High-res multi-view results

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

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

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

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

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

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

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

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

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

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

MP-MVS		83.19 18	83.54 23	82.14 19	90.54 4	79.00 8	86.42 22	83.59 73	71.31 39	81.26 102	90.96 39	74.57 47	84.69 67	78.41 21	94.78 27	82.74 167
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PMVS	70.70 6	81.70 32	83.15 31	77.36 77	90.35 5	82.82 2	82.15 54	79.22 150	74.08 20	87.16 28	91.97 19	84.80 2	76.97 196	64.98 119	93.61 60	72.28 296
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PS-CasMVS		80.41 47	82.86 36	73.07 135	89.93 6	39.21 315	77.15 111	81.28 107	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	72.74 148	89.84 7	37.34 335	77.16 110	81.81 97	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	73.11 133	89.83 8	39.02 318	77.06 113	82.61 86	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 63	70.19 48	83.86 71	90.72 49	75.20 40	86.27 21	79.41 14	94.25 50	83.95 128

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

MSP-MVS		80.49 45	79.67 58	82.96 5	89.70 11	77.46 19	87.16 11	85.10 39	64.94 89	81.05 105	88.38 114	57.10 209	87.10 8	79.75 7	83.87 228	84.31 121
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CP-MVSNet		79.48 54	81.65 45	72.98 138	89.66 12	39.06 317	76.76 114	80.46 127	78.91 7	90.32 7	91.70 25	68.49 91	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 33	67.96 59	84.91 59	90.88 42	75.59 36	86.57 14	78.16 22	94.71 30	83.82 130

WR-MVS_H		80.22 50	82.17 41	74.39 111	89.46 14	42.69 292	78.24 97	82.24 89	78.21 9	89.57 9	92.10 18	68.05 96	85.59 48	66.04 111	95.62 9	94.88 5

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

X-MVStestdata		76.81 77	74.79 100	82.85 8	89.43 15	77.61 14	86.80 17	84.66 52	72.71 27	82.87 82	9.95 394	73.86 52	86.31 19	78.84 19	94.03 53	84.64 104

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

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

UniMVSNet_ETH3D		76.74 78	79.02 61	69.92 191	89.27 19	43.81 280	74.47 149	71.70 229	72.33 35	85.50 50	93.65 3	77.98 21	76.88 199	54.60 210	91.64 86	89.08 32

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

ACMMP		84.22 6	84.84 8	82.35 17	89.23 21	76.66 22	87.65 6	85.89 25	71.03 42	85.85 42	90.58 51	78.77 16	85.78 42	79.37 15	95.17 16	84.62 106
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CPTT-MVS		81.51 34	81.76 43	80.76 34	89.20 22	78.75 9	86.48 21	82.03 93	68.80 53	80.92 107	88.52 110	72.00 63	82.39 100	74.80 44	93.04 68	81.14 193

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

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

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

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

TSAR-MVS + MP.		79.05 57	78.81 62	79.74 42	88.94 27	67.52 83	86.61 19	81.38 105	51.71 222	77.15 147	91.42 32	65.49 124	87.20 6	79.44 13	87.17 184	84.51 116
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
UA-Net		81.56 33	82.28 40	79.40 50	88.91 28	69.16 72	84.67 33	80.01 137	75.34 15	79.80 118	94.91 2	69.79 83	80.25 141	72.63 63	94.46 36	88.78 42

SMA-MVS		82.12 28	82.68 38	80.43 36	88.90 29	69.52 65	85.12 29	84.76 46	63.53 102	84.23 66	91.47 30	72.02 62	87.16 7	79.74 9	94.36 45	84.61 107
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MP-MVS-pluss		82.54 26	83.46 25	79.76 41	88.88 30	68.44 76	81.57 59	86.33 18	63.17 108	85.38 52	91.26 33	76.33 30	84.67 68	83.30 1	94.96 22	86.17 70
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 4	88.84 4	90.72 1	78.27 8	87.95 14	92.53 13	79.37 13	84.79 66	74.51 48	96.15 2	92.88 7

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

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

新几何1				69.99 189	88.37 34	71.34 51		62.08 302	43.85 292	74.99 184	86.11 164	52.85 230	70.57 267	50.99 237	83.23 237	68.05 330

HPM-MVS		84.12 8	84.63 9	82.60 13	88.21 35	74.40 31	85.24 28	87.21 13	70.69 45	85.14 54	90.42 58	78.99 15	86.62 13	80.83 5	94.93 23	86.79 63
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMM	69.25 9	82.11 29	83.31 27	78.49 64	88.17 36	73.96 34	83.11 49	84.52 56	66.40 69	87.45 22	89.16 94	81.02 8	80.52 137	74.27 51	95.73 7	80.98 199
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test222					87.30 37	69.15 73	67.85 233	59.59 312	41.06 314	73.05 216	85.72 172	48.03 262			80.65 264	66.92 335

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

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

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

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

EGC-MVSNET		64.77 231	61.17 264	75.60 98	86.90 42	74.47 30	84.04 35	68.62 261	0.60 396	1.13 398	91.61 28	65.32 127	74.15 230	64.01 126	88.28 160	78.17 245

OPM-MVS		80.99 41	81.63 46	79.07 54	86.86 43	69.39 68	79.41 84	84.00 69	65.64 73	85.54 49	89.28 87	76.32 31	83.47 84	74.03 52	93.57 62	84.35 120
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepC-MVS	72.44 4	81.00 40	80.83 50	81.50 22	86.70 44	70.03 64	82.06 55	87.00 14	59.89 130	80.91 108	90.53 53	72.19 60	88.56 1	73.67 55	94.52 35	85.92 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMP	69.50 8	82.64 25	83.38 26	80.40 37	86.50 45	69.44 67	82.30 53	86.08 23	66.80 65	86.70 30	89.99 76	81.64 6	85.95 34	74.35 50	96.11 3	85.81 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
XVG-OURS-SEG-HR		79.62 52	79.99 55	78.49 64	86.46 46	74.79 29	77.15 111	85.39 34	66.73 66	80.39 114	88.85 103	74.43 50	78.33 177	74.73 46	85.79 200	82.35 175

VDDNet		71.60 149	73.13 128	67.02 232	86.29 47	41.11 302	69.97 203	66.50 270	68.72 55	74.74 187	91.70 25	59.90 178	75.81 207	48.58 258	91.72 84	84.15 125

test_0728_SECOND				76.57 85	86.20 48	60.57 149	83.77 40	85.49 29					85.90 38	75.86 39	94.39 41	83.25 150

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

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

DVP-MVS		81.15 37	83.12 32	75.24 103	86.16 51	60.78 146	83.77 40	80.58 125	72.48 32	85.83 43	90.41 59	78.57 17	85.69 45	75.86 39	94.39 41	79.24 231
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					86.16 51	60.78 146	83.81 39	85.10 39	72.48 32	85.27 53	89.96 77	78.57 17

SED-MVS		81.78 31	83.48 24	76.67 83	86.12 53	61.06 139	83.62 42	84.72 48	72.61 30	87.38 24	89.70 81	77.48 23	85.89 40	75.29 42	94.39 41	83.08 156

IU-MVS					86.12 53	60.90 143		80.38 129	45.49 281	81.31 101				75.64 41	94.39 41	84.65 103

test_241102_ONE					86.12 53	61.06 139		84.72 48	72.64 29	87.38 24	89.47 84	77.48 23	85.74 44

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

test_part2					85.90 57	66.44 91				84.61 62

原ACMM1				73.90 118	85.90 57	65.15 106		81.67 99	50.97 234	74.25 198	86.16 161	61.60 157	83.54 82	56.75 187	91.08 104	73.00 287

testdata				64.13 253	85.87 59	63.34 119		61.80 305	47.83 264	76.42 170	86.60 148	48.83 256	62.31 321	54.46 212	81.26 258	66.74 339

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

test_one_0601					85.84 61	61.45 133		85.63 27	75.27 17	85.62 48	90.38 64	76.72 27

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

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

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

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

plane_prior7					85.18 66	66.21 94

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

bld_raw_dy_0_64		72.85 134	72.76 136	73.09 134	85.08 70	64.80 108	78.72 90	64.22 291	51.92 220	83.13 77	90.26 70	39.21 310	69.91 272	70.73 73	91.60 89	84.56 111

WR-MVS		71.20 152	72.48 141	67.36 227	84.98 71	35.70 345	64.43 280	68.66 260	65.05 86	81.49 99	86.43 153	57.57 205	76.48 203	50.36 242	93.32 65	89.90 23

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

LTVRE_ROB	75.46 1	84.22 6	84.98 7	81.94 20	84.82 73	75.40 25	91.60 3	87.80 7	73.52 24	88.90 11	93.06 6	71.39 68	81.53 114	81.53 3	92.15 82	88.91 38
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
mvs_tets		78.93 58	78.67 65	79.72 43	84.81 74	73.93 35	80.65 65	76.50 191	51.98 219	87.40 23	91.86 21	76.09 33	78.53 167	68.58 84	90.20 123	86.69 66

APDe-MVS		82.88 23	84.14 14	79.08 53	84.80 75	66.72 90	86.54 20	85.11 38	72.00 37	86.65 31	91.75 24	78.20 20	87.04 9	77.93 25	94.32 48	83.47 142
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MSLP-MVS++		74.48 105	75.78 92	70.59 175	84.66 76	62.40 124	78.65 91	84.24 62	60.55 125	77.71 142	81.98 221	63.12 140	77.64 191	62.95 140	88.14 162	71.73 301

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

TranMVSNet+NR-MVSNet		76.13 82	77.66 74	71.56 166	84.61 78	42.57 294	70.98 192	78.29 170	68.67 56	83.04 78	89.26 88	72.99 58	80.75 133	55.58 202	95.47 10	91.35 13

旧先验1					84.55 79	60.36 151		63.69 294			87.05 131	54.65 221			83.34 236	69.66 319

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

agg_prior					84.44 82	66.02 97		78.62 164		76.95 151			80.34 139

DeepPCF-MVS	71.07 5	78.48 65	77.14 79	82.52 16	84.39 83	77.04 21	76.35 121	84.05 67	56.66 162	80.27 115	85.31 175	68.56 90	87.03 10	67.39 99	91.26 95	83.50 138

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

plane_prior6					84.18 85	65.31 103						60.83 170

114514_t		73.40 116	73.33 125	73.64 123	84.15 86	57.11 173	78.20 98	80.02 136	43.76 295	72.55 222	86.07 166	64.00 136	83.35 87	60.14 164	91.03 105	80.45 214

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

DeepC-MVS_fast	69.89 7	77.17 75	76.33 86	79.70 44	83.90 88	67.94 78	80.06 79	83.75 70	56.73 161	74.88 186	85.32 174	65.54 123	87.79 2	65.61 115	91.14 100	83.35 148
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
h-mvs33		73.08 123	71.61 154	77.48 74	83.89 89	72.89 44	70.47 198	71.12 245	54.28 189	77.89 137	83.41 197	49.04 253	80.98 127	63.62 133	90.77 116	78.58 239

