High-res multi-view results

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

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

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

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

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

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

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

LCM-MVSNet		86.90 1	88.67 1	81.57 24	91.50 1	63.30 161	84.80 39	87.77 10	86.18 1	96.26 1	96.06 1	90.32 1	84.49 76	68.08 117	97.05 1	96.93 1

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 31	81.19 5	88.84 4	90.72 1	78.27 11	87.95 18	92.53 15	79.37 15	84.79 73	74.51 59	96.15 2	92.88 7

ACMP	69.50 8	82.64 29	83.38 31	80.40 40	86.50 45	69.44 84	82.30 63	86.08 24	66.80 76	86.70 38	89.99 83	81.64 6	85.95 36	74.35 61	96.11 3	85.81 99
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	66.64 10	81.20 43	82.48 45	77.35 88	81.16 140	62.39 167	80.51 78	87.80 8	73.02 30	87.57 25	91.08 43	80.28 9	82.44 115	64.82 153	96.10 4	87.21 63

LPG-MVS_test		83.47 19	84.33 16	80.90 35	87.00 39	70.41 75	82.04 66	86.35 17	69.77 59	87.75 20	91.13 41	81.83 3	86.20 27	77.13 40	95.96 5	86.08 92

LGP-MVS_train				80.90 35	87.00 39	70.41 75		86.35 17	69.77 59	87.75 20	91.13 41	81.83 3	86.20 27	77.13 40	95.96 5	86.08 92

ACMM	69.25 9	82.11 34	83.31 32	78.49 68	88.17 36	73.96 47	83.11 58	84.52 66	66.40 81	87.45 27	89.16 101	81.02 8	80.52 158	74.27 62	95.73 7	80.98 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
NR-MVSNet		73.62 127	74.05 131	72.33 184	83.50 101	43.71 395	65.65 348	77.32 225	64.32 107	75.59 231	87.08 148	62.45 187	81.34 136	54.90 287	95.63 8	91.93 8

WR-MVS_H		80.22 57	82.17 48	74.39 125	89.46 14	42.69 408	78.24 109	82.24 118	78.21 12	89.57 9	92.10 20	68.05 122	85.59 53	66.04 142	95.62 9	94.88 5

NormalMVS		76.15 91	75.08 109	79.36 52	83.87 98	70.01 80	79.92 91	84.34 70	58.60 161	75.21 245	84.02 235	52.85 306	81.82 128	61.45 195	95.50 10	86.24 87

lecture		83.41 20	85.02 10	78.58 65	83.87 98	67.26 108	84.47 41	88.27 6	73.64 27	87.35 32	91.96 23	78.55 21	82.92 105	81.59 3	95.50 10	85.56 108

TranMVSNet+NR-MVSNet		76.13 92	77.66 83	71.56 196	84.61 85	42.57 410	70.98 237	78.29 211	68.67 65	83.04 91	89.26 95	72.99 66	80.75 153	55.58 278	95.47 12	91.35 11

COLMAP_ROB	72.78 3	83.75 14	84.11 19	82.68 12	82.97 113	74.39 45	87.18 11	88.18 7	78.98 7	86.11 49	91.47 37	79.70 14	85.76 47	66.91 137	95.46 13	87.89 54
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CP-MVS		84.12 11	84.55 14	82.80 10	89.42 17	79.74 9	88.19 5	84.43 68	71.96 46	84.70 74	90.56 58	77.12 29	86.18 29	79.24 21	95.36 14	82.49 227

Baseline_NR-MVSNet		70.62 206	73.19 152	62.92 367	76.97 210	34.44 490	68.84 282	70.88 316	60.25 146	79.50 143	90.53 59	61.82 198	69.11 360	54.67 291	95.27 15	85.22 115

UniMVSNet (Re)		75.00 109	75.48 105	73.56 143	83.14 106	47.92 327	70.41 247	81.04 147	63.67 114	79.54 141	86.37 182	62.83 181	81.82 128	57.10 258	95.25 16	90.94 15

reproduce-ours		84.97 3	85.93 3	82.10 20	86.11 59	77.53 21	87.08 13	85.81 29	78.70 9	88.94 12	91.88 26	79.74 12	86.05 33	79.90 9	95.21 17	82.72 219

our_new_method		84.97 3	85.93 3	82.10 20	86.11 59	77.53 21	87.08 13	85.81 29	78.70 9	88.94 12	91.88 26	79.74 12	86.05 33	79.90 9	95.21 17	82.72 219

reproduce_model		84.87 5	85.80 5	82.05 22	85.52 68	78.14 16	87.69 6	85.36 39	79.26 6	89.12 11	92.10 20	77.52 26	85.92 40	80.47 8	95.20 19	82.10 237

