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	63.30 119	84.80 32	87.77 9	86.18 1	96.26 1	96.06 1	90.32 1	84.49 69	68.08 88	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	80.63 125	72.08 36	84.93 56	90.79 45	74.65 46	84.42 72	80.98 4	94.75 28	80.82 204

mPP-MVS		84.01 10	84.39 11	82.88 6	90.65 3	81.38 3	87.08 12	82.79 84	72.41 34	85.11 55	90.85 44	76.65 28	84.89 63	79.30 16	94.63 33	82.35 176

MP-MVS		83.19 18	83.54 23	82.14 19	90.54 4	79.00 8	86.42 22	83.59 74	71.31 39	81.26 102	90.96 39	74.57 47	84.69 67	78.41 21	94.78 27	82.74 166
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	79.22 152	74.08 20	87.16 28	91.97 19	84.80 2	76.97 197	64.98 119	93.61 60	72.28 304
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 47	82.86 36	73.07 133	89.93 6	39.21 318	77.15 110	81.28 109	79.74 5	90.87 4	92.73 11	75.03 43	84.93 62	63.83 131	95.19 15	95.07 3

DTE-MVSNet		80.35 48	82.89 35	72.74 146	89.84 7	37.34 338	77.16 109	81.81 99	80.45 3	90.92 3	92.95 7	74.57 47	86.12 29	63.65 132	94.68 31	94.76 6

PEN-MVS		80.46 46	82.91 34	73.11 132	89.83 8	39.02 321	77.06 112	82.61 88	80.04 4	90.60 6	92.85 9	74.93 44	85.21 57	63.15 139	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 64	70.19 48	83.86 71	90.72 49	75.20 40	86.27 21	79.41 14	94.25 50	83.95 127

ACMMPR		83.62 12	83.93 17	82.69 11	89.78 10	77.51 18	87.01 14	84.19 65	70.23 46	84.49 63	90.67 50	75.15 41	86.37 18	79.58 10	94.26 49	84.18 123

MSP-MVS		80.49 45	79.67 58	82.96 5	89.70 11	77.46 19	87.16 11	85.10 40	64.94 89	81.05 105	88.38 113	57.10 211	87.10 8	79.75 7	83.87 229	84.31 120
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CP-MVSNet		79.48 54	81.65 45	72.98 136	89.66 12	39.06 320	76.76 113	80.46 129	78.91 7	90.32 7	91.70 25	68.49 92	84.89 63	63.40 136	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 34	67.96 59	84.91 59	90.88 42	75.59 36	86.57 14	78.16 22	94.71 30	83.82 129

WR-MVS_H		80.22 50	82.17 41	74.39 111	89.46 14	42.69 295	78.24 96	82.24 91	78.21 9	89.57 9	92.10 18	68.05 97	85.59 48	66.04 112	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 53	72.71 27	82.87 81	90.39 62	73.86 52	86.31 19	78.84 19	94.03 53	84.64 103

X-MVStestdata		76.81 78	74.79 101	82.85 8	89.43 15	77.61 14	86.80 17	84.66 53	72.71 27	82.87 81	9.95 404	73.86 52	86.31 19	78.84 19	94.03 53	84.64 103

CP-MVS		84.12 8	84.55 10	82.80 10	89.42 17	79.74 5	88.19 5	84.43 58	71.96 38	84.70 61	90.56 52	77.12 25	86.18 26	79.24 17	95.36 12	82.49 174

ACMMP_NAP		82.33 27	83.28 28	79.46 49	89.28 18	69.09 74	83.62 42	84.98 42	64.77 90	83.97 69	91.02 38	75.53 39	85.93 37	82.00 2	94.36 45	83.35 147

UniMVSNet_ETH3D		76.74 79	79.02 61	69.92 191	89.27 19	43.81 283	74.47 148	71.70 231	72.33 35	85.50 50	93.65 3	77.98 21	76.88 200	54.60 213	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 61	70.23 46	84.47 64	90.43 57	76.79 26	85.94 35	79.58 10	94.23 51	82.82 163

ACMMP		84.22 6	84.84 8	82.35 17	89.23 21	76.66 22	87.65 6	85.89 26	71.03 42	85.85 42	90.58 51	78.77 16	85.78 42	79.37 15	95.17 16	84.62 105
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CPTT-MVS		81.51 34	81.76 43	80.76 34	89.20 22	78.75 9	86.48 21	82.03 95	68.80 53	80.92 107	88.52 109	72.00 64	82.39 101	74.80 44	93.04 68	81.14 194

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 25	67.39 60	84.02 68	90.39 62	74.73 45	86.46 15	80.73 6	94.43 40	84.60 108

HPM-MVS++		79.89 51	79.80 57	80.18 39	89.02 25	78.44 10	83.49 45	80.18 136	64.71 91	78.11 136	88.39 112	65.46 126	83.14 89	77.64 29	91.20 96	78.94 236

GST-MVS		82.79 24	83.27 29	81.34 27	88.99 26	73.29 41	85.94 25	85.13 38	68.58 57	84.14 67	90.21 72	73.37 56	86.41 16	79.09 18	93.98 56	84.30 122

TSAR-MVS + MP.		79.05 57	78.81 62	79.74 42	88.94 27	67.52 83	86.61 19	81.38 107	51.71 222	77.15 147	91.42 32	65.49 125	87.20 6	79.44 13	87.17 185	84.51 114
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
UA-Net		81.56 33	82.28 40	79.40 50	88.91 28	69.16 72	84.67 33	80.01 139	75.34 15	79.80 118	94.91 2	69.79 84	80.25 142	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 47	63.53 102	84.23 66	91.47 30	72.02 63	87.16 7	79.74 9	94.36 45	84.61 106
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MP-MVS-pluss		82.54 26	83.46 25	79.76 41	88.88 30	68.44 76	81.57 59	86.33 19	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 37	77.42 13	86.15 38	90.24 70	81.69 5	85.94 35	77.77 26	93.58 61	83.09 154

新几何1				69.99 189	88.37 34	71.34 51		62.08 305	43.85 295	74.99 185	86.11 163	52.85 233	70.57 269	50.99 240	83.23 238	68.05 340

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 57	66.40 69	87.45 22	89.16 93	81.02 8	80.52 138	74.27 51	95.73 7	80.98 200
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	59.59 315	41.06 319	73.05 217	85.72 171	48.03 267			80.65 265	66.92 345

XVG-ACMP-BASELINE		80.54 44	81.06 48	78.98 57	87.01 38	72.91 43	80.23 75	85.56 29	66.56 68	85.64 45	89.57 82	69.12 88	80.55 137	72.51 65	93.37 63	83.48 140

save fliter					87.00 39	67.23 86	79.24 85	77.94 178	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 17	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 17	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		64.77 235	61.17 268	75.60 98	86.90 42	74.47 30	84.04 35	68.62 265	0.60 406	1.13 408	91.61 28	65.32 128	74.15 232	64.01 126	88.28 160	78.17 246

OPM-MVS		80.99 41	81.63 46	79.07 54	86.86 43	69.39 68	79.41 84	84.00 70	65.64 73	85.54 49	89.28 86	76.32 31	83.47 83	74.03 52	93.57 62	84.35 119
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepC-MVS	72.44 4	81.00 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 24	66.80 65	86.70 30	89.99 75	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	77.15 110	85.39 35	66.73 66	80.39 114	88.85 102	74.43 50	78.33 178	74.73 46	85.79 201	82.35 176

VDDNet		71.60 150	73.13 130	67.02 235	86.29 47	41.11 305	69.97 205	66.50 274	68.72 55	74.74 188	91.70 25	59.90 179	75.81 208	48.58 261	91.72 84	84.15 124

test_0728_SECOND				76.57 85	86.20 48	60.57 150	83.77 40	85.49 30					85.90 38	75.86 39	94.39 41	83.25 149

SR-MVS-dyc-post		84.75 3	85.26 5	83.21 3	86.19 49	79.18 6	87.23 8	86.27 20	77.51 10	87.65 18	90.73 47	79.20 14	85.58 49	78.11 23	94.46 36	84.89 94

RE-MVS-def			85.50 3		86.19 49	79.18 6	87.23 8	86.27 20	77.51 10	87.65 18	90.73 47	81.38 7		78.11 23	94.46 36	84.89 94

DVP-MVS		81.15 37	83.12 32	75.24 103	86.16 51	60.78 147	83.77 40	80.58 127	72.48 32	85.83 43	90.41 59	78.57 17	85.69 45	75.86 39	94.39 41	79.24 232
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	60.78 147	83.81 39	85.10 40	72.48 32	85.27 53	89.96 76	78.57 17

SED-MVS		81.78 31	83.48 24	76.67 83	86.12 53	61.06 140	83.62 42	84.72 49	72.61 30	87.38 24	89.70 80	77.48 23	85.89 40	75.29 42	94.39 41	83.08 155

IU-MVS					86.12 53	60.90 144		80.38 131	45.49 283	81.31 101				75.64 41	94.39 41	84.65 102

test_241102_ONE					86.12 53	61.06 140		84.72 49	72.64 29	87.38 24	89.47 83	77.48 23	85.74 44

XVG-OURS		79.51 53	79.82 56	78.58 63	86.11 56	74.96 28	76.33 122	84.95 44	66.89 63	82.75 84	88.99 98	66.82 109	78.37 176	74.80 44	90.76 116	82.40 175

test_part2					85.90 57	66.44 91				84.61 62

原ACMM1				73.90 118	85.90 57	65.15 106		81.67 101	50.97 234	74.25 199	86.16 160	61.60 158	83.54 81	56.75 188	91.08 103	73.00 294

testdata				64.13 256	85.87 59	63.34 118		61.80 308	47.83 265	76.42 171	86.60 147	48.83 260	62.31 328	54.46 215	81.26 259	66.74 349

CNVR-MVS		78.49 64	78.59 66	78.16 68	85.86 60	67.40 84	78.12 99	81.50 103	63.92 96	77.51 144	86.56 148	68.43 94	84.82 65	73.83 53	91.61 88	82.26 180

test_one_0601					85.84 61	61.45 133		85.63 28	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	80.99 118	65.77 72	75.55 178	86.25 157	67.42 102	85.42 50	70.10 75	90.88 111	81.81 186

TEST9					85.47 63	69.32 70	76.42 118	78.69 163	53.73 204	76.97 149	86.74 138	66.84 108	81.10 123

train_agg		76.38 81	76.55 84	75.86 95	85.47 63	69.32 70	76.42 118	78.69 163	54.00 199	76.97 149	86.74 138	66.60 114	81.10 123	72.50 66	91.56 89	77.15 259

DPE-MVS		82.00 30	83.02 33	78.95 58	85.36 65	67.25 85	82.91 50	84.98 42	73.52 24	85.43 51	90.03 74	76.37 29	86.97 11	74.56 47	94.02 55	82.62 171
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 30	65.45 76	78.23 133	89.11 94	60.83 171	86.15 27	71.09 71	90.94 105	84.82 98

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 46	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	76.26 123	78.66 165	54.00 199	76.89 153	86.72 140	66.60 114	80.89 133

WR-MVS		71.20 153	72.48 142	67.36 230	84.98 70	35.70 348	64.43 283	68.66 264	65.05 86	81.49 98	86.43 152	57.57 207	76.48 204	50.36 245	93.32 65	89.90 23

PS-MVSNAJss		77.54 72	77.35 78	78.13 70	84.88 71	66.37 92	78.55 92	79.59 146	53.48 207	86.29 36	92.43 15	62.39 150	80.25 142	67.90 93	90.61 117	87.77 49

LTVRE_ROB	75.46 1	84.22 6	84.98 7	81.94 20	84.82 72	75.40 25	91.60 3	87.80 7	73.52 24	88.90 11	93.06 6	71.39 69	81.53 115	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 73	73.93 35	80.65 65	76.50 194	51.98 220	87.40 23	91.86 21	76.09 33	78.53 168	68.58 83	90.20 122	86.69 66

