 168	83.50 99	43.71 273	65.65 267	77.32 187	64.32 96	75.59 185	87.08 130	62.45 151	81.34 127	54.90 207	95.63 8	91.93 8

test_0402		78.17 78	79.48 66	74.24 126	83.50 99	59.15 166	72.52 167	74.60 212	75.34 17	88.69 13	91.81 23	75.06 44	82.37 112	65.10 123	88.68 166	81.20 194

OPU-MVS				78.65 68	83.44 102	66.85 94	83.62 45				86.12 167	66.82 114	86.01 31	61.72 152	89.79 146	83.08 159

NP-MVS					83.34 103	63.07 127					85.97 171

DVP-MVS++		81.24 39	82.74 40	76.76 93	83.14 104	60.90 148	91.64 1	85.49 34	74.03 23	84.93 58	90.38 68	66.82 114	85.90 39	77.43 33	90.78 124	83.49 144

MSC_two_6792asdad				79.02 61	83.14 104	67.03 92		80.75 127					86.24 22	77.27 35	94.85 26	83.78 137

No_MVS				79.02 61	83.14 104	67.03 92		80.75 127					86.24 22	77.27 35	94.85 26	83.78 137

RRT_MVS		78.18 77	77.69 82	79.66 53	83.14 104	61.34 141	83.29 52	80.34 140	57.43 158	86.65 33	91.79 24	50.52 241	86.01 31	71.36 76	94.65 34	91.62 12

UniMVSNet (Re)		75.00 107	75.48 105	73.56 137	83.14 104	47.92 237	70.41 202	81.04 124	63.67 102	79.54 127	86.37 158	62.83 146	81.82 121	57.10 187	95.25 15	90.94 18

hse-mvs2		72.32 152	70.66 174	77.31 89	83.10 109	71.77 52	69.19 215	71.45 239	54.28 195	77.89 146	78.26 267	49.04 252	79.23 167	63.62 138	89.13 161	80.92 202

UniMVSNet_NR-MVSNet		74.90 110	75.65 101	72.64 161	83.04 110	45.79 258	69.26 213	78.81 162	66.66 72	81.74 102	86.88 136	63.26 144	81.07 135	56.21 196	94.98 21	91.05 16

HyFIR lowres test		63.01 252	60.47 271	70.61 181	83.04 110	54.10 195	59.93 315	72.24 231	33.67 351	69.00 267	75.63 287	38.69 310	76.93 209	36.60 328	75.45 309	80.81 208

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 112	74.39 37	87.18 11	88.18 7	78.98 7	86.11 40	91.47 32	79.70 12	85.76 44	66.91 113	95.46 11	87.89 51
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AUN-MVS		70.22 170	67.88 206	77.22 90	82.96 113	71.61 53	69.08 216	71.39 240	49.17 258	71.70 235	78.07 272	37.62 317	79.21 168	61.81 149	89.15 159	80.82 205

DP-MVS Recon		73.57 124	72.69 146	76.23 102	82.85 114	63.39 123	74.32 155	82.96 90	57.75 151	70.35 254	81.98 220	64.34 141	84.41 76	49.69 246	89.95 141	80.89 203

APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 32	82.83 115	73.53 45	85.50 28	87.45 14	74.11 21	86.45 35	90.52 58	80.02 11	84.48 72	77.73 30	94.34 50	85.93 78

PVSNet_Blended_VisFu		70.04 172	68.88 189	73.53 138	82.71 116	63.62 122	74.81 146	81.95 105	48.53 263	67.16 283	79.18 257	51.42 237	78.38 186	54.39 215	79.72 281	78.60 236

DPM-MVS		69.98 174	69.22 185	72.26 169	82.69 117	58.82 168	70.53 199	81.23 118	47.79 271	64.16 298	80.21 240	51.32 238	83.12 99	60.14 167	84.95 219	74.83 269

EG-PatchMatch MVS		70.70 166	70.88 170	70.16 190	82.64 118	58.80 169	71.48 184	73.64 216	54.98 182	76.55 173	81.77 223	61.10 168	78.94 172	54.87 208	80.84 267	72.74 286

HQP-NCC					82.37 119		77.32 116		59.08 137	71.58 237

ACMP_Plane					82.37 119		77.32 116		59.08 137	71.58 237

HQP-MVS		75.24 103	75.01 107	75.94 105	82.37 119	58.80 169	77.32 116	84.12 74	59.08 137	71.58 237	85.96 172	58.09 195	85.30 55	67.38 109	89.16 157	83.73 140

test12				76.51 97	82.28 122	60.94 147		81.64 109		73.60 212		64.88 135	85.19 61		90.42 131	83.38 150

TAMVS		65.31 226	63.75 242	69.97 195	82.23 123	59.76 161	66.78 254	63.37 292	45.20 288	69.79 262	79.37 253	47.42 264	72.17 260	34.48 339	85.15 214	77.99 248

test_prior3		76.71 89	77.19 87	75.27 114	82.15 124	59.85 159	75.57 137	84.33 66	58.92 141	76.53 175	86.78 140	67.83 106	83.39 93	69.81 86	92.76 84	82.58 174

test_prior				75.27 114	82.15 124	59.85 159		84.33 66					83.39 93			82.58 174

SF-MVS		80.72 48	81.80 46	77.48 84	82.03 126	64.40 117	83.41 50	88.46 6	65.28 85	84.29 70	89.18 96	73.73 59	83.22 96	76.01 39	93.77 63	84.81 104

AdaColmap		74.22 117	74.56 110	73.20 143	81.95 127	60.97 146	79.43 88	80.90 126	65.57 79	72.54 227	81.76 224	70.98 78	85.26 56	47.88 262	90.00 139	73.37 279

PAPM_NR		73.91 119	74.16 118	73.16 144	81.90 128	53.50 199	81.28 65	81.40 113	66.17 75	73.30 218	83.31 205	59.96 176	83.10 100	58.45 179	81.66 258	82.87 165

DP-MVS		78.44 73	79.29 67	75.90 106	81.86 129	65.33 105	79.05 95	84.63 59	74.83 20	80.41 119	86.27 160	71.68 71	83.45 92	62.45 148	92.40 89	78.92 234

ETH3 D test6400		75.73 97	76.00 97	74.92 117	81.75 130	56.93 177	78.31 104	84.60 62	52.83 217	77.15 156	85.14 181	68.59 96	84.03 80	65.44 121	90.20 133	83.82 134

F-COLMAP		75.29 102	73.99 120	79.18 58	81.73 131	71.90 51	81.86 63	82.98 89	59.86 134	72.27 229	84.00 193	64.56 139	83.07 101	51.48 232	87.19 189	82.56 176

SixPastTwentyTwo		75.77 95	76.34 94	74.06 129	81.69 132	54.84 190	76.47 124	75.49 203	64.10 98	87.73 18	92.24 17	50.45 243	81.30 129	67.41 107	91.46 103	86.04 76

Vis-MVSNet		74.85 113	74.56 110	75.72 108	81.63 133	64.64 113	76.35 127	79.06 158	62.85 112	73.33 217	88.41 113	62.54 150	79.59 165	63.94 135	82.92 242	82.94 163
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_djsdf		78.88 66	78.27 76	80.70 40	81.42 134	71.24 58	83.98 39	75.72 201	52.27 221	87.37 27	92.25 16	68.04 104	80.56 146	72.28 71	91.15 111	90.32 23

3Dnovator+	73.19 2	81.08 44	80.48 57	82.87 9	81.41 135	72.03 50	84.38 37	86.23 25	77.28 16	80.65 116	90.18 79	59.80 180	87.58 4	73.06 63	91.34 106	89.01 37

MCST-MVS		73.42 126	73.34 134	73.63 136	81.28 136	59.17 165	74.80 148	83.13 88	45.50 282	72.84 223	83.78 196	65.15 133	80.99 137	64.54 127	89.09 162	80.73 210

MIMVSNet1		66.57 218	69.23 184	58.59 304	81.26 137	37.73 320	64.06 285	57.62 313	57.02 161	78.40 141	90.75 48	62.65 147	58.10 334	41.77 296	89.58 150	79.95 222

ACMH+	66.64 10	81.20 40	82.48 43	77.35 88	81.16 138	62.39 131	80.51 72	87.80 8	73.02 28	87.57 21	91.08 39	80.28 10	82.44 109	64.82 126	96.10 4	87.21 61

test_low_dy_conf_001		76.29 92	75.63 103	78.24 75	81.15 139	62.81 129	80.22 82	79.33 154	48.16 265	84.62 65	90.62 53	49.85 246	82.43 110	73.56 59	93.19 75	89.48 28

MVS_111021_HR		72.98 140	72.97 143	72.99 149	80.82 140	65.47 104	68.81 220	72.77 224	57.67 154	75.76 183	82.38 217	71.01 77	77.17 206	61.38 154	86.15 200	76.32 257

