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		95.70 1	96.40 1	93.61 2	98.67 1	85.39 46	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 15	85.07 73	99.27 1	99.54 1

FOURS1					96.08 11	87.41 18	96.19 2	95.83 4	92.95 2	96.57 2

DTE-MVSNet		89.98 50	91.91 18	84.21 192	96.51 7	57.84 433	88.93 96	92.84 115	91.92 3	96.16 3	96.23 23	86.95 56	95.99 11	79.05 154	98.57 14	98.80 6

PS-CasMVS		90.06 46	91.92 16	84.47 181	96.56 6	58.83 422	89.04 94	92.74 119	91.40 5	96.12 4	96.06 28	87.23 52	95.57 42	79.42 149	98.74 5	99.00 2

LCM-MVSNet-Re		83.48 207	85.06 155	78.75 350	85.94 328	55.75 452	80.05 330	94.27 25	76.47 149	96.09 5	94.54 72	83.31 104	89.75 280	59.95 408	94.89 195	90.75 294

PEN-MVS		90.03 48	91.88 19	84.48 180	96.57 5	58.88 419	88.95 95	93.19 94	91.62 4	96.01 6	96.16 26	87.02 55	95.60 41	78.69 158	98.72 8	98.97 3

CP-MVSNet		89.27 65	90.91 46	84.37 182	96.34 8	58.61 425	88.66 103	92.06 142	90.78 6	95.67 7	95.17 50	81.80 139	95.54 45	79.00 155	98.69 9	98.95 4

reproduce_model		92.89 4	93.18 7	92.01 12	94.20 53	88.23 12	92.87 13	94.32 22	90.25 10	95.65 8	95.74 32	87.75 45	95.72 37	89.60 4	98.27 27	92.08 251

LTVRE_ROB	86.10 1	93.04 3	93.44 3	91.82 21	93.73 68	85.72 42	96.79 1	95.51 9	88.86 15	95.63 9	96.99 12	84.81 87	93.16 153	91.10 1	97.53 81	96.58 33
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
WR-MVS_H		89.91 53	91.31 35	85.71 144	96.32 9	62.39 346	89.54 84	93.31 88	90.21 11	95.57 10	95.66 36	81.42 144	95.90 16	80.94 128	98.80 2	98.84 5

reproduce-ours		92.86 5	93.22 5	91.76 22	94.39 45	87.71 14	92.40 28	94.38 20	89.82 12	95.51 11	95.49 41	89.64 22	95.82 27	89.13 6	98.26 29	91.76 262

our_new_method		92.86 5	93.22 5	91.76 22	94.39 45	87.71 14	92.40 28	94.38 20	89.82 12	95.51 11	95.49 41	89.64 22	95.82 27	89.13 6	98.26 29	91.76 262

OurMVSNet-221017-0		90.01 49	89.74 59	90.83 35	93.16 86	80.37 100	91.91 41	93.11 99	81.10 90	95.32 13	97.24 9	72.94 270	94.85 76	85.07 73	97.78 58	97.26 16

anonymousdsp		89.73 56	88.88 76	92.27 7	89.82 190	86.67 24	90.51 59	90.20 214	69.87 276	95.06 14	96.14 27	84.28 92	93.07 157	87.68 23	96.34 121	97.09 20

wuyk23d		75.13 372	79.30 303	62.63 509	75.56 505	75.18 168	80.89 315	73.10 456	75.06 176	94.76 15	95.32 44	87.73 47	52.85 545	34.16 543	97.11 91	59.85 541

ACMH	76.49 14	89.34 62	91.14 37	83.96 200	92.50 103	70.36 235	89.55 82	93.84 55	81.89 82	94.70 16	95.44 43	90.69 9	88.31 320	83.33 96	98.30 26	93.20 179
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		87.20 96	87.45 96	86.45 121	92.52 102	69.19 253	87.84 117	88.05 267	81.66 84	94.64 17	96.53 19	65.94 328	94.75 80	83.02 102	96.83 101	95.41 59

mvs_tets		89.78 55	89.27 66	91.30 28	93.51 72	84.79 53	89.89 73	90.63 193	70.00 275	94.55 18	96.67 16	87.94 42	93.59 135	84.27 88	95.97 140	95.52 57

lecture		92.43 8	93.50 2	89.21 65	94.43 43	79.31 111	92.69 19	95.72 7	88.48 21	94.43 19	95.73 33	91.34 4	94.68 82	90.26 3	98.44 19	93.63 156

jajsoiax		89.41 60	88.81 80	91.19 31	93.38 78	84.72 54	89.70 76	90.29 211	69.27 284	94.39 20	96.38 20	86.02 72	93.52 140	83.96 90	95.92 146	95.34 61

test_0402		88.65 74	89.58 63	85.88 139	92.55 101	72.22 205	84.01 208	89.44 236	88.63 19	94.38 21	95.77 31	86.38 67	93.59 135	79.84 140	95.21 177	91.82 260

RoMa-HiRes		85.97 122	85.47 144	87.48 100	91.66 134	89.37 4	87.18 126	83.89 349	71.47 252	94.29 22	91.35 220	75.59 220	81.39 424	76.88 193	96.92 97	91.68 267

UniMVSNet_ETH3D		89.12 68	90.72 49	84.31 189	97.00 2	64.33 314	89.67 79	88.38 258	88.84 16	94.29 22	97.57 7	90.48 14	91.26 212	72.57 276	97.65 69	97.34 15

v7n		90.13 42	90.96 44	87.65 99	91.95 122	71.06 225	89.99 69	93.05 103	86.53 34	94.29 22	96.27 22	82.69 112	94.08 110	86.25 52	97.63 70	97.82 8

test_djsdf		89.62 57	89.01 70	91.45 25	92.36 107	82.98 72	91.98 39	90.08 217	71.54 249	94.28 25	96.54 18	81.57 142	94.27 97	86.26 50	96.49 114	97.09 20

PS-MVSNAJss		88.31 78	87.90 90	89.56 59	93.31 81	77.96 128	87.94 115	91.97 145	70.73 264	94.19 26	96.67 16	76.94 203	94.57 88	83.07 100	96.28 123	96.15 37

SED-MVS		90.46 39	91.64 22	86.93 111	94.18 54	72.65 191	90.47 60	93.69 64	83.77 60	94.11 27	94.27 84	90.28 15	95.84 25	86.03 56	97.92 51	92.29 240

test_241102_ONE					94.18 54	72.65 191		93.69 64	83.62 63	94.11 27	93.78 116	90.28 15	95.50 50

DPE-MVS		90.53 38	91.08 39	88.88 70	93.38 78	78.65 118	89.15 93	94.05 42	84.68 51	93.90 29	94.11 96	88.13 38	96.30 4	84.51 86	97.81 57	91.70 266
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		91.22 25	91.92 16	89.14 67	92.97 90	78.04 125	92.84 16	94.14 37	83.33 67	93.90 29	95.73 33	88.77 28	96.41 2	87.60 26	97.98 47	92.98 195
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMH+	77.89 11	90.73 33	91.50 26	88.44 82	93.00 89	76.26 152	89.65 80	95.55 8	87.72 26	93.89 31	94.94 55	91.62 3	93.44 144	78.35 162	98.76 3	95.61 56

TestfortrainingZip a		91.12 29	92.04 14	88.36 86	94.38 47	76.05 159	92.12 33	93.73 59	85.28 43	93.85 32	94.84 58	88.66 29	95.18 66	87.89 18	97.59 77	93.84 139

ACMM	79.39 9	90.65 34	90.99 43	89.63 57	95.03 33	83.53 65	89.62 81	93.35 84	79.20 116	93.83 33	93.60 125	90.81 8	92.96 160	85.02 76	98.45 18	92.41 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SR-MVS-dyc-post		92.41 9	92.41 10	92.39 4	94.13 59	88.95 7	92.87 13	94.16 33	88.75 17	93.79 34	94.43 77	88.83 27	95.51 48	87.16 37	97.60 74	92.73 204

RE-MVS-def			92.61 8		94.13 59	88.95 7	92.87 13	94.16 33	88.75 17	93.79 34	94.43 77	90.64 11		87.16 37	97.60 74	92.73 204

DVP-MVS		90.06 46	91.32 34	86.29 124	94.16 57	72.56 197	90.54 57	91.01 181	83.61 64	93.75 36	94.65 66	89.76 19	95.78 33	86.42 46	97.97 48	90.55 305
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
test_0728_THIRD									85.33 41	93.75 36	94.65 66	87.44 50	95.78 33	87.41 30	98.21 33	92.98 195

test0726					94.16 57	72.56 197	90.63 54	93.90 49	83.61 64	93.75 36	94.49 74	89.76 19

LPG-MVS_test		91.47 21	91.68 21	90.82 36	94.75 40	81.69 83	90.00 67	94.27 25	82.35 77	93.67 39	94.82 61	91.18 5	95.52 46	85.36 68	98.73 6	95.23 67

LGP-MVS_train				90.82 36	94.75 40	81.69 83		94.27 25	82.35 77	93.67 39	94.82 61	91.18 5	95.52 46	85.36 68	98.73 6	95.23 67

DVP-MVS++		90.07 45	91.09 38	87.00 108	91.55 141	72.64 193	96.19 2	94.10 40	85.33 41	93.49 41	94.64 69	81.12 147	95.88 17	87.41 30	95.94 144	92.48 221

test_241102_TWO								93.71 60	83.77 60	93.49 41	94.27 84	89.27 24	95.84 25	86.03 56	97.82 56	92.04 253

test_one_0601					93.85 66	73.27 183		94.11 39	86.57 33	93.47 43	94.64 69	88.42 30

aaatest				88.50 80	94.38 47	76.12 156	92.12 33	93.85 53	77.53 142	93.24 44	93.18 140		95.85 23	84.99 77	97.69 66	93.54 166

MED-MVS		90.78 32	91.50 26	88.60 78	94.38 47	76.12 156	92.12 33	93.85 53	85.28 43	93.24 44	94.84 58	87.06 54	95.85 23	84.99 77	97.78 58	93.84 139

SR-MVS		92.23 10	92.34 11	91.91 16	94.89 37	87.85 13	92.51 25	93.87 52	88.20 23	93.24 44	94.02 102	90.15 17	95.67 39	86.82 42	97.34 85	92.19 246

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 24	93.02 88	85.17 48	92.47 27	95.05 14	87.65 27	93.21 47	94.39 82	90.09 18	95.08 70	86.67 44	97.60 74	94.18 121

testf1		89.30 63	89.12 67	89.84 52	88.67 225	85.64 43	90.61 55	93.17 95	86.02 37	93.12 48	95.30 45	84.94 84	89.44 286	74.12 241	96.10 134	94.45 106

APD_test2		89.30 63	89.12 67	89.84 52	88.67 225	85.64 43	90.61 55	93.17 95	86.02 37	93.12 48	95.30 45	84.94 84	89.44 286	74.12 241	96.10 134	94.45 106

Anonymous20231211		88.40 76	89.62 62	84.73 171	90.46 174	65.27 301	88.86 97	93.02 107	87.15 29	93.05 50	97.10 10	82.28 125	92.02 186	76.70 194	97.99 45	96.88 26

RoMa-SfM		83.52 205	82.69 226	86.00 135	90.77 166	89.30 5	85.98 155	81.47 385	65.77 346	92.99 51	89.25 305	69.55 303	78.65 448	72.01 280	96.45 117	90.04 320

PMatch-Up-SfM		81.93 250	80.09 290	87.42 102	89.08 210	86.10 34	81.31 300	83.35 358	67.64 318	92.96 52	90.69 253	45.71 481	85.82 384	75.20 223	94.89 195	90.35 310

MP-MVS-pluss		90.81 31	91.08 39	89.99 49	95.97 13	79.88 103	88.13 110	94.51 19	75.79 163	92.94 53	94.96 54	88.36 32	95.01 72	90.70 2	98.40 21	95.09 74
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SD-MVS		88.96 70	89.88 56	86.22 128	91.63 135	77.07 142	89.82 74	93.77 57	78.90 120	92.88 54	92.29 183	86.11 70	90.22 257	86.24 53	97.24 88	91.36 277
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
ACMP	79.16 10	90.54 37	90.60 52	90.35 44	94.36 50	80.98 92	89.16 92	94.05 42	79.03 119	92.87 55	93.74 119	90.60 12	95.21 64	82.87 104	98.76 3	94.87 80
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
TDRefinement		93.52 2	93.39 4	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 56	96.29 21	88.16 37	94.17 107	86.07 55	98.48 17	97.22 18

SMA-MVS		90.31 40	90.48 53	89.83 54	95.31 29	79.52 110	90.98 51	93.24 92	75.37 173	92.84 57	95.28 47	85.58 79	96.09 7	87.92 17	97.76 61	93.88 137
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
test_part2					93.86 65	77.77 130				92.84 57

v10		86.54 108	87.10 102	84.84 165	88.16 246	63.28 325	86.64 141	92.20 137	75.42 172	92.81 59	94.50 73	74.05 249	94.06 111	83.88 91	96.28 123	97.17 19

dcpmvs_2		84.23 176	85.14 153	81.50 284	88.61 229	61.98 354	82.90 256	93.11 99	68.66 298	92.77 60	92.39 175	78.50 174	87.63 335	76.99 191	92.30 308	94.90 78

v8		86.22 114	86.83 111	84.36 184	87.82 254	62.35 348	86.42 145	91.33 168	76.78 148	92.73 61	94.48 75	73.41 262	93.72 126	83.10 99	95.41 169	97.01 23

nrg030		87.85 88	88.49 82	85.91 137	90.07 185	69.73 243	87.86 116	94.20 31	74.04 192	92.70 62	94.66 65	85.88 73	91.50 200	79.72 142	97.32 86	96.50 34

SteuartSystems-ACMMP		91.16 27	91.36 30	90.55 40	93.91 64	80.97 93	91.49 45	93.48 78	82.82 74	92.60 63	93.97 104	88.19 35	96.29 5	87.61 25	98.20 35	94.39 112
Skip Steuart: Steuart Systems R&D Blog.
OPM-MVS		89.80 54	89.97 55	89.27 63	94.76 39	79.86 104	86.76 138	92.78 118	78.78 122	92.51 64	93.64 124	88.13 38	93.84 122	84.83 82	97.55 78	94.10 127
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HPM-MVS_fast		92.50 7	92.54 9	92.37 5	95.93 15	85.81 41	92.99 12	94.23 28	85.21 45	92.51 64	95.13 51	90.65 10	95.34 58	88.06 15	98.15 38	95.95 45

K. test v3		85.14 145	84.73 163	86.37 122	91.13 158	69.63 245	85.45 170	76.68 428	84.06 58	92.44 66	96.99 12	62.03 356	94.65 84	80.58 134	93.24 271	94.83 89

aaEdge-Enhanced		90.09 43	90.66 50	88.38 84	92.82 97	76.12 156	89.40 90	93.70 61	83.72 62	92.39 67	93.18 140	88.02 41	95.47 51	84.99 77	97.69 66	93.54 166

ACMMP		91.91 14	91.87 20	92.03 11	95.53 26	85.91 36	93.35 11	94.16 33	82.52 76	92.39 67	94.14 94	89.15 26	95.62 40	87.35 32	98.24 31	94.56 97
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
SF-MVS		90.27 41	90.80 48	88.68 77	92.86 94	77.09 141	91.19 49	95.74 5	81.38 87	92.28 69	93.80 114	86.89 57	94.64 85	85.52 67	97.51 82	94.30 117

PMatch-SfM		81.28 263	79.37 301	87.00 108	89.23 203	85.40 45	81.27 305	81.28 387	65.97 337	92.13 70	90.30 274	44.94 493	85.43 388	74.06 244	95.14 182	90.18 317

LoFTR		76.52 352	76.53 347	76.49 402	83.36 390	80.97 93	80.82 318	68.96 488	62.47 396	92.13 70	89.95 285	51.45 442	74.61 469	64.97 362	94.67 211	73.87 520

COLMAP_ROB	83.01 3	91.97 13	91.95 15	92.04 10	93.68 69	86.15 31	93.37 10	95.10 13	90.28 9	92.11 72	95.03 53	89.75 21	94.93 74	79.95 139	98.27 27	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-ACMP-BASELINE		89.98 50	89.84 57	90.41 42	94.91 36	84.50 57	89.49 86	93.98 44	79.68 108	92.09 73	93.89 112	83.80 97	93.10 156	82.67 108	98.04 40	93.64 155

TranMVSNet+NR-MVSNet		87.86 87	88.76 81	85.18 157	94.02 62	64.13 315	84.38 199	91.29 169	84.88 49	92.06 74	93.84 113	86.45 64	93.73 125	73.22 267	98.66 10	97.69 9

DKM		82.99 220	82.10 236	85.66 145	90.69 170	88.83 9	82.94 253	78.86 406	66.54 333	92.02 75	88.74 319	67.79 314	78.28 450	74.39 231	96.96 95	89.85 325