APDe-MVS		82.88 23	84.14 14	79.08 53	84.80 74	66.72 90	86.54 20	85.11 39	72.00 37	86.65 31	91.75 24	78.20 20	87.04 9	77.93 25	94.32 48	83.47 141
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSLP-MVS++		74.48 106	75.78 93	70.59 175	84.66 75	62.40 123	78.65 90	84.24 63	60.55 125	77.71 142	81.98 219	63.12 141	77.64 192	62.95 140	88.14 162	71.73 309

jajsoiax		78.51 63	78.16 70	79.59 47	84.65 76	73.83 37	80.42 69	76.12 196	51.33 230	87.19 27	91.51 29	73.79 54	78.44 172	68.27 86	90.13 126	86.49 68

TranMVSNet+NR-MVSNet		76.13 83	77.66 75	71.56 164	84.61 77	42.57 297	70.98 192	78.29 172	68.67 56	83.04 77	89.26 87	72.99 58	80.75 134	55.58 205	95.47 10	91.35 13

旧先验1					84.55 78	60.36 152		63.69 297			87.05 130	54.65 224			83.34 237	69.66 328

APD-MVS		81.13 38	81.73 44	79.36 51	84.47 79	70.53 59	83.85 38	83.70 72	69.43 52	83.67 73	88.96 99	75.89 34	86.41 16	72.62 64	92.95 69	81.14 194
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
plane_prior1					84.46 80

agg_prior					84.44 81	66.02 97		78.62 166		76.95 151			80.34 140

DeepPCF-MVS	71.07 5	78.48 65	77.14 80	82.52 16	84.39 82	77.04 21	76.35 120	84.05 68	56.66 162	80.27 115	85.31 174	68.56 91	87.03 10	67.39 99	91.26 94	83.50 137

CDPH-MVS		77.33 74	77.06 81	78.14 69	84.21 83	63.98 114	76.07 126	83.45 75	54.20 194	77.68 143	87.18 125	69.98 81	85.37 51	68.01 90	92.72 74	85.08 91

plane_prior6					84.18 84	65.31 103						60.83 171

114514_t		73.40 117	73.33 127	73.64 122	84.15 85	57.11 174	78.20 97	80.02 138	43.76 298	72.55 223	86.07 165	64.00 137	83.35 86	60.14 164	91.03 104	80.45 215

ZD-MVS					83.91 86	69.36 69		81.09 115	58.91 140	82.73 85	89.11 94	75.77 35	86.63 12	72.73 62	92.93 70

DeepC-MVS_fast	69.89 7	77.17 76	76.33 87	79.70 44	83.90 87	67.94 78	80.06 79	83.75 71	56.73 161	74.88 187	85.32 173	65.54 124	87.79 2	65.61 116	91.14 99	83.35 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
h-mvs33		73.08 124	71.61 155	77.48 74	83.89 88	72.89 44	70.47 199	71.12 247	54.28 190	77.89 137	83.41 196	49.04 257	80.98 128	63.62 133	90.77 115	78.58 240

SD-MVS		80.28 49	81.55 47	76.47 88	83.57 89	67.83 80	83.39 47	85.35 36	64.42 92	86.14 39	87.07 129	74.02 51	80.97 129	77.70 28	92.32 80	80.62 212
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		74.91 101	75.57 96	72.93 140	83.50 90	45.79 269	69.47 211	80.14 137	65.22 82	81.74 95	87.08 127	61.82 156	81.07 125	56.21 197	94.98 20	91.93 8

NR-MVSNet		73.62 113	74.05 111	72.33 157	83.50 90	43.71 284	65.65 268	77.32 185	64.32 93	75.59 177	87.08 127	62.45 149	81.34 117	54.90 208	95.63 8	91.93 8

test_0402		78.17 69	79.48 59	74.24 113	83.50 90	59.15 161	72.52 163	74.60 211	75.34 15	88.69 13	91.81 22	75.06 42	82.37 102	65.10 117	88.68 157	81.20 192

OPU-MVS				78.65 62	83.44 93	66.85 89	83.62 42				86.12 162	66.82 109	86.01 31	61.72 147	89.79 134	83.08 155

NP-MVS					83.34 94	63.07 121					85.97 166

DVP-MVS++		81.24 35	82.74 37	76.76 82	83.14 95	60.90 144	91.64 1	85.49 30	74.03 21	84.93 56	90.38 64	66.82 109	85.90 38	77.43 30	90.78 113	83.49 138

MSC_two_6792asdad				79.02 55	83.14 95	67.03 87		80.75 120					86.24 22	77.27 33	94.85 25	83.78 131

No_MVS				79.02 55	83.14 95	67.03 87		80.75 120					86.24 22	77.27 33	94.85 25	83.78 131

RRT_MVS		78.18 68	77.69 73	79.66 46	83.14 95	61.34 135	83.29 48	80.34 134	57.43 154	86.65 31	91.79 23	50.52 246	86.01 31	71.36 70	94.65 32	91.62 11

UniMVSNet (Re)		75.00 99	75.48 97	73.56 124	83.14 95	47.92 243	70.41 201	81.04 117	63.67 100	79.54 120	86.37 153	62.83 144	81.82 111	57.10 187	95.25 14	90.94 17

hse-mvs2		72.32 143	70.66 167	77.31 79	83.10 100	71.77 47	69.19 216	71.45 237	54.28 190	77.89 137	78.26 274	49.04 257	79.23 156	63.62 133	89.13 151	80.92 201

UniMVSNet_NR-MVSNet		74.90 102	75.65 94	72.64 149	83.04 101	45.79 269	69.26 214	78.81 158	66.66 67	81.74 95	86.88 133	63.26 140	81.07 125	56.21 197	94.98 20	91.05 15

HyFIR lowres test		63.01 255	60.47 275	70.61 174	83.04 101	54.10 193	59.93 316	72.24 230	33.67 367	69.00 267	75.63 295	38.69 318	76.93 198	36.60 345	75.45 312	80.81 206

COLMAP_ROB	72.78 3	83.75 11	84.11 15	82.68 12	82.97 103	74.39 32	87.18 10	88.18 6	78.98 6	86.11 40	91.47 30	79.70 12	85.76 43	66.91 107	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		70.22 163	67.88 200	77.22 80	82.96 104	71.61 48	69.08 217	71.39 238	49.17 254	71.70 233	78.07 279	37.62 326	79.21 157	61.81 144	89.15 149	80.82 204

DP-MVS Recon		73.57 114	72.69 138	76.23 91	82.85 105	63.39 117	74.32 149	82.96 82	57.75 148	70.35 251	81.98 219	64.34 136	84.41 73	49.69 249	89.95 129	80.89 202

APD-MVS_3200maxsize		83.57 13	84.33 12	81.31 28	82.83 106	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		70.04 165	68.88 183	73.53 125	82.71 107	63.62 116	74.81 139	81.95 97	48.53 259	67.16 292	79.18 263	51.42 242	78.38 175	54.39 217	79.72 277	78.60 239

DPM-MVS		69.98 167	69.22 179	72.26 158	82.69 108	58.82 165	70.53 198	81.23 111	47.79 266	64.16 309	80.21 243	51.32 243	83.12 90	60.14 164	84.95 216	74.83 277

EG-PatchMatch MVS		70.70 159	70.88 164	70.16 185	82.64 109	58.80 166	71.48 182	73.64 215	54.98 177	76.55 165	81.77 222	61.10 168	78.94 162	54.87 209	80.84 263	72.74 299

HQP-NCC					82.37 110		77.32 106		59.08 134	71.58 235

ACMP_Plane					82.37 110		77.32 106		59.08 134	71.58 235

HQP-MVS		75.24 94	75.01 100	75.94 93	82.37 110	58.80 166	77.32 106	84.12 66	59.08 134	71.58 235	85.96 167	58.09 198	85.30 53	67.38 101	89.16 147	83.73 134

test12				76.51 86	82.28 113	60.94 143		81.64 102		73.60 208		64.88 131	85.19 59		90.42 120	83.38 145

TAMVS		65.31 228	63.75 247	69.97 190	82.23 114	59.76 156	66.78 254	63.37 299	45.20 287	69.79 259	79.37 259	47.42 270	72.17 252	34.48 358	85.15 211	77.99 251

test_prior				75.27 102	82.15 115	59.85 155		84.33 60					83.39 85			82.58 172

SF-MVS		80.72 43	81.80 42	77.48 74	82.03 116	64.40 111	83.41 46	88.46 5	65.28 81	84.29 65	89.18 91	73.73 55	83.22 88	76.01 38	93.77 58	84.81 100

AdaColmap		74.22 107	74.56 103	73.20 129	81.95 117	60.97 142	79.43 82	80.90 119	65.57 74	72.54 224	81.76 223	70.98 74	85.26 54	47.88 270	90.00 127	73.37 290

PAPM_NR		73.91 109	74.16 110	73.16 130	81.90 118	53.50 197	81.28 60	81.40 106	66.17 70	73.30 214	83.31 202	59.96 178	83.10 91	58.45 178	81.66 256	82.87 161

DP-MVS		78.44 66	79.29 60	75.90 94	81.86 119	65.33 102	79.05 87	84.63 55	74.83 18	80.41 113	86.27 155	71.68 65	83.45 84	62.45 143	92.40 77	78.92 237

F-COLMAP		75.29 92	73.99 112	79.18 52	81.73 120	71.90 46	81.86 58	82.98 81	59.86 131	72.27 227	84.00 189	64.56 134	83.07 92	51.48 235	87.19 184	82.56 173

SixPastTwentyTwo		75.77 85	76.34 86	74.06 116	81.69 121	54.84 187	76.47 115	75.49 203	64.10 95	87.73 17	92.24 17	50.45 248	81.30 119	67.41 97	91.46 91	86.04 73

Vis-MVSNet		74.85 105	74.56 103	75.72 96	81.63 122	64.64 109	76.35 120	79.06 154	62.85 110	73.33 213	88.41 111	62.54 148	79.59 153	63.94 130	82.92 239	82.94 159
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	81.42 123	71.24 52	83.98 36	75.72 201	52.27 215	87.37 26	92.25 16	68.04 98	80.56 135	72.28 67	91.15 98	90.32 22

3Dnovator+	73.19 2	81.08 39	80.48 51	82.87 7	81.41 124	72.03 45	84.38 34	86.23 23	77.28 14	80.65 111	90.18 73	59.80 182	87.58 5	73.06 59	91.34 93	89.01 34

tt0805		76.12 84	78.43 68	69.20 201	81.32 125	41.37 303	76.72 114	77.64 181	63.78 99	82.06 89	87.88 122	79.78 11	79.05 159	64.33 124	92.40 77	87.17 60

MCST-MVS		73.42 116	73.34 126	73.63 123	81.28 126	59.17 160	74.80 141	83.13 80	45.50 281	72.84 219	83.78 193	65.15 129	80.99 127	64.54 121	89.09 153	80.73 208

MIMVSNet1		66.57 218	69.23 178	58.59 306	81.26 127	37.73 335	64.06 286	57.62 319	57.02 157	78.40 132	90.75 46	62.65 145	58.10 345	41.77 309	89.58 140	79.95 221

ACMH+	66.64 10	81.20 36	82.48 39	77.35 78	81.16 128	62.39 124	80.51 67	87.80 7	73.02 26	87.57 20	91.08 36	80.28 9	82.44 100	64.82 120	96.10 4	87.21 57

MVS_111021_HR		72.98 131	72.97 135	72.99 135	80.82 129	65.47 100	68.81 221	72.77 223	57.67 150	75.76 175	82.38 216	71.01 73	77.17 195	61.38 149	86.15 196	76.32 265

9.14			80.22 53		80.68 130		80.35 72	87.69 10	59.90 129	83.00 78	88.20 116	74.57 47	81.75 113	73.75 54	93.78 57