9.14			80.22 59		80.68 141		80.35 77	87.69 11	59.90 132	83.00 85	88.20 119	74.57 51	81.75 124	73.75 57	93.78 62

ETH3D-3000-0.1		79.14 63	79.80 63	77.16 91	80.67 142	64.57 114	80.26 79	87.60 12	60.74 126	82.47 94	88.03 124	71.73 70	81.81 122	73.12 62	93.61 66	85.09 93

bld_raw_conf005		78.24 76	77.73 81	79.77 46	80.62 143	61.50 139	80.87 68	84.09 76	55.76 176	85.28 54	92.08 19	53.44 226	86.02 30	74.22 54	93.21 74	91.79 10

OMC-MVS		79.41 61	78.79 70	81.28 33	80.62 143	70.71 64	80.91 67	84.76 52	62.54 114	81.77 100	86.65 149	71.46 73	83.53 90	67.95 103	92.44 88	89.60 26

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 67	80.48 145	64.16 118	80.24 80	82.06 102	61.89 118	88.77 12	93.32 4	57.15 205	82.60 108	70.08 84	92.80 82	89.25 31

CDS-MVSNet		64.33 241	62.66 254	69.35 201	80.44 146	58.28 173	65.26 272	65.66 275	44.36 293	67.30 282	75.54 288	43.27 282	71.77 266	37.68 320	84.44 225	78.01 247
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		77.20 83	76.37 93	79.69 51	80.34 147	61.52 138	80.58 71	82.12 101	53.54 211	83.93 76	91.03 40	49.49 248	85.97 33	73.26 61	93.08 76	91.59 13

PLC	62.01 16	71.79 157	70.28 176	76.33 100	80.31 148	68.63 81	78.18 109	81.24 117	54.57 191	67.09 284	80.63 235	59.44 181	81.74 125	46.91 269	84.17 227	78.63 235
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ETH3D cwj APD-0.16		78.38 74	78.72 72	77.38 86	80.09 149	66.16 100	79.08 94	86.13 26	57.55 157	80.93 112	87.76 127	71.98 69	82.73 106	72.11 74	92.83 81	83.25 154

CHOSEN 1792x2688		58.09 289	56.30 300	63.45 264	79.95 150	50.93 211	54.07 337	65.59 276	28.56 366	61.53 313	74.33 301	41.09 296	66.52 306	33.91 342	67.69 349	72.92 283

K. test v3		73.67 122	73.61 129	73.87 131	79.78 151	55.62 188	74.69 152	62.04 302	66.16 76	84.76 63	93.23 5	49.47 249	80.97 139	65.66 119	86.67 196	85.02 97

VPNet		65.58 224	67.56 208	59.65 298	79.72 152	30.17 360	60.27 313	62.14 297	54.19 200	71.24 245	86.63 150	58.80 188	67.62 295	44.17 284	90.87 123	81.18 195

ACMH	63.62 14	77.50 81	80.11 60	69.68 197	79.61 153	56.28 181	78.81 97	83.62 82	63.41 108	87.14 31	90.23 77	76.11 34	73.32 247	67.58 105	94.44 42	79.44 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 157	79.60 154	56.83 180		57.37 316		83.80 78	89.01 103	47.45 263	78.74 176	64.39 129	86.49 198	82.69 172

MVS_111021_LR		72.10 154	71.82 158	72.95 151	79.53 155	73.90 41	70.45 201	66.64 271	56.87 162	76.81 168	81.76 224	68.78 94	71.76 267	61.81 149	83.74 234	73.18 281

Test_1112_low_res		58.78 286	58.69 283	59.04 302	79.41 156	38.13 316	57.62 325	66.98 270	34.74 344	59.62 327	77.56 276	42.92 285	63.65 318	38.66 312	70.73 334	75.35 266

CSCG		74.12 118	74.39 113	73.33 140	79.35 157	61.66 137	77.45 115	81.98 104	62.47 116	79.06 133	80.19 242	61.83 157	78.79 175	59.83 171	87.35 183	79.54 227

MVP-Stereo		61.56 266	59.22 278	68.58 216	79.28 158	60.44 154	69.20 214	71.57 235	43.58 300	56.42 337	78.37 266	39.57 306	76.46 217	34.86 338	60.16 360	68.86 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 169	71.34 167	67.85 224	79.26 159	40.42 301	74.67 153	75.15 209	58.41 145	68.74 273	88.14 123	56.08 215	83.69 86	59.90 170	81.71 257	79.43 229

IS-MVSNet		75.10 105	75.42 106	74.15 128	79.23 160	48.05 235	79.43 88	78.04 177	70.09 52	79.17 132	88.02 125	53.04 227	83.60 88	58.05 182	93.76 65	90.79 20

FC-MVSNet-test		73.32 129	74.78 109	68.93 210	79.21 161	36.57 325	71.82 182	79.54 152	57.63 156	82.57 93	90.38 68	59.38 183	78.99 171	57.91 183	94.56 36	91.23 15

AllTest		77.66 79	77.43 84	78.35 73	79.19 162	70.81 61	78.60 100	88.64 3	65.37 83	80.09 123	88.17 120	70.33 81	78.43 184	55.60 201	90.90 120	85.81 80

TestCases				78.35 73	79.19 162	70.81 61		88.64 3	65.37 83	80.09 123	88.17 120	70.33 81	78.43 184	55.60 201	90.90 120	85.81 80

xiu_mvs_v1_base_debu		67.87 204	67.07 215	70.26 187	79.13 164	61.90 134	67.34 242	71.25 244	47.98 267	67.70 277	74.19 305	61.31 163	72.62 254	56.51 190	78.26 293	76.27 258

xiu_mvs_v1_base		67.87 204	67.07 215	70.26 187	79.13 164	61.90 134	67.34 242	71.25 244	47.98 267	67.70 277	74.19 305	61.31 163	72.62 254	56.51 190	78.26 293	76.27 258

xiu_mvs_v1_base_debi		67.87 204	67.07 215	70.26 187	79.13 164	61.90 134	67.34 242	71.25 244	47.98 267	67.70 277	74.19 305	61.31 163	72.62 254	56.51 190	78.26 293	76.27 258

VDD-MVS		70.81 165	71.44 166	68.91 211	79.07 167	46.51 253	67.82 236	70.83 252	61.23 121	74.07 209	88.69 108	59.86 178	75.62 224	51.11 235	90.28 132	84.61 111

test1111		64.62 234	65.19 228	62.93 272	79.01 168	29.91 361	65.45 270	54.41 331	54.09 202	71.47 244	88.48 112	37.02 319	74.29 242	46.83 271	89.94 142	84.58 114

TSAR-MVS + GP.		73.08 133	71.60 163	77.54 83	78.99 169	70.73 63	74.96 143	69.38 259	60.73 127	74.39 204	78.44 265	57.72 202	82.78 104	60.16 166	89.60 148	79.11 232

test2506		61.23 268	60.85 268	62.38 277	78.80 170	27.88 367	67.33 245	37.42 375	54.23 197	67.55 280	88.68 109	17.87 379	74.39 240	46.33 274	89.41 153	84.86 100

ECVR-MVS		64.82 231	65.22 227	63.60 261	78.80 170	31.14 357	66.97 250	56.47 323	54.23 197	69.94 259	88.68 109	37.23 318	74.81 235	45.28 280	89.41 153	84.86 100

FIs		72.56 149	73.80 123	68.84 213	78.74 172	37.74 319	71.02 193	79.83 146	56.12 171	80.88 115	89.45 90	58.18 192	78.28 190	56.63 189	93.36 71	90.51 22

v7n		79.37 62	80.41 58	76.28 101	78.67 173	55.81 185	79.22 93	82.51 98	70.72 47	87.54 22	92.44 14	68.00 105	81.34 127	72.84 64	91.72 95	91.69 11

LS3D		80.99 46	80.85 55	81.41 29	78.37 174	71.37 56	87.45 8	85.87 31	77.48 13	81.98 97	89.95 83	69.14 91	85.26 56	66.15 114	91.24 108	87.61 55

CNLPA		73.44 125	73.03 141	74.66 118	78.27 175	75.29 31	75.99 133	78.49 170	65.39 82	75.67 184	83.22 210	61.23 166	66.77 304	53.70 222	85.33 210	81.92 188

EPP-MVSNet		73.86 120	73.38 132	75.31 113	78.19 176	53.35 201	80.45 73	77.32 187	65.11 88	76.47 177	86.80 138	49.47 249	83.77 83	53.89 220	92.72 86	88.81 44

PCF-MVS	63.80 13	72.70 147	71.69 159	75.72 108	78.10 177	60.01 158	73.04 162	81.50 110	45.34 286	79.66 126	84.35 189	65.15 133	82.65 107	48.70 254	89.38 156	84.50 121
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		73.14 131	73.77 125	71.26 177	78.09 178	52.64 204	74.32 155	79.56 151	56.32 170	76.35 180	83.36 204	70.76 79	77.96 197	63.32 142	81.84 253	83.18 157