MTAPA		91.52 18	91.60 23	91.29 29	96.59 4	86.29 28	92.02 38	91.81 153	84.07 57	92.00 76	94.40 81	86.63 60	95.28 61	88.59 10	98.31 25	92.30 238

ACMMP_NAP		90.65 34	91.07 41	89.42 61	95.93 15	79.54 109	89.95 71	93.68 68	77.65 138	91.97 77	94.89 56	88.38 31	95.45 53	89.27 5	97.87 55	93.27 174

FC-MVSNet-test		85.93 124	87.05 104	82.58 251	92.25 111	56.44 445	85.75 162	93.09 101	77.33 143	91.94 78	94.65 66	74.78 234	93.41 146	75.11 225	98.58 13	97.88 7

test-260524					93.36 80	75.43 166		93.68 68		91.87 79		86.66 59	95.37 56	85.83 63	97.78 58

sc_t1		87.70 91	88.94 73	83.99 198	93.47 73	67.15 276	85.05 180	88.21 266	86.81 31	91.87 79	97.65 5	85.51 81	87.91 327	74.22 235	97.63 70	96.92 25

lessismore_v0				85.95 136	91.10 159	70.99 226		70.91 477		91.79 81	94.42 79	61.76 357	92.93 162	79.52 148	93.03 278	93.93 134

HPM-MVS		92.13 11	92.20 13	91.91 16	95.58 25	84.67 55	93.51 8	94.85 15	82.88 73	91.77 82	93.94 110	90.55 13	95.73 36	88.50 11	98.23 32	95.33 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
PGM-MVS		91.20 26	90.95 45	91.93 14	95.67 22	85.85 39	90.00 67	93.90 49	80.32 99	91.74 83	94.41 80	88.17 36	95.98 12	86.37 48	97.99 45	93.96 133

mPP-MVS		91.69 15	91.47 28	92.37 5	96.04 12	88.48 11	92.72 18	92.60 126	83.09 70	91.54 84	94.25 88	87.67 48	95.51 48	87.21 36	98.11 39	93.12 185

HFP-MVS		91.30 23	91.39 29	91.02 32	95.43 28	84.66 56	92.58 23	93.29 90	81.99 79	91.47 85	93.96 107	88.35 33	95.56 43	87.74 21	97.74 63	92.85 201

ACMMPR		91.49 19	91.35 32	91.92 15	95.74 19	85.88 38	92.58 23	93.25 91	81.99 79	91.40 86	94.17 93	87.51 49	95.87 19	87.74 21	97.76 61	93.99 130

GST-MVS		90.96 30	91.01 42	90.82 36	95.45 27	82.73 74	91.75 43	93.74 58	80.98 92	91.38 87	93.80 114	87.20 53	95.80 29	87.10 39	97.69 66	93.93 134

test_fmvsmvis_n_1920		85.22 140	85.36 149	84.81 167	85.80 331	76.13 155	85.15 178	92.32 134	61.40 413	91.33 88	90.85 247	83.76 99	86.16 372	84.31 87	93.28 269	92.15 249

ANet_high		83.17 216	85.68 140	75.65 417	81.24 423	45.26 520	79.94 332	92.91 112	83.83 59	91.33 88	96.88 15	80.25 159	85.92 376	68.89 319	95.89 149	95.76 48

ZNCC-MVS		91.26 24	91.34 33	91.01 33	95.73 20	83.05 71	92.18 32	94.22 30	80.14 102	91.29 90	93.97 104	87.93 43	95.87 19	88.65 9	97.96 50	94.12 126

casdiffmvs_mvg		86.72 103	87.51 95	84.36 184	87.09 287	65.22 302	84.16 204	94.23 28	77.89 134	91.28 91	93.66 123	84.35 91	92.71 166	80.07 136	94.87 200	95.16 72
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DKM-HiRes		83.22 214	82.10 236	86.59 117	91.79 132	88.73 10	82.92 254	77.76 414	69.00 292	91.15 92	89.69 293	63.65 348	81.20 428	76.19 205	96.70 107	89.86 324

APD-MVS		89.54 59	89.63 61	89.26 64	92.57 100	81.34 90	90.19 66	93.08 102	80.87 94	91.13 93	93.19 139	86.22 68	95.97 13	82.23 114	97.18 90	90.45 307
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			89.29 65		91.84 129		88.80 99	95.32 12	75.14 175	91.07 94	92.89 156	87.27 51	93.78 124	83.69 95	97.55 78

CP-MVS		91.67 16	91.58 24	91.96 13	95.29 30	87.62 16	93.38 9	93.36 81	83.16 69	91.06 95	94.00 103	88.26 34	95.71 38	87.28 35	98.39 22	92.55 218

FIs		85.35 139	86.27 122	82.60 250	91.86 126	57.31 438	85.10 179	93.05 103	75.83 162	91.02 96	93.97 104	73.57 257	92.91 164	73.97 246	98.02 43	97.58 12

Casviewmamba		88.12 82	88.82 79	86.03 134	89.14 206	68.35 264	86.40 146	94.70 17	79.80 105	90.92 97	93.72 121	87.83 44	93.81 123	81.09 125	95.75 157	95.92 47

UniMVSNet_NR-MVSNet		86.84 101	87.06 103	86.17 131	92.86 94	67.02 281	82.55 265	91.56 159	83.08 71	90.92 97	91.82 201	78.25 177	93.99 113	74.16 239	98.35 23	97.49 13

DU-MVS		86.80 102	86.99 107	86.21 129	93.24 84	67.02 281	83.16 246	92.21 136	81.73 83	90.92 97	91.97 192	77.20 197	93.99 113	74.16 239	98.35 23	97.61 10

tt0805		88.09 83	89.79 58	82.98 233	93.26 83	63.94 318	91.10 50	89.64 230	85.07 46	90.91 100	91.09 233	89.16 25	91.87 191	82.03 116	95.87 150	93.13 182

V42		83.47 208	83.37 207	83.75 207	83.16 398	63.33 324	81.31 300	90.23 213	69.51 281	90.91 100	90.81 249	74.16 245	92.29 180	80.06 137	90.22 382	95.62 55

tt0320-xc		86.67 105	88.41 84	81.44 287	93.45 74	60.44 387	83.96 210	88.50 253	87.26 28	90.90 102	97.90 3	85.61 78	86.40 366	70.14 303	98.01 44	97.47 14

region2R		91.44 22	91.30 36	91.87 18	95.75 18	85.90 37	92.63 22	93.30 89	81.91 81	90.88 103	94.21 89	87.75 45	95.87 19	87.60 26	97.71 64	93.83 142

APD_test1		88.40 76	87.91 89	89.88 51	89.50 196	86.65 26	89.98 70	91.91 148	84.26 55	90.87 104	93.92 111	82.18 127	89.29 290	73.75 250	94.81 205	93.70 150

tt0320		86.63 107	88.36 85	81.41 288	93.57 71	60.73 384	84.37 200	88.61 252	87.00 30	90.75 105	97.98 2	85.54 80	86.45 363	69.75 308	97.70 65	97.06 22

WR-MVS		83.56 202	84.40 181	81.06 296	93.43 77	54.88 463	78.67 363	85.02 330	81.24 88	90.74 106	91.56 211	72.85 271	91.08 223	68.00 330	98.04 40	97.23 17

v1240		84.30 172	84.51 177	83.65 211	87.65 263	61.26 370	82.85 257	91.54 160	67.94 311	90.68 107	90.65 259	71.71 289	93.64 129	82.84 105	94.78 206	96.07 40

Elysia		88.71 72	88.89 74	88.19 90	91.26 152	72.96 187	88.10 111	93.59 73	84.31 53	90.42 108	94.10 97	74.07 246	94.82 77	88.19 13	95.92 146	96.80 27

StellarMVS		88.71 72	88.89 74	88.19 90	91.26 152	72.96 187	88.10 111	93.59 73	84.31 53	90.42 108	94.10 97	74.07 246	94.82 77	88.19 13	95.92 146	96.80 27

ZD-MVS					92.22 113	80.48 97		91.85 149	71.22 257	90.38 110	92.98 150	86.06 71	96.11 6	81.99 118	96.75 105

MIMVSNet1		83.63 199	84.59 172	80.74 302	94.06 61	62.77 333	82.72 259	84.53 342	77.57 140	90.34 111	95.92 30	76.88 209	85.83 383	61.88 393	97.42 83	93.62 157

LS3D		90.60 36	90.34 54	91.38 27	89.03 213	84.23 58	93.58 6	94.68 18	90.65 7	90.33 112	93.95 109	84.50 89	95.37 56	80.87 129	95.50 168	94.53 101

KD-MVS_self_test		81.93 250	83.14 214	78.30 361	84.75 356	52.75 479	80.37 327	89.42 237	70.24 273	90.26 113	93.39 130	74.55 241	86.77 356	68.61 325	96.64 108	95.38 60

PMVS	80.48 6	90.08 44	90.66 50	88.34 87	96.71 3	92.97 1	90.31 64	89.57 233	88.51 20	90.11 114	95.12 52	90.98 7	88.92 295	77.55 181	97.07 92	83.13 453
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PC_three_1452									58.96 441	90.06 115	91.33 221	80.66 154	93.03 159	75.78 212	95.94 144	92.48 221

v1921920		84.23 176	84.37 182	83.79 205	87.64 264	61.71 361	82.91 255	91.20 175	67.94 311	90.06 115	90.34 268	72.04 284	93.59 135	82.32 112	94.91 193	96.07 40

ITE_SJBPF				90.11 48	90.72 168	84.97 50		90.30 209	81.56 85	90.02 117	91.20 229	82.40 118	90.81 236	73.58 259	94.66 212	94.56 97

XVS		91.54 17	91.36 30	92.08 8	95.64 23	86.25 29	92.64 20	93.33 85	85.07 46	89.99 118	94.03 101	86.57 61	95.80 29	87.35 32	97.62 72	94.20 118

X-MVStestdata		85.04 149	82.70 225	92.08 8	95.64 23	86.25 29	92.64 20	93.33 85	85.07 46	89.99 118	16.05 552	86.57 61	95.80 29	87.35 32	97.62 72	94.20 118

v1192		84.57 162	84.69 168	84.21 192	87.75 258	62.88 329	83.02 249	91.43 163	69.08 289	89.98 120	90.89 244	72.70 274	93.62 133	82.41 111	94.97 192	96.13 38

Anonymous20240529		86.20 115	87.13 101	83.42 220	90.19 180	64.55 309	84.55 193	90.71 190	85.85 39	89.94 121	95.24 49	82.13 128	90.40 252	69.19 315	96.40 120	95.31 63

fmvsm_s_conf0.5_n_9		87.04 97	87.02 105	87.08 106	89.67 192	75.87 161	84.60 191	89.74 225	74.40 188	89.92 122	93.41 128	80.45 156	90.63 244	86.66 45	94.37 224	94.73 94

hybridcas		86.07 119	87.02 105	83.19 228	87.76 257	62.85 331	84.53 197	93.42 79	75.52 169	89.88 123	93.31 132	86.15 69	91.68 196	77.76 178	94.89 195	95.05 75

pmmvs6		86.52 109	88.06 88	81.90 271	92.22 113	62.28 349	84.66 190	89.15 242	83.54 66	89.85 124	97.32 8	88.08 40	86.80 355	70.43 300	97.30 87	96.62 31

v144192		84.24 175	84.41 180	83.71 209	87.59 266	61.57 362	82.95 252	91.03 180	67.82 315	89.80 125	90.49 265	73.28 266	93.51 141	81.88 121	94.89 195	96.04 42

v1144		84.54 166	84.72 165	84.00 197	87.67 262	62.55 338	82.97 251	90.93 185	70.32 270	89.80 125	90.99 237	73.50 258	93.48 142	81.69 122	94.65 213	95.97 43

MatchFormer		68.98 456	69.54 446	67.33 489	76.37 499	74.77 169	79.54 337	57.73 542	46.87 516	89.77 127	86.43 370	41.98 505	65.54 524	52.83 478	94.31 227	61.67 539

DeepC-MVS	82.31 4	89.15 67	89.08 69	89.37 62	93.64 70	79.07 114	88.54 106	94.20 31	73.53 204	89.71 128	94.82 61	85.09 83	95.77 35	84.17 89	98.03 42	93.26 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
RPSCF		88.00 85	86.93 109	91.22 30	90.08 183	89.30 5	89.68 78	91.11 177	79.26 115	89.68 129	94.81 64	82.44 116	87.74 332	76.54 199	88.74 411	96.61 32

IU-MVS					94.18 54	72.64 193		90.82 188	56.98 459	89.67 130				85.78 64	97.92 51	93.28 173

FMVSNet1		84.55 165	85.45 146	81.85 273	90.27 178	61.05 374	86.83 135	88.27 263	78.57 126	89.66 131	95.64 37	75.43 222	90.68 241	69.09 316	95.33 172	93.82 143

DenseAffine		81.00 271	79.38 300	85.84 140	90.25 179	87.48 17	81.47 295	78.40 410	65.68 349	89.63 132	86.45 369	58.79 379	82.05 419	67.78 333	95.99 139	87.99 376

IterMVS-LS		84.73 159	84.98 157	83.96 200	87.35 275	63.66 319	83.25 240	89.88 223	76.06 153	89.62 133	92.37 179	73.40 264	92.52 171	78.16 167	94.77 208	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DP-MVS		88.60 75	89.01 70	87.36 103	91.30 149	77.50 134	87.55 119	92.97 111	87.95 25	89.62 133	92.87 157	84.56 88	93.89 119	77.65 179	96.62 109	90.70 297

UniMVSNet (Re)		86.87 99	86.98 108	86.55 119	93.11 87	68.48 263	83.80 218	92.87 113	80.37 97	89.61 135	91.81 202	77.72 185	94.18 105	75.00 226	98.53 15	96.99 24

IS-MVSNet		86.66 106	86.82 112	86.17 131	92.05 119	66.87 285	91.21 48	88.64 250	86.30 36	89.60 136	92.59 167	69.22 306	94.91 75	73.89 247	97.89 54	96.72 29

v2v482		84.09 179	84.24 186	83.62 212	87.13 282	61.40 365	82.71 260	89.71 228	72.19 239	89.55 137	91.41 217	70.70 296	93.20 151	81.02 127	93.76 247	96.25 36

Baseline_NR-MVSNet		84.00 187	85.90 132	78.29 362	91.47 146	53.44 475	82.29 276	87.00 296	79.06 118	89.55 137	95.72 35	77.20 197	86.14 373	72.30 278	98.51 16	95.28 64

ArgMatch-SfM		79.08 303	77.37 333	84.22 191	87.80 255	86.73 23	79.32 347	78.45 408	56.81 461	89.54 139	84.95 401	55.35 417	79.21 442	68.89 319	95.21 177	86.73 401

CSCG		86.26 112	86.47 115	85.60 147	90.87 164	74.26 174	87.98 114	91.85 149	80.35 98	89.54 139	88.01 333	79.09 168	92.13 182	75.51 217	95.06 187	90.41 308

ambc				82.98 233	90.55 173	64.86 305	88.20 108	89.15 242		89.40 141	93.96 107	71.67 290	91.38 208	78.83 156	96.55 111	92.71 207

DeepPCF-MVS	81.24 5	87.28 95	86.21 124	90.49 41	91.48 145	84.90 51	83.41 235	92.38 131	70.25 272	89.35 142	90.68 255	82.85 111	94.57 88	79.55 146	95.95 143	92.00 255

LuminaMVS		83.94 190	83.51 199	85.23 155	89.78 191	71.74 211	84.76 186	87.27 281	72.60 230	89.31 143	90.60 263	64.04 341	90.95 227	79.08 153	94.11 234	92.99 193

MVStest1		70.05 444	69.26 447	72.41 453	58.62 552	55.59 454	76.61 403	65.58 506	53.44 483	89.28 144	93.32 131	22.91 551	71.44 481	74.08 243	89.52 393	90.21 316

ELoFTR		73.12 404	73.47 387	72.08 455	81.84 412	77.60 133	80.51 325	66.79 502	49.99 509	89.23 145	88.83 315	47.19 464	65.24 528	61.99 390	94.85 203	73.39 521

test_fmvsmconf0.01_n		86.68 104	86.52 114	87.18 104	85.94 328	78.30 121	86.93 131	92.20 137	65.94 339	89.16 146	93.16 143	83.10 105	89.89 274	87.81 20	94.43 220	93.35 169

MSP-MVS		89.08 69	88.16 87	91.83 19	95.76 17	86.14 32	92.75 17	93.90 49	78.43 127	89.16 146	92.25 185	72.03 285	96.36 3	88.21 12	90.93 354	92.98 195
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
XVG-OURS		89.18 66	88.83 78	90.23 46	94.28 51	86.11 33	85.91 156	93.60 72	80.16 101	89.13 148	93.44 127	83.82 96	90.98 226	83.86 92	95.30 176	93.60 159

fmvsm_s_conf0.5_n_10		85.20 142	85.25 152	85.02 162	86.01 326	71.31 220	84.96 181	91.76 155	69.10 287	88.90 149	92.56 170	73.84 253	90.63 244	86.88 40	93.26 270	93.13 182