SD-MVS		80.28 49	81.55 47	76.47 88	83.57 90	67.83 80	83.39 47	85.35 35	64.42 92	86.14 39	87.07 130	74.02 51	80.97 128	77.70 28	92.32 80	80.62 211
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
DU-MVS		74.91 100	75.57 95	72.93 142	83.50 91	45.79 266	69.47 209	80.14 135	65.22 82	81.74 96	87.08 128	61.82 155	81.07 124	56.21 194	94.98 20	91.93 8

NR-MVSNet		73.62 112	74.05 110	72.33 159	83.50 91	43.71 281	65.65 265	77.32 183	64.32 93	75.59 176	87.08 128	62.45 148	81.34 116	54.90 205	95.63 8	91.93 8

test_0402		78.17 69	79.48 59	74.24 113	83.50 91	59.15 160	72.52 163	74.60 208	75.34 15	88.69 13	91.81 22	75.06 42	82.37 101	65.10 117	88.68 157	81.20 191

OPU-MVS				78.65 62	83.44 94	66.85 89	83.62 42				86.12 163	66.82 108	86.01 31	61.72 147	89.79 135	83.08 156

NP-MVS					83.34 95	63.07 122					85.97 167

DVP-MVS++		81.24 35	82.74 37	76.76 82	83.14 96	60.90 143	91.64 1	85.49 29	74.03 21	84.93 56	90.38 64	66.82 108	85.90 38	77.43 30	90.78 114	83.49 139

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

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

RRT_MVS		78.18 68	77.69 73	79.66 46	83.14 96	61.34 134	83.29 48	80.34 132	57.43 154	86.65 31	91.79 23	50.52 243	86.01 31	71.36 70	94.65 32	91.62 11

UniMVSNet (Re)		75.00 98	75.48 96	73.56 125	83.14 96	47.92 243	70.41 200	81.04 115	63.67 100	79.54 120	86.37 154	62.83 143	81.82 110	57.10 186	95.25 14	90.94 17

hse-mvs2		72.32 143	70.66 166	77.31 79	83.10 101	71.77 47	69.19 214	71.45 235	54.28 189	77.89 137	78.26 273	49.04 253	79.23 155	63.62 133	89.13 151	80.92 200

UniMVSNet_NR-MVSNet		74.90 101	75.65 93	72.64 151	83.04 102	45.79 266	69.26 212	78.81 156	66.66 67	81.74 96	86.88 134	63.26 139	81.07 124	56.21 194	94.98 20	91.05 15

HyFIR lowres test		63.01 251	60.47 271	70.61 174	83.04 102	54.10 192	59.93 313	72.24 228	33.67 357	69.00 266	75.63 294	38.69 313	76.93 197	36.60 340	75.45 305	80.81 205

COLMAP_ROB	72.78 3	83.75 11	84.11 15	82.68 12	82.97 104	74.39 32	87.18 10	88.18 6	78.98 6	86.11 40	91.47 30	79.70 12	85.76 43	66.91 107	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		70.22 162	67.88 198	77.22 80	82.96 105	71.61 48	69.08 215	71.39 236	49.17 253	71.70 232	78.07 278	37.62 321	79.21 156	61.81 144	89.15 149	80.82 203

DP-MVS Recon		73.57 113	72.69 137	76.23 91	82.85 106	63.39 118	74.32 150	82.96 80	57.75 148	70.35 250	81.98 221	64.34 135	84.41 73	49.69 246	89.95 130	80.89 201

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

PVSNet_Blended_VisFu		70.04 164	68.88 181	73.53 126	82.71 108	63.62 117	74.81 140	81.95 95	48.53 258	67.16 290	79.18 262	51.42 239	78.38 174	54.39 214	79.72 276	78.60 238

DPM-MVS		69.98 166	69.22 177	72.26 160	82.69 109	58.82 164	70.53 197	81.23 109	47.79 265	64.16 307	80.21 242	51.32 240	83.12 90	60.14 164	84.95 215	74.83 274

EG-PatchMatch MVS		70.70 158	70.88 163	70.16 185	82.64 110	58.80 165	71.48 182	73.64 212	54.98 177	76.55 164	81.77 224	61.10 167	78.94 161	54.87 206	80.84 262	72.74 291

HQP-NCC					82.37 111		77.32 107		59.08 134	71.58 234

ACMP_Plane					82.37 111		77.32 107		59.08 134	71.58 234

HQP-MVS		75.24 93	75.01 99	75.94 93	82.37 111	58.80 165	77.32 107	84.12 65	59.08 134	71.58 234	85.96 168	58.09 196	85.30 53	67.38 101	89.16 147	83.73 135

test12				76.51 86	82.28 114	60.94 142		81.64 100		73.60 207		64.88 130	85.19 59		90.42 121	83.38 146

TAMVS		65.31 224	63.75 243	69.97 190	82.23 115	59.76 155	66.78 252	63.37 296	45.20 285	69.79 259	79.37 258	47.42 265	72.17 250	34.48 353	85.15 210	77.99 250

test_prior				75.27 102	82.15 116	59.85 154		84.33 59					83.39 86			82.58 171

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

AdaColmap		74.22 106	74.56 102	73.20 130	81.95 118	60.97 141	79.43 82	80.90 117	65.57 74	72.54 223	81.76 225	70.98 73	85.26 54	47.88 267	90.00 128	73.37 284

PAPM_NR		73.91 108	74.16 109	73.16 131	81.90 119	53.50 196	81.28 60	81.40 104	66.17 70	73.30 213	83.31 203	59.96 177	83.10 91	58.45 178	81.66 255	82.87 162

DP-MVS		78.44 66	79.29 60	75.90 94	81.86 120	65.33 102	79.05 87	84.63 54	74.83 18	80.41 113	86.27 156	71.68 64	83.45 85	62.45 143	92.40 77	78.92 236

F-COLMAP		75.29 91	73.99 111	79.18 52	81.73 121	71.90 46	81.86 58	82.98 79	59.86 131	72.27 226	84.00 190	64.56 133	83.07 92	51.48 232	87.19 183	82.56 172

SixPastTwentyTwo		75.77 84	76.34 85	74.06 116	81.69 122	54.84 186	76.47 116	75.49 200	64.10 95	87.73 17	92.24 17	50.45 245	81.30 118	67.41 97	91.46 92	86.04 73

Vis-MVSNet		74.85 104	74.56 102	75.72 96	81.63 123	64.64 110	76.35 121	79.06 152	62.85 110	73.33 212	88.41 112	62.54 147	79.59 152	63.94 130	82.92 238	82.94 160
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_djsdf		78.88 59	78.27 69	80.70 35	81.42 124	71.24 52	83.98 36	75.72 198	52.27 214	87.37 26	92.25 16	68.04 97	80.56 134	72.28 67	91.15 99	90.32 22

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

tt0805		76.12 83	78.43 68	69.20 200	81.32 126	41.37 300	76.72 115	77.64 179	63.78 99	82.06 90	87.88 123	79.78 11	79.05 158	64.33 124	92.40 77	87.17 60

MCST-MVS		73.42 115	73.34 124	73.63 124	81.28 127	59.17 159	74.80 142	83.13 78	45.50 279	72.84 218	83.78 194	65.15 128	80.99 126	64.54 121	89.09 153	80.73 207

MIMVSNet1		66.57 214	69.23 176	58.59 303	81.26 128	37.73 332	64.06 283	57.62 316	57.02 157	78.40 132	90.75 46	62.65 144	58.10 336	41.77 306	89.58 140	79.95 220

ACMH+	66.64 10	81.20 36	82.48 39	77.35 78	81.16 129	62.39 125	80.51 67	87.80 7	73.02 26	87.57 20	91.08 36	80.28 9	82.44 99	64.82 120	96.10 4	87.21 57

MVS_111021_HR		72.98 130	72.97 133	72.99 137	80.82 130	65.47 100	68.81 219	72.77 221	57.67 150	75.76 174	82.38 218	71.01 72	77.17 194	61.38 149	86.15 196	76.32 262

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

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

OurMVSNet-221017-0		78.57 62	78.53 67	78.67 61	80.48 133	64.16 113	80.24 74	82.06 92	61.89 116	88.77 12	93.32 4	57.15 207	82.60 98	70.08 77	92.80 71	89.25 28

CDS-MVSNet		64.33 239	62.66 255	69.35 198	80.44 134	58.28 169	65.26 270	65.66 276	44.36 290	67.30 289	75.54 295	43.27 283	71.77 256	37.68 332	84.44 223	78.01 249
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		77.20 74	76.37 84	79.69 45	80.34 135	61.52 132	80.58 66	82.12 91	53.54 205	83.93 70	91.03 37	49.49 249	85.97 33	73.26 57	93.08 67	91.59 12

PLC	62.01 16	71.79 148	70.28 168	76.33 89	80.31 136	68.63 75	78.18 99	81.24 108	54.57 186	67.09 291	80.63 237	59.44 182	81.74 113	46.91 274	84.17 225	78.63 237
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		58.09 290	56.30 301	63.45 262	79.95 137	50.93 210	54.07 346	65.59 277	28.56 373	61.53 324	74.33 307	41.09 297	66.52 305	33.91 356	67.69 358	72.92 288

K. test v3		73.67 111	73.61 119	73.87 119	79.78 138	55.62 184	74.69 146	62.04 304	66.16 71	84.76 60	93.23 5	49.47 250	80.97 128	65.66 114	86.67 191	85.02 94

VPNet		65.58 222	67.56 201	59.65 296	79.72 139	30.17 372	60.27 311	62.14 300	54.19 194	71.24 242	86.63 146	58.80 189	67.62 289	44.17 293	90.87 113	81.18 192

ACMH	63.62 14	77.50 72	80.11 54	69.68 193	79.61 140	56.28 177	78.81 89	83.62 72	63.41 106	87.14 29	90.23 72	76.11 32	73.32 236	67.58 95	94.44 39	79.44 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
lessismore_v0				72.75 147	79.60 141	56.83 176		57.37 319		83.80 72	89.01 98	47.45 264	78.74 165	64.39 123	86.49 194	82.69 168

MVS_111021_LR		72.10 145	71.82 150	72.95 139	79.53 142	73.90 36	70.45 199	66.64 269	56.87 158	76.81 157	81.76 225	68.78 88	71.76 257	61.81 144	83.74 230	73.18 286

Test_1112_low_res		58.78 286	58.69 283	59.04 301	79.41 143	38.13 328	57.62 326	66.98 268	34.74 350	59.62 340	77.56 282	42.92 286	63.65 316	38.66 324	70.73 340	75.35 271