LFMVS		67.06 216	67.89 205	64.56 252	78.02 179	38.25 314	70.81 198	59.60 308	65.18 87	71.06 248	86.56 153	43.85 279	75.22 228	46.35 273	89.63 147	80.21 220

anonymousdsp		78.60 68	77.80 80	81.00 36	78.01 180	74.34 38	80.09 83	76.12 197	50.51 245	89.19 10	90.88 43	71.45 74	77.78 201	73.38 60	90.60 129	90.90 19

BH-untuned		69.39 183	69.46 180	69.18 203	77.96 181	56.88 178	68.47 230	77.53 184	56.77 164	77.79 149	79.63 249	60.30 174	80.20 157	46.04 275	80.65 269	70.47 306

test_part1		76.97 86	78.21 77	73.25 142	77.87 182	45.76 260	78.27 106	87.26 15	66.69 71	85.31 53	91.43 34	55.95 216	84.24 79	65.71 118	95.43 12	89.75 25

1112_ss		59.48 281	58.99 281	60.96 290	77.84 183	42.39 287	61.42 304	68.45 265	37.96 330	59.93 326	67.46 353	45.11 272	65.07 311	40.89 301	71.81 328	75.41 264

PS-MVSNAJ		64.27 242	63.73 243	65.90 245	77.82 184	51.42 209	63.33 293	72.33 229	45.09 290	61.60 312	68.04 351	62.39 152	73.95 245	49.07 250	73.87 320	72.34 289

ambc				70.10 192	77.74 185	50.21 217	74.28 157	77.93 180		79.26 131	88.29 118	54.11 223	79.77 162	64.43 128	91.10 113	80.30 218

xiu_mvs_v2_base		64.43 239	63.96 240	65.85 246	77.72 186	51.32 210	63.63 290	72.31 230	45.06 291	61.70 311	69.66 340	62.56 148	73.93 246	49.06 251	73.91 319	72.31 290

Anonymous20231211		75.54 99	77.19 87	70.59 182	77.67 187	45.70 262	74.73 150	80.19 141	68.80 56	82.95 86	92.91 8	66.26 122	76.76 214	58.41 180	92.77 83	89.30 30

FMVSNet1		71.06 161	72.48 149	66.81 235	77.65 188	40.68 297	71.96 176	73.03 219	61.14 122	79.45 129	90.36 71	60.44 172	75.20 229	50.20 243	88.05 171	84.54 116

FPMVS		59.43 282	60.07 273	57.51 308	77.62 189	71.52 54	62.33 300	50.92 344	57.40 159	69.40 264	80.00 244	39.14 308	61.92 324	37.47 323	66.36 350	39.09 372

Effi-MVS+-dtu		75.43 100	72.28 152	84.91 2	77.05 190	83.58 1	78.47 102	77.70 181	57.68 152	74.89 193	78.13 271	64.80 136	84.26 78	56.46 193	85.32 211	86.88 65

mvs-test1		73.81 121	70.69 173	83.18 5	77.05 190	81.39 3	75.39 139	77.70 181	57.68 152	71.19 247	74.72 296	64.80 136	83.66 87	56.46 193	81.19 262	84.50 121

CLD-MVS		72.88 142	72.36 151	74.43 122	77.03 192	54.30 194	68.77 223	83.43 85	52.12 223	76.79 169	74.44 300	69.54 89	83.91 81	55.88 199	93.25 73	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 90	76.00 97	78.06 79	77.02 193	64.77 112	80.78 69	82.66 95	60.39 129	74.15 206	83.30 206	69.65 88	82.07 118	69.27 90	86.75 195	87.36 59

CS-MVS-test		74.89 111	74.23 117	76.86 92	77.01 194	62.94 128	78.98 96	84.61 61	58.62 144	70.17 257	80.80 232	66.74 117	81.96 119	61.74 151	89.40 155	85.69 86

Baseline_NR-MVSNet		70.62 167	73.19 136	62.92 273	76.97 195	34.44 341	68.84 218	70.88 251	60.25 130	79.50 128	90.53 56	61.82 158	69.11 286	54.67 210	95.27 14	85.22 89

ITE_SJBPF				80.35 43	76.94 196	73.60 43		80.48 134	66.87 67	83.64 80	86.18 163	70.25 83	79.90 161	61.12 159	88.95 164	87.56 56

jason		64.47 238	62.84 252	69.34 202	76.91 197	59.20 162	67.15 247	65.67 274	35.29 341	65.16 292	76.74 282	44.67 274	70.68 273	54.74 209	79.28 284	78.14 244
jason: jason.
ETV-MVS		72.72 146	72.16 155	74.38 124	76.90 198	55.95 182	73.34 161	84.67 56	62.04 117	72.19 232	70.81 330	65.90 126	85.24 58	58.64 177	84.96 218	81.95 187

Anonymous20240529		72.56 149	73.79 124	68.86 212	76.89 199	45.21 264	68.80 222	77.25 189	67.16 65	76.89 164	90.44 59	65.95 125	74.19 243	50.75 238	90.00 139	87.18 63

DROMVSNet		77.08 85	77.39 85	76.14 104	76.86 200	56.87 179	80.32 78	87.52 13	63.45 106	74.66 200	84.52 186	69.87 86	84.94 63	69.76 88	89.59 149	86.60 70

PM-MVS		64.49 237	63.61 244	67.14 233	76.68 201	75.15 32	68.49 229	42.85 362	51.17 238	77.85 148	80.51 236	45.76 265	66.31 307	52.83 227	76.35 301	59.96 355

TransMVSNet (Re)		69.62 178	71.63 161	63.57 262	76.51 202	35.93 331	65.75 266	71.29 243	61.05 123	75.02 191	89.90 84	65.88 127	70.41 279	49.79 245	89.48 151	84.38 124

BH-RMVSNet		68.69 194	68.20 202	70.14 191	76.40 203	53.90 198	64.62 279	73.48 218	58.01 148	73.91 211	81.78 222	59.09 185	78.22 191	48.59 255	77.96 296	78.31 240

PHI-MVS		74.92 108	74.36 115	76.61 95	76.40 203	62.32 132	80.38 75	83.15 87	54.16 201	73.23 219	80.75 233	62.19 155	83.86 82	68.02 99	90.92 119	83.65 141

UGNet		70.20 171	69.05 186	73.65 134	76.24 205	63.64 121	75.87 135	72.53 227	61.48 120	60.93 320	86.14 166	52.37 231	77.12 207	50.67 239	85.21 212	80.17 221
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		58.68 287	57.72 290	61.57 282	76.21 206	73.59 44	61.83 301	49.00 351	47.30 275	61.08 316	68.97 345	50.16 244	59.01 331	36.06 334	68.84 344	52.10 362

VPA-MVSNet		68.71 193	70.37 175	63.72 260	76.13 207	38.06 317	64.10 284	71.48 238	56.60 169	74.10 208	88.31 117	64.78 138	69.72 281	47.69 264	90.15 136	83.37 151

PAPM		61.79 265	60.37 272	66.05 243	76.09 208	41.87 289	69.30 212	76.79 194	40.64 319	53.80 349	79.62 250	44.38 276	82.92 103	29.64 357	73.11 323	73.36 280

BH-w/o		64.81 232	64.29 238	66.36 240	76.08 209	54.71 191	65.61 268	75.23 208	50.10 250	71.05 249	71.86 325	54.33 222	79.02 170	38.20 317	76.14 303	65.36 337

dcpmvs_2		71.02 163	72.65 147	66.16 242	76.06 210	50.49 214	71.97 175	79.36 153	50.34 246	82.81 90	83.63 197	64.38 140	67.27 298	61.54 153	83.71 236	80.71 212

pmmvs6		71.82 156	73.66 127	66.31 241	75.94 211	42.01 288	66.99 249	72.53 227	63.45 106	76.43 178	92.78 10	72.95 64	69.69 282	51.41 233	90.46 130	87.22 60

CANet		73.00 138	71.84 157	76.48 98	75.82 212	61.28 142	74.81 146	80.37 138	63.17 110	62.43 310	80.50 237	61.10 168	85.16 62	64.00 132	84.34 226	83.01 162

pmmvs-eth3d		64.41 240	63.27 248	67.82 226	75.81 213	60.18 156	69.49 209	62.05 301	38.81 327	74.13 207	82.23 218	43.76 280	68.65 288	42.53 290	80.63 271	74.63 270