MDA-MVSNet-bldmvs		77.47 334	76.90 341	79.16 341	79.03 467	64.59 306	66.58 508	75.67 435	73.15 217	88.86 150	88.99 313	66.94 320	81.23 427	64.71 364	88.22 423	91.64 269

EG-PatchMatch MVS		84.08 180	84.11 188	83.98 199	92.22 113	72.61 196	82.20 282	87.02 293	72.63 229	88.86 150	91.02 236	78.52 173	91.11 222	73.41 261	91.09 348	88.21 369

3Dnovator+	83.92 2	89.97 52	89.66 60	90.92 34	91.27 151	81.66 87	91.25 47	94.13 38	88.89 14	88.83 152	94.26 87	77.55 189	95.86 22	84.88 80	95.87 150	95.24 66

ArgMatch-Sym		78.58 318	76.86 342	83.71 209	87.61 265	86.40 27	78.19 369	77.45 417	55.72 466	88.82 153	82.01 453	59.68 372	78.75 447	67.43 336	94.86 201	85.98 407

fmvsm_s_conf0.5_n_5		84.56 163	84.71 166	84.11 196	87.92 251	72.09 207	84.80 182	88.64 250	64.43 370	88.77 154	91.78 204	78.07 179	87.95 326	85.85 62	92.18 316	92.30 238

EI-MVSNet-UG-set		85.04 149	84.44 179	86.85 113	83.87 377	72.52 199	83.82 216	85.15 326	80.27 100	88.75 155	85.45 390	79.95 162	91.90 189	81.92 120	90.80 363	96.13 38

EI-MVSNet-Vis-set		85.12 147	84.53 176	86.88 112	84.01 373	72.76 190	83.91 214	85.18 325	80.44 95	88.75 155	85.49 388	80.08 160	91.92 188	82.02 117	90.85 359	95.97 43

BridgeMVS		84.80 155	85.40 147	83.00 232	88.95 216	61.44 364	90.42 63	92.37 133	71.48 251	88.72 157	93.13 144	70.16 301	95.15 67	79.26 152	94.11 234	92.41 227

OMC-MVS		88.19 79	87.52 94	90.19 47	91.94 124	81.68 85	87.49 122	93.17 95	76.02 155	88.64 158	91.22 227	84.24 93	93.37 147	77.97 176	97.03 93	95.52 57

RRT-MVS		82.97 221	83.44 202	81.57 281	85.06 348	58.04 431	87.20 124	90.37 203	77.88 135	88.59 159	93.70 122	63.17 350	93.05 158	76.49 200	88.47 415	93.62 157

test_fmvsmconf0.1_n		86.18 117	85.88 133	87.08 106	85.26 344	78.25 122	85.82 160	91.82 151	65.33 356	88.55 160	92.35 182	82.62 115	89.80 276	86.87 41	94.32 226	93.18 181

UA-Net		91.49 19	91.53 25	91.39 26	94.98 34	82.95 73	93.52 7	92.79 117	88.22 22	88.53 161	97.64 6	83.45 102	94.55 90	86.02 59	98.60 12	96.67 30

MP-MVS		91.14 28	90.91 46	91.83 19	96.18 10	86.88 22	92.20 31	93.03 106	82.59 75	88.52 162	94.37 83	86.74 58	95.41 55	86.32 49	98.21 33	93.19 180
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
sasdasda		85.50 129	86.14 125	83.58 214	87.97 248	67.13 277	87.55 119	94.32 22	73.44 207	88.47 163	87.54 349	86.45 64	91.06 224	75.76 213	93.76 247	92.54 219

canonicalmvs		85.50 129	86.14 125	83.58 214	87.97 248	67.13 277	87.55 119	94.32 22	73.44 207	88.47 163	87.54 349	86.45 64	91.06 224	75.76 213	93.76 247	92.54 219

NR-MVSNet		86.00 120	86.22 123	85.34 154	93.24 84	64.56 308	82.21 280	90.46 199	80.99 91	88.42 165	91.97 192	77.56 188	93.85 120	72.46 277	98.65 11	97.61 10

alignmvs		83.94 190	83.98 191	83.80 204	87.80 255	67.88 271	84.54 195	91.42 165	73.27 215	88.41 166	87.96 334	72.33 278	90.83 235	76.02 210	94.11 234	92.69 208

TransMVSNet (Re)		84.02 186	85.74 139	78.85 347	91.00 161	55.20 461	82.29 276	87.26 282	79.65 109	88.38 167	95.52 40	83.00 107	86.88 351	67.97 331	96.60 110	94.45 106

PM-MVS		80.20 291	79.00 305	83.78 206	88.17 244	86.66 25	81.31 300	66.81 501	69.64 278	88.33 168	90.19 278	64.58 335	83.63 409	71.99 281	90.03 385	81.06 481

fmvsm_s_conf0.5_n_3		86.19 116	87.27 99	82.95 235	86.91 295	70.38 234	85.31 174	92.61 125	75.59 167	88.32 169	92.87 157	82.22 126	88.63 309	88.80 8	92.82 287	89.83 326

tttt0517		81.07 269	79.58 297	85.52 149	88.99 215	66.45 290	87.03 130	75.51 437	73.76 196	88.32 169	90.20 277	37.96 514	94.16 109	79.36 151	95.13 183	95.93 46

FE-MVSNET2		82.80 224	83.51 199	80.67 308	89.08 210	58.46 426	82.40 273	89.26 238	71.25 256	88.24 171	94.07 99	75.75 218	89.56 281	65.91 351	95.67 163	93.98 131

casdiffmvs		85.21 141	85.85 134	83.31 223	86.17 320	62.77 333	83.03 248	93.93 47	74.69 181	88.21 172	92.68 166	82.29 124	91.89 190	77.87 177	93.75 250	95.27 65
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_vis3_rt		71.42 428	70.67 429	73.64 439	69.66 536	70.46 232	66.97 507	89.73 226	42.68 535	88.20 173	83.04 436	43.77 497	60.07 536	65.35 358	86.66 450	90.39 309

SSC-MVS		77.55 333	81.64 248	65.29 502	90.46 174	20.33 556	73.56 451	68.28 490	85.44 40	88.18 174	94.64 69	70.93 294	81.33 425	71.25 287	92.03 320	94.20 118

balanced_ft_v1		83.49 206	83.93 193	82.19 263	86.46 305	59.61 403	90.81 52	90.92 186	71.78 246	88.08 175	92.56 170	66.97 319	94.54 91	75.34 221	92.42 304	92.42 225

MVSMamba_PlusPlus		87.53 93	88.86 77	83.54 218	92.03 120	62.26 350	91.49 45	92.62 123	88.07 24	88.07 176	96.17 25	72.24 280	95.79 32	84.85 81	94.16 233	92.58 216

MGCFI-Net		85.04 149	85.95 130	82.31 261	87.52 268	63.59 321	86.23 150	93.96 45	73.46 205	88.07 176	87.83 344	86.46 63	90.87 234	76.17 207	93.89 242	92.47 223

v148		82.31 234	82.48 232	81.81 276	85.59 337	59.66 401	81.47 295	86.02 309	72.85 224	88.05 178	90.65 259	70.73 295	90.91 231	75.15 224	91.79 328	94.87 80

AstraMVS		81.67 255	81.40 258	82.48 256	87.06 290	66.47 289	81.41 297	81.68 381	68.78 295	88.00 179	90.95 242	65.70 330	87.86 331	76.66 195	92.38 305	93.12 185

AllTest		87.97 86	87.40 98	89.68 55	91.59 136	83.40 66	89.50 85	95.44 10	79.47 110	88.00 179	93.03 148	82.66 113	91.47 202	70.81 291	96.14 131	94.16 123

TestCases				89.68 55	91.59 136	83.40 66		95.44 10	79.47 110	88.00 179	93.03 148	82.66 113	91.47 202	70.81 291	96.14 131	94.16 123

fmvsm_s_conf0.1_n_2		83.82 193	83.49 201	84.84 165	85.99 327	70.19 237	80.93 314	87.58 277	67.26 325	87.94 182	92.37 179	71.40 292	88.01 323	86.03 56	91.87 327	96.31 35

fmvsm_s_conf0.5_n_8		85.48 131	85.75 138	84.68 174	87.10 285	69.98 239	84.28 202	92.68 120	74.77 179	87.90 183	92.36 181	73.94 250	90.41 251	85.95 61	92.74 289	93.66 151

viewdifsd2359ckpt11		82.46 232	82.98 218	80.88 299	83.53 381	61.00 377	79.46 344	85.97 311	69.48 282	87.89 184	91.31 223	82.10 129	88.61 310	74.28 233	92.86 284	93.02 189

viewmsd2359difaftdt		82.46 232	82.99 217	80.88 299	83.52 382	61.00 377	79.46 344	85.97 311	69.48 282	87.89 184	91.31 223	82.10 129	88.61 310	74.28 233	92.86 284	93.02 189

pm-mvs1		83.69 196	84.95 159	79.91 324	90.04 187	59.66 401	82.43 271	87.44 278	75.52 169	87.85 186	95.26 48	81.25 146	85.65 387	68.74 323	96.04 136	94.42 110

PCF-MVS	74.62 15	82.15 242	80.92 270	85.84 140	89.43 198	72.30 203	80.53 324	91.82 151	57.36 455	87.81 187	89.92 288	77.67 186	93.63 130	58.69 417	95.08 186	91.58 271
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
fmvsm_s_conf0.5_n_2		83.62 200	83.29 208	84.62 175	85.43 341	70.18 238	80.61 323	87.24 283	67.14 326	87.79 188	91.87 194	71.79 288	87.98 325	86.00 60	91.77 330	95.71 50

guyue		81.57 257	81.37 260	82.15 265	86.39 308	66.13 293	81.54 294	83.21 360	69.79 277	87.77 189	89.95 285	65.36 333	87.64 334	75.88 211	92.49 302	92.67 209

KinetiMVS		85.95 123	86.10 127	85.50 151	87.56 267	69.78 241	83.70 221	89.83 224	80.42 96	87.76 190	93.24 138	73.76 255	91.54 199	85.03 75	93.62 256	95.19 69

mvs5depth		83.82 193	84.54 175	81.68 279	82.23 406	68.65 261	86.89 132	89.90 222	80.02 104	87.74 191	97.86 4	64.19 340	82.02 420	76.37 201	95.63 165	94.35 113

test_fmvsmconf_n		85.88 125	85.51 143	86.99 110	84.77 355	78.21 123	85.40 172	91.39 166	65.32 357	87.72 192	91.81 202	82.33 120	89.78 277	86.68 43	94.20 231	92.99 193

FMVSNet2		81.31 262	81.61 250	80.41 314	86.38 310	58.75 423	83.93 213	86.58 300	72.43 231	87.65 193	92.98 150	63.78 345	90.22 257	66.86 338	93.92 241	92.27 242

E5new		85.44 134	86.37 117	82.66 245	88.22 241	61.86 356	83.59 225	93.70 61	73.64 199	87.62 194	93.30 133	85.85 74	91.26 212	78.02 170	93.40 261	94.86 84

E5		85.44 134	86.37 117	82.66 245	88.22 241	61.86 356	83.59 225	93.70 61	73.64 199	87.62 194	93.30 133	85.85 74	91.26 212	78.02 170	93.40 261	94.86 84

E6new		85.44 134	86.37 117	82.66 245	88.23 239	61.86 356	83.59 225	93.69 64	73.64 199	87.61 196	93.30 133	85.85 74	91.26 212	78.02 170	93.40 261	94.86 84

E6		85.44 134	86.37 117	82.66 245	88.23 239	61.86 356	83.59 225	93.69 64	73.64 199	87.61 196	93.30 133	85.85 74	91.26 212	78.02 170	93.40 261	94.86 84

viewdifsd2359ckpt07		83.41 212	84.35 183	80.56 310	85.84 330	58.93 418	79.47 342	91.28 170	73.01 221	87.59 198	92.07 188	85.24 82	88.68 306	73.59 258	91.11 346	94.09 128

GeoE		85.45 133	85.81 135	84.37 182	90.08 183	67.07 280	85.86 159	91.39 166	72.33 236	87.59 198	90.25 275	84.85 86	92.37 176	78.00 174	91.94 324	93.66 151

VPA-MVSNet		83.47 208	84.73 163	79.69 329	90.29 177	57.52 436	81.30 303	88.69 249	76.29 151	87.58 200	94.44 76	80.60 155	87.20 344	66.60 343	96.82 102	94.34 114

casdiffseed414692147		85.64 128	86.08 128	84.32 187	87.49 270	65.55 300	85.81 161	93.00 110	75.85 161	87.50 201	93.40 129	83.10 105	91.71 195	73.70 255	94.84 204	95.69 51

CPTT-MVS		89.39 61	88.98 72	90.63 39	95.09 32	86.95 20	92.09 37	92.30 135	79.74 107	87.50 201	92.38 176	81.42 144	93.28 149	83.07 100	97.24 88	91.67 268

VDDNet		84.35 170	85.39 148	81.25 290	95.13 31	59.32 407	85.42 171	81.11 388	86.41 35	87.41 203	96.21 24	73.61 256	90.61 246	66.33 345	96.85 99	93.81 146

VortexMVS		80.51 280	80.63 274	80.15 320	83.36 390	61.82 360	80.63 322	88.00 269	67.11 327	87.23 204	89.10 311	63.98 342	88.00 324	73.63 257	92.63 292	90.64 302

ALIKED-LG		78.19 325	77.07 336	81.54 282	84.95 349	86.95 20	86.16 152	83.96 348	56.64 463	87.21 205	90.05 284	51.36 443	78.05 452	57.73 427	95.60 166	79.63 493

c3_l		81.64 256	81.59 251	81.79 278	80.86 431	59.15 413	78.61 364	90.18 215	68.36 302	87.20 206	87.11 361	69.39 304	91.62 197	78.16 167	94.43 220	94.60 96

VDD-MVS		84.23 176	84.58 173	83.20 226	91.17 157	65.16 304	83.25 240	84.97 333	79.79 106	87.18 207	94.27 84	74.77 235	90.89 232	69.24 312	96.54 112	93.55 165

SIFT-NCMNet		71.70 423	70.97 426	73.90 433	77.55 483	81.03 91	71.58 474	63.31 519	63.91 379	87.12 208	81.00 464	50.00 455	64.64 531	49.37 499	94.86 201	76.04 515

MSLP-MVS++		85.00 152	86.03 129	81.90 271	91.84 129	71.56 218	86.75 139	93.02 107	75.95 158	87.12 208	89.39 299	77.98 180	89.40 289	77.46 183	94.78 206	84.75 423

baseline		85.20 142	85.93 131	83.02 231	86.30 315	62.37 347	84.55 193	93.96 45	74.48 185	87.12 208	92.03 191	82.30 122	91.94 187	78.39 160	94.21 229	94.74 93

YYNet1		70.06 443	70.44 433	68.90 477	73.76 517	53.42 476	58.99 532	67.20 497	58.42 444	87.10 211	85.39 392	59.82 370	67.32 513	59.79 409	83.50 490	85.96 408

MDA-MVSNet_test_wron		70.05 444	70.44 433	68.88 478	73.84 516	53.47 474	58.93 533	67.28 496	58.43 443	87.09 212	85.40 391	59.80 371	67.25 514	59.66 410	83.54 489	85.92 410

test_fmvs3		75.72 366	75.20 364	77.27 385	75.01 512	69.47 247	78.93 356	84.88 336	46.67 518	87.08 213	87.84 343	50.44 453	71.62 479	77.42 186	88.53 413	90.72 295

CNVR-MVS		87.81 89	87.68 92	88.21 89	92.87 92	77.30 140	85.25 175	91.23 174	77.31 144	87.07 214	91.47 216	82.94 108	94.71 81	84.67 84	96.27 125	92.62 212

EPP-MVSNet		85.47 132	85.04 156	86.77 115	91.52 144	69.37 248	91.63 44	87.98 270	81.51 86	87.05 215	91.83 200	66.18 327	95.29 59	70.75 294	96.89 98	95.64 54

TinyColmap		81.25 264	82.34 234	77.99 368	85.33 342	60.68 385	82.32 275	88.33 260	71.26 255	86.97 216	92.22 187	77.10 200	86.98 349	62.37 384	95.17 180	86.31 405

eth_miper_zixun_eth		80.84 274	80.22 284	82.71 243	81.41 421	60.98 380	77.81 377	90.14 216	67.31 324	86.95 217	87.24 358	64.26 338	92.31 178	75.23 222	91.61 335	94.85 88

E4		84.75 158	85.46 145	82.61 249	88.17 244	61.55 363	81.39 298	93.55 76	73.13 219	86.83 218	92.83 159	84.17 94	91.48 201	76.92 192	92.19 315	94.80 91