CSCG		74.12 107	74.39 104	73.33 128	79.35 144	61.66 131	77.45 106	81.98 94	62.47 114	79.06 125	80.19 244	61.83 154	78.79 164	59.83 168	87.35 176	79.54 228

MVS_0304		76.32 81	75.96 91	77.42 76	79.33 145	60.86 145	80.18 76	74.88 205	66.93 62	69.11 264	88.95 101	57.84 203	86.12 29	76.63 37	89.77 136	85.28 86

MVP-Stereo		61.56 265	59.22 278	68.58 214	79.28 146	60.44 150	69.20 213	71.57 231	43.58 298	56.42 352	78.37 272	39.57 308	76.46 204	34.86 352	60.16 375	68.86 327
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MG-MVS		70.47 161	71.34 159	67.85 222	79.26 147	40.42 310	74.67 147	75.15 204	58.41 142	68.74 276	88.14 121	56.08 217	83.69 80	59.90 167	81.71 254	79.43 230

IS-MVSNet		75.10 95	75.42 97	74.15 115	79.23 148	48.05 241	79.43 82	78.04 174	70.09 49	79.17 124	88.02 122	53.04 229	83.60 81	58.05 181	93.76 59	90.79 19

FC-MVSNet-test		73.32 118	74.78 101	68.93 208	79.21 149	36.57 337	71.82 179	79.54 146	57.63 153	82.57 87	90.38 64	59.38 184	78.99 160	57.91 182	94.56 34	91.23 14

AllTest		77.66 70	77.43 75	78.35 66	79.19 150	70.81 55	78.60 92	88.64 3	65.37 79	80.09 116	88.17 118	70.33 76	78.43 172	55.60 199	90.90 110	85.81 76

TestCases				78.35 66	79.19 150	70.81 55		88.64 3	65.37 79	80.09 116	88.17 118	70.33 76	78.43 172	55.60 199	90.90 110	85.81 76

xiu_mvs_v1_base_debu		67.87 197	67.07 208	70.26 181	79.13 152	61.90 128	67.34 240	71.25 241	47.98 261	67.70 283	74.19 311	61.31 160	72.62 243	56.51 189	78.26 289	76.27 263

xiu_mvs_v1_base		67.87 197	67.07 208	70.26 181	79.13 152	61.90 128	67.34 240	71.25 241	47.98 261	67.70 283	74.19 311	61.31 160	72.62 243	56.51 189	78.26 289	76.27 263

xiu_mvs_v1_base_debi		67.87 197	67.07 208	70.26 181	79.13 152	61.90 128	67.34 240	71.25 241	47.98 261	67.70 283	74.19 311	61.31 160	72.62 243	56.51 189	78.26 289	76.27 263

VDD-MVS		70.81 157	71.44 158	68.91 209	79.07 155	46.51 260	67.82 234	70.83 249	61.23 119	74.07 202	88.69 106	59.86 179	75.62 210	51.11 235	90.28 122	84.61 107

test1111		64.62 232	65.19 228	62.93 269	79.01 156	29.91 373	65.45 268	54.41 339	54.09 196	71.47 241	88.48 111	37.02 323	74.29 228	46.83 276	89.94 131	84.58 110

TSAR-MVS + GP.		73.08 123	71.60 155	77.54 73	78.99 157	70.73 57	74.96 137	69.38 256	60.73 124	74.39 196	78.44 271	57.72 204	82.78 95	60.16 163	89.60 138	79.11 233

test2506		61.23 267	60.85 268	62.38 274	78.80 158	27.88 379	67.33 243	37.42 391	54.23 191	67.55 286	88.68 107	17.87 395	74.39 226	46.33 279	89.41 143	84.86 97

ECVR-MVS		64.82 229	65.22 227	63.60 259	78.80 158	31.14 369	66.97 248	56.47 330	54.23 191	69.94 256	88.68 107	37.23 322	74.81 221	45.28 289	89.41 143	84.86 97

FIs		72.56 139	73.80 114	68.84 211	78.74 160	37.74 331	71.02 191	79.83 139	56.12 166	80.88 110	89.45 85	58.18 193	78.28 178	56.63 188	93.36 64	90.51 21

v7n		79.37 56	80.41 52	76.28 90	78.67 161	55.81 181	79.22 86	82.51 88	70.72 44	87.54 21	92.44 14	68.00 98	81.34 116	72.84 61	91.72 84	91.69 10

LS3D		80.99 41	80.85 49	81.41 25	78.37 162	71.37 50	87.45 7	85.87 26	77.48 12	81.98 91	89.95 78	69.14 86	85.26 54	66.15 109	91.24 96	87.61 52

CNLPA		73.44 114	73.03 131	74.66 105	78.27 163	75.29 26	75.99 128	78.49 165	65.39 78	75.67 175	83.22 209	61.23 163	66.77 303	53.70 221	85.33 206	81.92 184

EPP-MVSNet		73.86 110	73.38 122	75.31 101	78.19 164	53.35 198	80.45 68	77.32 183	65.11 85	76.47 168	86.80 135	49.47 250	83.77 77	53.89 219	92.72 74	88.81 41

PCF-MVS	63.80 13	72.70 137	71.69 151	75.72 96	78.10 165	60.01 153	73.04 160	81.50 101	45.34 283	79.66 119	84.35 186	65.15 128	82.65 97	48.70 256	89.38 146	84.50 117
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		73.14 121	73.77 116	71.26 170	78.09 166	52.64 201	74.32 150	79.56 145	56.32 165	76.35 171	83.36 202	70.76 74	77.96 185	63.32 137	81.84 249	83.18 153

LFMVS		67.06 210	67.89 197	64.56 250	78.02 167	38.25 326	70.81 196	59.60 311	65.18 83	71.06 244	86.56 149	43.85 280	75.22 214	46.35 278	89.63 137	80.21 218

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

BH-untuned		69.39 176	69.46 172	69.18 201	77.96 169	56.88 174	68.47 228	77.53 180	56.77 160	77.79 140	79.63 253	60.30 175	80.20 144	46.04 281	80.65 264	70.47 312

1112_ss		59.48 281	58.99 281	60.96 288	77.84 170	42.39 295	61.42 301	68.45 262	37.96 335	59.93 337	67.46 356	45.11 273	65.07 309	40.89 312	71.81 334	75.41 269

PS-MVSNAJ		64.27 240	63.73 244	65.90 243	77.82 171	51.42 207	63.33 290	72.33 226	45.09 287	61.60 323	68.04 354	62.39 149	73.95 232	49.07 252	73.87 321	72.34 294

ambc				70.10 187	77.74 172	50.21 217	74.28 152	77.93 177		79.26 123	88.29 116	54.11 225	79.77 148	64.43 122	91.10 103	80.30 216

xiu_mvs_v2_base		64.43 237	63.96 241	65.85 244	77.72 173	51.32 208	63.63 287	72.31 227	45.06 288	61.70 322	69.66 342	62.56 145	73.93 233	49.06 253	73.91 320	72.31 295

Anonymous20231211		75.54 89	77.19 78	70.59 175	77.67 174	45.70 269	74.73 144	80.19 133	68.80 53	82.95 81	92.91 8	66.26 116	76.76 201	58.41 179	92.77 72	89.30 27

FMVSNet1		71.06 153	72.48 141	66.81 233	77.65 175	40.68 306	71.96 173	73.03 216	61.14 120	79.45 122	90.36 67	60.44 173	75.20 215	50.20 243	88.05 164	84.54 112

FPMVS		59.43 282	60.07 273	57.51 309	77.62 176	71.52 49	62.33 297	50.92 353	57.40 155	69.40 262	80.00 248	39.14 311	61.92 323	37.47 335	66.36 360	39.09 390

testing3		58.28 289	58.38 287	58.00 307	77.45 177	26.12 384	60.78 307	43.00 377	56.02 167	70.18 253	75.76 292	13.27 402	67.24 295	48.02 265	80.89 260	80.65 210

Effi-MVS+-dtu		75.43 90	72.28 145	84.91 2	77.05 178	83.58 1	78.47 94	77.70 178	57.68 149	74.89 185	78.13 277	64.80 131	84.26 74	56.46 192	85.32 207	86.88 62

CLD-MVS		72.88 133	72.36 144	74.43 110	77.03 179	54.30 190	68.77 222	83.43 75	52.12 216	76.79 158	74.44 306	69.54 85	83.91 75	55.88 197	93.25 66	85.09 91
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CS-MVS		76.51 79	76.00 89	78.06 71	77.02 180	64.77 109	80.78 64	82.66 85	60.39 126	74.15 199	83.30 204	69.65 84	82.07 107	69.27 82	86.75 190	87.36 55

CS-MVS-test		74.89 102	74.23 108	76.86 81	77.01 181	62.94 123	78.98 88	84.61 55	58.62 141	70.17 254	80.80 234	66.74 112	81.96 108	61.74 146	89.40 145	85.69 81

Baseline_NR-MVSNet		70.62 159	73.19 126	62.92 270	76.97 182	34.44 353	68.84 217	70.88 248	60.25 127	79.50 121	90.53 53	61.82 155	69.11 278	54.67 209	95.27 13	85.22 87

ITE_SJBPF				80.35 38	76.94 183	73.60 38		80.48 126	66.87 64	83.64 74	86.18 159	70.25 78	79.90 147	61.12 154	88.95 155	87.56 53

SSC-MVS		61.79 263	66.08 218	48.89 346	76.91 184	10.00 400	53.56 348	47.37 366	68.20 58	76.56 163	89.21 90	54.13 224	57.59 337	54.75 207	74.07 319	79.08 234

jason		64.47 236	62.84 253	69.34 199	76.91 184	59.20 156	67.15 245	65.67 275	35.29 347	65.16 300	76.74 288	44.67 275	70.68 265	54.74 208	79.28 279	78.14 246
jason: jason.
ETV-MVS		72.72 136	72.16 147	74.38 112	76.90 186	55.95 178	73.34 158	84.67 51	62.04 115	72.19 229	70.81 332	65.90 120	85.24 56	58.64 176	84.96 214	81.95 183

Anonymous20240529		72.56 139	73.79 115	68.86 210	76.89 187	45.21 271	68.80 221	77.25 185	67.16 61	76.89 153	90.44 56	65.95 119	74.19 229	50.75 238	90.00 128	87.18 59

EC-MVSNet		77.08 76	77.39 76	76.14 92	76.86 188	56.87 175	80.32 73	87.52 11	63.45 104	74.66 191	84.52 183	69.87 82	84.94 61	69.76 79	89.59 139	86.60 67

PM-MVS		64.49 235	63.61 245	67.14 231	76.68 189	75.15 27	68.49 227	42.85 378	51.17 233	77.85 139	80.51 238	45.76 267	66.31 306	52.83 227	76.35 297	59.96 367

TransMVSNet (Re)		69.62 171	71.63 153	63.57 260	76.51 190	35.93 343	65.75 264	71.29 240	61.05 121	75.02 183	89.90 79	65.88 121	70.41 271	49.79 245	89.48 141	84.38 119