OMC-MVS		79.41 55	78.79 63	81.28 29	80.62 131	70.71 58	80.91 63	84.76 47	62.54 112	81.77 93	86.65 144	71.46 67	83.53 82	67.95 92	92.44 76	89.60 24

OurMVSNet-221017-0		78.57 62	78.53 67	78.67 61	80.48 132	64.16 112	80.24 74	82.06 94	61.89 116	88.77 12	93.32 4	57.15 209	82.60 99	70.08 76	92.80 71	89.25 28

CDS-MVSNet		64.33 243	62.66 259	69.35 198	80.44 133	58.28 170	65.26 273	65.66 280	44.36 293	67.30 291	75.54 296	43.27 289	71.77 258	37.68 335	84.44 224	78.01 250
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		77.20 75	76.37 85	79.69 45	80.34 134	61.52 132	80.58 66	82.12 93	53.54 206	83.93 70	91.03 37	49.49 252	85.97 33	73.26 57	93.08 67	91.59 12

PLC	62.01 16	71.79 149	70.28 169	76.33 89	80.31 135	68.63 75	78.18 98	81.24 110	54.57 186	67.09 293	80.63 237	59.44 183	81.74 114	46.91 277	84.17 226	78.63 238
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MM		78.15 70	77.68 74	79.55 48	80.10 136	65.47 100	80.94 62	78.74 162	71.22 40	72.40 226	88.70 104	60.51 173	87.70 3	77.40 32	89.13 151	85.48 84

CHOSEN 1792x2688		58.09 294	56.30 305	63.45 265	79.95 137	50.93 210	54.07 355	65.59 281	28.56 383	61.53 328	74.33 309	41.09 303	66.52 308	33.91 361	67.69 368	72.92 295

K. test v3		73.67 112	73.61 120	73.87 119	79.78 138	55.62 185	74.69 145	62.04 307	66.16 71	84.76 60	93.23 5	49.47 253	80.97 129	65.66 115	86.67 192	85.02 93

VPNet		65.58 226	67.56 203	59.65 299	79.72 139	30.17 378	60.27 314	62.14 303	54.19 195	71.24 243	86.63 145	58.80 190	67.62 292	44.17 296	90.87 112	81.18 193

ACMH	63.62 14	77.50 73	80.11 54	69.68 193	79.61 140	56.28 178	78.81 89	83.62 73	63.41 106	87.14 29	90.23 71	76.11 32	73.32 238	67.58 94	94.44 39	79.44 230
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 145	79.60 141	56.83 177		57.37 322		83.80 72	89.01 97	47.45 269	78.74 166	64.39 123	86.49 195	82.69 167

MVS_111021_LR		72.10 146	71.82 151	72.95 137	79.53 142	73.90 36	70.45 200	66.64 273	56.87 158	76.81 157	81.76 223	68.78 89	71.76 259	61.81 144	83.74 231	73.18 292

Test_1112_low_res		58.78 290	58.69 287	59.04 304	79.41 143	38.13 331	57.62 330	66.98 272	34.74 360	59.62 344	77.56 283	42.92 292	63.65 323	38.66 327	70.73 350	75.35 274

CSCG		74.12 108	74.39 105	73.33 127	79.35 144	61.66 131	77.45 105	81.98 96	62.47 114	79.06 125	80.19 245	61.83 155	78.79 165	59.83 168	87.35 176	79.54 229

MVS_0304		76.32 82	75.96 92	77.42 76	79.33 145	60.86 146	80.18 76	74.88 208	66.93 62	69.11 265	88.95 100	57.84 205	86.12 29	76.63 37	89.77 135	85.28 86

MVP-Stereo		61.56 269	59.22 282	68.58 217	79.28 146	60.44 151	69.20 215	71.57 233	43.58 301	56.42 360	78.37 273	39.57 314	76.46 205	34.86 357	60.16 385	68.86 336
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 162	71.34 160	67.85 225	79.26 147	40.42 313	74.67 146	75.15 207	58.41 142	68.74 277	88.14 120	56.08 220	83.69 79	59.90 167	81.71 255	79.43 231

IS-MVSNet		75.10 96	75.42 98	74.15 115	79.23 148	48.05 241	79.43 82	78.04 176	70.09 49	79.17 124	88.02 121	53.04 232	83.60 80	58.05 181	93.76 59	90.79 19

FC-MVSNet-test		73.32 119	74.78 102	68.93 210	79.21 149	36.57 340	71.82 179	79.54 148	57.63 153	82.57 86	90.38 64	59.38 185	78.99 161	57.91 182	94.56 34	91.23 14

AllTest		77.66 71	77.43 76	78.35 66	79.19 150	70.81 55	78.60 91	88.64 3	65.37 79	80.09 116	88.17 117	70.33 77	78.43 173	55.60 202	90.90 109	85.81 76

TestCases				78.35 66	79.19 150	70.81 55		88.64 3	65.37 79	80.09 116	88.17 117	70.33 77	78.43 173	55.60 202	90.90 109	85.81 76

xiu_mvs_v1_base_debu		67.87 198	67.07 210	70.26 181	79.13 152	61.90 128	67.34 242	71.25 243	47.98 262	67.70 285	74.19 313	61.31 161	72.62 245	56.51 190	78.26 290	76.27 266

xiu_mvs_v1_base		67.87 198	67.07 210	70.26 181	79.13 152	61.90 128	67.34 242	71.25 243	47.98 262	67.70 285	74.19 313	61.31 161	72.62 245	56.51 190	78.26 290	76.27 266

xiu_mvs_v1_base_debi		67.87 198	67.07 210	70.26 181	79.13 152	61.90 128	67.34 242	71.25 243	47.98 262	67.70 285	74.19 313	61.31 161	72.62 245	56.51 190	78.26 290	76.27 266

VDD-MVS		70.81 158	71.44 159	68.91 211	79.07 155	46.51 263	67.82 236	70.83 251	61.23 119	74.07 203	88.69 105	59.86 180	75.62 211	51.11 238	90.28 121	84.61 106

test1111		64.62 236	65.19 232	62.93 272	79.01 156	29.91 379	65.45 271	54.41 343	54.09 197	71.47 242	88.48 110	37.02 328	74.29 230	46.83 279	89.94 130	84.58 109

TSAR-MVS + GP.		73.08 124	71.60 156	77.54 73	78.99 157	70.73 57	74.96 136	69.38 259	60.73 124	74.39 197	78.44 272	57.72 206	82.78 96	60.16 163	89.60 137	79.11 234

test2506		61.23 271	60.85 272	62.38 277	78.80 158	27.88 387	67.33 245	37.42 401	54.23 192	67.55 288	88.68 106	17.87 405	74.39 228	46.33 282	89.41 143	84.86 96

ECVR-MVS		64.82 233	65.22 230	63.60 262	78.80 158	31.14 373	66.97 250	56.47 333	54.23 192	69.94 257	88.68 106	37.23 327	74.81 223	45.28 292	89.41 143	84.86 96

FIs		72.56 139	73.80 115	68.84 213	78.74 160	37.74 334	71.02 191	79.83 141	56.12 166	80.88 110	89.45 84	58.18 194	78.28 179	56.63 189	93.36 64	90.51 21

v7n		79.37 56	80.41 52	76.28 90	78.67 161	55.81 182	79.22 86	82.51 90	70.72 44	87.54 21	92.44 14	68.00 99	81.34 117	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 27	77.48 12	81.98 90	89.95 77	69.14 87	85.26 54	66.15 109	91.24 95	87.61 52

CNLPA		73.44 115	73.03 133	74.66 105	78.27 163	75.29 26	75.99 127	78.49 167	65.39 78	75.67 176	83.22 208	61.23 164	66.77 306	53.70 224	85.33 207	81.92 185

EPP-MVSNet		73.86 111	73.38 123	75.31 101	78.19 164	53.35 199	80.45 68	77.32 185	65.11 85	76.47 169	86.80 134	49.47 253	83.77 77	53.89 222	92.72 74	88.81 41

PCF-MVS	63.80 13	72.70 137	71.69 152	75.72 96	78.10 165	60.01 154	73.04 160	81.50 103	45.34 285	79.66 119	84.35 185	65.15 129	82.65 98	48.70 259	89.38 146	84.50 115
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		73.14 122	73.77 117	71.26 168	78.09 166	52.64 202	74.32 149	79.56 147	56.32 165	76.35 172	83.36 201	70.76 75	77.96 186	63.32 137	81.84 250	83.18 152

LFMVS		67.06 212	67.89 199	64.56 253	78.02 167	38.25 329	70.81 196	59.60 314	65.18 83	71.06 245	86.56 148	43.85 286	75.22 216	46.35 281	89.63 136	80.21 219

anonymousdsp		78.60 61	77.80 72	81.00 31	78.01 168	74.34 33	80.09 77	76.12 196	50.51 239	89.19 10	90.88 42	71.45 68	77.78 190	73.38 56	90.60 118	90.90 18

BH-untuned		69.39 178	69.46 174	69.18 202	77.96 169	56.88 175	68.47 230	77.53 182	56.77 160	77.79 140	79.63 254	60.30 176	80.20 145	46.04 284	80.65 265	70.47 320

1112_ss		59.48 285	58.99 285	60.96 291	77.84 170	42.39 298	61.42 304	68.45 266	37.96 344	59.93 341	67.46 366	45.11 279	65.07 316	40.89 315	71.81 343	75.41 272

PS-MVSNAJ		64.27 244	63.73 248	65.90 246	77.82 171	51.42 207	63.33 293	72.33 228	45.09 289	61.60 327	68.04 363	62.39 150	73.95 234	49.07 255	73.87 328	72.34 302

ambc				70.10 187	77.74 172	50.21 217	74.28 151	77.93 179		79.26 123	88.29 115	54.11 228	79.77 149	64.43 122	91.10 102	80.30 217

xiu_mvs_v2_base		64.43 241	63.96 245	65.85 247	77.72 173	51.32 208	63.63 290	72.31 229	45.06 290	61.70 326	69.66 349	62.56 146	73.93 235	49.06 256	73.91 327	72.31 303

Anonymous20231211		75.54 90	77.19 79	70.59 175	77.67 174	45.70 272	74.73 143	80.19 135	68.80 53	82.95 80	92.91 8	66.26 117	76.76 202	58.41 179	92.77 72	89.30 27

FMVSNet1		71.06 154	72.48 142	66.81 236	77.65 175	40.68 309	71.96 173	73.03 218	61.14 120	79.45 122	90.36 67	60.44 174	75.20 217	50.20 246	88.05 164	84.54 110

FPMVS		59.43 286	60.07 277	57.51 312	77.62 176	71.52 49	62.33 300	50.92 360	57.40 155	69.40 263	80.00 249	39.14 316	61.92 330	37.47 338	66.36 370	39.09 400

testing3		58.28 293	58.38 291	58.00 310	77.45 177	26.12 394	60.78 310	43.00 387	56.02 167	70.18 254	75.76 293	13.27 412	67.24 298	48.02 268	80.89 261	80.65 211

Effi-MVS+-dtu		75.43 91	72.28 146	84.91 2	77.05 178	83.58 1	78.47 93	77.70 180	57.68 149	74.89 186	78.13 278	64.80 132	84.26 74	56.46 193	85.32 208	86.88 62

CLD-MVS		72.88 134	72.36 145	74.43 110	77.03 179	54.30 191	68.77 224	83.43 76	52.12 217	76.79 158	74.44 308	69.54 86	83.91 75	55.88 200	93.25 66	85.09 90
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 80	76.00 90	78.06 71	77.02 180	64.77 108	80.78 64	82.66 87	60.39 126	74.15 200	83.30 203	69.65 85	82.07 108	69.27 81	86.75 191	87.36 55