Anonymous20240521		80.18 292	81.25 262	76.95 392	83.15 399	60.84 382	82.46 268	85.99 310	68.76 296	86.78 219	93.73 120	59.13 376	77.44 454	73.71 251	97.55 78	92.56 217

Patchmatch-RL test		74.48 384	73.68 383	76.89 395	84.83 353	66.54 287	72.29 466	69.16 487	57.70 450	86.76 220	86.33 373	45.79 480	82.59 413	69.63 309	90.65 374	81.54 472

XVG-OURS-SEG-HR		89.59 58	89.37 64	90.28 45	94.47 42	85.95 35	86.84 134	93.91 48	80.07 103	86.75 221	93.26 137	93.64 2	90.93 229	84.60 85	90.75 364	93.97 132

fmvsm_l_conf0.5_n_9		83.98 188	84.46 178	82.53 254	86.11 323	70.65 230	82.45 270	89.17 241	67.72 317	86.74 222	91.49 213	79.20 166	85.86 382	84.71 83	92.60 298	91.07 283

h-mvs33		84.25 174	82.76 224	88.72 74	91.82 131	82.60 75	84.00 209	84.98 332	71.27 253	86.70 223	90.55 264	63.04 353	93.92 118	78.26 165	94.20 231	89.63 330

hse-mvs2		83.47 208	81.81 245	88.47 81	91.03 160	82.27 79	82.61 261	83.69 353	71.27 253	86.70 223	86.05 379	63.04 353	92.41 174	78.26 165	93.62 256	90.71 296

HPM-MVS++		88.93 71	88.45 83	90.38 43	94.92 35	85.85 39	89.70 76	91.27 173	78.20 130	86.69 225	92.28 184	80.36 158	95.06 71	86.17 54	96.49 114	90.22 312

TSAR-MVS + MP.		88.14 80	87.82 91	89.09 68	95.72 21	76.74 145	92.49 26	91.19 176	67.85 314	86.63 226	94.84 58	79.58 165	95.96 14	87.62 24	94.50 216	94.56 97
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SSM_0404		85.16 144	85.09 154	85.36 153	90.14 182	69.52 246	86.17 151	91.58 157	74.41 186	86.55 227	91.49 213	78.54 171	93.97 115	73.71 251	93.21 274	92.59 215

EI-MVSNet		82.61 227	82.42 233	83.20 226	83.25 395	63.66 319	83.50 232	85.07 327	76.06 153	86.55 227	85.10 396	73.41 262	90.25 254	78.15 169	90.67 371	95.68 53

HQP_MVS		87.75 90	87.43 97	88.70 76	93.45 74	76.42 149	89.45 87	93.61 70	79.44 112	86.55 227	92.95 154	74.84 232	95.22 62	80.78 131	95.83 152	94.46 104

plane_prior3						76.85 144			77.79 137	86.55 227

BH-untuned		80.96 272	80.99 268	80.84 301	88.55 232	68.23 265	80.33 328	88.46 255	72.79 227	86.55 227	86.76 365	74.72 236	91.77 194	61.79 394	88.99 406	82.52 461

MVSTER		77.09 340	75.70 357	81.25 290	75.27 509	61.08 373	77.49 386	85.07 327	60.78 427	86.55 227	88.68 320	43.14 502	90.25 254	73.69 256	90.67 371	92.42 225

旧先验2							81.73 290		56.88 460	86.54 233			84.90 394	72.81 274

IterMVS-SCA-FT		80.64 278	79.41 298	84.34 186	83.93 375	69.66 244	76.28 409	81.09 389	72.43 231	86.47 234	90.19 278	60.46 363	93.15 154	77.45 184	86.39 454	90.22 312

WB-MVS		76.06 360	80.01 292	64.19 506	89.96 189	20.58 555	72.18 468	68.19 491	83.21 68	86.46 235	93.49 126	70.19 300	78.97 444	65.96 347	90.46 380	93.02 189

test_fmvsm_n_1920		83.60 201	82.89 220	85.74 143	85.22 345	77.74 131	84.12 206	90.48 197	59.87 438	86.45 236	91.12 232	75.65 219	85.89 380	82.28 113	90.87 357	93.58 161

fmvsm_s_conf0.5_n_4		84.38 168	84.27 185	84.74 170	87.25 278	70.84 227	83.55 230	88.45 256	68.64 299	86.29 237	91.31 223	74.97 229	88.42 316	87.87 19	90.07 384	94.95 77

DIV-MVS_self_test		80.43 282	80.23 282	81.02 297	79.99 452	59.25 409	77.07 392	87.02 293	67.38 321	86.19 238	89.22 307	63.09 351	90.16 261	76.32 202	95.80 154	93.66 151

CDPH-MVS		86.17 118	85.54 142	88.05 94	92.25 111	75.45 165	83.85 215	92.01 143	65.91 341	86.19 238	91.75 206	83.77 98	94.98 73	77.43 185	96.71 106	93.73 149

cl____		80.42 283	80.23 282	81.02 297	79.99 452	59.25 409	77.07 392	87.02 293	67.37 322	86.18 240	89.21 308	63.08 352	90.16 261	76.31 203	95.80 154	93.65 154

MVS_111021_LR		84.28 173	83.76 196	85.83 142	89.23 203	83.07 70	80.99 312	83.56 355	72.71 228	86.07 241	89.07 312	81.75 141	86.19 371	77.11 189	93.36 265	88.24 368

viewmacassd2359aftdt		84.04 185	84.78 162	81.81 276	86.43 307	60.32 389	81.95 285	92.82 116	71.56 248	86.06 242	92.98 150	81.79 140	90.28 253	76.18 206	93.24 271	94.82 90

SP-DiffGlue		78.90 308	78.86 308	79.02 342	80.36 442	79.68 108	81.86 286	80.17 396	71.69 247	86.02 243	83.77 421	57.33 396	69.38 489	79.38 150	89.12 403	88.02 375

E2		84.06 181	84.61 170	82.40 259	87.49 270	61.31 367	81.03 310	93.36 81	71.83 244	86.02 243	91.87 194	82.91 109	91.37 209	75.66 215	91.33 341	94.53 101

E3		84.06 181	84.61 170	82.40 259	87.49 270	61.30 368	81.03 310	93.36 81	71.83 244	86.01 245	91.87 194	82.91 109	91.36 210	75.66 215	91.33 341	94.53 101

GBi-Net		82.02 246	82.07 238	81.85 273	86.38 310	61.05 374	86.83 135	88.27 263	72.43 231	86.00 246	95.64 37	63.78 345	90.68 241	65.95 348	93.34 266	93.82 143

test1		82.02 246	82.07 238	81.85 273	86.38 310	61.05 374	86.83 135	88.27 263	72.43 231	86.00 246	95.64 37	63.78 345	90.68 241	65.95 348	93.34 266	93.82 143

FMVSNet3		78.80 312	78.55 315	79.57 332	82.89 403	56.89 443	81.76 289	85.77 314	69.04 290	86.00 246	90.44 266	51.75 441	90.09 267	65.95 348	93.34 266	91.72 264

miper_ehance_all_eth		80.34 286	80.04 291	81.24 293	79.82 456	58.95 417	77.66 379	89.66 229	65.75 347	85.99 249	85.11 395	68.29 311	91.42 206	76.03 209	92.03 320	93.33 170

tfpnnormal		81.79 254	82.95 219	78.31 360	88.93 217	55.40 457	80.83 317	82.85 365	76.81 147	85.90 250	94.14 94	74.58 239	86.51 361	66.82 341	95.68 161	93.01 192

TAPA-MVS	77.73 12	85.71 127	84.83 161	88.37 85	88.78 224	79.72 105	87.15 128	93.50 77	69.17 285	85.80 251	89.56 295	80.76 152	92.13 182	73.21 272	95.51 167	93.25 177
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mamba_0408		83.44 211	82.88 221	85.11 158	89.13 207	68.97 256	72.73 463	91.28 170	72.90 222	85.68 252	90.61 261	76.78 210	93.97 115	73.37 263	93.47 258	92.38 232

SSM_04072		81.44 260	82.88 221	77.10 388	89.13 207	68.97 256	72.73 463	91.28 170	72.90 222	85.68 252	90.61 261	76.78 210	69.94 487	73.37 263	93.47 258	92.38 232

SSM_0407		84.89 154	84.85 160	85.01 163	89.13 207	68.97 256	85.60 166	91.58 157	74.41 186	85.68 252	91.49 213	78.54 171	93.69 127	73.71 251	93.47 258	92.38 232

TSAR-MVS + GP.		83.95 189	82.69 226	87.72 97	89.27 202	81.45 89	83.72 220	81.58 384	74.73 180	85.66 255	86.06 378	72.56 276	92.69 168	75.44 219	95.21 177	89.01 354

EU-MVSNet		75.12 373	74.43 376	77.18 387	83.11 400	59.48 405	85.71 164	82.43 372	39.76 540	85.64 256	88.76 317	44.71 495	87.88 329	73.86 248	85.88 462	84.16 434

SIFT-ConvMatch		74.17 388	72.94 397	77.87 371	80.47 439	83.15 69	74.56 435	63.87 516	63.44 381	85.61 257	83.95 418	53.15 429	69.97 486	57.21 431	94.21 229	80.48 486

MonoMVSNet		76.66 347	77.26 335	74.86 425	79.86 455	54.34 467	86.26 149	86.08 306	71.08 259	85.59 258	88.68 320	53.95 424	85.93 375	63.86 372	80.02 510	84.32 429

LF4IMVS		82.75 226	81.93 243	85.19 156	82.08 407	80.15 102	85.53 167	88.76 247	68.01 308	85.58 259	87.75 345	71.80 287	86.85 353	74.02 245	93.87 243	88.58 361

Patchmtry		76.56 351	77.46 330	73.83 435	79.37 463	46.60 513	82.41 272	76.90 425	73.81 195	85.56 260	92.38 176	48.07 462	83.98 406	63.36 378	95.31 175	90.92 289

MVS_111021_HR		84.63 160	84.34 184	85.49 152	90.18 181	75.86 162	79.23 353	87.13 287	73.35 209	85.56 260	89.34 301	83.60 101	90.50 248	76.64 196	94.05 238	90.09 319

testdata				79.54 334	92.87 92	72.34 202		80.14 397	59.91 437	85.47 262	91.75 206	67.96 313	85.24 390	68.57 327	92.18 316	81.06 481

FE-MVSNET		78.46 320	79.36 302	75.75 414	86.53 301	54.53 465	78.03 370	85.35 321	69.01 291	85.41 263	90.68 255	64.27 337	85.73 385	62.59 383	92.35 307	87.00 396

viewcassd2359sk11		83.53 204	83.96 192	82.25 262	86.97 294	61.13 372	80.80 319	93.22 93	70.97 261	85.36 264	91.08 234	81.84 138	91.29 211	74.79 228	90.58 376	94.33 115

mvsmamba		80.30 288	78.87 307	84.58 177	88.12 247	67.55 273	92.35 30	84.88 336	63.15 385	85.33 265	90.91 243	50.71 449	95.20 65	66.36 344	87.98 425	90.99 286

SIFT-UM-Cal		73.50 398	72.76 400	75.71 416	79.21 465	81.68 85	72.85 462	68.91 489	62.93 386	85.31 266	83.39 432	52.88 431	67.56 512	54.97 456	94.42 223	77.89 509

test1111		78.53 319	78.85 310	77.56 376	92.22 113	47.49 508	82.61 261	69.24 486	72.43 231	85.28 267	94.20 90	51.91 438	90.07 269	65.36 357	96.45 117	95.11 73

thisisatest0530		79.07 304	77.33 334	84.26 190	87.13 282	64.58 307	83.66 223	75.95 432	68.86 294	85.22 268	87.36 355	38.10 511	93.57 138	75.47 218	94.28 228	94.62 95

XFeat-MNN		64.44 485	63.82 485	66.28 495	61.83 551	67.23 274	61.52 525	63.95 515	44.72 526	85.19 269	74.40 523	36.05 519	66.04 522	55.58 446	91.14 345	65.57 534

BP-MVS1		82.81 223	81.67 247	86.23 126	87.88 253	68.53 262	86.06 154	84.36 343	75.65 165	85.14 270	90.19 278	45.84 479	94.42 94	85.18 71	94.72 210	95.75 49

NormalMVS		86.47 110	85.32 150	89.94 50	94.43 43	80.42 98	88.63 104	93.59 73	74.56 183	85.12 271	90.34 268	66.19 325	94.20 102	76.57 197	98.44 19	95.19 69

SymmetryMVS		84.79 157	83.54 198	88.55 79	92.44 105	80.42 98	88.63 104	82.37 373	74.56 183	85.12 271	90.34 268	66.19 325	94.20 102	76.57 197	95.68 161	91.03 285

EC-MVSNet		88.01 84	88.32 86	87.09 105	89.28 201	72.03 208	90.31 64	96.31 3	80.88 93	85.12 271	89.67 294	84.47 90	95.46 52	82.56 109	96.26 126	93.77 148

dtuonlycased		77.13 339	76.99 339	77.55 379	88.60 230	57.48 437	74.18 440	81.70 380	55.62 468	85.10 274	88.40 325	74.87 230	82.26 417	56.73 435	87.66 433	92.90 200

CLD-MVS		83.18 215	82.64 228	84.79 168	89.05 212	67.82 272	77.93 375	92.52 127	68.33 303	85.07 275	81.54 460	82.06 131	92.96 160	69.35 311	97.91 53	93.57 162
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
fmvsm_s_conf0.1_n_a		82.58 229	81.93 243	84.50 178	87.68 261	73.35 180	86.14 153	77.70 415	61.64 411	85.02 276	91.62 208	77.75 183	86.24 368	82.79 106	87.07 442	93.91 136

FA-MVS(test-final)		83.13 217	83.02 216	83.43 219	86.16 322	66.08 294	88.00 113	88.36 259	75.55 168	85.02 276	92.75 164	65.12 334	92.50 172	74.94 227	91.30 343	91.72 264

viewdifsd2359ckpt09		83.64 198	83.18 212	85.03 161	87.26 277	66.99 283	85.32 173	93.83 56	65.57 351	84.99 278	89.40 298	77.30 193	93.57 138	71.16 290	93.80 245	94.54 100

DeepC-MVS_fast	80.27 8	86.23 113	85.65 141	87.96 95	91.30 149	76.92 143	87.19 125	91.99 144	70.56 265	84.96 279	90.69 253	80.01 161	95.14 68	78.37 161	95.78 156	91.82 260
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
3Dnovator	80.37 7	84.80 155	84.71 166	85.06 160	86.36 313	74.71 170	88.77 100	90.00 219	75.65 165	84.96 279	93.17 142	74.06 248	91.19 219	78.28 164	91.09 348	89.29 341

QAPM		82.59 228	82.59 230	82.58 251	86.44 306	66.69 286	89.94 72	90.36 204	67.97 310	84.94 281	92.58 169	72.71 273	92.18 181	70.63 297	87.73 430	88.85 356

fmvsm_l_conf0.5_n_3		85.11 148	84.96 158	85.56 148	87.49 270	75.69 163	84.71 188	90.61 195	67.64 318	84.88 282	92.05 189	82.30 122	88.36 318	83.84 93	91.10 347	92.62 212

VPNet		80.25 289	81.68 246	75.94 411	92.46 104	47.98 506	76.70 399	81.67 382	73.45 206	84.87 283	92.82 160	74.66 238	86.51 361	61.66 396	96.85 99	93.33 170

NCCC		87.36 94	86.87 110	88.83 71	92.32 110	78.84 117	86.58 142	91.09 179	78.77 123	84.85 284	90.89 244	80.85 150	95.29 59	81.14 124	95.32 173	92.34 235

viewmanbaseed2359cas		82.95 222	83.43 203	81.52 283	85.18 346	60.03 394	81.36 299	92.38 131	69.55 280	84.84 285	91.38 218	79.85 164	90.09 267	74.22 235	92.09 318	94.43 109

SIFT-NCM-Cal		73.77 394	72.70 402	76.99 390	82.03 408	83.73 63	75.59 420	63.01 522	63.50 380	84.80 286	83.94 419	55.86 410	67.80 508	52.94 475	92.62 293	79.44 495

E3new		83.08 219	83.39 205	82.14 266	86.49 303	61.00 377	80.64 321	93.12 98	70.30 271	84.78 287	90.34 268	80.85 150	91.24 217	74.20 238	89.83 389	94.17 122

PHI-MVS		86.38 111	85.81 135	88.08 92	88.44 235	77.34 138	89.35 91	93.05 103	73.15 217	84.76 288	87.70 346	78.87 170	94.18 105	80.67 133	96.29 122	92.73 204