BH-RMVSNet		68.69 186	68.20 194	70.14 186	76.40 191	53.90 195	64.62 277	73.48 214	58.01 145	73.91 206	81.78 223	59.09 186	78.22 179	48.59 257	77.96 292	78.31 242

PHI-MVS		74.92 99	74.36 106	76.61 84	76.40 191	62.32 126	80.38 70	83.15 77	54.16 195	73.23 214	80.75 235	62.19 152	83.86 76	68.02 90	90.92 109	83.65 136

UGNet		70.20 163	69.05 178	73.65 122	76.24 193	63.64 116	75.87 131	72.53 224	61.48 118	60.93 331	86.14 162	52.37 232	77.12 195	50.67 239	85.21 208	80.17 219
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
PatchMatch-RL		58.68 287	57.72 291	61.57 280	76.21 194	73.59 39	61.83 298	49.00 360	47.30 269	61.08 327	68.97 347	50.16 246	59.01 330	36.06 347	68.84 351	52.10 377

VPA-MVSNet		68.71 185	70.37 167	63.72 258	76.13 195	38.06 329	64.10 282	71.48 234	56.60 164	74.10 201	88.31 115	64.78 132	69.72 273	47.69 269	90.15 125	83.37 147

WB-MVS		60.04 277	64.19 239	47.59 348	76.09 196	10.22 399	52.44 353	46.74 367	65.17 84	74.07 202	87.48 125	53.48 227	55.28 340	49.36 250	72.84 326	77.28 255

PAPM		61.79 263	60.37 272	66.05 241	76.09 196	41.87 297	69.30 211	76.79 190	40.64 322	53.80 364	79.62 254	44.38 277	82.92 94	29.64 372	73.11 325	73.36 285

BH-w/o		64.81 230	64.29 238	66.36 238	76.08 198	54.71 187	65.61 266	75.23 203	50.10 245	71.05 245	71.86 327	54.33 223	79.02 159	38.20 329	76.14 299	65.36 345

dcpmvs_2		71.02 155	72.65 138	66.16 240	76.06 199	50.49 213	71.97 172	79.36 147	50.34 241	82.81 84	83.63 195	64.38 134	67.27 294	61.54 148	83.71 232	80.71 209

pmmvs6		71.82 147	73.66 117	66.31 239	75.94 200	42.01 296	66.99 247	72.53 224	63.45 104	76.43 169	92.78 10	72.95 59	69.69 274	51.41 233	90.46 120	87.22 56

CANet		73.00 128	71.84 149	76.48 87	75.82 201	61.28 135	74.81 140	80.37 130	63.17 108	62.43 321	80.50 239	61.10 167	85.16 60	64.00 127	84.34 224	83.01 159

pmmvs-eth3d		64.41 238	63.27 249	67.82 224	75.81 202	60.18 152	69.49 208	62.05 303	38.81 332	74.13 200	82.23 219	43.76 281	68.65 281	42.53 300	80.63 266	74.63 275

TR-MVS		64.59 233	63.54 246	67.73 225	75.75 203	50.83 211	63.39 289	70.29 252	49.33 251	71.55 238	74.55 304	50.94 241	78.46 170	40.43 314	75.69 301	73.89 281

tttt0517		69.46 174	67.79 200	74.46 107	75.34 204	52.72 200	75.05 136	63.27 297	54.69 183	78.87 127	84.37 185	26.63 374	81.15 120	63.95 128	87.93 168	89.51 25

cascas		64.59 233	62.77 254	70.05 188	75.27 205	50.02 219	61.79 299	71.61 230	42.46 305	63.68 314	68.89 349	49.33 252	80.35 138	47.82 268	84.05 227	79.78 223

API-MVS		70.97 156	71.51 157	69.37 196	75.20 206	55.94 179	80.99 61	76.84 188	62.48 113	71.24 242	77.51 283	61.51 159	80.96 131	52.04 228	85.76 201	71.22 306

EIA-MVS		68.59 188	67.16 207	72.90 143	75.18 207	55.64 183	69.39 210	81.29 106	52.44 213	64.53 303	70.69 333	60.33 174	82.30 103	54.27 216	76.31 298	80.75 206

PAPR		69.20 178	68.66 187	70.82 172	75.15 208	47.77 246	75.31 134	81.11 111	49.62 250	66.33 293	79.27 259	61.53 158	82.96 93	48.12 264	81.50 257	81.74 187

MVSFormer		69.93 167	69.03 179	72.63 152	74.93 209	59.19 157	83.98 36	75.72 198	52.27 214	63.53 317	76.74 288	43.19 284	80.56 134	72.28 67	78.67 285	78.14 246

lupinMVS		63.36 246	61.49 262	68.97 206	74.93 209	59.19 157	65.80 263	64.52 288	34.68 352	63.53 317	74.25 309	43.19 284	70.62 266	53.88 220	78.67 285	77.10 259

nrg030		74.87 103	75.99 90	71.52 167	74.90 211	49.88 226	74.10 154	82.58 87	54.55 187	83.50 75	89.21 90	71.51 65	75.74 209	61.24 150	92.34 79	88.94 37

TAPA-MVS	65.27 12	75.16 94	74.29 107	77.77 72	74.86 212	68.08 77	77.89 101	84.04 68	55.15 176	76.19 173	83.39 198	66.91 106	80.11 145	60.04 166	90.14 126	85.13 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
RPSCF		75.76 85	74.37 105	79.93 40	74.81 213	77.53 16	77.53 105	79.30 149	59.44 133	78.88 126	89.80 80	71.26 69	73.09 238	57.45 183	80.89 260	89.17 31

EI-MVSNet-Vis-set		72.78 135	71.87 148	75.54 99	74.77 214	59.02 163	72.24 165	71.56 232	63.92 96	78.59 128	71.59 328	66.22 117	78.60 166	67.58 95	80.32 267	89.00 35

v1240		73.06 125	73.14 127	72.84 145	74.74 215	47.27 254	71.88 178	81.11 111	51.80 221	82.28 89	84.21 187	56.22 216	82.34 102	68.82 83	87.17 184	88.91 38

v1921920		72.96 131	72.98 132	72.89 144	74.67 216	47.58 249	71.92 176	80.69 120	51.70 223	81.69 98	83.89 192	56.58 214	82.25 104	68.34 86	87.36 175	88.82 40

EI-MVSNet-UG-set		72.63 138	71.68 152	75.47 100	74.67 216	58.64 168	72.02 170	71.50 233	63.53 102	78.58 130	71.39 331	65.98 118	78.53 167	67.30 104	80.18 269	89.23 29

Fast-Effi-MVS+		68.81 183	68.30 190	70.35 180	74.66 218	48.61 234	66.06 258	78.32 168	50.62 238	71.48 240	75.54 295	68.75 89	79.59 152	50.55 241	78.73 284	82.86 163

v1192		73.40 116	73.42 120	73.32 129	74.65 219	48.67 233	72.21 166	81.73 98	52.76 211	81.85 92	84.56 182	57.12 208	82.24 105	68.58 84	87.33 177	89.06 33

v144192		72.99 129	73.06 130	72.77 146	74.58 220	47.48 250	71.90 177	80.44 128	51.57 224	81.46 100	84.11 189	58.04 200	82.12 106	67.98 92	87.47 173	88.70 43

MAR-MVS		67.72 200	66.16 217	72.40 157	74.45 221	64.99 107	74.87 138	77.50 181	48.67 257	65.78 297	68.58 353	57.01 211	77.79 188	46.68 277	81.92 246	74.42 277
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
v10		75.69 86	76.20 87	74.16 114	74.44 222	48.69 232	75.84 132	82.93 81	59.02 138	85.92 41	89.17 93	58.56 191	82.74 96	70.73 73	89.14 150	91.05 15

canonicalmvs		72.29 144	73.38 122	69.04 203	74.23 223	47.37 252	73.93 156	83.18 76	54.36 188	76.61 162	81.64 227	72.03 61	75.34 213	57.12 185	87.28 179	84.40 118

Anonymous202405211		66.02 219	66.89 213	63.43 263	74.22 224	38.14 327	59.00 317	66.13 272	63.33 107	69.76 260	85.95 169	51.88 234	70.50 268	44.23 292	87.52 171	81.64 188

Effi-MVS+		72.10 145	72.28 145	71.58 165	74.21 225	50.33 215	74.72 145	82.73 83	62.62 111	70.77 246	76.83 287	69.96 81	80.97 128	60.20 161	78.43 287	83.45 144

FE-MVS		68.29 193	66.96 212	72.26 160	74.16 226	54.24 191	77.55 104	73.42 215	57.65 152	72.66 220	84.91 179	32.02 347	81.49 115	48.43 260	81.85 248	81.04 195

v1144		73.29 119	73.39 121	73.01 136	74.12 227	48.11 239	72.01 171	81.08 114	53.83 202	81.77 94	84.68 180	58.07 199	81.91 109	68.10 88	86.86 186	88.99 36

FA-MVS(test-final)		71.27 151	71.06 161	71.92 163	73.96 228	52.32 204	76.45 118	76.12 193	59.07 137	74.04 204	86.18 159	52.18 233	79.43 154	59.75 170	81.76 250	84.03 126

EI-MVSNet		69.61 172	69.01 180	71.41 169	73.94 229	49.90 222	71.31 187	71.32 238	58.22 143	75.40 181	70.44 334	58.16 194	75.85 205	62.51 141	79.81 273	88.48 44

CVMVSNet		59.21 283	58.44 286	61.51 281	73.94 229	47.76 247	71.31 187	64.56 287	26.91 379	60.34 333	70.44 334	36.24 327	67.65 288	53.57 222	68.66 352	69.12 325

IterMVS-LS		73.01 127	73.12 129	72.66 150	73.79 231	49.90 222	71.63 181	78.44 166	58.22 143	80.51 112	86.63 146	58.15 195	79.62 150	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.
iter_conf_final		68.69 186	67.00 211	73.76 121	73.68 232	52.33 203	75.96 129	73.54 213	50.56 239	69.90 257	82.85 210	24.76 383	83.73 78	65.40 116	86.33 195	85.22 87

alignmvs		70.54 160	71.00 162	69.15 202	73.50 233	48.04 242	69.85 206	79.62 141	53.94 201	76.54 165	82.00 220	59.00 187	74.68 222	57.32 184	87.21 182	84.72 102

Fast-Effi-MVS+-dtu		70.00 165	68.74 185	73.77 120	73.47 234	64.53 111	71.36 185	78.14 173	55.81 171	68.84 274	74.71 303	65.36 126	75.75 208	52.00 229	79.00 281	81.03 196

v8		75.07 96	75.64 94	73.35 127	73.42 235	47.46 251	75.20 135	81.45 103	60.05 128	85.64 45	89.26 88	58.08 198	81.80 111	69.71 81	87.97 167	90.79 19

tfpnnormal		66.48 215	67.93 196	62.16 276	73.40 236	36.65 336	63.45 288	64.99 282	55.97 168	72.82 219	87.80 124	57.06 210	69.10 279	48.31 262	87.54 170	80.72 208