CS-MVS-test		74.89 103	74.23 109	76.86 81	77.01 181	62.94 122	78.98 88	84.61 56	58.62 141	70.17 255	80.80 234	66.74 113	81.96 109	61.74 146	89.40 145	85.69 81

Baseline_NR-MVSNet		70.62 160	73.19 128	62.92 273	76.97 182	34.44 356	68.84 219	70.88 250	60.25 127	79.50 121	90.53 53	61.82 156	69.11 279	54.67 212	95.27 13	85.22 87

ITE_SJBPF				80.35 38	76.94 183	73.60 38		80.48 128	66.87 64	83.64 74	86.18 158	70.25 79	79.90 148	61.12 154	88.95 155	87.56 53

SSC-MVS		61.79 267	66.08 220	48.89 356	76.91 184	10.00 410	53.56 357	47.37 375	68.20 58	76.56 164	89.21 89	54.13 227	57.59 346	54.75 210	74.07 326	79.08 235

jason		64.47 240	62.84 257	69.34 199	76.91 184	59.20 157	67.15 247	65.67 279	35.29 357	65.16 302	76.74 289	44.67 281	70.68 267	54.74 211	79.28 280	78.14 247
jason: jason.
ETV-MVS		72.72 136	72.16 148	74.38 112	76.90 186	55.95 179	73.34 158	84.67 52	62.04 115	72.19 230	70.81 336	65.90 121	85.24 56	58.64 176	84.96 215	81.95 184

Anonymous20240529		72.56 139	73.79 116	68.86 212	76.89 187	45.21 274	68.80 223	77.25 187	67.16 61	76.89 153	90.44 56	65.95 120	74.19 231	50.75 241	90.00 127	87.18 59

EC-MVSNet		77.08 77	77.39 77	76.14 92	76.86 188	56.87 176	80.32 73	87.52 11	63.45 104	74.66 192	84.52 182	69.87 83	84.94 61	69.76 78	89.59 138	86.60 67

PM-MVS		64.49 239	63.61 249	67.14 234	76.68 189	75.15 27	68.49 229	42.85 388	51.17 233	77.85 139	80.51 238	45.76 273	66.31 309	52.83 230	76.35 303	59.96 377

TransMVSNet (Re)		69.62 173	71.63 154	63.57 263	76.51 190	35.93 346	65.75 267	71.29 242	61.05 121	75.02 184	89.90 78	65.88 122	70.41 273	49.79 248	89.48 141	84.38 118

BH-RMVSNet		68.69 188	68.20 196	70.14 186	76.40 191	53.90 196	64.62 280	73.48 216	58.01 145	73.91 207	81.78 221	59.09 187	78.22 180	48.59 260	77.96 294	78.31 243

PHI-MVS		74.92 100	74.36 107	76.61 84	76.40 191	62.32 125	80.38 70	83.15 79	54.16 196	73.23 215	80.75 235	62.19 153	83.86 76	68.02 89	90.92 108	83.65 135

UGNet		70.20 164	69.05 180	73.65 121	76.24 193	63.64 115	75.87 129	72.53 226	61.48 118	60.93 335	86.14 161	52.37 235	77.12 196	50.67 242	85.21 209	80.17 220
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		58.68 291	57.72 295	61.57 283	76.21 194	73.59 39	61.83 301	49.00 369	47.30 270	61.08 331	68.97 354	50.16 249	59.01 339	36.06 352	68.84 361	52.10 387

VPA-MVSNet		68.71 187	70.37 168	63.72 261	76.13 195	38.06 332	64.10 285	71.48 236	56.60 164	74.10 202	88.31 114	64.78 133	69.72 274	47.69 272	90.15 124	83.37 146

WB-MVS		60.04 281	64.19 243	47.59 358	76.09 196	10.22 409	52.44 362	46.74 376	65.17 84	74.07 203	87.48 124	53.48 230	55.28 349	49.36 253	72.84 334	77.28 256

PAPM		61.79 267	60.37 276	66.05 244	76.09 196	41.87 300	69.30 213	76.79 193	40.64 327	53.80 373	79.62 255	44.38 283	82.92 95	29.64 378	73.11 333	73.36 291

BH-w/o		64.81 234	64.29 242	66.36 241	76.08 198	54.71 188	65.61 269	75.23 206	50.10 245	71.05 246	71.86 330	54.33 226	79.02 160	38.20 332	76.14 305	65.36 355

dcpmvs_2		71.02 156	72.65 139	66.16 243	76.06 199	50.49 213	71.97 172	79.36 149	50.34 240	82.81 83	83.63 194	64.38 135	67.27 297	61.54 148	83.71 233	80.71 210

pmmvs6		71.82 148	73.66 118	66.31 242	75.94 200	42.01 299	66.99 249	72.53 226	63.45 104	76.43 170	92.78 10	72.95 59	69.69 275	51.41 236	90.46 119	87.22 56

CANet		73.00 129	71.84 150	76.48 87	75.82 201	61.28 136	74.81 139	80.37 132	63.17 108	62.43 325	80.50 239	61.10 168	85.16 60	64.00 127	84.34 225	83.01 158

pmmvs-eth3d		64.41 242	63.27 253	67.82 227	75.81 202	60.18 153	69.49 210	62.05 306	38.81 339	74.13 201	82.23 217	43.76 287	68.65 283	42.53 303	80.63 267	74.63 278

TR-MVS		64.59 237	63.54 250	67.73 228	75.75 203	50.83 211	63.39 292	70.29 254	49.33 252	71.55 239	74.55 306	50.94 244	78.46 171	40.43 317	75.69 308	73.89 287

tttt0517		69.46 176	67.79 202	74.46 107	75.34 204	52.72 201	75.05 135	63.27 300	54.69 183	78.87 127	84.37 184	26.63 379	81.15 121	63.95 128	87.93 168	89.51 25

cascas		64.59 237	62.77 258	70.05 188	75.27 205	50.02 219	61.79 302	71.61 232	42.46 310	63.68 316	68.89 357	49.33 255	80.35 139	47.82 271	84.05 228	79.78 224

API-MVS		70.97 157	71.51 158	69.37 196	75.20 206	55.94 180	80.99 61	76.84 191	62.48 113	71.24 243	77.51 284	61.51 160	80.96 132	52.04 231	85.76 202	71.22 314

EIA-MVS		68.59 189	67.16 209	72.90 141	75.18 207	55.64 184	69.39 212	81.29 108	52.44 214	64.53 305	70.69 337	60.33 175	82.30 104	54.27 219	76.31 304	80.75 207

PAPR		69.20 180	68.66 189	70.82 172	75.15 208	47.77 246	75.31 133	81.11 113	49.62 250	66.33 295	79.27 260	61.53 159	82.96 94	48.12 267	81.50 258	81.74 188

MVSFormer		69.93 169	69.03 181	72.63 150	74.93 209	59.19 158	83.98 36	75.72 201	52.27 215	63.53 319	76.74 289	43.19 290	80.56 135	72.28 67	78.67 286	78.14 247

lupinMVS		63.36 250	61.49 266	68.97 208	74.93 209	59.19 158	65.80 266	64.52 292	34.68 362	63.53 319	74.25 311	43.19 290	70.62 268	53.88 223	78.67 286	77.10 260

nrg030		74.87 104	75.99 91	71.52 165	74.90 211	49.88 226	74.10 153	82.58 89	54.55 187	83.50 75	89.21 89	71.51 66	75.74 210	61.24 150	92.34 79	88.94 37

TAPA-MVS	65.27 12	75.16 95	74.29 108	77.77 72	74.86 212	68.08 77	77.89 100	84.04 69	55.15 176	76.19 174	83.39 197	66.91 107	80.11 146	60.04 166	90.14 125	85.13 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RPSCF		75.76 86	74.37 106	79.93 40	74.81 213	77.53 16	77.53 104	79.30 151	59.44 133	78.88 126	89.80 79	71.26 70	73.09 240	57.45 183	80.89 261	89.17 31

EI-MVSNet-Vis-set		72.78 135	71.87 149	75.54 99	74.77 214	59.02 164	72.24 165	71.56 234	63.92 96	78.59 128	71.59 331	66.22 118	78.60 167	67.58 94	80.32 268	89.00 35

v1240		73.06 126	73.14 129	72.84 143	74.74 215	47.27 255	71.88 178	81.11 113	51.80 221	82.28 88	84.21 186	56.22 219	82.34 103	68.82 82	87.17 185	88.91 38

v1921920		72.96 132	72.98 134	72.89 142	74.67 216	47.58 249	71.92 176	80.69 122	51.70 223	81.69 97	83.89 191	56.58 216	82.25 105	68.34 85	87.36 175	88.82 40

EI-MVSNet-UG-set		72.63 138	71.68 153	75.47 100	74.67 216	58.64 169	72.02 170	71.50 235	63.53 102	78.58 130	71.39 335	65.98 119	78.53 168	67.30 104	80.18 270	89.23 29

Fast-Effi-MVS+		68.81 185	68.30 192	70.35 180	74.66 218	48.61 234	66.06 261	78.32 170	50.62 238	71.48 241	75.54 296	68.75 90	79.59 153	50.55 244	78.73 285	82.86 162

v1192		73.40 117	73.42 121	73.32 128	74.65 219	48.67 233	72.21 166	81.73 100	52.76 212	81.85 91	84.56 181	57.12 210	82.24 106	68.58 83	87.33 177	89.06 33

v144192		72.99 130	73.06 132	72.77 144	74.58 220	47.48 250	71.90 177	80.44 130	51.57 224	81.46 99	84.11 188	58.04 202	82.12 107	67.98 91	87.47 173	88.70 43

MAR-MVS		67.72 201	66.16 219	72.40 155	74.45 221	64.99 107	74.87 137	77.50 183	48.67 258	65.78 299	68.58 361	57.01 213	77.79 189	46.68 280	81.92 247	74.42 283
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 87	76.20 88	74.16 114	74.44 222	48.69 232	75.84 130	82.93 83	59.02 138	85.92 41	89.17 92	58.56 192	82.74 97	70.73 73	89.14 150	91.05 15

MGCFI-Net		72.29 144	73.38 123	69.04 204	74.23 223	47.37 252	73.93 155	83.18 77	54.36 188	76.61 162	81.64 225	72.03 61	75.34 214	57.12 185	87.28 179	84.40 116

canonicalmvs		72.29 144	73.38 123	69.04 204	74.23 223	47.37 252	73.93 155	83.18 77	54.36 188	76.61 162	81.64 225	72.03 61	75.34 214	57.12 185	87.28 179	84.40 116

Anonymous202405211		66.02 223	66.89 214	63.43 266	74.22 225	38.14 330	59.00 320	66.13 276	63.33 107	69.76 260	85.95 168	51.88 237	70.50 270	44.23 295	87.52 171	81.64 189

Effi-MVS+		72.10 146	72.28 146	71.58 163	74.21 226	50.33 215	74.72 144	82.73 85	62.62 111	70.77 247	76.83 288	69.96 82	80.97 129	60.20 161	78.43 288	83.45 143

FE-MVS		68.29 194	66.96 213	72.26 158	74.16 227	54.24 192	77.55 103	73.42 217	57.65 152	72.66 221	84.91 178	32.02 352	81.49 116	48.43 263	81.85 249	81.04 196

v1144		73.29 120	73.39 122	73.01 134	74.12 228	48.11 239	72.01 171	81.08 116	53.83 203	81.77 93	84.68 179	58.07 201	81.91 110	68.10 87	86.86 187	88.99 36

FA-MVS(test-final)		71.27 152	71.06 162	71.92 161	73.96 229	52.32 204	76.45 117	76.12 196	59.07 137	74.04 205	86.18 158	52.18 236	79.43 155	59.75 170	81.76 251	84.03 125

EI-MVSNet		69.61 174	69.01 182	71.41 167	73.94 230	49.90 222	71.31 187	71.32 240	58.22 143	75.40 182	70.44 338	58.16 195	75.85 206	62.51 141	79.81 274	88.48 44