SIFT-UMatch		73.61 396	72.65 404	76.46 403	80.19 450	82.31 78	74.23 439	64.86 510	64.03 376	84.69 289	84.19 415	50.89 447	67.79 509	57.03 432	93.79 246	79.28 497

pmmvs-eth3d		78.42 324	77.04 338	82.57 253	87.44 274	74.41 173	80.86 316	79.67 399	55.68 467	84.69 289	90.31 273	60.91 361	85.42 389	62.20 386	91.59 336	87.88 381

test_prior2							83.37 236		75.43 171	84.58 291	91.57 210	81.92 136		79.54 147	96.97 94

fmvsm_s_conf0.5_n_6		84.05 183	84.14 187	83.81 203	87.75 258	71.17 223	83.42 234	91.10 178	67.90 313	84.53 292	90.70 252	73.01 269	88.73 303	85.09 72	93.72 252	91.53 274

fmvsm_s_conf0.5_n_a		82.21 238	81.51 256	84.32 187	86.56 300	73.35 180	85.46 169	77.30 421	61.81 407	84.51 293	90.88 246	77.36 191	86.21 370	82.72 107	86.97 447	93.38 168

TEST9					92.34 108	79.70 106	83.94 211	90.32 206	65.41 355	84.49 294	90.97 238	82.03 132	93.63 130

train_agg		85.98 121	85.28 151	88.07 93	92.34 108	79.70 106	83.94 211	90.32 206	65.79 343	84.49 294	90.97 238	81.93 134	93.63 130	81.21 123	96.54 112	90.88 291

fmvsm_s_conf0.1_n		82.17 240	81.59 251	83.94 202	86.87 298	71.57 217	85.19 177	77.42 419	62.27 402	84.47 296	91.33 221	76.43 213	85.91 378	83.14 97	87.14 440	94.33 115

Gipuma		84.44 167	86.33 121	78.78 349	84.20 368	73.57 178	89.55 82	90.44 200	84.24 56	84.38 297	94.89 56	76.35 216	80.40 435	76.14 208	96.80 104	82.36 463
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_f		64.31 487	65.85 473	59.67 520	66.54 542	62.24 352	57.76 535	70.96 476	40.13 538	84.36 298	82.09 450	46.93 465	51.67 546	61.99 390	81.89 500	65.12 535

test_8					92.09 117	78.87 116	83.82 216	90.31 208	65.79 343	84.36 298	90.96 240	81.93 134	93.44 144

SIFT-MNN		74.38 387	73.27 390	77.72 374	82.37 405	83.68 64	76.29 408	67.76 493	64.16 373	84.33 300	84.30 410	50.36 454	68.84 498	57.79 426	92.07 319	80.66 485

cl22		78.97 306	78.21 321	81.24 293	77.74 478	59.01 416	77.46 387	87.13 287	65.79 343	84.32 301	85.10 396	58.96 378	90.88 233	75.36 220	92.03 320	93.84 139

CS-MVS		88.14 80	87.67 93	89.54 60	89.56 194	79.18 113	90.47 60	94.77 16	79.37 114	84.32 301	89.33 302	83.87 95	94.53 92	82.45 110	94.89 195	94.90 78

agg_prior					91.58 139	77.69 132		90.30 209		84.32 301			93.18 152

Anonymous202405211		80.51 280	81.19 266	78.49 355	88.48 233	57.26 439	76.63 401	82.49 369	81.21 89	84.30 304	92.24 186	67.99 312	86.24 368	62.22 385	95.13 183	91.98 257

LFMVS		80.15 293	80.56 276	78.89 344	89.19 205	55.93 448	85.22 176	73.78 449	82.96 72	84.28 305	92.72 165	57.38 394	90.07 269	63.80 373	95.75 157	90.68 298

Vis-MVSNet		86.86 100	86.58 113	87.72 97	92.09 117	77.43 137	87.35 123	92.09 141	78.87 121	84.27 306	94.05 100	78.35 176	93.65 128	80.54 135	91.58 337	92.08 251
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ECVR-MVS		78.44 323	78.63 314	77.88 370	91.85 127	48.95 502	83.68 222	69.91 481	72.30 237	84.26 307	94.20 90	51.89 439	89.82 275	63.58 374	96.02 137	94.87 80

FE-MVS		79.98 297	78.86 308	83.36 221	86.47 304	66.45 290	89.73 75	84.74 340	72.80 226	84.22 308	91.38 218	44.95 492	93.60 134	63.93 371	91.50 338	90.04 320

SIFT-PointCN		72.17 417	71.14 425	75.23 421	77.93 477	79.30 112	72.22 467	64.71 512	62.60 390	84.13 309	81.00 464	46.91 466	67.69 511	55.17 453	95.64 164	78.70 503

ETV-MVS		84.31 171	83.91 195	85.52 149	88.58 231	70.40 233	84.50 198	93.37 80	78.76 124	84.07 310	78.72 488	80.39 157	95.13 69	73.82 249	92.98 280	91.04 284

SIFT-PCN-Cal		71.86 419	71.21 423	73.82 436	77.43 484	78.37 120	71.75 471	65.73 505	62.15 404	84.04 311	81.59 459	50.59 451	64.96 529	52.46 480	95.15 181	78.14 508

fmvsm_s_conf0.5_n		81.91 252	81.30 261	83.75 207	86.02 325	71.56 218	84.73 187	77.11 424	62.44 399	84.00 312	90.68 255	76.42 214	85.89 380	83.14 97	87.11 441	93.81 146

MCST-MVS		84.36 169	83.93 193	85.63 146	91.59 136	71.58 216	83.52 231	92.13 139	61.82 406	83.96 313	89.75 291	79.93 163	93.46 143	78.33 163	94.34 225	91.87 259

新几何1				82.95 235	93.96 63	78.56 119		80.24 395	55.45 470	83.93 314	91.08 234	71.19 293	88.33 319	65.84 352	93.07 277	81.95 468

mmtdpeth		85.13 146	85.78 137	83.17 229	84.65 357	74.71 170	85.87 158	90.35 205	77.94 133	83.82 315	96.96 14	77.75 183	80.03 438	78.44 159	96.21 127	94.79 92

fmvsm_l_conf0.5_n		82.06 244	81.54 255	83.60 213	83.94 374	73.90 176	83.35 237	86.10 305	58.97 440	83.80 316	90.36 267	74.23 243	86.94 350	82.90 103	90.22 382	89.94 322

GDP-MVS		82.17 240	80.85 273	86.15 133	88.65 227	68.95 259	85.65 165	93.02 107	68.42 301	83.73 317	89.54 296	45.07 491	94.31 96	79.66 144	93.87 243	95.19 69

viewdifsd2359ckpt13		82.22 237	81.98 242	82.95 235	85.48 340	64.44 311	83.17 245	92.11 140	65.97 337	83.72 318	89.73 292	77.60 187	90.80 237	70.61 298	89.42 395	93.59 160

BH-RMVSNet		80.53 279	80.22 284	81.49 285	87.19 281	66.21 292	77.79 378	86.23 303	74.21 190	83.69 319	88.50 324	73.25 267	90.75 238	63.18 380	87.90 426	87.52 388

USDC		76.63 348	76.73 345	76.34 406	83.46 385	57.20 440	80.02 331	88.04 268	52.14 495	83.65 320	91.25 226	63.24 349	86.65 358	54.66 459	94.11 234	85.17 418

miper_enhance_ethall		77.83 328	76.93 340	80.51 311	76.15 500	58.01 432	75.47 423	88.82 244	58.05 448	83.59 321	80.69 467	64.41 336	91.20 218	73.16 273	92.03 320	92.33 237

MM		87.64 92	87.15 100	89.09 68	89.51 195	76.39 151	88.68 102	86.76 297	84.54 52	83.58 322	93.78 116	73.36 265	96.48 1	87.98 16	96.21 127	94.41 111

Effi-MVS+-dtu		85.82 126	83.38 206	93.14 3	87.13 282	91.15 2	87.70 118	88.42 257	74.57 182	83.56 323	85.65 384	78.49 175	94.21 101	72.04 279	92.88 283	94.05 129

CNLPA		83.55 203	83.10 215	84.90 164	89.34 200	83.87 61	84.54 195	88.77 245	79.09 117	83.54 324	88.66 323	74.87 230	81.73 422	66.84 340	92.29 310	89.11 347

SIFT-CM-Cal		73.20 403	71.85 412	77.25 386	79.80 457	82.49 77	73.51 452	64.83 511	62.27 402	83.49 325	82.81 444	51.79 440	69.71 488	53.70 466	94.43 220	79.53 494

SDMVSNet		81.90 253	83.17 213	78.10 365	88.81 222	62.45 345	76.08 413	86.05 308	73.67 197	83.41 326	93.04 146	82.35 119	80.65 432	70.06 305	95.03 188	91.21 279

sd_testset		79.95 298	81.39 259	75.64 418	88.81 222	58.07 430	76.16 412	82.81 366	73.67 197	83.41 326	93.04 146	80.96 149	77.65 453	58.62 418	95.03 188	91.21 279

viewmamba		81.97 249	82.13 235	81.47 286	80.43 440	62.46 340	79.31 348	89.99 221	71.08 259	83.39 328	90.21 276	78.08 178	88.73 303	77.55 181	89.16 402	93.23 178

diffmvs_AUTHOR		81.24 265	81.55 254	80.30 316	80.61 436	60.22 390	77.98 374	90.48 197	67.77 316	83.34 329	89.50 297	74.69 237	87.42 339	78.78 157	90.81 362	93.27 174

OpenMVS_ROB	70.19 17	77.77 331	77.46 330	78.71 351	84.39 364	61.15 371	81.18 308	82.52 368	62.45 398	83.34 329	87.37 354	66.20 324	88.66 308	64.69 365	85.02 472	86.32 404

thres100view900		75.45 369	75.05 369	76.66 399	87.27 276	51.88 487	81.07 309	73.26 454	75.68 164	83.25 331	86.37 372	45.54 482	88.80 298	51.98 484	90.99 350	89.31 338

miper_lstm_enhance		76.45 354	76.10 353	77.51 380	76.72 493	60.97 381	64.69 515	85.04 329	63.98 378	83.20 332	88.22 329	56.67 399	78.79 446	73.22 267	93.12 276	92.78 203

IterMVS		76.91 343	76.34 351	78.64 352	80.91 428	64.03 316	76.30 407	79.03 403	64.88 365	83.11 333	89.16 309	59.90 369	84.46 399	68.61 325	85.15 470	87.42 389
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
thres600view7		75.97 363	75.35 363	77.85 373	87.01 291	51.84 488	80.45 326	73.26 454	75.20 174	83.10 334	86.31 375	45.54 482	89.05 292	55.03 455	92.24 312	92.66 210

mvs_anonymous		78.13 326	78.76 312	76.23 409	79.24 464	50.31 498	78.69 362	84.82 338	61.60 412	83.09 335	92.82 160	73.89 252	87.01 346	68.33 329	86.41 453	91.37 276

MASt3R-SfM		63.18 489	63.70 487	61.64 514	63.57 548	67.13 277	64.25 518	57.31 543	37.50 546	82.96 336	80.95 466	45.96 476	49.82 547	54.93 457	85.89 461	67.95 531

fmvsm_l_conf0.5_n_a		81.46 259	80.87 272	83.25 224	83.73 380	73.21 185	83.00 250	85.59 318	58.22 446	82.96 336	90.09 283	72.30 279	86.65 358	81.97 119	89.95 387	89.88 323

SP-SuperGlue		80.13 294	80.14 286	80.11 321	79.95 454	80.97 93	80.94 313	80.77 392	76.46 150	82.92 338	85.73 383	58.75 380	70.83 483	85.20 70	90.50 377	88.53 362

dtuplus		78.46 320	78.13 323	79.45 337	80.90 430	59.52 404	77.65 380	86.72 298	61.21 420	82.91 339	89.26 304	73.46 261	87.27 343	63.53 376	87.49 435	91.55 272

test_fmvs2		73.57 397	72.80 398	75.90 412	72.74 527	68.84 260	77.07 392	84.32 345	45.14 524	82.89 340	84.22 414	48.37 460	70.36 485	73.40 262	87.03 444	88.52 363

MVS_Test		82.47 231	83.22 209	80.22 318	82.62 404	57.75 435	82.54 266	91.96 146	71.16 258	82.89 340	92.52 173	77.41 190	90.50 248	80.04 138	87.84 429	92.40 229

reproduce_monomvs		74.09 390	73.23 391	76.65 401	76.52 494	54.54 464	77.50 385	81.40 386	65.85 342	82.86 342	86.67 366	27.38 544	84.53 398	70.24 302	90.66 373	90.89 290

TestfortrainingZip				84.49 179	88.84 220	70.49 231	92.12 33	91.01 181	84.70 50	82.82 343	89.25 305	74.30 242	94.06 111		90.73 369	88.92 355

test12				86.57 118	90.74 167	72.63 195		90.69 191		82.76 344		79.20 166	94.80 79		95.32 173	92.27 242

fmvsm_s_conf0.5_n_11		84.56 163	84.69 168	84.15 195	86.53 301	71.29 221	85.53 167	92.62 123	70.54 266	82.75 345	91.20 229	77.33 192	88.55 314	83.80 94	91.93 325	92.61 214

原ACMM1				84.60 176	92.81 98	74.01 175		91.50 161	62.59 391	82.73 346	90.67 258	76.53 212	94.25 99	69.24 312	95.69 160	85.55 414

test_yl		78.71 316	78.51 316	79.32 339	84.32 365	58.84 420	78.38 365	85.33 322	75.99 156	82.49 347	86.57 367	58.01 388	90.02 271	62.74 381	92.73 290	89.10 348

DCV-MVSNet		78.71 316	78.51 316	79.32 339	84.32 365	58.84 420	78.38 365	85.33 322	75.99 156	82.49 347	86.57 367	58.01 388	90.02 271	62.74 381	92.73 290	89.10 348

diffmvs		80.40 284	80.48 279	80.17 319	79.02 468	60.04 392	77.54 383	90.28 212	66.65 332	82.40 349	87.33 356	73.50 258	87.35 341	77.98 175	89.62 392	93.13 182
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmambaseed2359dif		78.80 312	78.47 318	79.78 325	80.26 448	59.28 408	77.31 389	87.13 287	60.42 431	82.37 350	88.67 322	74.58 239	87.87 330	67.78 333	87.73 430	92.19 246

test222					93.31 81	76.54 146	79.38 346	77.79 413	52.59 489	82.36 351	90.84 248	66.83 322			91.69 333	81.25 476

D2MVS		76.84 344	75.67 358	80.34 315	80.48 438	62.16 353	73.50 453	84.80 339	57.61 452	82.24 352	87.54 349	51.31 444	87.65 333	70.40 301	93.19 275	91.23 278

VNet		79.31 302	80.27 281	76.44 404	87.92 251	53.95 471	75.58 421	84.35 344	74.39 189	82.23 353	90.72 251	72.84 272	84.39 401	60.38 406	93.98 239	90.97 287

Vis-MVSNet (Re-imp)		77.82 329	77.79 328	77.92 369	88.82 221	51.29 492	83.28 238	71.97 469	74.04 192	82.23 353	89.78 290	57.38 394	89.41 288	57.22 430	95.41 169	93.05 188

API-MVS		82.28 235	82.61 229	81.30 289	86.29 316	69.79 240	88.71 101	87.67 276	78.42 128	82.15 355	84.15 417	77.98 180	91.59 198	65.39 356	92.75 288	82.51 462

icg_test_0407_2		78.46 320	79.68 295	74.78 427	85.76 332	62.46 340	68.51 495	87.91 271	65.23 358	82.12 356	87.92 338	77.27 195	72.67 474	71.67 282	90.74 365	89.20 342

IMVS_0407		81.08 268	81.23 264	80.62 309	85.76 332	62.46 340	82.46 268	87.91 271	65.23 358	82.12 356	87.92 338	77.27 195	90.18 259	71.67 282	90.74 365	89.20 342

IMVS_0403		80.93 273	81.00 267	80.72 304	85.76 332	62.46 340	81.82 288	87.91 271	65.23 358	82.07 358	87.92 338	75.91 217	90.50 248	71.67 282	90.74 365	89.20 342

DP-MVS Recon		84.05 183	83.22 209	86.52 120	91.73 133	75.27 167	83.23 243	92.40 129	72.04 241	82.04 359	88.33 328	77.91 182	93.95 117	66.17 346	95.12 185	90.34 311

onestephybrid01		81.22 266	80.90 271	82.18 264	80.05 451	64.49 310	79.47 342	89.23 239	69.10 287	81.96 360	89.27 303	75.02 227	89.12 291	73.71 251	90.24 381	92.92 199

MSDG		80.06 296	79.99 293	80.25 317	83.91 376	68.04 270	77.51 384	89.19 240	77.65 138	81.94 361	83.45 429	76.37 215	86.31 367	63.31 379	86.59 451	86.41 403

test2506		74.12 389	73.39 388	76.28 407	91.85 127	44.20 523	84.06 207	48.20 550	72.30 237	81.90 362	94.20 90	27.22 546	89.77 278	64.81 363	96.02 137	94.87 80