IterMVS-SCA-FT		67.68 201	66.07 219	72.49 155	73.34 237	58.20 170	63.80 285	65.55 278	48.10 260	76.91 152	82.64 215	45.20 271	78.84 162	61.20 151	77.89 293	80.44 215

VNet		64.01 243	65.15 231	60.57 290	73.28 238	35.61 346	57.60 327	67.08 267	54.61 185	66.76 292	83.37 200	56.28 215	66.87 299	42.19 302	85.20 209	79.23 232

3Dnovator	65.95 11	71.50 150	71.22 160	72.34 158	73.16 239	63.09 121	78.37 95	78.32 168	57.67 150	72.22 228	84.61 181	54.77 219	78.47 169	60.82 157	81.07 259	75.45 268

GBi-Net		68.30 191	68.79 182	66.81 233	73.14 240	40.68 306	71.96 173	73.03 216	54.81 178	74.72 188	90.36 67	48.63 259	75.20 215	47.12 271	85.37 203	84.54 112

test1		68.30 191	68.79 182	66.81 233	73.14 240	40.68 306	71.96 173	73.03 216	54.81 178	74.72 188	90.36 67	48.63 259	75.20 215	47.12 271	85.37 203	84.54 112

FMVSNet2		67.48 203	68.21 193	65.29 245	73.14 240	38.94 319	68.81 219	71.21 244	54.81 178	76.73 159	86.48 151	48.63 259	74.60 223	47.98 266	86.11 198	82.35 175

thisisatest0530		67.05 211	65.16 229	72.73 149	73.10 243	50.55 212	71.26 189	63.91 293	50.22 243	74.46 195	80.75 235	26.81 373	80.25 141	59.43 172	86.50 193	87.37 54

pm-mvs1		68.40 189	69.85 171	64.04 256	73.10 243	39.94 312	64.61 278	70.50 250	55.52 173	73.97 205	89.33 86	63.91 137	68.38 283	49.68 247	88.02 165	83.81 131

pmmvs4		60.78 271	59.04 280	66.00 242	73.06 245	57.67 172	64.53 279	60.22 309	36.91 341	65.96 294	77.27 284	39.66 307	68.54 282	38.87 322	74.89 309	71.80 300

SDMVSNet		66.36 217	67.85 199	61.88 278	73.04 246	46.14 265	58.54 321	71.36 237	51.42 227	68.93 270	82.72 213	65.62 122	62.22 322	54.41 213	84.67 216	77.28 255

sd_testset		63.55 244	65.38 225	58.07 306	73.04 246	38.83 321	57.41 328	65.44 279	51.42 227	68.93 270	82.72 213	63.76 138	58.11 335	41.05 310	84.67 216	77.28 255

v2v482		72.55 141	72.58 139	72.43 156	72.92 248	46.72 258	71.41 184	79.13 151	55.27 174	81.17 104	85.25 176	55.41 218	81.13 121	67.25 105	85.46 202	89.43 26

casdiffmvs_mvg		75.26 92	76.18 88	72.52 153	72.87 249	49.47 227	72.94 161	84.71 50	59.49 132	80.90 109	88.81 104	70.07 79	79.71 149	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		54.39 307	56.12 303	49.20 342	72.57 250	30.91 370	59.98 312	48.43 362	41.66 308	55.94 354	83.86 193	41.19 296	50.42 347	26.05 382	75.38 306	66.27 340

Patchmatch-RL test		59.95 278	59.12 279	62.44 273	72.46 251	54.61 189	59.63 314	47.51 365	41.05 315	74.58 193	74.30 308	31.06 356	65.31 307	51.61 231	79.85 272	67.39 332

CL-MVSNet_self_test		62.44 258	63.40 247	59.55 297	72.34 252	32.38 362	56.39 332	64.84 284	51.21 232	67.46 287	81.01 232	50.75 242	63.51 317	38.47 327	88.12 163	82.75 166

Vis-MVSNet (Re-imp)		62.74 255	63.21 250	61.34 284	72.19 253	31.56 366	67.31 244	53.87 340	53.60 204	69.88 258	83.37 200	40.52 301	70.98 264	41.40 308	86.78 189	81.48 190

thres100view900		61.17 268	61.09 265	61.39 283	72.14 254	35.01 349	65.42 269	56.99 324	55.23 175	70.71 247	79.90 249	32.07 345	72.09 251	35.61 348	81.73 251	77.08 260

ab-mvs		64.11 241	65.13 232	61.05 286	71.99 255	38.03 330	67.59 235	68.79 259	49.08 255	65.32 299	86.26 157	58.02 201	66.85 301	39.33 318	79.79 275	78.27 243

thres600view7		61.82 262	61.38 263	63.12 265	71.81 256	34.93 350	64.64 276	56.99 324	54.78 182	70.33 251	79.74 251	32.07 345	72.42 248	38.61 325	83.46 235	82.02 181

QAPM		69.18 179	69.26 175	68.94 207	71.61 257	52.58 202	80.37 71	78.79 159	49.63 249	73.51 208	85.14 177	53.66 226	79.12 157	55.11 204	75.54 303	75.11 273

baseline		73.10 122	73.96 112	70.51 177	71.46 258	46.39 263	72.08 169	84.40 58	55.95 169	76.62 161	86.46 152	67.20 102	78.03 184	64.22 125	87.27 180	87.11 61

casdiffmvs		73.06 125	73.84 113	70.72 173	71.32 259	46.71 259	70.93 193	84.26 61	55.62 172	77.46 145	87.10 127	67.09 104	77.81 187	63.95 128	86.83 188	87.64 51
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmvis_n_1920		72.36 142	72.49 140	71.96 162	71.29 260	64.06 114	72.79 162	81.82 96	40.23 324	81.25 103	81.04 231	70.62 75	68.69 280	69.74 80	83.60 234	83.14 154

Anonymous20231206		54.13 308	55.82 305	49.04 345	70.89 261	35.96 342	51.73 354	50.87 354	34.86 348	62.49 320	79.22 260	42.52 290	44.29 373	27.95 379	81.88 247	66.88 336

fmvsm_s_conf0.1_n_a		67.37 206	66.36 215	70.37 179	70.86 262	61.17 137	74.00 155	57.18 323	40.77 319	68.83 275	80.88 233	63.11 141	67.61 290	66.94 106	74.72 310	82.33 178

tfpn200view9		60.35 275	59.97 274	61.51 281	70.78 263	35.35 347	63.27 291	57.47 317	53.00 209	68.31 278	77.09 285	32.45 342	72.09 251	35.61 348	81.73 251	77.08 260

thres400		60.77 272	59.97 274	63.15 264	70.78 263	35.35 347	63.27 291	57.47 317	53.00 209	68.31 278	77.09 285	32.45 342	72.09 251	35.61 348	81.73 251	82.02 181

MSDG		67.47 204	67.48 204	67.46 226	70.70 265	54.69 188	66.90 250	78.17 171	60.88 123	70.41 249	74.76 301	61.22 165	73.18 237	47.38 270	76.87 295	74.49 276

test_yl		65.11 225	65.09 233	65.18 246	70.59 266	40.86 304	63.22 293	72.79 219	57.91 146	68.88 272	79.07 265	42.85 287	74.89 219	45.50 286	84.97 211	79.81 221

DCV-MVSNet		65.11 225	65.09 233	65.18 246	70.59 266	40.86 304	63.22 293	72.79 219	57.91 146	68.88 272	79.07 265	42.85 287	74.89 219	45.50 286	84.97 211	79.81 221

test_fmvsm_n_1920		69.63 170	68.45 188	73.16 131	70.56 268	65.86 98	70.26 201	78.35 167	37.69 336	74.29 197	78.89 267	61.10 167	68.10 285	65.87 113	79.07 280	85.53 83

OpenMVS	62.51 15	68.76 184	68.75 184	68.78 212	70.56 268	53.91 194	78.29 96	77.35 182	48.85 256	70.22 252	83.52 196	52.65 231	76.93 197	55.31 203	81.99 245	75.49 267

DELS-MVS		68.83 182	68.31 189	70.38 178	70.55 270	48.31 235	63.78 286	82.13 90	54.00 198	68.96 268	75.17 299	58.95 188	80.06 146	58.55 177	82.74 240	82.76 165
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
LCM-MVSNet-Re		69.10 180	71.57 156	61.70 279	70.37 271	34.30 355	61.45 300	79.62 141	56.81 159	89.59 8	88.16 120	68.44 92	72.94 239	42.30 301	87.33 177	77.85 252

patch_mono-2		62.73 256	64.08 240	58.68 302	70.36 272	55.87 180	60.84 306	64.11 292	41.23 312	64.04 308	78.22 274	60.00 176	48.80 352	54.17 217	83.71 232	71.37 303

SCA		58.57 288	58.04 289	60.17 293	70.17 273	41.07 303	65.19 271	53.38 346	43.34 302	61.00 330	73.48 315	45.20 271	69.38 276	40.34 315	70.31 343	70.05 315

ET-MVSNet_ETH3D		63.32 247	60.69 270	71.20 171	70.15 274	55.66 182	65.02 273	64.32 289	43.28 303	68.99 267	72.05 326	25.46 380	78.19 182	54.16 218	82.80 239	79.74 224

APD_test1		75.04 97	75.38 98	74.02 117	69.89 275	70.15 62	76.46 117	79.71 140	65.50 75	82.99 80	88.60 109	66.94 105	72.35 249	59.77 169	88.54 158	79.56 225

iter_conf05		67.34 207	65.62 222	72.50 154	69.82 276	47.06 256	72.19 167	76.86 187	45.32 284	72.86 217	82.85 210	20.53 390	83.73 78	61.13 153	89.02 154	86.70 65

PVSNet_BlendedMVS		65.38 223	64.30 237	68.61 213	69.81 277	49.36 228	65.60 267	78.96 153	45.50 279	59.98 334	78.61 269	51.82 235	78.20 180	44.30 290	84.11 226	78.27 243

PVSNet_Blended		62.90 253	61.64 259	66.69 236	69.81 277	49.36 228	61.23 303	78.96 153	42.04 306	59.98 334	68.86 350	51.82 235	78.20 180	44.30 290	77.77 294	72.52 292

OpenMVS_ROB	54.93 17	63.23 249	63.28 248	63.07 266	69.81 277	45.34 270	68.52 226	67.14 266	43.74 296	70.61 248	79.22 260	47.90 263	72.66 242	48.75 255	73.84 322	71.21 307

test_fmvsmconf0.01_n		73.91 108	73.64 118	74.71 104	69.79 280	66.25 93	75.90 130	79.90 138	46.03 276	76.48 167	85.02 178	67.96 99	73.97 231	74.47 49	87.22 181	83.90 129

fmvsm_s_conf0.5_n_a		67.00 212	65.95 221	70.17 184	69.72 281	61.16 138	73.34 158	56.83 326	40.96 316	68.36 277	80.08 247	62.84 142	67.57 291	66.90 108	74.50 314	81.78 186