CVMVSNet		59.21 287	58.44 290	61.51 284	73.94 230	47.76 247	71.31 187	64.56 291	26.91 389	60.34 337	70.44 338	36.24 332	67.65 291	53.57 225	68.66 362	69.12 334

IterMVS-LS		73.01 128	73.12 131	72.66 148	73.79 232	49.90 222	71.63 181	78.44 168	58.22 143	80.51 112	86.63 145	58.15 196	79.62 151	62.51 141	88.20 161	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UWE-MVS		52.94 324	52.70 327	53.65 329	73.56 233	27.49 388	57.30 333	49.57 366	38.56 341	62.79 323	71.42 334	19.49 401	60.41 333	24.33 397	77.33 298	73.06 293

alignmvs		70.54 161	71.00 163	69.15 203	73.50 234	48.04 242	69.85 208	79.62 143	53.94 202	76.54 166	82.00 218	59.00 188	74.68 224	57.32 184	87.21 183	84.72 101

Fast-Effi-MVS+-dtu		70.00 166	68.74 187	73.77 120	73.47 235	64.53 110	71.36 185	78.14 175	55.81 171	68.84 275	74.71 305	65.36 127	75.75 209	52.00 232	79.00 282	81.03 197

v8		75.07 97	75.64 95	73.35 126	73.42 236	47.46 251	75.20 134	81.45 105	60.05 128	85.64 45	89.26 87	58.08 200	81.80 112	69.71 80	87.97 167	90.79 19

tfpnnormal		66.48 219	67.93 198	62.16 279	73.40 237	36.65 339	63.45 291	64.99 286	55.97 168	72.82 220	87.80 123	57.06 212	69.10 280	48.31 265	87.54 170	80.72 209

IterMVS-SCA-FT		67.68 202	66.07 221	72.49 153	73.34 238	58.20 171	63.80 288	65.55 282	48.10 261	76.91 152	82.64 213	45.20 277	78.84 163	61.20 151	77.89 295	80.44 216

iter_conf05_11		66.64 216	65.20 231	70.94 170	73.28 239	46.89 258	66.09 260	77.03 189	43.44 303	63.43 321	74.09 316	47.19 271	83.26 87	56.25 195	86.01 199	82.66 168

bld_raw_dy_0_64		69.94 168	69.64 173	70.84 171	73.28 239	46.85 259	75.82 131	86.52 16	40.43 329	81.41 100	74.77 302	48.70 263	83.01 93	56.25 195	89.59 138	82.66 168

VNet		64.01 247	65.15 235	60.57 293	73.28 239	35.61 349	57.60 331	67.08 271	54.61 185	66.76 294	83.37 199	56.28 218	66.87 302	42.19 305	85.20 210	79.23 233

3Dnovator	65.95 11	71.50 151	71.22 161	72.34 156	73.16 242	63.09 120	78.37 94	78.32 170	57.67 150	72.22 229	84.61 180	54.77 222	78.47 170	60.82 157	81.07 260	75.45 271

GBi-Net		68.30 192	68.79 184	66.81 236	73.14 243	40.68 309	71.96 173	73.03 218	54.81 178	74.72 189	90.36 67	48.63 264	75.20 217	47.12 274	85.37 204	84.54 110

test1		68.30 192	68.79 184	66.81 236	73.14 243	40.68 309	71.96 173	73.03 218	54.81 178	74.72 189	90.36 67	48.63 264	75.20 217	47.12 274	85.37 204	84.54 110

FMVSNet2		67.48 204	68.21 195	65.29 248	73.14 243	38.94 322	68.81 221	71.21 246	54.81 178	76.73 159	86.48 150	48.63 264	74.60 225	47.98 269	86.11 198	82.35 176

thisisatest0530		67.05 213	65.16 233	72.73 147	73.10 246	50.55 212	71.26 189	63.91 296	50.22 243	74.46 196	80.75 235	26.81 378	80.25 142	59.43 172	86.50 194	87.37 54

pm-mvs1		68.40 190	69.85 172	64.04 259	73.10 246	39.94 315	64.61 281	70.50 252	55.52 173	73.97 206	89.33 85	63.91 138	68.38 285	49.68 250	88.02 165	83.81 130

pmmvs4		60.78 275	59.04 284	66.00 245	73.06 248	57.67 173	64.53 282	60.22 312	36.91 351	65.96 296	77.27 285	39.66 313	68.54 284	38.87 325	74.89 316	71.80 308

SDMVSNet		66.36 221	67.85 201	61.88 281	73.04 249	46.14 268	58.54 324	71.36 239	51.42 227	68.93 271	82.72 211	65.62 123	62.22 329	54.41 216	84.67 217	77.28 256

sd_testset		63.55 248	65.38 228	58.07 309	73.04 249	38.83 324	57.41 332	65.44 283	51.42 227	68.93 271	82.72 211	63.76 139	58.11 344	41.05 313	84.67 217	77.28 256

v2v482		72.55 141	72.58 140	72.43 154	72.92 251	46.72 261	71.41 184	79.13 153	55.27 174	81.17 104	85.25 175	55.41 221	81.13 122	67.25 105	85.46 203	89.43 26

casdiffmvs_mvg		75.26 93	76.18 89	72.52 151	72.87 252	49.47 227	72.94 161	84.71 51	59.49 132	80.90 109	88.81 103	70.07 80	79.71 150	67.40 98	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		54.39 313	56.12 307	49.20 352	72.57 253	30.91 374	59.98 315	48.43 371	41.66 313	55.94 362	83.86 192	41.19 302	50.42 357	26.05 388	75.38 313	66.27 350

Patchmatch-RL test		59.95 282	59.12 283	62.44 276	72.46 254	54.61 190	59.63 317	47.51 374	41.05 320	74.58 194	74.30 310	31.06 361	65.31 314	51.61 234	79.85 273	67.39 342

CL-MVSNet_self_test		62.44 262	63.40 251	59.55 300	72.34 255	32.38 365	56.39 337	64.84 288	51.21 232	67.46 289	81.01 232	50.75 245	63.51 324	38.47 330	88.12 163	82.75 165

Vis-MVSNet (Re-imp)		62.74 259	63.21 254	61.34 287	72.19 256	31.56 370	67.31 246	53.87 345	53.60 205	69.88 258	83.37 199	40.52 307	70.98 266	41.40 311	86.78 190	81.48 191

thres100view900		61.17 272	61.09 269	61.39 286	72.14 257	35.01 352	65.42 272	56.99 327	55.23 175	70.71 248	79.90 250	32.07 350	72.09 253	35.61 353	81.73 252	77.08 261

ab-mvs		64.11 245	65.13 236	61.05 289	71.99 258	38.03 333	67.59 237	68.79 263	49.08 256	65.32 301	86.26 156	58.02 203	66.85 304	39.33 321	79.79 276	78.27 244

thres600view7		61.82 266	61.38 267	63.12 268	71.81 259	34.93 353	64.64 279	56.99 327	54.78 182	70.33 252	79.74 252	32.07 350	72.42 250	38.61 328	83.46 236	82.02 182

QAPM		69.18 181	69.26 177	68.94 209	71.61 260	52.58 203	80.37 71	78.79 161	49.63 249	73.51 209	85.14 176	53.66 229	79.12 158	55.11 207	75.54 310	75.11 276

WB-MVSnew		53.94 319	54.76 316	51.49 341	71.53 261	28.05 385	58.22 327	50.36 363	37.94 345	59.16 345	70.17 343	49.21 256	51.94 354	24.49 395	71.80 344	74.47 282

baseline		73.10 123	73.96 113	70.51 177	71.46 262	46.39 266	72.08 169	84.40 59	55.95 169	76.62 161	86.46 151	67.20 103	78.03 185	64.22 125	87.27 181	87.11 61

casdiffmvs		73.06 126	73.84 114	70.72 173	71.32 263	46.71 262	70.93 193	84.26 62	55.62 172	77.46 145	87.10 126	67.09 105	77.81 188	63.95 128	86.83 189	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	72.49 141	71.96 160	71.29 264	64.06 113	72.79 162	81.82 98	40.23 330	81.25 103	81.04 231	70.62 76	68.69 282	69.74 79	83.60 235	83.14 153

Anonymous20231206		54.13 314	55.82 309	49.04 355	70.89 265	35.96 345	51.73 364	50.87 361	34.86 358	62.49 324	79.22 261	42.52 296	44.29 383	27.95 385	81.88 248	66.88 346

fmvsm_s_conf0.1_n_a		67.37 208	66.36 217	70.37 179	70.86 266	61.17 138	74.00 154	57.18 326	40.77 324	68.83 276	80.88 233	63.11 142	67.61 293	66.94 106	74.72 317	82.33 179

tfpn200view9		60.35 279	59.97 278	61.51 284	70.78 267	35.35 350	63.27 294	57.47 320	53.00 210	68.31 280	77.09 286	32.45 347	72.09 253	35.61 353	81.73 252	77.08 261

thres400		60.77 276	59.97 278	63.15 267	70.78 267	35.35 350	63.27 294	57.47 320	53.00 210	68.31 280	77.09 286	32.45 347	72.09 253	35.61 353	81.73 252	82.02 182

MSDG		67.47 206	67.48 206	67.46 229	70.70 269	54.69 189	66.90 252	78.17 173	60.88 123	70.41 250	74.76 303	61.22 166	73.18 239	47.38 273	76.87 300	74.49 281

testing91		55.74 304	55.29 314	57.08 313	70.63 270	30.85 375	54.94 350	56.31 336	50.34 240	57.08 353	70.10 345	24.50 389	65.86 310	36.98 343	76.75 301	74.53 280

test_yl		65.11 229	65.09 237	65.18 249	70.59 271	40.86 307	63.22 296	72.79 221	57.91 146	68.88 273	79.07 266	42.85 293	74.89 221	45.50 289	84.97 212	79.81 222

DCV-MVSNet		65.11 229	65.09 237	65.18 249	70.59 271	40.86 307	63.22 296	72.79 221	57.91 146	68.88 273	79.07 266	42.85 293	74.89 221	45.50 289	84.97 212	79.81 222

test_fmvsm_n_1920		69.63 172	68.45 190	73.16 130	70.56 273	65.86 98	70.26 202	78.35 169	37.69 346	74.29 198	78.89 268	61.10 168	68.10 288	65.87 114	79.07 281	85.53 83

OpenMVS	62.51 15	68.76 186	68.75 186	68.78 214	70.56 273	53.91 195	78.29 95	77.35 184	48.85 257	70.22 253	83.52 195	52.65 234	76.93 198	55.31 206	81.99 246	75.49 270

DELS-MVS		68.83 184	68.31 191	70.38 178	70.55 275	48.31 235	63.78 289	82.13 92	54.00 199	68.96 269	75.17 300	58.95 189	80.06 147	58.55 177	82.74 241	82.76 164
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		53.37 320	52.50 330	55.98 320	70.51 276	29.68 380	56.20 340	51.85 358	46.19 276	56.76 357	68.94 355	19.18 402	65.39 313	25.87 391	76.98 299	72.87 296

testing11		53.13 322	52.26 332	55.75 321	70.44 277	31.73 369	54.75 351	52.40 356	44.81 291	52.36 377	68.40 362	21.83 394	65.74 312	32.64 367	72.73 335	69.78 326

LCM-MVSNet-Re		69.10 182	71.57 157	61.70 282	70.37 278	34.30 358	61.45 303	79.62 143	56.81 159	89.59 8	88.16 119	68.44 93	72.94 241	42.30 304	87.33 177	77.85 253

patch_mono-2		62.73 260	64.08 244	58.68 305	70.36 279	55.87 181	60.84 309	64.11 295	41.23 317	64.04 310	78.22 275	60.00 177	48.80 362	54.17 220	83.71 233	71.37 311