Fast-Effi-MVS+		81.04 270	80.57 275	82.46 257	87.50 269	63.22 326	78.37 367	89.63 231	68.01 308	81.87 363	82.08 451	82.31 121	92.65 169	67.10 337	88.30 422	91.51 275

testgi		72.36 413	74.61 372	65.59 499	80.56 437	42.82 529	68.29 496	73.35 453	66.87 330	81.84 364	89.93 287	72.08 283	66.92 516	46.05 520	92.54 300	87.01 395

tfpn200view9		74.86 379	74.23 377	76.74 398	86.24 317	52.12 484	79.24 351	73.87 447	73.34 210	81.82 365	84.60 407	46.02 473	88.80 298	51.98 484	90.99 350	89.31 338

thres400		75.14 371	74.23 377	77.86 372	86.24 317	52.12 484	79.24 351	73.87 447	73.34 210	81.82 365	84.60 407	46.02 473	88.80 298	51.98 484	90.99 350	92.66 210

CL-MVSNet_self_test		76.81 345	77.38 332	75.12 423	86.90 296	51.34 490	73.20 457	80.63 394	68.30 304	81.80 367	88.40 325	66.92 321	80.90 429	55.35 451	94.90 194	93.12 185

OpenMVS	76.72 13	81.98 248	82.00 241	81.93 270	84.42 363	68.22 266	88.50 107	89.48 234	66.92 329	81.80 367	91.86 197	72.59 275	90.16 261	71.19 289	91.25 344	87.40 390

SIFT-NN-CMatch		72.68 409	71.28 420	76.88 396	78.79 470	82.59 76	73.68 448	61.02 532	60.35 432	81.79 369	83.09 435	52.94 430	68.88 497	57.28 429	92.53 301	79.16 499

MGCNet		85.37 138	84.58 173	87.75 96	85.28 343	73.36 179	86.54 144	85.71 315	77.56 141	81.78 370	92.47 174	70.29 299	96.02 10	85.59 66	95.96 141	93.87 138

AdaColmap		83.66 197	83.69 197	83.57 216	90.05 186	72.26 204	86.29 148	90.00 219	78.19 131	81.65 371	87.16 359	83.40 103	94.24 100	61.69 395	94.76 209	84.21 433

SPE-MVS-test		87.00 98	86.43 116	88.71 75	89.46 197	77.46 135	89.42 89	95.73 6	77.87 136	81.64 372	87.25 357	82.43 117	94.53 92	77.65 179	96.46 116	94.14 125

DELS-MVS		81.44 260	81.25 262	82.03 268	84.27 367	62.87 330	76.47 406	92.49 128	70.97 261	81.64 372	83.83 420	75.03 226	92.70 167	74.29 232	92.22 314	90.51 306
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
SP-LightGlue		79.92 299	79.74 294	80.46 312	80.22 449	81.52 88	81.28 304	81.81 378	75.89 160	81.60 374	84.90 402	55.82 411	71.10 482	85.62 65	90.47 378	88.76 358

SP-MNN		77.71 332	77.85 326	77.29 384	78.48 473	75.90 160	79.14 354	79.46 400	69.61 279	81.56 375	84.60 407	54.98 421	69.02 496	81.08 126	91.72 332	86.95 397

114514_t		83.10 218	82.54 231	84.77 169	92.90 91	69.10 255	86.65 140	90.62 194	54.66 476	81.46 376	90.81 249	76.98 202	94.38 95	72.62 275	96.18 129	90.82 293

TR-MVS		76.77 346	75.79 355	79.72 328	86.10 324	65.79 297	77.14 390	83.02 363	65.20 362	81.40 377	82.10 449	66.30 323	90.73 240	55.57 447	85.27 466	82.65 456

TAMVS		78.08 327	76.36 350	83.23 225	90.62 171	72.87 189	79.08 355	80.01 398	61.72 409	81.35 378	86.92 364	63.96 344	88.78 301	50.61 490	93.01 279	88.04 374

Effi-MVS+		83.90 192	84.01 190	83.57 216	87.22 280	65.61 299	86.55 143	92.40 129	78.64 125	81.34 379	84.18 416	83.65 100	92.93 162	74.22 235	87.87 427	92.17 248

hybridnocas07		79.65 301	79.65 296	79.63 331	78.06 474	59.34 406	77.00 396	88.72 248	66.51 334	81.08 380	89.36 300	72.35 277	87.12 345	74.56 229	89.20 400	92.44 224

testing3		71.53 427	70.79 428	73.77 438	88.89 219	41.86 531	76.60 404	59.12 537	72.83 225	80.97 381	82.08 451	19.80 553	87.33 342	65.12 359	91.68 334	92.13 250

new-patchmatchnet		70.10 442	73.37 389	60.29 519	81.23 424	16.95 558	59.54 529	74.62 440	62.93 386	80.97 381	87.93 337	62.83 355	71.90 477	55.24 452	95.01 191	92.00 255

SIFT-NN-PointCN		72.35 414	71.17 424	75.90 412	77.68 480	80.93 96	73.48 454	63.14 521	60.88 425	80.94 383	82.91 441	52.54 435	67.74 510	55.98 442	92.95 282	79.05 501

PVSNet_Blended_VisFu		81.55 258	80.49 278	84.70 173	91.58 139	73.24 184	84.21 203	91.67 156	62.86 388	80.94 383	87.16 359	67.27 317	92.87 165	69.82 307	88.94 408	87.99 376

BH-w/o		76.57 349	76.07 354	78.10 365	86.88 297	65.92 296	77.63 381	86.33 301	65.69 348	80.89 385	79.95 476	68.97 309	90.74 239	53.01 474	85.25 467	77.62 511

PAPM_NR		83.23 213	83.19 211	83.33 222	90.90 163	65.98 295	88.19 109	90.78 189	78.13 132	80.87 386	87.92 338	73.49 260	92.42 173	70.07 304	88.40 416	91.60 270

ab-mvs		79.67 300	80.56 276	76.99 390	88.48 233	56.93 441	84.70 189	86.06 307	68.95 293	80.78 387	93.08 145	75.30 224	84.62 396	56.78 434	90.90 355	89.43 336

XXY-MVS		74.44 386	76.19 352	69.21 475	84.61 358	52.43 483	71.70 472	77.18 423	60.73 428	80.60 388	90.96 240	75.44 221	69.35 492	56.13 440	88.33 418	85.86 411

HQP4-MVS										80.56 389			94.61 86			93.56 163

HQP-NCC					91.19 154		84.77 183		73.30 212	80.55 390

ACMP_Plane					91.19 154		84.77 183		73.30 212	80.55 390

HQP-MVS		84.61 161	84.06 189	86.27 125	91.19 154	70.66 228	84.77 183	92.68 120	73.30 212	80.55 390	90.17 281	72.10 281	94.61 86	77.30 187	94.47 218	93.56 163

ALIKED-MNN		76.42 355	75.39 362	79.52 335	84.57 359	84.06 60	84.33 201	82.48 370	49.85 510	80.53 393	88.35 327	54.52 422	77.10 457	56.89 433	96.96 95	77.39 512

hybrid		79.06 305	78.94 306	79.40 338	77.99 476	59.05 415	77.07 392	88.49 254	64.42 371	80.52 394	88.78 316	71.45 291	86.82 354	73.23 266	88.52 414	92.34 235

test_cas_vis1_n_1920		69.20 455	69.12 448	69.43 474	73.68 518	62.82 332	70.38 486	77.21 422	46.18 521	80.46 395	78.95 485	52.03 437	65.53 525	65.77 354	77.45 524	79.95 490

AUN-MVS		81.18 267	78.78 311	88.39 83	90.93 162	82.14 80	82.51 267	83.67 354	64.69 367	80.29 396	85.91 382	51.07 446	92.38 175	76.29 204	93.63 255	90.65 301

HyFIR lowres test		75.12 373	72.66 403	82.50 255	91.44 147	65.19 303	72.47 465	87.31 280	46.79 517	80.29 396	84.30 410	52.70 434	92.10 185	51.88 488	86.73 449	90.22 312

test20.03		73.75 395	74.59 374	71.22 460	81.11 425	51.12 494	70.15 487	72.10 468	70.42 267	80.28 398	91.50 212	64.21 339	74.72 468	46.96 515	94.58 214	87.82 384

mvsany_test3		65.48 480	62.97 491	73.03 445	69.99 535	76.17 154	64.83 513	43.71 552	43.68 530	80.25 399	87.05 363	52.83 433	63.09 535	51.92 487	72.44 533	79.84 492

SIFT-NN-UMatch		72.46 411	71.25 421	76.08 410	78.57 472	81.88 82	74.36 436	61.59 530	61.99 405	80.24 400	83.46 428	51.20 445	68.08 507	57.95 425	91.91 326	78.28 506

F-COLMAP		84.97 153	83.42 204	89.63 57	92.39 106	83.40 66	88.83 98	91.92 147	73.19 216	80.18 401	89.15 310	77.04 201	93.28 149	65.82 353	92.28 311	92.21 245

GA-MVS		75.83 364	74.61 372	79.48 336	81.87 410	59.25 409	73.42 455	82.88 364	68.68 297	79.75 402	81.80 455	50.62 450	89.46 284	66.85 339	85.64 463	89.72 327

xiu_mvs_v1_base_debu		80.84 274	80.14 286	82.93 238	88.31 236	71.73 212	79.53 338	87.17 284	65.43 352	79.59 403	82.73 445	76.94 203	90.14 264	73.22 267	88.33 418	86.90 398

xiu_mvs_v1_base		80.84 274	80.14 286	82.93 238	88.31 236	71.73 212	79.53 338	87.17 284	65.43 352	79.59 403	82.73 445	76.94 203	90.14 264	73.22 267	88.33 418	86.90 398

xiu_mvs_v1_base_debi		80.84 274	80.14 286	82.93 238	88.31 236	71.73 212	79.53 338	87.17 284	65.43 352	79.59 403	82.73 445	76.94 203	90.14 264	73.22 267	88.33 418	86.90 398

test_fmvs1_n		70.94 433	70.41 435	72.53 451	73.92 515	66.93 284	75.99 414	84.21 347	43.31 532	79.40 406	79.39 481	43.47 498	68.55 501	69.05 317	84.91 475	82.10 466

blended_shiyan8		76.05 361	75.11 365	78.86 346	81.76 413	59.18 412	75.09 427	83.81 350	64.70 366	79.37 407	78.35 491	58.30 384	88.68 306	62.03 389	92.56 299	88.73 359

blended_shiyan6		76.05 361	75.11 365	78.87 345	81.74 414	59.15 413	75.08 428	83.79 351	64.69 367	79.37 407	78.37 490	58.30 384	88.69 305	61.99 390	92.61 294	88.77 357

patch_mono-2		78.89 309	79.39 299	77.41 382	84.78 354	68.11 268	75.60 418	83.11 362	60.96 424	79.36 409	89.89 289	75.18 225	72.97 473	73.32 265	92.30 308	91.15 281

UnsupCasMVSNet_eth		71.63 425	72.30 409	69.62 472	76.47 496	52.70 481	70.03 488	80.97 390	59.18 439	79.36 409	88.21 330	60.50 362	69.12 494	58.33 421	77.62 522	87.04 394

ppachtmachnet_test		74.73 383	74.00 379	76.90 394	80.71 434	56.89 443	71.53 476	78.42 409	58.24 445	79.32 411	82.92 440	57.91 391	84.26 403	65.60 355	91.36 340	89.56 332

gbinet_0.2-2-1-0.02		76.14 358	74.88 370	79.92 323	80.33 447	60.02 395	75.80 416	82.44 371	66.36 336	79.24 412	75.07 520	56.11 407	90.17 260	64.60 368	93.95 240	89.58 331

MG-MVS		80.32 287	80.94 269	78.47 356	88.18 243	52.62 482	82.29 276	85.01 331	72.01 242	79.24 412	92.54 172	69.36 305	93.36 148	70.65 296	89.19 401	89.45 333

usedtu_dtu_shiyan2		78.92 307	78.15 322	81.25 290	91.33 148	73.10 186	80.75 320	79.00 405	74.19 191	79.17 414	92.04 190	67.17 318	81.33 425	42.86 526	96.81 103	89.31 338

Fast-Effi-MVS+-dtu		82.54 230	81.41 257	85.90 138	85.60 336	76.53 148	83.07 247	89.62 232	73.02 220	79.11 415	83.51 426	80.74 153	90.24 256	68.76 322	89.29 397	90.94 288

SSC-MVS3.2		73.90 392	75.67 358	68.61 483	84.11 370	41.28 532	64.17 519	72.83 459	72.09 240	79.08 416	87.94 335	70.31 298	73.89 471	55.99 441	94.49 217	90.67 300

CDS-MVSNet		77.32 336	75.40 360	83.06 230	89.00 214	72.48 200	77.90 376	82.17 375	60.81 426	78.94 417	83.49 427	59.30 374	88.76 302	54.64 460	92.37 306	87.93 380
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
baseline1		73.26 400	73.54 385	72.43 452	84.92 351	47.79 507	79.89 333	74.00 445	65.93 340	78.81 418	86.28 376	56.36 401	81.63 423	56.63 436	79.04 517	87.87 382

SIFT-NN-NCMNet		72.70 408	71.25 421	77.06 389	81.65 417	84.07 59	75.19 425	63.15 520	61.29 417	78.74 419	83.21 433	53.60 426	69.25 493	53.99 463	90.47 378	77.86 510

ttmdpeth		71.72 422	70.67 429	74.86 425	73.08 524	55.88 449	77.41 388	69.27 485	55.86 465	78.66 420	93.77 118	38.01 513	75.39 465	60.12 407	89.87 388	93.31 172

EIA-MVS		82.19 239	81.23 264	85.10 159	87.95 250	69.17 254	83.22 244	93.33 85	70.42 267	78.58 421	79.77 479	77.29 194	94.20 102	71.51 286	88.96 407	91.93 258

thres200		72.34 415	71.55 417	74.70 429	83.48 384	51.60 489	75.02 429	73.71 450	70.14 274	78.56 422	80.57 470	46.20 471	88.20 321	46.99 514	89.29 397	84.32 429

usedtu_dtu_shiyan1		75.70 367	75.08 367	77.56 376	84.10 371	55.50 455	73.58 449	84.89 334	62.48 393	78.16 423	84.24 412	58.14 386	87.47 337	59.35 412	90.82 360	89.72 327

FE-MVSNET3		75.70 367	75.08 367	77.56 376	84.10 371	55.50 455	73.58 449	84.89 334	62.48 393	78.16 423	84.24 412	58.14 386	87.47 337	59.34 413	90.82 360	89.72 327

fmvsm_s_conf0.5_n_7		82.04 245	82.05 240	82.01 269	86.98 293	71.07 224	78.70 361	89.45 235	68.07 307	78.14 425	91.61 209	74.19 244	85.92 376	79.61 145	91.73 331	89.05 351

our_test_3		71.85 420	71.59 414	72.62 449	80.71 434	53.78 472	69.72 490	71.71 473	58.80 442	78.03 426	80.51 472	56.61 400	78.84 445	62.20 386	86.04 460	85.23 417

KD-MVS_2432*1600		66.87 467	65.81 475	70.04 466	67.50 539	47.49 508	62.56 522	79.16 401	61.21 420	77.98 427	80.61 468	25.29 549	82.48 414	53.02 472	84.92 473	80.16 488

miper_refine_blended		66.87 467	65.81 475	70.04 466	67.50 539	47.49 508	62.56 522	79.16 401	61.21 420	77.98 427	80.61 468	25.29 549	82.48 414	53.02 472	84.92 473	80.16 488

jason		77.42 335	75.75 356	82.43 258	87.10 285	69.27 249	77.99 373	81.94 377	51.47 499	77.84 429	85.07 399	60.32 365	89.00 293	70.74 295	89.27 399	89.03 352
jason: jason.
MAR-MVS		80.24 290	78.74 313	84.73 171	86.87 298	78.18 124	85.75 162	87.81 275	65.67 350	77.84 429	78.50 489	73.79 254	90.53 247	61.59 397	90.87 357	85.49 416
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
FPMVS		72.29 416	72.00 410	73.14 443	88.63 228	85.00 49	74.65 433	67.39 495	71.94 243	77.80 431	87.66 347	50.48 452	75.83 462	49.95 494	79.51 511	58.58 543

test_fmvs1		69.57 450	69.05 450	71.14 462	69.15 538	65.77 298	73.98 444	83.32 359	42.83 534	77.77 432	78.27 493	43.39 501	68.50 502	68.39 328	84.38 482	79.15 500

pmmvs4		74.92 378	72.98 396	80.73 303	84.95 349	71.71 215	76.23 410	77.59 416	52.83 488	77.73 433	86.38 371	56.35 402	84.97 393	57.72 428	87.05 443	85.51 415

wanda-best-256-512		74.97 376	73.85 380	78.35 358	80.36 442	58.13 427	73.10 459	83.53 356	64.04 375	77.62 434	75.71 514	56.22 404	88.60 312	61.42 398	92.61 294	88.32 365