FMVSNet3		65.00 228	65.16 229	64.52 251	69.47 282	37.56 334	66.63 253	70.38 251	51.55 225	74.72 188	83.27 205	37.89 319	74.44 225	47.12 271	85.37 203	81.57 189

MS-PatchMatch		55.59 301	54.89 310	57.68 308	69.18 283	49.05 231	61.00 305	62.93 298	35.98 344	58.36 343	68.93 348	36.71 325	66.59 304	37.62 334	63.30 367	57.39 373

baseline1		57.82 292	58.36 288	56.19 313	69.17 284	30.76 371	62.94 295	55.21 334	46.04 275	63.83 312	78.47 270	41.20 295	63.68 315	39.44 317	68.99 350	74.13 278

v148		69.38 177	69.39 173	69.36 197	69.14 285	44.56 275	68.83 218	72.70 222	54.79 181	78.59 128	84.12 188	54.69 220	76.74 202	59.40 173	82.20 243	86.79 63

test_fmvsmconf0.1_n		73.26 120	72.82 135	74.56 106	69.10 286	66.18 95	74.65 148	79.34 148	45.58 278	75.54 178	83.91 191	67.19 103	73.88 234	73.26 57	86.86 186	83.63 137

fmvsm_s_conf0.1_n		66.60 213	65.54 223	69.77 192	68.99 287	59.15 160	72.12 168	56.74 328	40.72 321	68.25 280	80.14 246	61.18 166	66.92 297	67.34 103	74.40 315	83.23 152

Syy-MVS		54.13 308	55.45 308	50.18 336	68.77 288	23.59 388	55.02 341	44.55 372	43.80 293	58.05 345	64.07 365	46.22 266	58.83 331	46.16 280	72.36 329	68.12 328

myMVS_eth3d		50.36 331	50.52 336	49.88 337	68.77 288	22.69 390	55.02 341	44.55 372	43.80 293	58.05 345	64.07 365	14.16 401	58.83 331	33.90 357	72.36 329	68.12 328

test_fmvsmconf_n		72.91 132	72.40 143	74.46 107	68.62 290	66.12 96	74.21 153	78.80 158	45.64 277	74.62 192	83.25 206	66.80 111	73.86 235	72.97 60	86.66 192	83.39 145

CANet_DTU		64.04 242	63.83 242	64.66 249	68.39 291	42.97 290	73.45 157	74.50 209	52.05 218	54.78 359	75.44 298	43.99 279	70.42 270	53.49 223	78.41 288	80.59 212

EU-MVSNet		60.82 270	60.80 269	60.86 289	68.37 292	41.16 301	72.27 164	68.27 263	26.96 377	69.08 265	75.71 293	32.09 344	67.44 292	55.59 201	78.90 282	73.97 279

PVSNet	43.83 21	51.56 325	51.17 328	52.73 326	68.34 293	38.27 325	48.22 362	53.56 344	36.41 342	54.29 362	64.94 364	34.60 331	54.20 344	30.34 367	69.87 346	65.71 343

EPNet		69.10 180	67.32 205	74.46 107	68.33 294	61.27 136	77.56 103	63.57 295	60.95 122	56.62 351	82.75 212	51.53 238	81.24 119	54.36 215	90.20 123	80.88 202
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n		66.34 218	65.27 226	69.57 195	68.20 295	59.14 162	71.66 180	56.48 329	40.92 317	67.78 282	79.46 255	61.23 163	66.90 298	67.39 99	74.32 318	82.66 169

IB-MVS	49.67 18	59.69 280	56.96 296	67.90 221	68.19 296	50.30 216	61.42 301	65.18 281	47.57 267	55.83 355	67.15 360	23.77 386	79.60 151	43.56 296	79.97 271	73.79 282
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		60.62 273	59.97 274	62.58 272	68.13 297	47.28 253	68.59 224	73.96 211	32.19 361	59.94 336	68.86 350	50.48 244	77.64 191	41.85 305	75.74 300	62.83 356

eth_miper_zixun_eth		69.42 175	68.73 186	71.50 168	67.99 298	46.42 261	67.58 236	78.81 156	50.72 237	78.13 135	80.34 241	50.15 247	80.34 139	60.18 162	84.65 218	87.74 50

TinyColmap		67.98 196	69.28 174	64.08 254	67.98 299	46.82 257	70.04 202	75.26 202	53.05 208	77.36 146	86.79 136	59.39 183	72.59 246	45.64 284	88.01 166	72.83 289

EPNet_dtu		58.93 285	58.52 284	60.16 294	67.91 300	47.70 248	69.97 203	58.02 315	49.73 248	47.28 380	73.02 320	38.14 315	62.34 320	36.57 341	85.99 199	70.43 313
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		57.55 293	57.02 295	59.17 298	67.89 301	34.93 350	58.91 319	57.25 321	50.24 242	64.01 309	71.46 330	32.49 341	71.39 261	31.31 364	79.57 277	71.19 308

our_test_3		56.46 296	56.51 299	56.30 312	67.70 302	39.66 314	55.36 340	52.34 351	40.57 323	63.85 311	69.91 341	40.04 304	58.22 334	43.49 297	75.29 308	71.03 310

ppachtmachnet_test		60.26 276	59.61 277	62.20 275	67.70 302	44.33 277	58.18 324	60.96 307	40.75 320	65.80 296	72.57 322	41.23 294	63.92 314	46.87 275	82.42 242	78.33 241

MVS_Test		69.84 168	70.71 165	67.24 228	67.49 304	43.25 288	69.87 205	81.22 110	52.69 212	71.57 237	86.68 142	62.09 153	74.51 224	66.05 110	78.74 283	83.96 127

thisisatest0515		60.48 274	57.86 290	68.34 216	67.25 305	46.42 261	60.58 309	62.14 300	40.82 318	63.58 316	69.12 345	26.28 376	78.34 176	48.83 254	82.13 244	80.26 217

V42		71.06 153	70.83 164	71.72 164	67.25 305	47.14 255	65.94 259	80.35 131	51.35 229	83.40 76	83.23 207	59.25 185	78.80 163	65.91 112	80.81 263	89.23 29

GA-MVS		62.91 252	61.66 258	66.66 237	67.09 307	44.49 276	61.18 304	69.36 257	51.33 230	69.33 263	74.47 305	36.83 324	74.94 218	50.60 240	74.72 310	80.57 213

testf1		75.66 87	76.57 81	72.95 139	67.07 308	67.62 81	76.10 125	80.68 121	64.95 87	86.58 33	90.94 40	71.20 70	71.68 259	60.46 159	91.13 101	79.56 225

APD_test2		75.66 87	76.57 81	72.95 139	67.07 308	67.62 81	76.10 125	80.68 121	64.95 87	86.58 33	90.94 40	71.20 70	71.68 259	60.46 159	91.13 101	79.56 225

HY-MVS	49.31 19	57.96 291	57.59 292	59.10 300	66.85 310	36.17 340	65.13 272	65.39 280	39.24 329	54.69 361	78.14 276	44.28 278	67.18 296	33.75 358	70.79 339	73.95 280

CR-MVSNet		58.96 284	58.49 285	60.36 292	66.37 311	48.24 237	70.93 193	56.40 331	32.87 360	61.35 325	86.66 143	33.19 336	63.22 318	48.50 259	70.17 344	69.62 320

RPMNet		65.77 221	65.08 235	67.84 223	66.37 311	48.24 237	70.93 193	86.27 19	54.66 184	61.35 325	86.77 138	33.29 335	85.67 47	55.93 196	70.17 344	69.62 320

IterMVS		63.12 250	62.48 256	65.02 248	66.34 313	52.86 199	63.81 284	62.25 299	46.57 273	71.51 239	80.40 240	44.60 276	66.82 302	51.38 234	75.47 304	75.38 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
c3_l		69.82 169	69.89 170	69.61 194	66.24 314	43.48 284	68.12 231	79.61 143	51.43 226	77.72 141	80.18 245	54.61 222	78.15 183	63.62 133	87.50 172	87.20 58

tpm2		56.12 297	54.64 311	60.55 291	66.24 314	36.01 341	68.14 230	56.77 327	33.60 358	58.25 344	75.52 297	30.25 362	74.33 227	33.27 359	69.76 348	71.32 304

Anonymous20240521		63.55 244	66.07 219	55.99 314	66.18 316	44.04 279	68.77 222	68.80 258	46.99 270	72.57 221	85.84 170	39.87 305	50.22 348	53.40 226	92.23 81	73.71 283

Patchmtry		60.91 269	63.01 252	54.62 319	66.10 317	26.27 383	67.47 238	56.40 331	54.05 197	72.04 230	86.66 143	33.19 336	60.17 327	43.69 294	87.45 174	77.42 253

FMVSNet5		55.08 304	55.54 307	53.71 321	65.80 318	33.50 359	56.22 334	52.50 350	43.72 297	61.06 328	83.38 199	25.46 380	54.87 341	30.11 369	81.64 256	72.75 290

1314		59.83 279	58.86 282	62.74 271	65.71 319	44.78 274	68.59 224	72.63 223	33.54 359	61.05 329	67.29 359	43.62 282	71.26 262	49.49 249	67.84 357	72.19 297

MDTV_nov1_ep13			54.05 314		65.54 320	29.30 375	59.00 317	55.22 333	35.96 345	52.44 366	75.98 291	30.77 359	59.62 328	38.21 328	73.33 324

baseline2		55.57 302	52.74 319	64.05 255	65.26 321	44.11 278	62.38 296	54.43 338	39.03 330	51.21 370	67.35 358	33.66 334	72.45 247	37.14 337	64.22 365	75.60 266

USDC		62.80 254	63.10 251	61.89 277	65.19 322	43.30 287	67.42 239	74.20 210	35.80 346	72.25 227	84.48 184	45.67 268	71.95 255	37.95 331	84.97 211	70.42 314

tpm		50.60 329	52.42 322	45.14 359	65.18 323	26.29 382	60.30 310	43.50 374	37.41 338	57.01 348	79.09 264	30.20 364	42.32 378	32.77 361	66.36 360	66.81 338

PatchmatchNet		54.60 306	54.27 312	55.59 315	65.17 324	39.08 316	66.92 249	51.80 352	39.89 325	58.39 342	73.12 319	31.69 350	58.33 333	43.01 299	58.38 381	69.38 323
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_ehance_all_eth		68.36 190	68.16 195	68.98 205	65.14 325	43.34 286	67.07 246	78.92 155	49.11 254	76.21 172	77.72 280	53.48 227	77.92 186	61.16 152	84.59 220	85.68 82

cl____		68.26 195	68.26 191	68.29 217	64.98 326	43.67 282	65.89 260	74.67 206	50.04 246	76.86 155	82.42 217	48.74 257	75.38 211	60.92 156	89.81 133	85.80 80