TR-MVS		64.59 235	63.54 245	67.73 227	75.75 214	50.83 212	63.39 292	70.29 255	49.33 256	71.55 241	74.55 298	50.94 239	78.46 182	40.43 303	75.69 305	73.89 276

tttt0517		69.46 181	67.79 207	74.46 120	75.34 215	52.72 203	75.05 142	63.27 293	54.69 188	78.87 135	84.37 188	26.63 360	81.15 131	63.95 133	87.93 175	89.51 27

cascas		64.59 235	62.77 253	70.05 193	75.27 216	50.02 218	61.79 302	71.61 234	42.46 307	63.68 304	68.89 347	49.33 251	80.35 150	47.82 263	84.05 229	79.78 225

API-MVS		70.97 164	71.51 165	69.37 199	75.20 217	55.94 183	80.99 66	76.84 192	62.48 115	71.24 245	77.51 277	61.51 162	80.96 143	52.04 228	85.76 205	71.22 301

EIA-MVS		68.59 196	67.16 214	72.90 153	75.18 218	55.64 187	69.39 211	81.29 115	52.44 220	64.53 294	70.69 331	60.33 173	82.30 114	54.27 217	76.31 302	80.75 209

PAPR		69.20 186	68.66 195	70.82 179	75.15 219	47.77 239	75.31 140	81.11 120	49.62 255	66.33 286	79.27 254	61.53 161	82.96 102	48.12 260	81.50 260	81.74 190

MVSFormer		69.93 175	69.03 187	72.63 162	74.93 220	59.19 163	83.98 39	75.72 201	52.27 221	63.53 306	76.74 282	43.19 283	80.56 146	72.28 71	78.67 289	78.14 244

lupinMVS		63.36 247	61.49 262	68.97 208	74.93 220	59.19 163	65.80 265	64.52 285	34.68 346	63.53 306	74.25 303	43.19 283	70.62 274	53.88 221	78.67 289	77.10 253

nrg030		74.87 112	75.99 99	71.52 174	74.90 222	49.88 222	74.10 158	82.58 97	54.55 192	83.50 81	89.21 95	71.51 72	75.74 223	61.24 155	92.34 90	88.94 40

TAPA-MVS	65.27 12	75.16 104	74.29 116	77.77 81	74.86 223	68.08 83	77.89 111	84.04 78	55.15 181	76.19 182	83.39 200	66.91 112	80.11 159	60.04 169	90.14 137	85.13 92
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RPSCF		75.76 96	74.37 114	79.93 45	74.81 224	77.53 20	77.53 114	79.30 155	59.44 136	78.88 134	89.80 85	71.26 76	73.09 249	57.45 184	80.89 266	89.17 34

EI-MVSNet-Vis-set		72.78 145	71.87 156	75.54 111	74.77 225	59.02 167	72.24 169	71.56 236	63.92 99	78.59 136	71.59 326	66.22 123	78.60 178	67.58 105	80.32 272	89.00 38

Regformer-3		72.86 143	72.28 152	74.62 119	74.74 226	60.18 156	72.91 163	71.76 232	64.74 92	78.42 140	72.07 320	67.00 111	76.28 218	67.97 102	80.91 264	87.39 57

Regformer-4		74.64 114	73.67 126	77.55 82	74.74 226	64.49 116	72.91 163	75.42 205	67.45 63	80.24 122	72.07 320	68.98 93	80.19 158	70.29 82	80.91 264	87.98 50

v1240		73.06 135	73.14 137	72.84 155	74.74 226	47.27 247	71.88 181	81.11 120	51.80 228	82.28 96	84.21 190	56.22 214	82.34 113	68.82 91	87.17 190	88.91 41

v1921920		72.96 141	72.98 142	72.89 154	74.67 229	47.58 242	71.92 179	80.69 129	51.70 230	81.69 104	83.89 194	56.58 212	82.25 115	68.34 95	87.36 182	88.82 43

EI-MVSNet-UG-set		72.63 148	71.68 160	75.47 112	74.67 229	58.64 172	72.02 173	71.50 237	63.53 104	78.58 138	71.39 329	65.98 124	78.53 179	67.30 111	80.18 274	89.23 32

Fast-Effi-MVS+		68.81 191	68.30 197	70.35 186	74.66 231	48.61 228	66.06 260	78.32 172	50.62 243	71.48 243	75.54 288	68.75 95	79.59 165	50.55 241	78.73 288	82.86 166

v1192		73.40 127	73.42 130	73.32 141	74.65 232	48.67 227	72.21 170	81.73 107	52.76 218	81.85 98	84.56 185	57.12 206	82.24 116	68.58 93	87.33 184	89.06 36

v144192		72.99 139	73.06 140	72.77 156	74.58 233	47.48 243	71.90 180	80.44 136	51.57 231	81.46 106	84.11 192	58.04 199	82.12 117	67.98 101	87.47 180	88.70 46

MAR-MVS		67.72 207	66.16 222	72.40 166	74.45 234	64.99 110	74.87 144	77.50 185	48.67 262	65.78 290	68.58 350	57.01 209	77.79 200	46.68 272	81.92 251	74.42 272
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 98	76.20 96	74.16 127	74.44 235	48.69 226	75.84 136	82.93 91	59.02 140	85.92 41	89.17 98	58.56 190	82.74 105	70.73 79	89.14 160	91.05 16

canonicalmvs		72.29 153	73.38 132	69.04 205	74.23 236	47.37 245	73.93 159	83.18 86	54.36 194	76.61 172	81.64 226	72.03 66	75.34 227	57.12 186	87.28 186	84.40 123

Anonymous202405211		66.02 221	66.89 219	63.43 265	74.22 237	38.14 315	59.00 319	66.13 273	63.33 109	69.76 263	85.95 173	51.88 232	70.50 276	44.23 283	87.52 178	81.64 191

Regformer-1		74.28 116	73.63 128	76.21 103	74.22 237	64.12 119	72.77 165	75.46 204	66.86 68	79.27 130	72.08 318	69.29 90	78.74 176	68.73 92	84.02 230	85.77 85

Regformer-2		75.32 101	74.47 112	77.88 80	74.22 237	66.65 96	72.77 165	77.54 183	68.47 61	80.44 118	72.08 318	70.60 80	80.97 139	70.08 84	84.02 230	86.01 77

Effi-MVS+		72.10 154	72.28 152	71.58 172	74.21 240	50.33 215	74.72 151	82.73 93	62.62 113	70.77 250	76.83 281	69.96 85	80.97 139	60.20 164	78.43 291	83.45 149

v1144		73.29 130	73.39 131	73.01 148	74.12 241	48.11 234	72.01 174	81.08 123	53.83 208	81.77 100	84.68 183	58.07 198	81.91 120	68.10 97	86.86 192	88.99 39

EI-MVSNet		69.61 179	69.01 188	71.41 176	73.94 242	49.90 219	71.31 189	71.32 241	58.22 146	75.40 189	70.44 332	58.16 193	75.85 219	62.51 146	79.81 278	88.48 47

CVMVSNet		59.21 283	58.44 286	61.51 283	73.94 242	47.76 240	71.31 189	64.56 284	26.91 370	60.34 322	70.44 332	36.24 322	67.65 294	53.57 223	68.66 345	69.12 319

IterMVS-LS		73.01 137	73.12 139	72.66 160	73.79 244	49.90 219	71.63 183	78.44 171	58.22 146	80.51 117	86.63 150	58.15 194	79.62 163	62.51 146	88.20 168	88.48 47
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
iter_conf_final		68.69 194	67.00 218	73.76 133	73.68 245	52.33 206	75.96 134	73.54 217	50.56 244	69.90 260	82.85 211	24.76 368	83.73 84	65.40 122	86.33 199	85.22 89

alignmvs		70.54 168	71.00 169	69.15 204	73.50 246	48.04 236	69.85 207	79.62 147	53.94 207	76.54 174	82.00 219	59.00 186	74.68 236	57.32 185	87.21 188	84.72 106

Fast-Effi-MVS+-dtu		70.00 173	68.74 193	73.77 132	73.47 247	64.53 115	71.36 187	78.14 176	55.81 175	68.84 272	74.71 297	65.36 131	75.75 222	52.00 229	79.00 285	81.03 198

v8		75.07 106	75.64 102	73.35 139	73.42 248	47.46 244	75.20 141	81.45 112	60.05 131	85.64 45	89.26 93	58.08 197	81.80 123	69.71 89	87.97 174	90.79 20

tfpnnormal		66.48 219	67.93 204	62.16 279	73.40 249	36.65 324	63.45 291	64.99 280	55.97 172	72.82 224	87.80 126	57.06 208	69.10 287	48.31 259	87.54 177	80.72 211

IterMVS-SCA-FT		67.68 208	66.07 223	72.49 164	73.34 250	58.20 174	63.80 288	65.55 277	48.10 266	76.91 163	82.64 214	45.20 270	78.84 173	61.20 156	77.89 297	80.44 217

VNet		64.01 245	65.15 231	60.57 292	73.28 251	35.61 334	57.60 326	67.08 269	54.61 190	66.76 285	83.37 202	56.28 213	66.87 300	42.19 292	85.20 213	79.23 231