ETVMVS		50.32 342	49.87 350	51.68 339	70.30 280	26.66 391	52.33 363	43.93 383	43.54 302	54.91 367	67.95 364	20.01 400	60.17 335	22.47 399	73.40 330	68.22 337

SCA		58.57 292	58.04 293	60.17 296	70.17 281	41.07 306	65.19 274	53.38 351	43.34 307	61.00 334	73.48 318	45.20 277	69.38 277	40.34 318	70.31 353	70.05 323

ET-MVSNet_ETH3D		63.32 251	60.69 274	71.20 169	70.15 282	55.66 183	65.02 276	64.32 293	43.28 308	68.99 268	72.05 329	25.46 385	78.19 183	54.16 221	82.80 240	79.74 225

testing99		55.16 309	54.56 318	56.98 315	70.13 283	30.58 377	54.55 353	54.11 344	49.53 251	56.76 357	70.14 344	22.76 393	65.79 311	36.99 342	76.04 306	74.57 279

APD_test1		75.04 98	75.38 99	74.02 117	69.89 284	70.15 62	76.46 116	79.71 142	65.50 75	82.99 79	88.60 108	66.94 106	72.35 251	59.77 169	88.54 158	79.56 226

iter_conf05		67.34 209	65.62 225	72.50 152	69.82 285	47.06 257	72.19 167	76.86 190	45.32 286	72.86 218	82.85 209	20.53 397	83.73 78	61.13 153	89.02 154	86.70 65

PVSNet_BlendedMVS		65.38 227	64.30 241	68.61 216	69.81 286	49.36 228	65.60 270	78.96 155	45.50 281	59.98 338	78.61 270	51.82 238	78.20 181	44.30 293	84.11 227	78.27 244

PVSNet_Blended		62.90 257	61.64 263	66.69 239	69.81 286	49.36 228	61.23 306	78.96 155	42.04 311	59.98 338	68.86 358	51.82 238	78.20 181	44.30 293	77.77 296	72.52 300

OpenMVS_ROB	54.93 17	63.23 253	63.28 252	63.07 269	69.81 286	45.34 273	68.52 228	67.14 270	43.74 299	70.61 249	79.22 261	47.90 268	72.66 244	48.75 258	73.84 329	71.21 315

test_fmvsmconf0.01_n		73.91 109	73.64 119	74.71 104	69.79 289	66.25 93	75.90 128	79.90 140	46.03 278	76.48 168	85.02 177	67.96 100	73.97 233	74.47 49	87.22 182	83.90 128

fmvsm_s_conf0.5_n_a		67.00 214	65.95 224	70.17 184	69.72 290	61.16 139	73.34 158	56.83 329	40.96 321	68.36 279	80.08 248	62.84 143	67.57 294	66.90 108	74.50 321	81.78 187

FMVSNet3		65.00 232	65.16 233	64.52 254	69.47 291	37.56 337	66.63 255	70.38 253	51.55 225	74.72 189	83.27 204	37.89 324	74.44 227	47.12 274	85.37 204	81.57 190

MS-PatchMatch		55.59 306	54.89 315	57.68 311	69.18 292	49.05 231	61.00 308	62.93 301	35.98 354	58.36 348	68.93 356	36.71 330	66.59 307	37.62 337	63.30 377	57.39 383

baseline1		57.82 296	58.36 292	56.19 318	69.17 293	30.76 376	62.94 298	55.21 338	46.04 277	63.83 314	78.47 271	41.20 301	63.68 322	39.44 320	68.99 360	74.13 284

v148		69.38 179	69.39 175	69.36 197	69.14 294	44.56 278	68.83 220	72.70 224	54.79 181	78.59 128	84.12 187	54.69 223	76.74 203	59.40 173	82.20 244	86.79 63

test_fmvsmconf0.1_n		73.26 121	72.82 137	74.56 106	69.10 295	66.18 95	74.65 147	79.34 150	45.58 280	75.54 179	83.91 190	67.19 104	73.88 236	73.26 57	86.86 187	83.63 136

fmvsm_s_conf0.1_n		66.60 217	65.54 226	69.77 192	68.99 296	59.15 161	72.12 168	56.74 331	40.72 326	68.25 282	80.14 247	61.18 167	66.92 300	67.34 103	74.40 322	83.23 151

Syy-MVS		54.13 314	55.45 312	50.18 346	68.77 297	23.59 398	55.02 347	44.55 381	43.80 296	58.05 350	64.07 375	46.22 272	58.83 340	46.16 283	72.36 338	68.12 338

myMVS_eth3d		50.36 341	50.52 346	49.88 347	68.77 297	22.69 400	55.02 347	44.55 381	43.80 296	58.05 350	64.07 375	14.16 411	58.83 340	33.90 362	72.36 338	68.12 338

test_fmvsmconf_n		72.91 133	72.40 144	74.46 107	68.62 299	66.12 96	74.21 152	78.80 160	45.64 279	74.62 193	83.25 205	66.80 112	73.86 237	72.97 60	86.66 193	83.39 144

CANet_DTU		64.04 246	63.83 246	64.66 252	68.39 300	42.97 293	73.45 157	74.50 212	52.05 219	54.78 368	75.44 299	43.99 285	70.42 272	53.49 226	78.41 289	80.59 213

EU-MVSNet		60.82 274	60.80 273	60.86 292	68.37 301	41.16 304	72.27 164	68.27 267	26.96 387	69.08 266	75.71 294	32.09 349	67.44 295	55.59 204	78.90 283	73.97 285

PVSNet	43.83 21	51.56 335	51.17 338	52.73 334	68.34 302	38.27 328	48.22 372	53.56 349	36.41 352	54.29 371	64.94 374	34.60 336	54.20 353	30.34 373	69.87 356	65.71 353

EPNet		69.10 182	67.32 207	74.46 107	68.33 303	61.27 137	77.56 102	63.57 298	60.95 122	56.62 359	82.75 210	51.53 241	81.24 120	54.36 218	90.20 122	80.88 203
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n		66.34 222	65.27 229	69.57 195	68.20 304	59.14 163	71.66 180	56.48 332	40.92 322	67.78 284	79.46 256	61.23 164	66.90 301	67.39 99	74.32 325	82.66 168

IB-MVS	49.67 18	59.69 284	56.96 300	67.90 224	68.19 305	50.30 216	61.42 304	65.18 285	47.57 268	55.83 363	67.15 370	23.77 391	79.60 152	43.56 299	79.97 272	73.79 288
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		60.62 277	59.97 278	62.58 275	68.13 306	47.28 254	68.59 226	73.96 214	32.19 371	59.94 340	68.86 358	50.48 247	77.64 192	41.85 308	75.74 307	62.83 366

eth_miper_zixun_eth		69.42 177	68.73 188	71.50 166	67.99 307	46.42 264	67.58 238	78.81 158	50.72 237	78.13 135	80.34 242	50.15 250	80.34 140	60.18 162	84.65 219	87.74 50

TinyColmap		67.98 197	69.28 176	64.08 257	67.98 308	46.82 260	70.04 203	75.26 205	53.05 209	77.36 146	86.79 135	59.39 184	72.59 248	45.64 287	88.01 166	72.83 297

EPNet_dtu		58.93 289	58.52 288	60.16 297	67.91 309	47.70 248	69.97 205	58.02 318	49.73 248	47.28 390	73.02 323	38.14 320	62.34 327	36.57 346	85.99 200	70.43 321
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 297	57.02 299	59.17 301	67.89 310	34.93 353	58.91 322	57.25 324	50.24 242	64.01 311	71.46 333	32.49 346	71.39 263	31.31 370	79.57 278	71.19 316

our_test_3		56.46 300	56.51 303	56.30 317	67.70 311	39.66 317	55.36 346	52.34 357	40.57 328	63.85 313	69.91 348	40.04 310	58.22 343	43.49 300	75.29 315	71.03 318

ppachtmachnet_test		60.26 280	59.61 281	62.20 278	67.70 311	44.33 280	58.18 328	60.96 310	40.75 325	65.80 298	72.57 325	41.23 300	63.92 321	46.87 278	82.42 243	78.33 242

MVS_Test		69.84 170	70.71 166	67.24 231	67.49 313	43.25 291	69.87 207	81.22 112	52.69 213	71.57 238	86.68 141	62.09 154	74.51 226	66.05 111	78.74 284	83.96 126

fmvsm_l_conf0.5_n		67.48 204	66.88 215	69.28 200	67.41 314	62.04 126	70.69 197	69.85 256	39.46 333	69.59 261	81.09 230	58.15 196	68.73 281	67.51 96	78.16 293	77.07 263

thisisatest0515		60.48 278	57.86 294	68.34 219	67.25 315	46.42 264	60.58 312	62.14 303	40.82 323	63.58 318	69.12 352	26.28 381	78.34 177	48.83 257	82.13 245	80.26 218

V42		71.06 154	70.83 165	71.72 162	67.25 315	47.14 256	65.94 262	80.35 133	51.35 229	83.40 76	83.23 206	59.25 186	78.80 164	65.91 113	80.81 264	89.23 29

fmvsm_l_conf0.5_n_a		66.66 215	65.97 223	68.72 215	67.09 317	61.38 134	70.03 204	69.15 261	38.59 340	68.41 278	80.36 241	56.56 217	68.32 286	66.10 110	77.45 297	76.46 264

GA-MVS		62.91 256	61.66 262	66.66 240	67.09 317	44.49 279	61.18 307	69.36 260	51.33 230	69.33 264	74.47 307	36.83 329	74.94 220	50.60 243	74.72 317	80.57 214

testf1		75.66 88	76.57 82	72.95 137	67.07 319	67.62 81	76.10 124	80.68 123	64.95 87	86.58 33	90.94 40	71.20 71	71.68 261	60.46 159	91.13 100	79.56 226

APD_test2		75.66 88	76.57 82	72.95 137	67.07 319	67.62 81	76.10 124	80.68 123	64.95 87	86.58 33	90.94 40	71.20 71	71.68 261	60.46 159	91.13 100	79.56 226

HY-MVS	49.31 19	57.96 295	57.59 296	59.10 303	66.85 321	36.17 343	65.13 275	65.39 284	39.24 336	54.69 370	78.14 277	44.28 284	67.18 299	33.75 363	70.79 349	73.95 286

CR-MVSNet		58.96 288	58.49 289	60.36 295	66.37 322	48.24 237	70.93 193	56.40 334	32.87 370	61.35 329	86.66 142	33.19 341	63.22 325	48.50 262	70.17 354	69.62 329

RPMNet		65.77 225	65.08 239	67.84 226	66.37 322	48.24 237	70.93 193	86.27 20	54.66 184	61.35 329	86.77 137	33.29 340	85.67 47	55.93 199	70.17 354	69.62 329

IterMVS		63.12 254	62.48 260	65.02 251	66.34 324	52.86 200	63.81 287	62.25 302	46.57 274	71.51 240	80.40 240	44.60 282	66.82 305	51.38 237	75.47 311	75.38 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		69.82 171	69.89 171	69.61 194	66.24 325	43.48 287	68.12 233	79.61 145	51.43 226	77.72 141	80.18 246	54.61 225	78.15 184	63.62 133	87.50 172	87.20 58

tpm2		56.12 301	54.64 317	60.55 294	66.24 325	36.01 344	68.14 232	56.77 330	33.60 368	58.25 349	75.52 298	30.25 367	74.33 229	33.27 364	69.76 358	71.32 312

Anonymous20240521		63.55 248	66.07 221	55.99 319	66.18 327	44.04 282	68.77 224	68.80 262	46.99 271	72.57 222	85.84 169	39.87 311	50.22 358	53.40 229	92.23 81	73.71 289

Patchmtry		60.91 273	63.01 256	54.62 326	66.10 328	26.27 393	67.47 240	56.40 334	54.05 198	72.04 231	86.66 142	33.19 341	60.17 335	43.69 297	87.45 174	77.42 254