DIV-MVS_self_test		68.27 194	68.26 191	68.29 217	64.98 326	43.67 282	65.89 260	74.67 206	50.04 246	76.86 155	82.43 216	48.74 257	75.38 211	60.94 155	89.81 133	85.81 76

tpm cat1		54.02 311	52.63 320	58.19 305	64.85 328	39.86 313	66.26 257	57.28 320	32.16 362	56.90 349	70.39 336	32.75 340	65.30 308	34.29 354	58.79 378	69.41 322

XXY-MVS		55.19 303	57.40 294	48.56 347	64.45 329	34.84 352	51.54 355	53.59 342	38.99 331	63.79 313	79.43 256	56.59 213	45.57 363	36.92 339	71.29 336	65.25 346

PatchT		53.35 313	56.47 300	43.99 364	64.19 330	17.46 395	59.15 315	43.10 376	52.11 217	54.74 360	86.95 132	29.97 365	49.98 349	43.62 295	74.40 315	64.53 353

D2MVS		62.58 257	61.05 266	67.20 229	63.85 331	47.92 243	56.29 333	69.58 255	39.32 327	70.07 255	78.19 275	34.93 330	72.68 241	53.44 224	83.74 230	81.00 198

mvs_anonymous		65.08 227	65.49 224	63.83 257	63.79 332	37.60 333	66.52 255	69.82 254	43.44 299	73.46 210	86.08 165	58.79 190	71.75 258	51.90 230	75.63 302	82.15 180

CostFormer		57.35 294	56.14 302	60.97 287	63.76 333	38.43 323	67.50 237	60.22 309	37.14 340	59.12 341	76.34 290	32.78 339	71.99 254	39.12 321	69.27 349	72.47 293

Gipuma		69.55 173	72.83 134	59.70 295	63.63 334	53.97 193	80.08 78	75.93 196	64.24 94	73.49 209	88.93 102	57.89 202	62.46 319	59.75 170	91.55 91	62.67 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
cl22		67.14 208	66.51 214	69.03 204	63.20 335	43.46 285	66.88 251	76.25 192	49.22 252	74.48 194	77.88 279	45.49 270	77.40 193	60.64 158	84.59 220	86.24 69

gg-mvs-nofinetune		55.75 299	56.75 298	52.72 327	62.87 336	28.04 378	68.92 216	41.36 386	71.09 41	50.80 372	92.63 12	20.74 389	66.86 300	29.97 370	72.41 328	63.25 355

gm-plane-assit					62.51 337	33.91 357			37.25 339		62.71 370		72.74 240	38.70 323

MVS-HIRNet		45.53 344	47.29 344	40.24 370	62.29 338	26.82 381	56.02 336	37.41 392	29.74 372	43.69 390	81.27 228	33.96 332	55.48 339	24.46 388	56.79 382	38.43 391

diffmvs		67.42 205	67.50 203	67.20 229	62.26 339	45.21 271	64.87 274	77.04 186	48.21 259	71.74 231	79.70 252	58.40 192	71.17 263	64.99 118	80.27 268	85.22 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CHOSEN 280x420		41.62 355	39.89 360	46.80 352	61.81 340	51.59 205	33.56 388	35.74 393	27.48 376	37.64 394	53.53 384	23.24 387	42.09 379	27.39 380	58.64 379	46.72 383

KD-MVS_self_test		66.38 216	67.51 202	62.97 268	61.76 341	34.39 354	58.11 325	75.30 201	50.84 236	77.12 148	85.42 173	56.84 212	69.44 275	51.07 236	91.16 98	85.08 92

MDA-MVSNet-bldmvs		62.34 259	61.73 257	64.16 252	61.64 342	49.90 222	48.11 363	57.24 322	53.31 207	80.95 106	79.39 257	49.00 255	61.55 324	45.92 282	80.05 270	81.03 196

miper_enhance_ethall		65.86 220	65.05 236	68.28 219	61.62 343	42.62 293	64.74 275	77.97 175	42.52 304	73.42 211	72.79 321	49.66 248	77.68 190	58.12 180	84.59 220	84.54 112

WTY-MVS		49.39 335	50.31 338	46.62 353	61.22 344	32.00 365	46.61 368	49.77 357	33.87 355	54.12 363	69.55 344	41.96 291	45.40 365	31.28 365	64.42 364	62.47 360

CMPMVS	48.73 20	61.54 266	60.89 267	63.52 261	61.08 345	51.55 206	68.07 232	68.00 264	33.88 354	65.87 295	81.25 229	37.91 318	67.71 287	49.32 251	82.60 241	71.31 305
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test-LLR		50.43 330	50.69 335	49.64 340	60.76 346	41.87 297	53.18 349	45.48 370	43.41 300	49.41 377	60.47 378	29.22 368	44.73 370	42.09 303	72.14 332	62.33 362

test-mter		48.56 337	48.20 342	49.64 340	60.76 346	41.87 297	53.18 349	45.48 370	31.91 366	49.41 377	60.47 378	18.34 393	44.73 370	42.09 303	72.14 332	62.33 362

GG-mvs-BLEND				52.24 328	60.64 348	29.21 376	69.73 207	42.41 379		45.47 383	52.33 387	20.43 391	68.16 284	25.52 386	65.42 362	59.36 369

tpmvs		55.84 298	55.45 308	57.01 310	60.33 349	33.20 360	65.89 260	59.29 313	47.52 268	56.04 353	73.60 314	31.05 357	68.06 286	40.64 313	64.64 363	69.77 318

miper_lstm_enhance		61.97 260	61.63 260	62.98 267	60.04 350	45.74 268	47.53 365	70.95 246	44.04 291	73.06 215	78.84 268	39.72 306	60.33 326	55.82 198	84.64 219	82.88 161

dmvs_re		49.91 334	50.77 334	47.34 349	59.98 351	38.86 320	53.18 349	53.58 343	39.75 326	55.06 358	61.58 374	36.42 326	44.40 372	29.15 377	68.23 353	58.75 370

PVSNet_0	36.71 22	41.12 356	40.78 359	42.14 366	59.97 352	40.13 311	40.97 377	42.24 383	30.81 370	44.86 386	49.41 390	40.70 300	45.12 367	23.15 389	34.96 393	41.16 389

dmvs_testset		45.26 345	47.51 343	38.49 373	59.96 353	14.71 397	58.50 322	43.39 375	41.30 311	51.79 369	56.48 382	39.44 309	49.91 351	21.42 391	55.35 387	50.85 378

new-patchmatchnet		52.89 315	55.76 306	44.26 363	59.94 354	6.31 401	37.36 385	50.76 355	41.10 313	64.28 306	79.82 250	44.77 274	48.43 356	36.24 344	87.61 169	78.03 248

test20.03		55.74 300	57.51 293	50.42 335	59.89 355	32.09 364	50.63 357	49.01 359	50.11 244	65.07 301	83.23 207	45.61 269	48.11 357	30.22 368	83.82 229	71.07 309

MVSTER		63.29 248	61.60 261	68.36 215	59.77 356	46.21 264	60.62 308	71.32 238	41.83 307	75.40 181	79.12 263	30.25 362	75.85 205	56.30 193	79.81 273	83.03 158

N_pmnet		52.06 321	51.11 329	54.92 316	59.64 357	71.03 53	37.42 384	61.62 306	33.68 356	57.12 347	72.10 323	37.94 317	31.03 391	29.13 378	71.35 335	62.70 357

test_vis1_n_1920		52.96 314	53.50 315	51.32 332	59.15 358	44.90 273	56.13 335	64.29 290	30.56 371	59.87 338	60.68 376	40.16 303	47.47 358	48.25 263	62.46 369	61.58 364

JIA-IIPM		54.03 310	51.62 324	61.25 285	59.14 359	55.21 185	59.10 316	47.72 363	50.85 235	50.31 376	85.81 171	20.10 392	63.97 313	36.16 345	55.41 386	64.55 352

LF4IMVS		67.50 202	67.31 206	68.08 220	58.86 360	61.93 127	71.43 183	75.90 197	44.67 289	72.42 224	80.20 243	57.16 206	70.44 269	58.99 175	86.12 197	71.88 299

UnsupCasMVSNet_bld		50.01 333	51.03 331	46.95 350	58.61 361	32.64 361	48.31 361	53.27 347	34.27 353	60.47 332	71.53 329	41.40 293	47.07 360	30.68 366	60.78 374	61.13 365

dp		44.09 351	44.88 353	41.72 369	58.53 362	23.18 389	54.70 344	42.38 381	34.80 349	44.25 388	65.61 362	24.48 385	44.80 369	29.77 371	49.42 389	57.18 374

testgi		54.00 312	56.86 297	45.45 357	58.20 363	25.81 385	49.05 359	49.50 358	45.43 282	67.84 281	81.17 230	51.81 237	43.20 377	29.30 373	79.41 278	67.34 334

wuyk23d		61.97 260	66.25 216	49.12 344	58.19 364	60.77 148	66.32 256	52.97 348	55.93 170	90.62 5	86.91 133	73.07 57	35.98 389	20.63 393	91.63 87	50.62 379

ANet_high		67.08 209	69.94 169	58.51 304	57.55 365	27.09 380	58.43 323	76.80 189	63.56 101	82.40 88	91.93 20	59.82 180	64.98 310	50.10 244	88.86 156	83.46 143

Patchmatch-test		47.93 338	49.96 339	41.84 367	57.42 366	24.26 387	48.75 360	41.49 385	39.30 328	56.79 350	73.48 315	30.48 361	33.87 390	29.29 374	72.61 327	67.39 332

test_vis1_n		51.27 327	50.41 337	53.83 320	56.99 367	50.01 220	56.75 330	60.53 308	25.68 381	59.74 339	57.86 381	29.40 367	47.41 359	43.10 298	63.66 366	64.08 354

new_pmnet		37.55 359	39.80 361	30.79 375	56.83 368	16.46 396	39.35 381	30.65 395	25.59 382	45.26 384	61.60 373	24.54 384	28.02 394	21.60 390	52.80 388	47.90 382

pmmvs3		46.71 341	45.09 351	51.55 331	56.76 369	48.25 236	55.78 338	39.53 390	24.13 386	50.35 375	63.40 367	15.90 398	51.08 346	29.29 374	70.69 341	55.33 376

sss		47.59 340	48.32 340	45.40 358	56.73 370	33.96 356	45.17 371	48.51 361	32.11 365	52.37 367	65.79 361	40.39 302	41.91 381	31.85 362	61.97 371	60.35 366

tpmrst		50.15 332	51.38 327	46.45 354	56.05 371	24.77 386	64.40 281	49.98 356	36.14 343	53.32 365	69.59 343	35.16 329	48.69 353	39.24 319	58.51 380	65.89 341