FE-blended-shiyan7		74.97 376	73.85 380	78.35 358	80.36 442	58.13 427	73.10 459	83.53 356	64.03 376	77.62 434	75.71 514	56.22 404	88.60 312	61.42 398	92.61 294	88.32 365

usedtu_blend_shiyan5		77.07 341	76.43 349	78.99 343	80.36 442	59.77 399	83.25 240	88.32 261	74.91 177	77.62 434	75.71 514	56.22 404	88.89 296	58.91 415	92.61 294	88.32 365

ET-MVSNet_ETH3D		75.28 370	72.77 399	82.81 242	83.03 401	68.11 268	77.09 391	76.51 429	60.67 429	77.60 437	80.52 471	38.04 512	91.15 221	70.78 293	90.68 370	89.17 346

testing3-2		70.72 437	70.97 426	69.95 468	88.93 217	34.80 545	69.85 489	66.59 503	78.42 128	77.58 438	85.55 385	31.83 530	82.08 418	46.28 517	93.73 251	92.98 195

UnsupCasMVSNet_bld		69.21 454	69.68 442	67.82 486	79.42 461	51.15 493	67.82 500	75.79 433	54.15 479	77.47 439	85.36 394	59.26 375	70.64 484	48.46 505	79.35 513	81.66 470

blend_shiyan4		70.82 435	68.15 460	78.83 348	81.06 426	59.77 399	74.58 434	83.79 351	64.94 364	77.34 440	75.47 518	29.39 537	88.89 296	58.91 415	67.86 543	87.84 383

XFeat-NN		59.92 503	59.04 505	62.58 510	63.37 549	64.42 312	55.18 538	60.26 535	41.73 536	77.26 441	69.20 532	31.98 529	58.40 541	48.23 509	84.12 484	64.93 536

WBMVS		68.76 458	68.43 457	69.75 471	83.29 393	40.30 535	67.36 503	72.21 466	57.09 458	77.05 442	85.53 387	33.68 524	80.51 433	48.79 503	90.90 355	88.45 364

Anonymous20231206		71.38 429	71.88 411	69.88 469	86.31 314	54.37 466	70.39 485	74.62 440	52.57 490	76.73 443	88.76 317	59.94 368	72.06 476	44.35 524	93.23 273	83.23 451

CMPMVS	59.41 20	75.12 373	73.57 384	79.77 326	75.84 503	67.22 275	81.21 307	82.18 374	50.78 504	76.50 444	87.66 347	55.20 418	82.99 412	62.17 388	90.64 375	89.09 350
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
FMVSNet5		72.10 418	71.69 413	73.32 440	81.57 419	53.02 478	76.77 398	78.37 411	63.31 382	76.37 445	91.85 198	36.68 517	78.98 443	47.87 510	92.45 303	87.95 378

CVMVSNet		72.62 410	71.41 418	76.28 407	83.25 395	60.34 388	83.50 232	79.02 404	37.77 545	76.33 446	85.10 396	49.60 458	87.41 340	70.54 299	77.54 523	81.08 479

PLC	73.85 16	82.09 243	80.31 280	87.45 101	90.86 165	80.29 101	85.88 157	90.65 192	68.17 306	76.32 447	86.33 373	73.12 268	92.61 170	61.40 400	90.02 386	89.44 334
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSFormer		82.23 236	81.57 253	84.19 194	85.54 338	69.26 250	91.98 39	90.08 217	71.54 249	76.23 448	85.07 399	58.69 381	94.27 97	86.26 50	88.77 409	89.03 352

lupinMVS		76.37 356	74.46 375	82.09 267	85.54 338	69.26 250	76.79 397	80.77 392	50.68 506	76.23 448	82.82 442	58.69 381	88.94 294	69.85 306	88.77 409	88.07 371

UWE-MVS		66.43 473	65.56 478	69.05 476	84.15 369	40.98 533	73.06 461	64.71 512	54.84 474	76.18 450	79.62 480	29.21 539	80.50 434	38.54 537	89.75 390	85.66 413

PatchMatch-RL		74.48 384	73.22 392	78.27 363	87.70 260	85.26 47	75.92 415	70.09 479	64.34 372	76.09 451	81.25 462	65.87 329	78.07 451	53.86 464	83.82 487	71.48 525

thisisatest0515		73.00 406	70.52 432	80.46 312	81.45 420	59.90 397	73.16 458	74.31 444	57.86 449	76.08 452	77.78 495	37.60 515	92.12 184	65.00 360	91.45 339	89.35 337

SP-NN		76.57 349	76.54 346	76.66 399	77.40 485	75.50 164	78.02 371	78.77 407	68.60 300	75.98 453	83.71 423	55.56 414	66.71 517	82.06 115	88.74 411	87.76 385

SIFT-NN		71.05 432	69.58 444	75.45 420	80.35 446	81.93 81	74.31 437	63.57 518	61.17 423	75.98 453	81.67 458	46.63 469	65.25 527	53.44 470	89.09 404	79.18 498

MS-PatchMatch		70.93 434	70.22 436	73.06 444	81.85 411	62.50 339	73.82 447	77.90 412	52.44 491	75.92 455	81.27 461	55.67 413	81.75 421	55.37 450	77.70 521	74.94 518

CHOSEN 1792x2688		72.45 412	70.56 431	78.13 364	90.02 188	63.08 327	68.72 494	83.16 361	42.99 533	75.92 455	85.46 389	57.22 397	85.18 392	49.87 496	81.67 501	86.14 406

CR-MVSNet		74.00 391	73.04 395	76.85 397	79.58 458	62.64 336	82.58 263	76.90 425	50.50 507	75.72 457	92.38 176	48.07 462	84.07 405	68.72 324	82.91 494	83.85 438

RPMNet		78.88 310	78.28 320	80.68 307	79.58 458	62.64 336	82.58 263	94.16 33	74.80 178	75.72 457	92.59 167	48.69 459	95.56 43	73.48 260	82.91 494	83.85 438

DPM-MVS		80.10 295	79.18 304	82.88 241	90.71 169	69.74 242	78.87 359	90.84 187	60.29 434	75.64 459	85.92 381	67.28 316	93.11 155	71.24 288	91.79 328	85.77 412

test_vis1_n		70.29 439	69.99 440	71.20 461	75.97 502	66.50 288	76.69 400	80.81 391	44.22 528	75.43 460	77.23 503	50.00 455	68.59 500	66.71 342	82.85 496	78.52 505

PVSNet_BlendedMVS		78.80 312	77.84 327	81.65 280	84.43 361	63.41 322	79.49 341	90.44 200	61.70 410	75.43 460	87.07 362	69.11 307	91.44 204	60.68 404	92.24 312	90.11 318

PVSNet_Blended		76.49 353	75.40 360	79.76 327	84.43 361	63.41 322	75.14 426	90.44 200	57.36 455	75.43 460	78.30 492	69.11 307	91.44 204	60.68 404	87.70 432	84.42 428

PAPR		78.84 311	78.10 325	81.07 295	85.17 347	60.22 390	82.21 280	90.57 196	62.51 392	75.32 463	84.61 406	74.99 228	92.30 179	59.48 411	88.04 424	90.68 298

N_pmnet		70.20 440	68.80 455	74.38 430	80.91 428	84.81 52	59.12 531	76.45 431	55.06 472	75.31 464	82.36 448	55.74 412	54.82 543	47.02 513	87.24 439	83.52 443

cascas		76.29 357	74.81 371	80.72 304	84.47 360	62.94 328	73.89 446	87.34 279	55.94 464	75.16 465	76.53 509	63.97 343	91.16 220	65.00 360	90.97 353	88.06 373

SD_0403		76.08 359	76.77 343	73.98 432	87.08 289	49.45 501	83.62 224	84.68 341	63.31 382	75.13 466	87.47 352	71.85 286	84.56 397	49.97 493	87.86 428	87.94 379

PRO-TEST		78.77 315	78.12 324	80.70 306	83.83 378	62.76 335	82.20 282	88.77 245	64.67 369	75.01 467	83.52 425	70.67 297	89.92 273	67.67 335	86.75 448	89.44 334

SCA		73.32 399	72.57 406	75.58 419	81.62 418	55.86 450	78.89 358	71.37 474	61.73 408	74.93 468	83.42 430	60.46 363	87.01 346	58.11 423	82.63 499	83.88 435

test_vis1_n_1920		71.30 430	71.58 416	70.47 464	77.58 482	59.99 396	74.25 438	84.22 346	51.06 501	74.85 469	79.10 483	55.10 419	68.83 499	68.86 321	79.20 516	82.58 458

xiu_mvs_v2_base		77.19 338	76.75 344	78.52 354	87.01 291	61.30 368	75.55 422	87.12 291	61.24 419	74.45 470	78.79 487	77.20 197	90.93 229	64.62 367	84.80 479	83.32 449

CANet		83.79 195	82.85 223	86.63 116	86.17 320	72.21 206	83.76 219	91.43 163	77.24 145	74.39 471	87.45 353	75.36 223	95.42 54	77.03 190	92.83 286	92.25 244

PS-MVSNAJ		77.04 342	76.53 347	78.56 353	87.09 287	61.40 365	75.26 424	87.13 287	61.25 418	74.38 472	77.22 504	76.94 203	90.94 228	64.63 366	84.83 478	83.35 448

ALIKED-NN		74.80 381	73.22 392	79.55 333	82.93 402	83.79 62	81.84 287	82.56 367	47.43 515	74.33 473	88.03 332	53.21 428	76.31 459	54.08 462	94.57 215	78.54 504

MVP-Stereo		75.81 365	73.51 386	82.71 243	89.35 199	73.62 177	80.06 329	85.20 324	60.30 433	73.96 474	87.94 335	57.89 392	89.45 285	52.02 483	74.87 528	85.06 420
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
WB-MVSnew		68.72 459	69.01 451	67.85 485	83.22 397	43.98 524	74.93 430	65.98 504	55.09 471	73.83 475	79.11 482	65.63 331	71.89 478	38.21 538	85.04 471	87.69 386

UGNet		82.78 225	81.64 248	86.21 129	86.20 319	76.24 153	86.86 133	85.68 316	77.07 146	73.76 476	92.82 160	69.64 302	91.82 193	69.04 318	93.69 253	90.56 304
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
1112_ss		74.82 380	73.74 382	78.04 367	89.57 193	60.04 392	76.49 405	87.09 292	54.31 477	73.66 477	79.80 477	60.25 366	86.76 357	58.37 419	84.15 483	87.32 391

FBQ-MVS		71.59 426	69.67 443	77.34 383	84.84 352	56.41 446	81.26 306	76.51 429	62.70 389	73.28 478	75.95 511	36.93 516	88.04 322	48.28 507	87.27 437	87.56 387

Test_1112_low_res		73.90 392	73.08 394	76.35 405	90.35 176	55.95 447	73.40 456	86.17 304	50.70 505	73.14 479	85.94 380	58.31 383	85.90 379	56.51 437	83.22 491	87.20 393

1314		73.22 401	72.56 407	75.20 422	80.41 441	57.84 433	81.64 292	85.36 320	51.68 498	73.10 480	76.65 508	61.45 358	85.19 391	63.54 375	79.21 515	82.59 457

test_vis1_rt		65.64 479	64.09 483	70.31 465	66.09 543	70.20 236	61.16 526	81.60 383	38.65 542	72.87 481	69.66 531	52.84 432	60.04 537	56.16 439	77.77 520	80.68 483

Patchmatch-test		65.91 476	67.38 463	61.48 516	75.51 506	43.21 528	68.84 493	63.79 517	62.48 393	72.80 482	83.42 430	44.89 494	59.52 538	48.27 508	86.45 452	81.70 469

PatchmatchNet		69.71 449	68.83 454	72.33 454	77.66 481	53.60 473	79.29 349	69.99 480	57.66 451	72.53 483	82.93 439	46.45 470	80.08 437	60.91 403	72.09 534	83.31 450
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		67.95 461	68.08 462	67.55 487	78.74 471	43.53 526	75.60 418	67.10 500	54.92 473	72.23 484	88.10 331	42.87 503	75.97 461	52.21 481	80.95 509	83.15 452

IMVS_0404		77.24 337	77.75 329	75.73 415	85.76 332	62.46 340	70.84 481	87.91 271	65.23 358	72.21 485	87.92 338	67.48 315	75.53 464	71.67 282	90.74 365	89.20 342

pmmvs5		70.73 436	70.07 437	72.72 447	77.03 490	52.73 480	74.14 441	75.65 436	50.36 508	72.17 486	85.37 393	55.42 416	80.67 431	52.86 476	87.59 434	84.77 422

PatchT		70.52 438	72.76 400	63.79 508	79.38 462	33.53 546	77.63 381	65.37 508	73.61 203	71.77 487	92.79 163	44.38 496	75.65 463	64.53 369	85.37 465	82.18 465

MVS		73.21 402	72.59 405	75.06 424	80.97 427	60.81 383	81.64 292	85.92 313	46.03 522	71.68 488	77.54 498	68.47 310	89.77 278	55.70 445	85.39 464	74.60 519

MIMVSNet		71.09 431	71.59 414	69.57 473	87.23 279	50.07 499	78.91 357	71.83 470	60.20 436	71.26 489	91.76 205	55.08 420	76.09 460	41.06 530	87.02 445	82.54 460

WTY-MVS		67.91 462	68.35 458	66.58 494	80.82 432	48.12 505	65.96 510	72.60 461	53.67 482	71.20 490	81.68 457	58.97 377	69.06 495	48.57 504	81.67 501	82.55 459

test0.0.03 1		64.66 483	64.36 482	65.57 500	75.03 511	46.89 512	64.69 515	61.58 531	62.43 400	71.18 491	77.54 498	43.41 499	68.47 503	40.75 532	82.65 497	81.35 473

CostFormer		69.98 446	68.68 456	73.87 434	77.14 488	50.72 496	79.26 350	74.51 442	51.94 497	70.97 492	84.75 404	45.16 490	87.49 336	55.16 454	79.23 514	83.40 447

Syy-MVS		69.40 452	70.03 439	67.49 488	81.72 415	38.94 537	71.00 478	61.99 524	61.38 414	70.81 493	72.36 528	61.37 359	79.30 440	64.50 370	85.18 468	84.22 431

myMVS_eth3d		64.66 483	63.89 484	66.97 492	81.72 415	37.39 540	71.00 478	61.99 524	61.38 414	70.81 493	72.36 528	20.96 552	79.30 440	49.59 497	85.18 468	84.22 431

nomal-1		66.61 471	65.11 481	71.13 463	75.60 504	61.96 355	65.47 512	69.28 484	57.45 454	70.78 495	77.26 502	35.65 520	73.16 472	50.42 491	84.07 486	78.25 507

testing91		69.94 447	68.99 452	72.80 446	83.81 379	45.89 516	71.57 475	73.64 452	68.24 305	70.77 496	77.82 494	34.37 522	84.44 400	53.64 467	87.00 446	88.07 371

GLUNet-SfM		36.71 513	36.32 516	37.87 530	23.81 556	32.04 547	38.61 545	29.05 556	18.10 549	70.60 497	50.66 545	18.79 554	40.81 552	17.68 552	59.57 546	40.74 546

testing99		69.27 453	68.15 460	72.63 448	83.29 393	45.45 518	71.15 477	71.08 475	67.34 323	70.43 498	77.77 496	32.24 528	84.35 402	53.72 465	86.33 455	88.10 370

tpmvs		70.16 441	69.56 445	71.96 456	74.71 513	48.13 504	79.63 335	75.45 438	65.02 363	70.26 499	81.88 454	45.34 487	85.68 386	58.34 420	75.39 527	82.08 467

sss		66.92 466	67.26 464	65.90 497	77.23 487	51.10 495	64.79 514	71.72 472	52.12 496	70.13 500	80.18 474	57.96 390	65.36 526	50.21 492	81.01 507	81.25 476

tpm2		68.45 460	66.83 468	73.30 442	78.93 469	48.50 503	79.76 334	71.76 471	47.50 514	69.92 501	83.60 424	42.07 504	88.40 317	48.44 506	79.51 511	83.01 454

myMVS_eth3d28		65.83 478	65.85 473	65.78 498	83.42 387	35.71 543	67.29 504	68.01 492	67.58 320	69.80 502	77.72 497	32.29 527	74.30 470	37.49 539	89.06 405	87.32 391

testing222		66.93 465	65.30 479	71.81 457	83.38 388	45.83 517	72.06 469	67.50 494	64.12 374	69.68 503	76.37 510	27.34 545	83.00 411	38.88 534	88.38 417	86.62 402

HY-MVS	64.64 18	73.03 405	72.47 408	74.71 428	83.36 390	54.19 469	82.14 284	81.96 376	56.76 462	69.57 504	86.21 377	60.03 367	84.83 395	49.58 498	82.65 497	85.11 419

dmvs_re		66.81 469	66.98 466	66.28 495	76.87 491	58.68 424	71.66 473	72.24 464	60.29 434	69.52 505	73.53 524	52.38 436	64.40 532	44.90 522	81.44 504	75.76 516