FMVSNet5		55.08 310	55.54 311	53.71 328	65.80 329	33.50 362	56.22 339	52.50 355	43.72 300	61.06 332	83.38 198	25.46 385	54.87 350	30.11 375	81.64 257	72.75 298

1314		59.83 283	58.86 286	62.74 274	65.71 330	44.78 277	68.59 226	72.63 225	33.54 369	61.05 333	67.29 369	43.62 288	71.26 264	49.49 252	67.84 367	72.19 305

MDTV_nov1_ep13			54.05 321		65.54 331	29.30 382	59.00 320	55.22 337	35.96 355	52.44 375	75.98 292	30.77 364	59.62 337	38.21 331	73.33 332

baseline2		55.57 307	52.74 326	64.05 258	65.26 332	44.11 281	62.38 299	54.43 342	39.03 337	51.21 380	67.35 368	33.66 339	72.45 249	37.14 340	64.22 375	75.60 269

USDC		62.80 258	63.10 255	61.89 280	65.19 333	43.30 290	67.42 241	74.20 213	35.80 356	72.25 228	84.48 183	45.67 274	71.95 257	37.95 334	84.97 212	70.42 322

tpm		50.60 339	52.42 331	45.14 369	65.18 334	26.29 392	60.30 313	43.50 384	37.41 348	57.01 354	79.09 265	30.20 369	42.32 388	32.77 366	66.36 370	66.81 348

PatchmatchNet		54.60 312	54.27 319	55.59 322	65.17 335	39.08 319	66.92 251	51.80 359	39.89 331	58.39 347	73.12 322	31.69 355	58.33 342	43.01 302	58.38 391	69.38 332
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_ehance_all_eth		68.36 191	68.16 197	68.98 207	65.14 336	43.34 289	67.07 248	78.92 157	49.11 255	76.21 173	77.72 281	53.48 230	77.92 187	61.16 152	84.59 221	85.68 82

cl____		68.26 196	68.26 193	68.29 220	64.98 337	43.67 285	65.89 263	74.67 209	50.04 246	76.86 155	82.42 215	48.74 261	75.38 212	60.92 156	89.81 132	85.80 80

DIV-MVS_self_test		68.27 195	68.26 193	68.29 220	64.98 337	43.67 285	65.89 263	74.67 209	50.04 246	76.86 155	82.43 214	48.74 261	75.38 212	60.94 155	89.81 132	85.81 76

tpm cat1		54.02 317	52.63 328	58.19 308	64.85 339	39.86 316	66.26 259	57.28 323	32.16 372	56.90 355	70.39 340	32.75 345	65.30 315	34.29 359	58.79 388	69.41 331

XXY-MVS		55.19 308	57.40 298	48.56 357	64.45 340	34.84 355	51.54 365	53.59 347	38.99 338	63.79 315	79.43 257	56.59 215	45.57 373	36.92 344	71.29 346	65.25 356

PatchT		53.35 321	56.47 304	43.99 374	64.19 341	17.46 405	59.15 318	43.10 386	52.11 218	54.74 369	86.95 131	29.97 370	49.98 359	43.62 298	74.40 322	64.53 363

D2MVS		62.58 261	61.05 270	67.20 232	63.85 342	47.92 243	56.29 338	69.58 258	39.32 334	70.07 256	78.19 276	34.93 335	72.68 243	53.44 227	83.74 231	81.00 199

mvs_anonymous		65.08 231	65.49 227	63.83 260	63.79 343	37.60 336	66.52 257	69.82 257	43.44 303	73.46 211	86.08 164	58.79 191	71.75 260	51.90 233	75.63 309	82.15 181

CostFormer		57.35 298	56.14 306	60.97 290	63.76 344	38.43 326	67.50 239	60.22 312	37.14 350	59.12 346	76.34 291	32.78 344	71.99 256	39.12 324	69.27 359	72.47 301

Gipuma		69.55 175	72.83 136	59.70 298	63.63 345	53.97 194	80.08 78	75.93 199	64.24 94	73.49 210	88.93 101	57.89 204	62.46 326	59.75 170	91.55 90	62.67 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
cl22		67.14 210	66.51 216	69.03 206	63.20 346	43.46 288	66.88 253	76.25 195	49.22 253	74.48 195	77.88 280	45.49 276	77.40 194	60.64 158	84.59 221	86.24 69

gg-mvs-nofinetune		55.75 303	56.75 302	52.72 335	62.87 347	28.04 386	68.92 218	41.36 396	71.09 41	50.80 382	92.63 12	20.74 396	66.86 303	29.97 376	72.41 337	63.25 365

gm-plane-assit					62.51 348	33.91 360			37.25 349		62.71 380		72.74 242	38.70 326

MVS-HIRNet		45.53 355	47.29 355	40.24 380	62.29 349	26.82 390	56.02 342	37.41 402	29.74 382	43.69 400	81.27 227	33.96 337	55.48 348	24.46 396	56.79 392	38.43 401

diffmvs		67.42 207	67.50 205	67.20 232	62.26 350	45.21 274	64.87 277	77.04 188	48.21 260	71.74 232	79.70 253	58.40 193	71.17 265	64.99 118	80.27 269	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		41.62 366	39.89 371	46.80 362	61.81 351	51.59 205	33.56 398	35.74 403	27.48 386	37.64 404	53.53 394	23.24 392	42.09 389	27.39 386	58.64 389	46.72 393

KD-MVS_self_test		66.38 220	67.51 204	62.97 271	61.76 352	34.39 357	58.11 329	75.30 204	50.84 236	77.12 148	85.42 172	56.84 214	69.44 276	51.07 239	91.16 97	85.08 91

MDA-MVSNet-bldmvs		62.34 263	61.73 261	64.16 255	61.64 353	49.90 222	48.11 373	57.24 325	53.31 208	80.95 106	79.39 258	49.00 259	61.55 331	45.92 285	80.05 271	81.03 197

miper_enhance_ethall		65.86 224	65.05 240	68.28 222	61.62 354	42.62 296	64.74 278	77.97 177	42.52 309	73.42 212	72.79 324	49.66 251	77.68 191	58.12 180	84.59 221	84.54 110

WTY-MVS		49.39 346	50.31 348	46.62 363	61.22 355	32.00 368	46.61 378	49.77 365	33.87 365	54.12 372	69.55 351	41.96 297	45.40 375	31.28 371	64.42 374	62.47 370

CMPMVS	48.73 20	61.54 270	60.89 271	63.52 264	61.08 356	51.55 206	68.07 234	68.00 268	33.88 364	65.87 297	81.25 228	37.91 323	67.71 290	49.32 254	82.60 242	71.31 313
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 340	50.69 345	49.64 350	60.76 357	41.87 300	53.18 358	45.48 379	43.41 305	49.41 387	60.47 388	29.22 373	44.73 380	42.09 306	72.14 341	62.33 372

test-mter		48.56 348	48.20 353	49.64 350	60.76 357	41.87 300	53.18 358	45.48 379	31.91 376	49.41 387	60.47 388	18.34 403	44.73 380	42.09 306	72.14 341	62.33 372

GG-mvs-BLEND				52.24 336	60.64 359	29.21 383	69.73 209	42.41 389		45.47 393	52.33 397	20.43 398	68.16 287	25.52 393	65.42 372	59.36 379

tpmvs		55.84 302	55.45 312	57.01 314	60.33 360	33.20 363	65.89 263	59.29 316	47.52 269	56.04 361	73.60 317	31.05 362	68.06 289	40.64 316	64.64 373	69.77 327

miper_lstm_enhance		61.97 264	61.63 264	62.98 270	60.04 361	45.74 271	47.53 375	70.95 248	44.04 294	73.06 216	78.84 269	39.72 312	60.33 334	55.82 201	84.64 220	82.88 160

dmvs_re		49.91 345	50.77 344	47.34 359	59.98 362	38.86 323	53.18 358	53.58 348	39.75 332	55.06 366	61.58 384	36.42 331	44.40 382	29.15 383	68.23 363	58.75 380

PVSNet_0	36.71 22	41.12 367	40.78 370	42.14 376	59.97 363	40.13 314	40.97 387	42.24 393	30.81 380	44.86 396	49.41 400	40.70 306	45.12 377	23.15 398	34.96 403	41.16 399

dmvs_testset		45.26 356	47.51 354	38.49 383	59.96 364	14.71 407	58.50 325	43.39 385	41.30 316	51.79 379	56.48 392	39.44 315	49.91 361	21.42 401	55.35 397	50.85 388

new-patchmatchnet		52.89 325	55.76 310	44.26 373	59.94 365	6.31 411	37.36 395	50.76 362	41.10 318	64.28 308	79.82 251	44.77 280	48.43 366	36.24 349	87.61 169	78.03 249

test20.03		55.74 304	57.51 297	50.42 345	59.89 366	32.09 367	50.63 367	49.01 368	50.11 244	65.07 303	83.23 206	45.61 275	48.11 367	30.22 374	83.82 230	71.07 317

MVSTER		63.29 252	61.60 265	68.36 218	59.77 367	46.21 267	60.62 311	71.32 240	41.83 312	75.40 182	79.12 264	30.25 367	75.85 206	56.30 194	79.81 274	83.03 157

N_pmnet		52.06 331	51.11 339	54.92 323	59.64 368	71.03 53	37.42 394	61.62 309	33.68 366	57.12 352	72.10 326	37.94 322	31.03 401	29.13 384	71.35 345	62.70 367

test_vis1_n_1920		52.96 323	53.50 322	51.32 342	59.15 369	44.90 276	56.13 341	64.29 294	30.56 381	59.87 342	60.68 386	40.16 309	47.47 368	48.25 266	62.46 379	61.58 374

JIA-IIPM		54.03 316	51.62 334	61.25 288	59.14 370	55.21 186	59.10 319	47.72 372	50.85 235	50.31 386	85.81 170	20.10 399	63.97 320	36.16 350	55.41 396	64.55 362

LF4IMVS		67.50 203	67.31 208	68.08 223	58.86 371	61.93 127	71.43 183	75.90 200	44.67 292	72.42 225	80.20 244	57.16 208	70.44 271	58.99 175	86.12 197	71.88 307

UnsupCasMVSNet_bld		50.01 344	51.03 341	46.95 360	58.61 372	32.64 364	48.31 371	53.27 352	34.27 363	60.47 336	71.53 332	41.40 299	47.07 370	30.68 372	60.78 384	61.13 375

dp		44.09 362	44.88 364	41.72 379	58.53 373	23.18 399	54.70 352	42.38 391	34.80 359	44.25 398	65.61 372	24.48 390	44.80 379	29.77 377	49.42 399	57.18 384

testgi		54.00 318	56.86 301	45.45 367	58.20 374	25.81 395	49.05 369	49.50 367	45.43 284	67.84 283	81.17 229	51.81 240	43.20 387	29.30 379	79.41 279	67.34 344

wuyk23d		61.97 264	66.25 218	49.12 354	58.19 375	60.77 149	66.32 258	52.97 353	55.93 170	90.62 5	86.91 132	73.07 57	35.98 399	20.63 403	91.63 87	50.62 389

ANet_high		67.08 211	69.94 170	58.51 307	57.55 376	27.09 389	58.43 326	76.80 192	63.56 101	82.40 87	91.93 20	59.82 181	64.98 317	50.10 247	88.86 156	83.46 142