TESTMET0.1,1		45.17 346	44.93 352	45.89 356	56.02 372	38.31 324	53.18 349	41.94 384	27.85 374	44.86 386	56.47 383	17.93 394	41.50 382	38.08 330	68.06 354	57.85 371

ADS-MVSNet2		48.76 336	47.25 345	53.29 325	55.90 373	40.54 309	47.34 366	54.99 336	31.41 368	50.48 373	72.06 324	31.23 353	54.26 343	25.93 383	55.93 383	65.07 347

ADS-MVSNet		44.62 349	45.58 348	41.73 368	55.90 373	20.83 393	47.34 366	39.94 389	31.41 368	50.48 373	72.06 324	31.23 353	39.31 385	25.93 383	55.93 383	65.07 347

test0.0.03 1		47.72 339	48.31 341	45.93 355	55.53 375	29.39 374	46.40 369	41.21 387	43.41 300	55.81 356	67.65 355	29.22 368	43.77 376	25.73 385	69.87 346	64.62 351

UnsupCasMVSNet_eth		52.26 320	53.29 318	49.16 343	55.08 376	33.67 358	50.03 358	58.79 314	37.67 337	63.43 319	74.75 302	41.82 292	45.83 362	38.59 326	59.42 377	67.98 331

pmmvs5		52.49 319	52.58 321	52.21 329	54.99 377	32.38 362	55.45 339	53.84 341	32.15 363	55.49 357	74.81 300	38.08 316	57.37 338	34.02 355	74.40 315	66.88 336

DSMNet-mixed		43.18 354	44.66 354	38.75 372	54.75 378	28.88 377	57.06 329	27.42 397	13.47 393	47.27 381	77.67 281	38.83 312	39.29 386	25.32 387	60.12 376	48.08 381

MDA-MVSNet_test_wron		52.57 318	53.49 317	49.81 339	54.24 379	36.47 338	40.48 379	46.58 368	38.13 333	75.47 180	73.32 317	41.05 299	43.85 375	40.98 311	71.20 337	69.10 326

YYNet1		52.58 317	53.50 315	49.85 338	54.15 380	36.45 339	40.53 378	46.55 369	38.09 334	75.52 179	73.31 318	41.08 298	43.88 374	41.10 309	71.14 338	69.21 324

EPMVS		45.74 343	46.53 346	43.39 365	54.14 381	22.33 392	55.02 341	35.00 394	34.69 351	51.09 371	70.20 338	25.92 378	42.04 380	37.19 336	55.50 385	65.78 342

test_cas_vis1_n_1920		50.90 328	50.92 332	50.83 334	54.12 382	47.80 245	51.44 356	54.61 337	26.95 378	63.95 310	60.85 375	37.86 320	44.97 368	45.53 285	62.97 368	59.72 368

test_fmvs3		56.78 295	55.99 304	59.12 299	53.96 383	48.09 240	58.76 320	66.22 271	27.54 375	76.66 160	68.69 352	25.32 382	51.31 345	53.42 225	73.38 323	77.97 251

test_fmvs1_n		52.70 316	52.01 323	54.76 317	53.83 384	50.36 214	55.80 337	65.90 273	24.96 383	65.39 298	60.64 377	27.69 371	48.46 354	45.88 283	67.99 355	65.46 344

KD-MVS_2432*1600		52.05 322	51.58 325	53.44 323	52.11 385	31.20 367	44.88 372	64.83 285	41.53 309	64.37 304	70.03 339	15.61 399	64.20 311	36.25 342	74.61 312	64.93 349

miper_refine_blended		52.05 322	51.58 325	53.44 323	52.11 385	31.20 367	44.88 372	64.83 285	41.53 309	64.37 304	70.03 339	15.61 399	64.20 311	36.25 342	74.61 312	64.93 349

test_fmvs2		54.80 305	54.11 313	56.88 311	51.76 387	49.95 221	56.70 331	65.80 274	26.22 380	69.42 261	65.25 363	31.82 348	49.98 349	49.63 248	70.36 342	70.71 311

E-PMN		45.17 346	45.36 349	44.60 361	50.07 388	42.75 291	38.66 382	42.29 382	46.39 274	39.55 391	51.15 388	26.00 377	45.37 366	37.68 332	76.41 296	45.69 385

PMMVS		44.69 348	43.95 356	46.92 351	50.05 389	53.47 197	48.08 364	42.40 380	22.36 389	44.01 389	53.05 386	42.60 289	45.49 364	31.69 363	61.36 373	41.79 388

test_fmvs1		51.51 326	50.86 333	53.48 322	49.72 390	49.35 230	54.11 345	64.96 283	24.64 385	63.66 315	59.61 380	28.33 370	48.45 355	45.38 288	67.30 359	62.66 359

EMVS		44.61 350	44.45 355	45.10 360	48.91 391	43.00 289	37.92 383	41.10 388	46.75 272	38.00 393	48.43 391	26.42 375	46.27 361	37.11 338	75.38 306	46.03 384

mvsany_test3		43.76 353	41.01 357	52.01 330	48.09 392	57.74 171	42.47 376	23.85 400	23.30 388	64.80 302	62.17 372	27.12 372	40.59 383	29.17 376	48.11 390	57.69 372

mvsany_test1		37.88 357	35.74 362	44.28 362	47.28 393	49.90 222	36.54 386	24.37 399	19.56 392	45.76 382	53.46 385	32.99 338	37.97 388	26.17 381	35.52 392	44.99 387

test_vis3_rt		51.94 324	51.04 330	54.65 318	46.32 394	50.13 218	44.34 374	78.17 171	23.62 387	68.95 269	62.81 369	21.41 388	38.52 387	41.49 307	72.22 331	75.30 272

test_vis1_rt		46.70 342	45.24 350	51.06 333	44.58 395	51.04 209	39.91 380	67.56 265	21.84 391	51.94 368	50.79 389	33.83 333	39.77 384	35.25 351	61.50 372	62.38 361

MVE	27.91 23	36.69 360	35.64 363	39.84 371	43.37 396	35.85 344	19.49 390	24.61 398	24.68 384	39.05 392	62.63 371	38.67 314	27.10 395	21.04 392	47.25 391	56.56 375
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.74 358	40.87 358	28.36 376	42.41 397	5.35 402	24.61 389	27.75 396	32.15 363	47.85 379	70.27 337	35.85 328	29.51 393	19.08 394	67.85 356	50.22 380

test_f		43.79 352	45.63 347	38.24 374	42.29 398	38.58 322	34.76 387	47.68 364	22.22 390	67.34 288	63.15 368	31.82 348	30.60 392	39.19 320	62.28 370	45.53 386

DeepMVS_CX				11.83 378	15.51 399	13.86 398		11.25 403	5.76 394	20.85 396	26.46 393	17.06 397	9.22 397	9.69 397	13.82 396	12.42 393

test_method		19.26 361	19.12 365	19.71 377	9.09 400	1.91 404	7.79 392	53.44 345	1.42 395	10.27 397	35.80 392	17.42 396	25.11 396	12.44 395	24.38 395	32.10 392

tmp_tt		11.98 363	14.73 366	3.72 379	2.28 401	4.62 403	19.44 391	14.50 402	0.47 397	21.55 395	9.58 395	25.78 379	4.57 398	11.61 396	27.37 394	1.96 394

test123		4.43 366	5.78 369	0.39 381	0.97 402	0.28 405	46.33 370	0.45 404	0.31 398	0.62 399	1.50 398	0.61 404	0.11 400	0.56 398	0.63 397	0.77 396

testmvs		4.06 367	5.28 370	0.41 380	0.64 403	0.16 406	42.54 375	0.31 405	0.26 399	0.50 400	1.40 399	0.77 403	0.17 399	0.56 398	0.55 398	0.90 395

test_blank		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

eth-test2					0.00 404

eth-test					0.00 404

uanet_test		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

DCPMVS		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

cdsmvs_eth3d_5k		17.71 362	23.62 364	0.00 382	0.00 404	0.00 407	0.00 393	70.17 253	0.00 400	0.00 401	74.25 309	68.16 95	0.00 401	0.00 400	0.00 399	0.00 397

pcd_1.5k_mvsjas		5.20 365	6.93 368	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	62.39 149	0.00 401	0.00 400	0.00 399	0.00 397

sosnet-low-res		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

sosnet		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

uncertanet		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

Regformer		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

ab-mvs-re		5.62 364	7.50 367	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	67.46 356	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

uanet		0.00 368	0.00 371	0.00 382	0.00 404	0.00 407	0.00 393	0.00 406	0.00 400	0.00 401	0.00 400	0.00 405	0.00 401	0.00 400	0.00 399	0.00 397

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

WAC-MVS						22.69 390								36.10 346

PC_three_1452									46.98 271	81.83 93	86.28 155	66.55 115	84.47 71	63.31 138	90.78 114	83.49 139

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

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

GSMVS																70.05 315

sam_mvs1												31.41 351				70.05 315

sam_mvs												31.21 355

MTGPA								80.63 123

test_post1							66.63 253				2.08 396	30.66 360	59.33 329	40.34 315

test_post											1.99 397	30.91 358	54.76 342

patchmatchnet-post											68.99 346	31.32 352	69.38 276

MTMP							84.83 31	19.26 401

test9_res														72.12 69	91.37 93	77.40 254

agg_prior2														70.70 75	90.93 108	78.55 240

test_prior4						70.14 63	77.57 102

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

旧先验2							71.17 190		45.11 286	78.54 131			61.28 325	59.19 174

新几何2							71.33 186

无先验							74.82 139	70.94 247	47.75 266				76.85 200	54.47 211		72.09 298

原ACMM2							74.78 143

testdata2													67.30 293	48.34 261

segment_acmp												68.30 94

testdata1							68.34 229		57.24 156

plane_prior5								85.49 29					86.15 27	71.09 71	90.94 106	84.82 99

plane_prior4											89.11 95

plane_prior3						65.67 99			63.82 98	78.23 133

plane_prior2							82.74 51		65.45 76

plane_prior						65.18 104	80.06 79		61.88 117						89.91 132

n2								0.00 406

nn								0.00 406

door-mid								55.02 335

test11								82.71 84

door								52.91 349

HQP5-MVS						58.80 165

BP-MVS														67.38 101

HQP4-MVS										71.59 233			85.31 52			83.74 134

HQP3-MVS								84.12 65							89.16 147

HQP2-MVS												58.09 196

MDTV_nov1_ep13_2view						18.41 394	53.74 347		31.57 367	44.89 385		29.90 366		32.93 360		71.48 302

ACMMP++_ref															89.47 142

ACMMP++															91.96 83

Test By Simon												62.56 145

High-resolution multi-view benchmark