3Dnovator	65.95 11	71.50 159	71.22 168	72.34 167	73.16 252	63.09 126	78.37 103	78.32 172	57.67 154	72.22 231	84.61 184	54.77 218	78.47 181	60.82 162	81.07 263	75.45 263

GBi-Net		68.30 199	68.79 190	66.81 235	73.14 253	40.68 297	71.96 176	73.03 219	54.81 183	74.72 197	90.36 71	48.63 258	75.20 229	47.12 266	85.37 207	84.54 116

test1		68.30 199	68.79 190	66.81 235	73.14 253	40.68 297	71.96 176	73.03 219	54.81 183	74.72 197	90.36 71	48.63 258	75.20 229	47.12 266	85.37 207	84.54 116

FMVSNet2		67.48 210	68.21 201	65.29 247	73.14 253	38.94 310	68.81 220	71.21 247	54.81 183	76.73 170	86.48 155	48.63 258	74.60 237	47.98 261	86.11 202	82.35 180

thisisatest0530		67.05 217	65.16 229	72.73 159	73.10 256	50.55 213	71.26 191	63.91 289	50.22 248	74.46 203	80.75 233	26.81 359	80.25 154	59.43 173	86.50 197	87.37 58

pm-mvs1		68.40 197	69.85 179	64.04 258	73.10 256	39.94 303	64.61 280	70.50 253	55.52 178	73.97 210	89.33 91	63.91 143	68.38 290	49.68 247	88.02 172	83.81 136

pmmvs4		60.78 272	59.04 280	66.00 244	73.06 258	57.67 175	64.53 281	60.22 306	36.91 335	65.96 287	77.27 278	39.66 305	68.54 289	38.87 310	74.89 313	71.80 295

v2v482		72.55 151	72.58 148	72.43 165	72.92 259	46.72 251	71.41 186	79.13 157	55.27 179	81.17 109	85.25 180	55.41 217	81.13 132	67.25 112	85.46 206	89.43 29

MIMVSNet		54.39 304	56.12 302	49.20 330	72.57 260	30.91 358	59.98 314	48.43 353	41.66 310	55.94 339	83.86 195	41.19 295	50.42 342	26.05 364	75.38 310	66.27 333

Patchmatch-RL test		59.95 278	59.12 279	62.44 276	72.46 261	54.61 193	59.63 316	47.51 355	41.05 316	74.58 201	74.30 302	31.06 346	65.31 309	51.61 231	79.85 277	67.39 325

CL-MVSNet_self_test		62.44 260	63.40 246	59.55 299	72.34 262	32.38 350	56.39 328	64.84 281	51.21 237	67.46 281	81.01 231	50.75 240	63.51 319	38.47 315	88.12 170	82.75 170

Vis-MVSNet (Re-imp)		62.74 256	63.21 249	61.34 286	72.19 263	31.56 354	67.31 246	53.87 332	53.60 210	69.88 261	83.37 202	40.52 300	70.98 272	41.40 297	86.78 194	81.48 193

thres100view900		61.17 269	61.09 265	61.39 285	72.14 264	35.01 337	65.42 271	56.99 320	55.23 180	70.71 251	79.90 245	32.07 338	72.09 261	35.61 335	81.73 254	77.08 254

ab-mvs		64.11 243	65.13 232	61.05 288	71.99 265	38.03 318	67.59 237	68.79 262	49.08 260	65.32 291	86.26 161	58.02 200	66.85 302	39.33 307	79.79 280	78.27 241

thres600view7		61.82 264	61.38 263	63.12 268	71.81 266	34.93 338	64.64 278	56.99 320	54.78 187	70.33 255	79.74 247	32.07 338	72.42 259	38.61 313	83.46 238	82.02 185

QAPM		69.18 187	69.26 183	68.94 209	71.61 267	52.58 205	80.37 76	78.79 164	49.63 254	73.51 213	85.14 181	53.66 224	79.12 169	55.11 206	75.54 307	75.11 267

baseline		73.10 132	73.96 121	70.51 184	71.46 268	46.39 256	72.08 172	84.40 65	55.95 173	76.62 171	86.46 156	67.20 109	78.03 196	64.22 130	87.27 187	87.11 64

casdiffmvs		73.06 135	73.84 122	70.72 180	71.32 269	46.71 252	70.93 195	84.26 70	55.62 177	77.46 154	87.10 129	67.09 110	77.81 199	63.95 133	86.83 193	87.64 54

Anonymous20231206		54.13 305	55.82 303	49.04 333	70.89 270	35.96 330	51.73 342	50.87 345	34.86 342	62.49 309	79.22 255	42.52 289	44.29 360	27.95 362	81.88 252	66.88 329

tfpn200view9		60.35 276	59.97 274	61.51 283	70.78 271	35.35 335	63.27 294	57.47 314	53.00 215	68.31 274	77.09 279	32.45 335	72.09 261	35.61 335	81.73 254	77.08 254

thres400		60.77 273	59.97 274	63.15 267	70.78 271	35.35 335	63.27 294	57.47 314	53.00 215	68.31 274	77.09 279	32.45 335	72.09 261	35.61 335	81.73 254	82.02 185

MSDG		67.47 211	67.48 211	67.46 228	70.70 273	54.69 192	66.90 252	78.17 175	60.88 125	70.41 253	74.76 294	61.22 167	73.18 248	47.38 265	76.87 299	74.49 271

test_yl		65.11 227	65.09 233	65.18 248	70.59 274	40.86 295	63.22 296	72.79 222	57.91 149	68.88 270	79.07 260	42.85 286	74.89 233	45.50 278	84.97 215	79.81 223

DCV-MVSNet		65.11 227	65.09 233	65.18 248	70.59 274	40.86 295	63.22 296	72.79 222	57.91 149	68.88 270	79.07 260	42.85 286	74.89 233	45.50 278	84.97 215	79.81 223

OpenMVS	62.51 15	68.76 192	68.75 192	68.78 214	70.56 276	53.91 197	78.29 105	77.35 186	48.85 261	70.22 256	83.52 198	52.65 230	76.93 209	55.31 205	81.99 250	75.49 262

DELS-MVS		68.83 190	68.31 196	70.38 185	70.55 277	48.31 230	63.78 289	82.13 100	54.00 204	68.96 269	75.17 292	58.95 187	80.06 160	58.55 178	82.74 244	82.76 169
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
LCM-MVSNet-Re		69.10 188	71.57 164	61.70 281	70.37 278	34.30 343	61.45 303	79.62 147	56.81 163	89.59 8	88.16 122	68.44 99	72.94 250	42.30 291	87.33 184	77.85 249

patch_mono-2		62.73 257	64.08 239	58.68 303	70.36 279	55.87 184	60.84 309	64.11 288	41.23 313	64.04 299	78.22 268	60.00 175	48.80 345	54.17 218	83.71 236	71.37 298

SCA		58.57 288	58.04 288	60.17 295	70.17 280	41.07 294	65.19 273	53.38 337	43.34 304	61.00 319	73.48 309	45.20 270	69.38 284	40.34 304	70.31 336	70.05 309

ET-MVSNet_ETH3D		63.32 248	60.69 270	71.20 178	70.15 281	55.66 186	65.02 275	64.32 286	43.28 305	68.99 268	72.05 324	25.46 366	78.19 194	54.16 219	82.80 243	79.74 226

iter_conf05		67.34 213	65.62 225	72.50 163	69.82 282	47.06 249	72.19 171	76.86 191	45.32 287	72.86 222	82.85 211	20.53 374	83.73 84	61.13 158	89.02 163	86.70 68

PVSNet_BlendedMVS		65.38 225	64.30 237	68.61 215	69.81 283	49.36 223	65.60 269	78.96 159	45.50 282	59.98 323	78.61 263	51.82 233	78.20 192	44.30 281	84.11 228	78.27 241

PVSNet_Blended		62.90 254	61.64 259	66.69 238	69.81 283	49.36 223	61.23 306	78.96 159	42.04 308	59.98 323	68.86 348	51.82 233	78.20 192	44.30 281	77.77 298	72.52 287

OpenMVS_ROB	54.93 17	63.23 250	63.28 247	63.07 269	69.81 283	45.34 263	68.52 228	67.14 268	43.74 298	70.61 252	79.22 255	47.90 262	72.66 253	48.75 253	73.84 321	71.21 302

FMVSNet3		65.00 230	65.16 229	64.52 253	69.47 286	37.56 322	66.63 255	70.38 254	51.55 232	74.72 197	83.27 207	37.89 316	74.44 239	47.12 266	85.37 207	81.57 192

MVS_0304		62.51 259	62.27 256	63.25 266	69.39 287	48.47 229	64.05 286	62.48 295	59.69 135	54.10 348	81.04 230	45.71 266	66.31 307	41.38 298	82.58 246	74.96 268