Patchmatch-test		47.93 349	49.96 349	41.84 377	57.42 377	24.26 397	48.75 370	41.49 395	39.30 335	56.79 356	73.48 318	30.48 366	33.87 400	29.29 380	72.61 336	67.39 342

test_vis1_n		51.27 337	50.41 347	53.83 327	56.99 378	50.01 220	56.75 335	60.53 311	25.68 391	59.74 343	57.86 391	29.40 372	47.41 369	43.10 301	63.66 376	64.08 364

new_pmnet		37.55 370	39.80 372	30.79 385	56.83 379	16.46 406	39.35 391	30.65 405	25.59 392	45.26 394	61.60 383	24.54 388	28.02 404	21.60 400	52.80 398	47.90 392

pmmvs3		46.71 352	45.09 362	51.55 340	56.76 380	48.25 236	55.78 344	39.53 400	24.13 396	50.35 385	63.40 377	15.90 408	51.08 356	29.29 380	70.69 351	55.33 386

sss		47.59 351	48.32 351	45.40 368	56.73 381	33.96 359	45.17 381	48.51 370	32.11 375	52.37 376	65.79 371	40.39 308	41.91 391	31.85 368	61.97 381	60.35 376

tpmrst		50.15 343	51.38 337	46.45 364	56.05 382	24.77 396	64.40 284	49.98 364	36.14 353	53.32 374	69.59 350	35.16 334	48.69 363	39.24 322	58.51 390	65.89 351

TESTMET0.1,1		45.17 357	44.93 363	45.89 366	56.02 383	38.31 327	53.18 358	41.94 394	27.85 384	44.86 396	56.47 393	17.93 404	41.50 392	38.08 333	68.06 364	57.85 381

ADS-MVSNet2		48.76 347	47.25 356	53.29 333	55.90 384	40.54 312	47.34 376	54.99 340	31.41 378	50.48 383	72.06 327	31.23 358	54.26 352	25.93 389	55.93 393	65.07 357

ADS-MVSNet		44.62 360	45.58 359	41.73 378	55.90 384	20.83 403	47.34 376	39.94 399	31.41 378	50.48 383	72.06 327	31.23 358	39.31 395	25.93 389	55.93 393	65.07 357

test0.0.03 1		47.72 350	48.31 352	45.93 365	55.53 386	29.39 381	46.40 379	41.21 397	43.41 305	55.81 364	67.65 365	29.22 373	43.77 386	25.73 392	69.87 356	64.62 361

UnsupCasMVSNet_eth		52.26 330	53.29 325	49.16 353	55.08 387	33.67 361	50.03 368	58.79 317	37.67 347	63.43 321	74.75 304	41.82 298	45.83 372	38.59 329	59.42 387	67.98 341

pmmvs5		52.49 329	52.58 329	52.21 337	54.99 388	32.38 365	55.45 345	53.84 346	32.15 373	55.49 365	74.81 301	38.08 321	57.37 347	34.02 360	74.40 322	66.88 346

DSMNet-mixed		43.18 365	44.66 365	38.75 382	54.75 389	28.88 384	57.06 334	27.42 407	13.47 403	47.27 391	77.67 282	38.83 317	39.29 396	25.32 394	60.12 386	48.08 391

MDA-MVSNet_test_wron		52.57 328	53.49 324	49.81 349	54.24 390	36.47 341	40.48 389	46.58 377	38.13 342	75.47 181	73.32 320	41.05 305	43.85 385	40.98 314	71.20 347	69.10 335

YYNet1		52.58 327	53.50 322	49.85 348	54.15 391	36.45 342	40.53 388	46.55 378	38.09 343	75.52 180	73.31 321	41.08 304	43.88 384	41.10 312	71.14 348	69.21 333

EPMVS		45.74 354	46.53 357	43.39 375	54.14 392	22.33 402	55.02 347	35.00 404	34.69 361	51.09 381	70.20 342	25.92 383	42.04 390	37.19 339	55.50 395	65.78 352

test_cas_vis1_n_1920		50.90 338	50.92 342	50.83 344	54.12 393	47.80 245	51.44 366	54.61 341	26.95 388	63.95 312	60.85 385	37.86 325	44.97 378	45.53 288	62.97 378	59.72 378

test_fmvs3		56.78 299	55.99 308	59.12 302	53.96 394	48.09 240	58.76 323	66.22 275	27.54 385	76.66 160	68.69 360	25.32 387	51.31 355	53.42 228	73.38 331	77.97 252

test_fmvs1_n		52.70 326	52.01 333	54.76 324	53.83 395	50.36 214	55.80 343	65.90 277	24.96 393	65.39 300	60.64 387	27.69 376	48.46 364	45.88 286	67.99 365	65.46 354

KD-MVS_2432*1600		52.05 332	51.58 335	53.44 331	52.11 396	31.20 371	44.88 382	64.83 289	41.53 314	64.37 306	70.03 346	15.61 409	64.20 318	36.25 347	74.61 319	64.93 359

miper_refine_blended		52.05 332	51.58 335	53.44 331	52.11 396	31.20 371	44.88 382	64.83 289	41.53 314	64.37 306	70.03 346	15.61 409	64.20 318	36.25 347	74.61 319	64.93 359

test_fmvs2		54.80 311	54.11 320	56.88 316	51.76 398	49.95 221	56.70 336	65.80 278	26.22 390	69.42 262	65.25 373	31.82 353	49.98 359	49.63 251	70.36 352	70.71 319

E-PMN		45.17 357	45.36 360	44.60 371	50.07 399	42.75 294	38.66 392	42.29 392	46.39 275	39.55 401	51.15 398	26.00 382	45.37 376	37.68 335	76.41 302	45.69 395

PMMVS		44.69 359	43.95 367	46.92 361	50.05 400	53.47 198	48.08 374	42.40 390	22.36 399	44.01 399	53.05 396	42.60 295	45.49 374	31.69 369	61.36 383	41.79 398

test_fmvs1		51.51 336	50.86 343	53.48 330	49.72 401	49.35 230	54.11 354	64.96 287	24.64 395	63.66 317	59.61 390	28.33 375	48.45 365	45.38 291	67.30 369	62.66 369

EMVS		44.61 361	44.45 366	45.10 370	48.91 402	43.00 292	37.92 393	41.10 398	46.75 273	38.00 403	48.43 401	26.42 380	46.27 371	37.11 341	75.38 313	46.03 394

mvsany_test3		43.76 364	41.01 368	52.01 338	48.09 403	57.74 172	42.47 386	23.85 410	23.30 398	64.80 304	62.17 382	27.12 377	40.59 393	29.17 382	48.11 400	57.69 382

mvsany_test1		37.88 368	35.74 373	44.28 372	47.28 404	49.90 222	36.54 396	24.37 409	19.56 402	45.76 392	53.46 395	32.99 343	37.97 398	26.17 387	35.52 402	44.99 397

test_vis3_rt		51.94 334	51.04 340	54.65 325	46.32 405	50.13 218	44.34 384	78.17 173	23.62 397	68.95 270	62.81 379	21.41 395	38.52 397	41.49 310	72.22 340	75.30 275

test_vis1_rt		46.70 353	45.24 361	51.06 343	44.58 406	51.04 209	39.91 390	67.56 269	21.84 401	51.94 378	50.79 399	33.83 338	39.77 394	35.25 356	61.50 382	62.38 371

MVE	27.91 23	36.69 371	35.64 374	39.84 381	43.37 407	35.85 347	19.49 400	24.61 408	24.68 394	39.05 402	62.63 381	38.67 319	27.10 405	21.04 402	47.25 401	56.56 385
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 369	40.87 369	28.36 386	42.41 408	5.35 412	24.61 399	27.75 406	32.15 373	47.85 389	70.27 341	35.85 333	29.51 403	19.08 404	67.85 366	50.22 390

test_f		43.79 363	45.63 358	38.24 384	42.29 409	38.58 325	34.76 397	47.68 373	22.22 400	67.34 290	63.15 378	31.82 353	30.60 402	39.19 323	62.28 380	45.53 396

DeepMVS_CX				11.83 388	15.51 410	13.86 408		11.25 413	5.76 404	20.85 406	26.46 403	17.06 407	9.22 407	9.69 407	13.82 406	12.42 403

test_method		19.26 372	19.12 376	19.71 387	9.09 411	1.91 414	7.79 402	53.44 350	1.42 405	10.27 407	35.80 402	17.42 406	25.11 406	12.44 405	24.38 405	32.10 402

tmp_tt		11.98 374	14.73 377	3.72 389	2.28 412	4.62 413	19.44 401	14.50 412	0.47 407	21.55 405	9.58 405	25.78 384	4.57 408	11.61 406	27.37 404	1.96 404

test123		4.43 377	5.78 380	0.39 391	0.97 413	0.28 415	46.33 380	0.45 414	0.31 408	0.62 409	1.50 408	0.61 414	0.11 410	0.56 408	0.63 407	0.77 406

testmvs		4.06 378	5.28 381	0.41 390	0.64 414	0.16 416	42.54 385	0.31 415	0.26 409	0.50 410	1.40 409	0.77 413	0.17 409	0.56 408	0.55 408	0.90 405

test_blank		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

eth-test2					0.00 415

eth-test					0.00 415

uanet_test		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

DCPMVS		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

cdsmvs_eth3d_5k		17.71 373	23.62 375	0.00 392	0.00 415	0.00 417	0.00 403	70.17 255	0.00 410	0.00 411	74.25 311	68.16 96	0.00 411	0.00 410	0.00 409	0.00 407

pcd_1.5k_mvsjas		5.20 376	6.93 379	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	62.39 150	0.00 411	0.00 410	0.00 409	0.00 407

sosnet-low-res		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

sosnet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uncertanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

Regformer		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

ab-mvs-re		5.62 375	7.50 378	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	67.46 366	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

uanet		0.00 379	0.00 382	0.00 392	0.00 415	0.00 417	0.00 403	0.00 416	0.00 410	0.00 411	0.00 410	0.00 415	0.00 411	0.00 410	0.00 409	0.00 407

WAC-MVS						22.69 400								36.10 351

PC_three_1452									46.98 272	81.83 92	86.28 154	66.55 116	84.47 71	63.31 138	90.78 113	83.49 138

test_241102_TWO								84.80 45	72.61 30	84.93 56	89.70 80	77.73 22	85.89 40	75.29 42	94.22 52	83.25 149

test_0728_THIRD									74.03 21	85.83 43	90.41 59	75.58 37	85.69 45	77.43 30	94.74 29	84.31 120

GSMVS																70.05 323

sam_mvs1												31.41 356				70.05 323

sam_mvs												31.21 360

MTGPA								80.63 125

test_post1							66.63 255				2.08 406	30.66 365	59.33 338	40.34 318

test_post											1.99 407	30.91 363	54.76 351

patchmatchnet-post											68.99 353	31.32 357	69.38 277

MTMP							84.83 31	19.26 411

test9_res														72.12 69	91.37 92	77.40 255

agg_prior2														70.70 74	90.93 107	78.55 241

test_prior4						70.14 63	77.57 101

test_prior2							75.57 132		58.92 139	76.53 167	86.78 136	67.83 101		69.81 77	92.76 73

旧先验2							71.17 190		45.11 288	78.54 131			61.28 332	59.19 174

新几何2							71.33 186

无先验							74.82 138	70.94 249	47.75 267				76.85 201	54.47 214		72.09 306

原ACMM2							74.78 142

testdata2													67.30 296	48.34 264

segment_acmp												68.30 95

testdata1							68.34 231		57.24 156

plane_prior5								85.49 30					86.15 27	71.09 71	90.94 105	84.82 98

plane_prior4											89.11 94

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						89.91 131

n2								0.00 416

nn								0.00 416

door-mid								55.02 339

test11								82.71 86

door								52.91 354

HQP5-MVS						58.80 166

BP-MVS														67.38 101

HQP4-MVS										71.59 234			85.31 52			83.74 133

HQP3-MVS								84.12 66							89.16 147

HQP2-MVS												58.09 198

MDTV_nov1_ep13_2view						18.41 404	53.74 356		31.57 377	44.89 395		29.90 371		32.93 365		71.48 310

ACMMP++_ref															89.47 142

ACMMP++															91.96 83

Test By Simon												62.56 146

High-resolution multi-view benchmark