ETVMVS		64.67 482	63.34 490	68.64 480	83.44 386	41.89 530	69.56 492	61.70 529	61.33 416	68.74 506	75.76 513	28.76 540	79.35 439	34.65 542	86.16 459	84.67 424

tpm cat1		66.76 470	65.21 480	71.42 459	77.09 489	50.62 497	78.01 372	73.68 451	44.89 525	68.64 507	79.00 484	45.51 484	82.42 416	49.91 495	70.15 537	81.23 478

IB-MVS	62.13 19	71.64 424	68.97 453	79.66 330	80.80 433	62.26 350	73.94 445	76.90 425	63.27 384	68.63 508	76.79 506	33.83 523	91.84 192	59.28 414	87.26 438	84.88 421
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
EPNet		80.37 285	78.41 319	86.23 126	76.75 492	73.28 182	87.18 126	77.45 417	76.24 152	68.14 509	88.93 314	65.41 332	93.85 120	69.47 310	96.12 133	91.55 272
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dtuonly		66.56 472	67.23 465	64.55 504	69.44 537	43.53 526	66.34 509	72.11 467	48.23 513	68.04 510	83.21 433	55.95 408	66.59 519	55.55 448	86.17 458	83.53 442

PVSNet	58.17 21	66.41 474	65.63 477	68.75 479	81.96 409	49.88 500	62.19 524	72.51 463	51.03 502	68.04 510	75.34 519	50.84 448	74.77 466	45.82 521	82.96 492	81.60 471

tpmrst		66.28 475	66.69 470	65.05 503	72.82 526	39.33 536	78.20 368	70.69 478	53.16 486	67.88 512	80.36 473	48.18 461	74.75 467	58.13 422	70.79 536	81.08 479

CANet_DTU		77.81 330	77.05 337	80.09 322	81.37 422	59.90 397	83.26 239	88.29 262	69.16 286	67.83 513	83.72 422	60.93 360	89.47 283	69.22 314	89.70 391	90.88 291

EPMVS		62.47 491	62.63 493	62.01 511	70.63 534	38.74 538	74.76 431	52.86 546	53.91 480	67.71 514	80.01 475	39.40 509	66.60 518	55.54 449	68.81 542	80.68 483

UBG		64.34 486	63.35 489	67.30 490	83.50 383	40.53 534	67.46 502	65.02 509	54.77 475	67.54 515	74.47 522	32.99 526	78.50 449	40.82 531	83.58 488	82.88 455

MDTV_nov1_ep13			68.29 459		78.03 475	43.87 525	74.12 442	72.22 465	52.17 493	67.02 516	85.54 386	45.36 486	80.85 430	55.73 443	84.42 481

testing11		67.38 463	65.93 472	71.73 458	83.37 389	46.60 513	70.95 480	69.40 483	62.47 396	66.14 517	76.66 507	31.22 531	84.10 404	49.10 501	84.10 485	84.49 425

pmmvs3		62.47 491	60.02 503	69.80 470	71.58 531	64.00 317	70.52 484	58.44 540	39.77 539	66.05 518	75.84 512	27.10 547	72.28 475	46.15 519	84.77 480	73.11 523

ADS-MVSNet2		65.87 477	63.64 488	72.55 450	73.16 522	56.92 442	67.10 505	74.81 439	49.74 511	66.04 519	82.97 437	46.71 467	77.26 455	42.29 527	69.96 538	83.46 445

ADS-MVSNet		61.90 494	62.19 495	61.03 517	73.16 522	36.42 542	67.10 505	61.75 527	49.74 511	66.04 519	82.97 437	46.71 467	63.21 533	42.29 527	69.96 538	83.46 445

mvsany_test1		58.48 505	56.47 512	64.50 505	65.90 545	68.21 267	56.95 536	42.11 553	38.30 543	65.69 521	77.19 505	56.96 398	59.35 539	46.16 518	58.96 547	65.93 533

dmvs_testset		60.59 502	62.54 494	54.72 526	77.26 486	27.74 551	74.05 443	61.00 533	60.48 430	65.62 522	67.03 536	55.93 409	68.23 505	32.07 546	69.46 541	68.17 530

DSMNet-mixed		60.98 500	61.61 497	59.09 523	72.88 525	45.05 521	74.70 432	46.61 551	26.20 548	65.34 523	90.32 272	55.46 415	63.12 534	41.72 529	81.30 506	69.09 529

JIA-IIPM		69.41 451	66.64 471	77.70 375	73.19 521	71.24 222	75.67 417	65.56 507	70.42 267	65.18 524	92.97 153	33.64 525	83.06 410	53.52 469	69.61 540	78.79 502

test-LLR		67.21 464	66.74 469	68.63 481	76.45 497	55.21 459	67.89 497	67.14 498	62.43 400	65.08 525	72.39 526	43.41 499	69.37 490	61.00 401	84.89 476	81.31 474

test-mter		65.00 481	63.79 486	68.63 481	76.45 497	55.21 459	67.89 497	67.14 498	50.98 503	65.08 525	72.39 526	28.27 542	69.37 490	61.00 401	84.89 476	81.31 474

PMMVS2		55.64 510	59.27 504	44.74 528	64.30 547	12.32 560	40.60 544	49.79 548	53.19 485	65.06 527	84.81 403	53.60 426	49.76 548	32.68 545	89.41 396	72.15 524

baseline2		69.77 448	66.89 467	78.41 357	79.51 460	58.09 429	76.23 410	69.57 482	57.50 453	64.82 528	77.45 500	46.02 473	88.44 315	53.08 471	77.83 519	88.70 360

gg-mvs-nofinetune		68.96 457	69.11 449	68.52 484	76.12 501	45.32 519	83.59 225	55.88 544	86.68 32	64.62 529	97.01 11	30.36 534	83.97 407	44.78 523	82.94 493	76.26 514

PAPM		71.77 421	70.06 438	76.92 393	86.39 308	53.97 470	76.62 402	86.62 299	53.44 483	63.97 530	84.73 405	57.79 393	92.34 177	39.65 533	81.33 505	84.45 427

PDCNetPlus		57.49 507	56.93 510	59.15 522	56.36 553	47.35 511	52.32 542	77.34 420	39.50 541	63.50 531	73.19 525	13.19 557	56.86 542	47.51 511	89.48 394	73.22 522

new_pmnet		55.69 509	57.66 509	49.76 527	75.47 507	30.59 549	59.56 528	51.45 547	43.62 531	62.49 532	75.48 517	40.96 507	49.15 549	37.39 540	72.52 532	69.55 528

UWE-MVS-28		58.44 506	57.71 508	60.65 518	73.58 519	31.23 548	69.68 491	48.80 549	53.12 487	61.79 533	78.83 486	30.98 532	68.40 504	21.58 549	80.99 508	82.33 464

MDTV_nov1_ep13_2view						27.60 552	70.76 483		46.47 520	61.27 534		45.20 488		49.18 500		83.75 440

dp		60.70 501	60.29 502	61.92 513	72.04 529	38.67 539	70.83 482	64.08 514	51.28 500	60.75 535	77.28 501	36.59 518	71.58 480	47.41 512	62.34 545	75.52 517

TESTMET0.1,1		61.29 497	60.32 501	64.19 506	72.06 528	51.30 491	67.89 497	62.09 523	45.27 523	60.65 536	69.01 533	27.93 543	64.74 530	56.31 438	81.65 503	76.53 513

0.4-1-1-0.1		64.02 488	60.59 499	74.31 431	73.99 514	55.62 453	67.66 501	72.78 460	55.53 469	60.35 537	58.45 541	29.26 538	86.88 351	52.84 477	74.42 529	80.42 487

PMMVS		61.65 495	60.38 500	65.47 501	65.40 546	69.26 250	63.97 520	61.73 528	36.80 547	60.11 538	68.43 534	59.42 373	66.35 520	48.97 502	78.57 518	60.81 540

PVSNet_0	51.08 22	56.10 508	54.97 513	59.48 521	75.12 510	53.28 477	55.16 539	61.89 526	44.30 527	59.16 539	62.48 539	54.22 423	65.91 523	35.40 541	47.01 548	59.25 542

MVS-HIRNet		61.16 498	62.92 492	55.87 524	79.09 466	35.34 544	71.83 470	57.98 541	46.56 519	59.05 540	91.14 231	49.95 457	76.43 458	38.74 535	71.92 535	55.84 544

E-PMN		61.59 496	61.62 496	61.49 515	66.81 541	55.40 457	53.77 540	60.34 534	66.80 331	58.90 541	65.50 537	40.48 508	66.12 521	55.72 444	86.25 456	62.95 538

GG-mvs-BLEND				67.16 491	73.36 520	46.54 515	84.15 205	55.04 545		58.64 542	61.95 540	29.93 535	83.87 408	38.71 536	76.92 525	71.07 526

EPNet_dtu		72.87 407	71.33 419	77.49 381	77.72 479	60.55 386	82.35 274	75.79 433	66.49 335	58.39 543	81.06 463	53.68 425	85.98 374	53.55 468	92.97 281	85.95 409
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
dongtai		41.90 512	42.65 515	39.67 529	70.86 532	21.11 553	61.01 527	21.42 559	57.36 455	57.97 544	50.06 546	16.40 555	58.73 540	21.03 550	27.69 552	39.17 547

0.4-1-1-0.2		62.43 493	58.81 507	73.31 441	70.85 533	54.20 468	64.36 517	72.99 457	53.70 481	57.51 545	54.59 543	29.52 536	86.44 364	51.70 489	74.02 530	79.30 496

0.3-1-1-0.015		62.57 490	58.82 506	73.82 436	71.85 530	54.96 462	65.63 511	72.97 458	54.16 478	56.95 546	55.43 542	26.76 548	86.59 360	52.05 482	73.55 531	79.92 491

EMVS		61.10 499	60.81 498	61.99 512	65.96 544	55.86 450	53.10 541	58.97 539	67.06 328	56.89 547	63.33 538	40.98 506	67.03 515	54.79 458	86.18 457	63.08 537

CHOSEN 280x420		59.08 504	56.52 511	66.76 493	76.51 495	64.39 313	49.62 543	59.00 538	43.86 529	55.66 548	68.41 535	35.55 521	68.21 506	43.25 525	76.78 526	67.69 532

MVE	40.22 23	51.82 511	50.47 514	55.87 524	62.66 550	51.91 486	31.61 547	39.28 554	40.65 537	50.76 549	74.98 521	56.24 403	44.67 550	33.94 544	64.11 544	71.04 527
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
kuosan		30.83 514	32.17 517	26.83 532	53.36 554	19.02 557	57.90 534	20.44 560	38.29 544	38.01 550	37.82 548	15.18 556	33.45 553	7.74 555	20.76 555	28.03 548

DeepMVS_CX				24.13 533	32.95 555	29.49 550		21.63 558	12.07 550	37.95 551	45.07 547	30.84 533	19.21 554	17.94 551	33.06 551	23.69 549

tmp_tt		20.25 517	24.50 520	7.49 536	4.47 560	8.70 562	34.17 546	25.16 557	1.00 555	32.43 552	18.49 551	39.37 510	9.21 556	21.64 548	43.75 549	4.57 552

MVS_clip		14.31 518	16.37 521	8.11 535	18.08 557	12.42 559	12.95 549	3.12 562	3.73 552	28.79 553	35.98 549	8.84 558	4.85 557	12.31 553	23.54 553	7.07 550

VLMVS_CLIP		13.55 519	14.55 522	10.53 534	11.59 559	10.03 561	11.68 550	18.47 561	4.20 551	20.50 554	24.42 550	8.69 559	16.48 555	8.18 554	23.25 554	5.10 551

test_method		30.46 515	29.60 518	33.06 531	17.99 558	3.84 563	13.62 548	73.92 446	2.79 553	18.29 555	53.41 544	28.53 541	43.25 551	22.56 547	35.27 550	52.11 545

MVS_baseline		4.35 524	5.47 527	0.99 538	3.75 561	0.34 567	2.10 551	0.79 565	0.13 559	12.26 556	14.40 553	2.36 561	0.00 561	1.87 556	11.56 556	2.62 554

VLMVS		3.03 525	3.34 528	2.13 537	3.00 562	1.87 564	1.95 552	1.16 563	0.16 558	5.10 557	6.49 554	5.23 560	1.51 558	1.34 557	5.59 557	3.02 553

EGC-MVSNET		74.79 382	69.99 440	89.19 66	94.89 37	87.00 19	91.89 42	86.28 302	1.09 554	2.23 558	95.98 29	81.87 137	89.48 282	79.76 141	95.96 141	91.10 282

testmvs		5.91 523	7.65 526	0.72 540	1.20 563	0.37 566	59.14 530	0.67 566	0.49 557	1.11 559	2.76 557	0.94 563	0.24 560	1.02 559	1.47 558	1.55 556

test123		6.27 522	8.08 525	0.84 539	1.11 564	0.57 565	62.90 521	0.82 564	0.54 556	1.07 560	2.75 558	1.26 562	0.30 559	1.04 558	1.26 559	1.66 555

mmdepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

monomultidepth		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

test_blank		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet_test		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

DCPMVS		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

cdsmvs_eth3d_5k		20.81 516	27.75 519	0.00 541	0.00 565	0.00 568	0.00 553	85.44 319	0.00 560	0.00 561	82.82 442	81.46 143	0.00 561	0.00 560	0.00 560	0.00 557

pcd_1.5k_mvsjas		6.41 521	8.55 524	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	76.94 203	0.00 561	0.00 560	0.00 560	0.00 557

sosnet-low-res		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

sosnet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uncertanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

Regformer		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

ab-mvs-re		6.65 520	8.87 523	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	79.80 477	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

uanet		0.00 526	0.00 529	0.00 541	0.00 565	0.00 568	0.00 553	0.00 567	0.00 560	0.00 561	0.00 559	0.00 564	0.00 561	0.00 560	0.00 560	0.00 557

PatchmatchNet2					0.00 565	20.88 554	55.62 537	59.13 536	52.38 492
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														46.85 516	87.28 436	83.48 444
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													54.72 544
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						37.39 540								52.61 479

MSC_two_6792asdad				88.81 72	91.55 141	77.99 126		91.01 181					96.05 8	87.45 28	98.17 36	92.40 229

No_MVS				88.81 72	91.55 141	77.99 126		91.01 181					96.05 8	87.45 28	98.17 36	92.40 229

eth-test2					0.00 565

eth-test					0.00 565

OPU-MVS				88.27 88	91.89 125	77.83 129	90.47 60				91.22 227	81.12 147	94.68 82	74.48 230	95.35 171	92.29 240

save fliter					93.75 67	77.44 136	86.31 147	89.72 227	70.80 263

test_0728_SECOND				86.79 114	94.25 52	72.45 201	90.54 57	94.10 40					95.88 17	86.42 46	97.97 48	92.02 254

GSMVS																83.88 435

sam_mvs1												46.11 472				83.88 435

sam_mvs												45.92 478

MTGPA								91.81 153

test_post1							78.85 360				3.13 555	45.19 489	80.13 436	58.11 423

test_post											3.10 556	45.43 485	77.22 456

patchmatchnet-post											81.71 456	45.93 477	87.01 346

MTMP							90.66 53	33.14 555

gm-plane-assit					75.42 508	44.97 522			52.17 493		72.36 528		87.90 328	54.10 461

test9_res														80.83 130	96.45 117	90.57 303

agg_prior2														79.68 143	96.16 130	90.22 312

test_prior4						78.97 115	84.59 192

test_prior				86.32 123	90.59 172	71.99 209		92.85 114					94.17 107			92.80 202

新几何2							81.72 291

旧先验1					91.97 121	71.77 210		81.78 379			91.84 199	73.92 251			93.65 254	83.61 441

无先验							82.81 258	85.62 317	58.09 447				91.41 207	67.95 332		84.48 426

原ACMM2							82.26 279

testdata2													86.43 365	63.52 377

segment_acmp												81.94 133

testdata1							79.62 336		73.95 194

plane_prior7					93.45 74	77.31 139

plane_prior6					92.61 99	76.54 146						74.84 232

plane_prior5								93.61 70					95.22 62	80.78 131	95.83 152	94.46 104

plane_prior4											92.95 154

plane_prior2							89.45 87		79.44 112

plane_prior1					92.83 96

plane_prior						76.42 149	87.15 128		75.94 159						95.03 188

n2								0.00 567

nn								0.00 567

door-mid								74.45 443

test11								91.46 162

door								72.57 462

HQP5-MVS						70.66 228

BP-MVS														77.30 187

HQP3-MVS								92.68 120							94.47 218

HQP2-MVS												72.10 281

NP-MVS					91.95 122	74.55 172					90.17 281

ACMMP++_ref															95.74 159

ACMMP++															97.35 84

Test By Simon												79.09 168

High-resolution multi-view benchmark