MS-PatchMatch		55.59 299	54.89 307	57.68 307	69.18 288	49.05 225	61.00 308	62.93 294	35.98 338	58.36 330	68.93 346	36.71 321	66.59 305	37.62 322	63.30 356	57.39 358

baseline1		57.82 291	58.36 287	56.19 311	69.17 289	30.76 359	62.94 298	55.21 327	46.04 281	63.83 302	78.47 264	41.20 294	63.68 317	39.44 306	68.99 343	74.13 273

v148		69.38 184	69.39 181	69.36 200	69.14 290	44.56 267	68.83 219	72.70 225	54.79 186	78.59 136	84.12 191	54.69 219	76.74 215	59.40 174	82.20 248	86.79 66

CANet_DTU		64.04 244	63.83 241	64.66 251	68.39 291	42.97 282	73.45 160	74.50 213	52.05 225	54.78 343	75.44 291	43.99 278	70.42 278	53.49 224	78.41 292	80.59 214

EU-MVSNet		60.82 271	60.80 269	60.86 291	68.37 292	41.16 292	72.27 168	68.27 266	26.96 369	69.08 266	75.71 286	32.09 337	67.44 296	55.59 203	78.90 286	73.97 274

PVSNet	43.83 21	51.56 318	51.17 322	52.73 320	68.34 293	38.27 313	48.22 349	53.56 335	36.41 336	54.29 346	64.94 360	34.60 326	54.20 340	30.34 352	69.87 339	65.71 336

EPNet		69.10 188	67.32 212	74.46 120	68.33 294	61.27 143	77.56 113	63.57 291	60.95 124	56.62 336	82.75 213	51.53 236	81.24 130	54.36 216	90.20 133	80.88 204
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IB-MVS	49.67 18	59.69 280	56.96 295	67.90 223	68.19 295	50.30 216	61.42 304	65.18 279	47.57 273	55.83 340	67.15 357	23.77 371	79.60 164	43.56 287	79.97 276	73.79 277
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		60.62 274	59.97 274	62.58 275	68.13 296	47.28 246	68.59 225	73.96 215	32.19 355	59.94 325	68.86 348	50.48 242	77.64 203	41.85 295	75.74 304	62.83 347

eth_miper_zixun_eth		69.42 182	68.73 194	71.50 175	67.99 297	46.42 254	67.58 238	78.81 162	50.72 242	78.13 144	80.34 239	50.15 245	80.34 151	60.18 165	84.65 220	87.74 53

TinyColmap		67.98 203	69.28 182	64.08 256	67.98 298	46.82 250	70.04 203	75.26 207	53.05 214	77.36 155	86.79 139	59.39 182	72.59 257	45.64 277	88.01 173	72.83 284

EPNet_dtu		58.93 285	58.52 284	60.16 296	67.91 299	47.70 241	69.97 204	58.02 312	49.73 253	47.28 363	73.02 314	38.14 312	62.34 322	36.57 329	85.99 203	70.43 307
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 292	57.02 294	59.17 300	67.89 300	34.93 338	58.91 321	57.25 318	50.24 247	64.01 300	71.46 328	32.49 334	71.39 269	31.31 349	79.57 282	71.19 303

our_test_3		56.46 294	56.51 298	56.30 310	67.70 301	39.66 305	55.36 334	52.34 342	40.57 320	63.85 301	69.91 339	40.04 302	58.22 333	43.49 288	75.29 312	71.03 305

ppachtmachnet_test		60.26 277	59.61 277	62.20 278	67.70 301	44.33 269	58.18 323	60.96 305	40.75 318	65.80 289	72.57 316	41.23 293	63.92 316	46.87 270	82.42 247	78.33 239

MVS_Test		69.84 176	70.71 172	67.24 230	67.49 303	43.25 280	69.87 206	81.22 119	52.69 219	71.57 240	86.68 146	62.09 156	74.51 238	66.05 115	78.74 287	83.96 132

thisisatest0515		60.48 275	57.86 289	68.34 218	67.25 304	46.42 254	60.58 311	62.14 297	40.82 317	63.58 305	69.12 343	26.28 362	78.34 188	48.83 252	82.13 249	80.26 219

V42		71.06 161	70.83 171	71.72 171	67.25 304	47.14 248	65.94 261	80.35 139	51.35 234	83.40 82	83.23 208	59.25 184	78.80 174	65.91 117	80.81 268	89.23 32

GA-MVS		62.91 253	61.66 258	66.66 239	67.09 306	44.49 268	61.18 307	69.36 260	51.33 235	69.33 265	74.47 299	36.83 320	74.94 232	50.60 240	74.72 314	80.57 215

HY-MVS	49.31 19	57.96 290	57.59 291	59.10 301	66.85 307	36.17 328	65.13 274	65.39 278	39.24 324	54.69 345	78.14 270	44.28 277	67.18 299	33.75 343	70.79 333	73.95 275

CR-MVSNet		58.96 284	58.49 285	60.36 294	66.37 308	48.24 232	70.93 195	56.40 324	32.87 354	61.35 314	86.66 147	33.19 330	63.22 320	48.50 257	70.17 337	69.62 314

RPMNet		65.77 223	65.08 235	67.84 225	66.37 308	48.24 232	70.93 195	86.27 22	54.66 189	61.35 314	86.77 142	33.29 329	85.67 48	55.93 198	70.17 337	69.62 314

IterMVS		63.12 251	62.48 255	65.02 250	66.34 310	52.86 202	63.81 287	62.25 296	46.57 279	71.51 242	80.40 238	44.60 275	66.82 303	51.38 234	75.47 308	75.38 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		69.82 177	69.89 178	69.61 198	66.24 311	43.48 276	68.12 233	79.61 149	51.43 233	77.72 150	80.18 243	54.61 221	78.15 195	63.62 138	87.50 179	87.20 62

tpm2		56.12 295	54.64 308	60.55 293	66.24 311	36.01 329	68.14 232	56.77 322	33.60 352	58.25 331	75.52 290	30.25 352	74.33 241	33.27 344	69.76 341	71.32 299

Anonymous20240521		63.55 246	66.07 223	55.99 312	66.18 313	44.04 271	68.77 223	68.80 261	46.99 276	72.57 225	85.84 174	39.87 303	50.22 343	53.40 226	92.23 92	73.71 278

Patchmtry		60.91 270	63.01 251	54.62 315	66.10 314	26.27 371	67.47 240	56.40 324	54.05 203	72.04 233	86.66 147	33.19 330	60.17 328	43.69 285	87.45 181	77.42 250

FMVSNet5		55.08 302	55.54 305	53.71 316	65.80 315	33.50 347	56.22 330	52.50 341	43.72 299	61.06 317	83.38 201	25.46 366	54.87 337	30.11 354	81.64 259	72.75 285

1314		59.83 279	58.86 282	62.74 274	65.71 316	44.78 266	68.59 225	72.63 226	33.54 353	61.05 318	67.29 356	43.62 281	71.26 270	49.49 248	67.84 348	72.19 292

MDTV_nov1_ep13			54.05 310		65.54 317	29.30 363	59.00 319	55.22 326	35.96 339	52.44 351	75.98 285	30.77 349	59.62 329	38.21 316	73.33 322

baseline2		55.57 300	52.74 314	64.05 257	65.26 318	44.11 270	62.38 299	54.43 330	39.03 325	51.21 353	67.35 355	33.66 328	72.45 258	37.14 325	64.22 355	75.60 261

USDC		62.80 255	63.10 250	61.89 280	65.19 319	43.30 279	67.42 241	74.20 214	35.80 340	72.25 230	84.48 187	45.67 267	71.95 265	37.95 319	84.97 215	70.42 308

tpm		50.60 319	52.42 317	45.14 344	65.18 320	26.29 370	60.30 312	43.50 360	37.41 332	57.01 333	79.09 259	30.20 354	42.32 365	32.77 346	66.36 350	66.81 331

PatchmatchNet		54.60 303	54.27 309	55.59 313	65.17 321	39.08 307	66.92 251	51.80 343	39.89 321	58.39 329	73.12 313	31.69 340	58.33 332	43.01 289	58.38 366	69.38 317
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_ehance_all_eth		68.36 198	68.16 203	68.98 207	65.14 322	43.34 278	67.07 248	78.92 161	49.11 259	76.21 181	77.72 274	53.48 225	77.92 198	61.16 157	84.59 222	85.68 87

cl____		68.26 202	68.26 198	68.29 219	64.98 323	43.67 274	65.89 262	74.67 210	50.04 251	76.86 166	82.42 216	48.74 256	75.38 225	60.92 161	89.81 144	85.80 84

DIV-MVS_self_test		68.27 201	68.26 198	68.29 219	64.98 323	43.67 274	65.89 262	74.67 210	50.04 251	76.86 166	82.43 215	48.74 256	75.38 225	60.94 160	89.81 144	85.81 80

tpm cat1		54.02 307	52.63 315	58.19 306	64.85 325	39.86 304	66.26 259	57.28 317	32.16 356	56.90 334	70.39 334	32.75 333	65.30 310	34.29 340	58.79 363	69.41 316

XXY-MVS		55.19 301	57.40 293	48.56 334	64.45 326	34.84 340	51.54 343	53.59 334	38.99 326	63.79 303	79.43 251	56.59 211	45.57 352	36.92 327	71.29 330	65.25 338

PatchT		53.35 309	56.47 299	43.99 348	64.19 327	17.46 379	59.15 317	43.10 361	52.11 224	54.74 344	86.95 134	29.97 355	49.98 344	43.62 286	74.40 317	64.53 345

D2MVS		62.58 258	61.05 266	67.20 231	63.85 328	47.92 237	56.29 329	69.58 258	39.32 322	70.07 258	78.19 269	34.93 325	72.68 252	53.44 225	83.74 234	81.00 200

mvs_anonymous		65.08 229	65.49 226	63.83 259	63.79 329	37.60 321	66.52 257	69.82 257	43.44 301	73.46 215	86.08 169	58.79 189	71.75 268	51.90 230	75.63 306	82.15 184

CostFormer		57.35 293	56.14 301	60.97 289	63.76 330	38.43 311	67.50 239	60.22 306	37.14 334	59.12 328	76.34 284	32.78 332	71.99 264	39.12 309	69.27 342	72.47 288

Gipuma		69.55 180	72.83 144	59.70 297	63.63 331	53.97 196	80.08 84	75.93 199	64.24 97	73.49 214	88.93 106	57.89 201	62.46 321	59.75 172	91.55 102	62.67 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
cl22		67.14 214	66.51 220	69.03 206	63.20 332	43.46 277	66.88 253	76.25 196	49.22 257	74.48 202	77.88 273	45.49 269	77.40 205	60.64 163	84.59 222	86.24 72

gg-mvs-nofinetune		55.75 297	56.75 297	52.72 321	62.87 333	28.04 366	68.92 217	41.36 370	71.09 44	50.80 355	92.63 12	20.74 373	66.86 301	29.97 355	72.41 325	63.25 346

gm-plane-assit					62.51 334	33.91 345			37.25 333		62.71 362		72.74 251	38.70 311

MVS-HIRNet		45.53 331	47.29 331	40.24 354	62.29 335	26.82 369	56.02 331	37.41 376	29.74 365	43.69 372	81.27 227	33.96 327	55.48 336	24.46 370	56.79 367	38.43 373

diffmvs		67.42 212	67.50 210	67.20 231	62.26 336	45.21 264	64.87 276	77.04 190	48.21 264	71.74 234	79.70 248	58.40 191	71.17 271	64.99 124	80.27 273	85.22 89

CHOSEN 280x420		41.62 339	39.89 344	46.80 337	61.81 337	51.59 207	33.56 370	35.74 377	27.48 368	37.64 376	53.53 368	23.24 372	42.09 366	27.39 363	58.64 364	46.72 367

KD-MVS_self_test		66.38 220	67.51 209	62.97 271	61.76 338	34.39 342	58.11 324	75.30 206	50.84 241	77.12 158	85.42 177	56.84 210	69.44 283	51.07 236	91.16 110	85.08 95

MDA-MVSNet-bldmvs		62.34 261	61.73 257	64.16 254	61.64 339	49.90 219	48.11 350	57.24 319	53.31 213	80.95 111	79.39 252	49.00 254	61.55 325	45.92 276	80.05 275	81.03 198

miper_enhance_ethall		65.86 222	65.05 236	68.28 221	61.62 340	42.62 285	64.74 277	77.97 178	42.52 306	73.42 216	72.79 315	49.66 247	77.68 202	58.12 181	84.59 222	84.54 116

WTY-MVS		49.39 323	50.31 326	46.62 338	61.22 341	32.00 353	46.61 355	49.77 348	33.87 349	54.12 347	69.55 342	41.96 290	45.40 354	31.28 350	64.42 354	62.47 350

CMPMVS	48.73 20	61.54 267	60.89 267	63.52 263	61.08 342	51.55 208	68.07 234	68.00 267	33.88 348	65.87 288	81.25 228	37.91 315	67.71 293	49.32 249	82.60 245	71.31 300
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 320	50.69 325	49.64 328	60.76 343	41.87 289	53.18 339	45.48 358	43.41 302	49.41 360	60.47 365	29.22 357	44.73 358	42.09 293	72.14 326	62.33 351

test-mter		48.56 325	48.20 330	49.64 328	60.76 343	41.87 289	53.18 339	45.48 358	31.91 360	49.41 360	60.47 365	18.34 377	44.73 358	42.09 293	72.14 326	62.33 351

GG-mvs-BLEND				52.24 322	60.64 345	29.21 364	69.73 208	42.41 363		45.47 365	52.33 370	20.43 375	68.16 291	25.52 368	65.42 352	59.36 356

tpmvs		55.84 296	55.45 306	57.01 309	60.33 346	33.20 348	65.89 262	59.29 310	47.52 274	56.04 338	73.60 308	31.05 347	68.06 292	40.64 302	64.64 353	69.77 312

miper_lstm_enhance		61.97 262	61.63 260	62.98 270	60.04 347	45.74 261	47.53 352	70.95 249	44.04 294	73.06 220	78.84 262	39.72 304	60.33 327	55.82 200	84.64 221	82.88 164

PVSNet_0	36.71 22	41.12 340	40.78 343	42.14 350	59.97 348	40.13 302	40.97 362	42.24 367	30.81 364	44.86 368	49.41 372	40.70 299	45.12 356	23.15 371	34.96 375	41.16 371

new-patchmatchnet		52.89 310	55.76 304	44.26 347	59.94 349	6.31 382	37.36 369	50.76 346	41.10 314	64.28 297	79.82 246	44.77 273	48.43 347	36.24 332	87.61 176	78.03 246

test20.03		55.74 298	57.51 292	50.42 325	59.89 350	32.09 352	50.63 344	49.01 350	50.11 249	65.07 293	83.23 208	45.61 268	48.11 348	30.22 353	83.82 233	71.07 304

MVSTER		63.29 249	61.60 261	68.36 217	59.77 351	46.21 257	60.62 310	71.32 241	41.83 309	75.40 189	79.12 258	30.25 352	75.85 219	56.30 195	79.81 278	83.03 161

N_pmnet		52.06 315	51.11 323	54.92 314	59.64 352	71.03 59	37.42 368	61.62 304	33.68 350	57.12 332	72.10 317	37.94 314	31.03 374	29.13 361	71.35 329	62.70 348

JIA-IIPM		54.03 306	51.62 318	61.25 287	59.14 353	55.21 189	59.10 318	47.72 354	50.85 240	50.31 359	85.81 175	20.10 376	63.97 315	36.16 333	55.41 371	64.55 344

LF4IMVS		67.50 209	67.31 213	68.08 222	58.86 354	61.93 133	71.43 185	75.90 200	44.67 292	72.42 228	80.20 241	57.16 204	70.44 277	58.99 176	86.12 201	71.88 294

UnsupCasMVSNet_bld		50.01 322	51.03 324	46.95 335	58.61 355	32.64 349	48.31 348	53.27 338	34.27 347	60.47 321	71.53 327	41.40 292	47.07 349	30.68 351	60.78 359	61.13 353

dp		44.09 337	44.88 338	41.72 353	58.53 356	23.18 375	54.70 336	42.38 365	34.80 343	44.25 370	65.61 359	24.48 370	44.80 357	29.77 356	49.42 373	57.18 359

testgi		54.00 308	56.86 296	45.45 342	58.20 357	25.81 372	49.05 346	49.50 349	45.43 285	67.84 276	81.17 229	51.81 235	43.20 364	29.30 358	79.41 283	67.34 327

wuyk23d		61.97 262	66.25 221	49.12 332	58.19 358	60.77 152	66.32 258	52.97 339	55.93 174	90.62 5	86.91 135	73.07 62	35.98 372	20.63 374	91.63 98	50.62 363

ANet_high		67.08 215	69.94 177	58.51 305	57.55 359	27.09 368	58.43 322	76.80 193	63.56 103	82.40 95	91.93 21	59.82 179	64.98 312	50.10 244	88.86 165	83.46 148

Patchmatch-test		47.93 326	49.96 327	41.84 351	57.42 360	24.26 374	48.75 347	41.49 369	39.30 323	56.79 335	73.48 309	30.48 351	33.87 373	29.29 359	72.61 324	67.39 325

new_pmnet		37.55 342	39.80 345	30.79 357	56.83 361	16.46 380	39.35 365	30.65 379	25.59 371	45.26 366	61.60 364	24.54 369	28.02 376	21.60 372	52.80 372	47.90 366

pmmvs3		46.71 329	45.09 336	51.55 324	56.76 362	48.25 231	55.78 332	39.53 374	24.13 373	50.35 358	63.40 361	15.90 382	51.08 341	29.29 359	70.69 335	55.33 361

sss		47.59 328	48.32 328	45.40 343	56.73 363	33.96 344	45.17 358	48.51 352	32.11 359	52.37 352	65.79 358	40.39 301	41.91 368	31.85 347	61.97 357	60.35 354

tpmrst		50.15 321	51.38 321	46.45 339	56.05 364	24.77 373	64.40 283	49.98 347	36.14 337	53.32 350	69.59 341	35.16 324	48.69 346	39.24 308	58.51 365	65.89 334

TESTMET0.1,1		45.17 332	44.93 337	45.89 341	56.02 365	38.31 312	53.18 339	41.94 368	27.85 367	44.86 368	56.47 367	17.93 378	41.50 369	38.08 318	68.06 346	57.85 357

ADS-MVSNet2		48.76 324	47.25 332	53.29 319	55.90 366	40.54 300	47.34 353	54.99 329	31.41 362	50.48 356	72.06 322	31.23 343	54.26 339	25.93 365	55.93 368	65.07 339

ADS-MVSNet		44.62 335	45.58 334	41.73 352	55.90 366	20.83 377	47.34 353	39.94 373	31.41 362	50.48 356	72.06 322	31.23 343	39.31 370	25.93 365	55.93 368	65.07 339

test0.0.03 1		47.72 327	48.31 329	45.93 340	55.53 368	29.39 362	46.40 356	41.21 371	43.41 302	55.81 341	67.65 352	29.22 357	43.77 363	25.73 367	69.87 339	64.62 343

UnsupCasMVSNet_eth		52.26 314	53.29 313	49.16 331	55.08 369	33.67 346	50.03 345	58.79 311	37.67 331	63.43 308	74.75 295	41.82 291	45.83 351	38.59 314	59.42 362	67.98 324

pmmvs5		52.49 313	52.58 316	52.21 323	54.99 370	32.38 350	55.45 333	53.84 333	32.15 357	55.49 342	74.81 293	38.08 313	57.37 335	34.02 341	74.40 317	66.88 329

DSMNet-mixed		43.18 338	44.66 339	38.75 356	54.75 371	28.88 365	57.06 327	27.42 381	13.47 375	47.27 364	77.67 275	38.83 309	39.29 371	25.32 369	60.12 361	48.08 365

MDA-MVSNet_test_wron		52.57 312	53.49 312	49.81 327	54.24 372	36.47 326	40.48 364	46.58 356	38.13 328	75.47 188	73.32 311	41.05 298	43.85 362	40.98 300	71.20 331	69.10 320

YYNet1		52.58 311	53.50 311	49.85 326	54.15 373	36.45 327	40.53 363	46.55 357	38.09 329	75.52 187	73.31 312	41.08 297	43.88 361	41.10 299	71.14 332	69.21 318

EPMVS		45.74 330	46.53 333	43.39 349	54.14 374	22.33 376	55.02 335	35.00 378	34.69 345	51.09 354	70.20 336	25.92 364	42.04 367	37.19 324	55.50 370	65.78 335

KD-MVS_2432*1600		52.05 316	51.58 319	53.44 317	52.11 375	31.20 355	44.88 359	64.83 282	41.53 311	64.37 295	70.03 337	15.61 383	64.20 313	36.25 330	74.61 315	64.93 341

miper_refine_blended		52.05 316	51.58 319	53.44 317	52.11 375	31.20 355	44.88 359	64.83 282	41.53 311	64.37 295	70.03 337	15.61 383	64.20 313	36.25 330	74.61 315	64.93 341

E-PMN		45.17 332	45.36 335	44.60 346	50.07 377	42.75 283	38.66 366	42.29 366	46.39 280	39.55 373	51.15 371	26.00 363	45.37 355	37.68 320	76.41 300	45.69 369

PMMVS		44.69 334	43.95 341	46.92 336	50.05 378	53.47 200	48.08 351	42.40 364	22.36 374	44.01 371	53.05 369	42.60 288	45.49 353	31.69 348	61.36 358	41.79 370

EMVS		44.61 336	44.45 340	45.10 345	48.91 379	43.00 281	37.92 367	41.10 372	46.75 278	38.00 375	48.43 373	26.42 361	46.27 350	37.11 326	75.38 310	46.03 368

MVE	27.91 23	36.69 343	35.64 346	39.84 355	43.37 380	35.85 332	19.49 372	24.61 382	24.68 372	39.05 374	62.63 363	38.67 311	27.10 377	21.04 373	47.25 374	56.56 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 341	40.87 342	28.36 358	42.41 381	5.35 383	24.61 371	27.75 380	32.15 357	47.85 362	70.27 335	35.85 323	29.51 375	19.08 375	67.85 347	50.22 364

DeepMVS_CX				11.83 360	15.51 382	13.86 381		11.25 385	5.76 376	20.85 378	26.46 375	17.06 381	9.22 379	9.69 378	13.82 378	12.42 375

test_method		19.26 344	19.12 348	19.71 359	9.09 383	1.91 385	7.79 374	53.44 336	1.42 377	10.27 379	35.80 374	17.42 380	25.11 378	12.44 376	24.38 377	32.10 374

tmp_tt		11.98 346	14.73 349	3.72 361	2.28 384	4.62 384	19.44 373	14.50 384	0.47 379	21.55 377	9.58 377	25.78 365	4.57 380	11.61 377	27.37 376	1.96 376

test123		4.43 349	5.78 352	0.39 363	0.97 385	0.28 386	46.33 357	0.45 386	0.31 380	0.62 381	1.50 380	0.61 386	0.11 382	0.56 379	0.63 379	0.77 378

testmvs		4.06 350	5.28 353	0.41 362	0.64 386	0.16 387	42.54 361	0.31 387	0.26 381	0.50 382	1.40 381	0.77 385	0.17 381	0.56 379	0.55 380	0.90 377

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

eth-test2					0.00 387

eth-test					0.00 387

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		17.71 345	23.62 347	0.00 364	0.00 387	0.00 388	0.00 375	70.17 256	0.00 382	0.00 383	74.25 303	68.16 102	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		5.20 348	6.93 351	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	62.39 152	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		5.62 347	7.50 350	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	67.46 353	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

PC_three_1452									46.98 277	81.83 99	86.28 159	66.55 120	84.47 73	63.31 143	90.78 124	83.49 144

test_241102_TWO								84.80 50	72.61 32	84.93 58	89.70 86	77.73 23	85.89 41	75.29 45	94.22 56	83.25 154

test_0728_THIRD									74.03 23	85.83 43	90.41 63	75.58 39	85.69 46	77.43 33	94.74 31	84.31 126

GSMVS																70.05 309

sam_mvs1												31.41 341				70.05 309

sam_mvs												31.21 345

MTGPA								80.63 130

test_post1							66.63 255				2.08 378	30.66 350	59.33 330	40.34 304

test_post											1.99 379	30.91 348	54.76 338

patchmatchnet-post											68.99 344	31.32 342	69.38 284

MTMP							84.83 33	19.26 383

test9_res														72.12 73	91.37 105	77.40 251

agg_prior2														70.70 81	90.93 118	78.55 238

test_prior4						70.14 68	77.57 112

test_prior2							75.57 137		58.92 141	76.53 175	86.78 140	67.83 106		69.81 86	92.76 84

旧先验2							71.17 192		45.11 289	78.54 139			61.28 326	59.19 175

新几何2							71.33 188

无先验							74.82 145	70.94 250	47.75 272				76.85 212	54.47 212		72.09 293

原ACMM2							74.78 149

testdata2													67.30 297	48.34 258

segment_acmp												68.30 101

testdata1							68.34 231		57.24 160

plane_prior5								85.49 34					86.15 27	71.09 77	90.94 116	84.82 102

plane_prior4											89.11 100

plane_prior3						65.67 103			63.82 101	78.23 142

plane_prior2							82.74 55		65.45 80

plane_prior						65.18 107	80.06 85		61.88 119						89.91 143

n2								0.00 388

nn								0.00 388

door-mid								55.02 328

test11								82.71 94

door								52.91 340

HQP5-MVS						58.80 169

BP-MVS														67.38 109

HQP4-MVS										71.59 236			85.31 54			83.74 139

HQP3-MVS								84.12 74							89.16 157

HQP2-MVS												58.09 195

MDTV_nov1_ep13_2view						18.41 378	53.74 338		31.57 361	44.89 367		29.90 356		32.93 345		71.48 297

ACMMP++_ref															89.47 152

ACMMP++															91.96 94

Test By Simon												62.56 148

High-resolution multi-view benchmark