PS-CasMVS		80.41 54	82.86 41	73.07 155	89.93 6	39.21 445	77.15 124	81.28 138	79.74 5	90.87 4	92.73 13	75.03 50	84.93 69	63.83 169	95.19 20	95.07 3

ACMMP		84.22 9	84.84 12	82.35 17	89.23 21	76.66 31	87.65 7	85.89 27	71.03 51	85.85 51	90.58 57	78.77 18	85.78 46	79.37 19	95.17 21	84.62 144
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
PEN-MVS		80.46 53	82.91 39	73.11 153	89.83 8	39.02 449	77.06 126	82.61 108	80.04 4	90.60 6	92.85 11	74.93 51	85.21 64	63.15 179	95.15 22	95.09 2

CP-MVSNet		79.48 61	81.65 52	72.98 159	89.66 12	39.06 448	76.76 127	80.46 161	78.91 8	90.32 7	91.70 32	68.49 115	84.89 70	63.40 176	95.12 23	95.01 4

SteuartSystems-ACMMP		83.07 25	83.64 27	81.35 29	85.14 75	71.00 68	85.53 33	84.78 53	70.91 52	85.64 54	90.41 65	75.55 44	87.69 4	79.75 11	95.08 24	85.36 113
Skip Steuart: Steuart Systems R&D Blog.
UniMVSNet_NR-MVSNet		74.90 112	75.65 102	72.64 176	83.04 111	45.79 368	69.26 270	78.81 197	66.66 79	81.74 113	86.88 155	63.26 175	81.07 144	56.21 268	94.98 25	91.05 13

DU-MVS		74.91 111	75.57 104	72.93 163	83.50 101	45.79 368	69.47 264	80.14 168	65.22 95	81.74 113	87.08 148	61.82 198	81.07 144	56.21 268	94.98 25	91.93 8

MP-MVS-pluss		82.54 30	83.46 30	79.76 44	88.88 30	68.44 96	81.57 69	86.33 19	63.17 122	85.38 64	91.26 40	76.33 36	84.67 75	83.30 1	94.96 27	86.17 91
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HPM-MVS		84.12 11	84.63 13	82.60 13	88.21 35	74.40 44	85.24 35	87.21 14	70.69 54	85.14 66	90.42 64	78.99 17	86.62 14	80.83 6	94.93 28	86.79 72
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HPM-MVS_fast		84.59 7	85.10 9	83.06 4	88.60 32	75.83 33	86.27 27	86.89 16	73.69 26	86.17 46	91.70 32	78.23 22	85.20 65	79.45 16	94.91 29	88.15 52

MSC_two_6792asdad				79.02 57	83.14 106	67.03 113		80.75 151					86.24 25	77.27 38	94.85 30	83.78 174

No_MVS				79.02 57	83.14 106	67.03 113		80.75 151					86.24 25	77.27 38	94.85 30	83.78 174

MP-MVS		83.19 22	83.54 28	82.14 19	90.54 4	79.00 12	86.42 25	83.59 87	71.31 47	81.26 120	90.96 45	74.57 55	84.69 74	78.41 25	94.78 32	82.74 218
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		83.19 22	83.87 23	81.13 33	91.16 2	78.16 15	84.87 37	80.63 157	72.08 44	84.93 68	90.79 51	74.65 54	84.42 79	80.98 5	94.75 33	80.82 269

test_0728_THIRD									74.03 24	85.83 52	90.41 65	75.58 43	85.69 49	77.43 35	94.74 34	84.31 160

PGM-MVS		83.07 25	83.25 35	82.54 15	89.57 13	77.21 28	82.04 66	85.40 37	67.96 68	84.91 71	90.88 48	75.59 42	86.57 15	78.16 27	94.71 35	83.82 172

DTE-MVSNet		80.35 55	82.89 40	72.74 173	89.84 7	37.34 469	77.16 123	81.81 126	80.45 3	90.92 3	92.95 9	74.57 55	86.12 32	63.65 172	94.68 36	94.76 6

mPP-MVS		84.01 13	84.39 15	82.88 6	90.65 3	81.38 4	87.08 13	82.79 102	72.41 41	85.11 67	90.85 50	76.65 33	84.89 70	79.30 20	94.63 37	82.35 230

FC-MVSNet-test		73.32 139	74.78 112	68.93 265	79.21 166	36.57 472	71.82 222	79.54 185	57.63 176	82.57 101	90.38 70	59.38 238	78.99 181	57.91 248	94.56 38	91.23 12

DeepC-MVS	72.44 4	81.00 47	80.83 57	81.50 25	86.70 44	70.03 79	82.06 65	87.00 15	59.89 149	80.91 126	90.53 59	72.19 71	88.56 1	73.67 70	94.52 39	85.92 98
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS-dyc-post		84.75 6	85.26 8	83.21 3	86.19 52	79.18 10	87.23 9	86.27 20	77.51 13	87.65 23	90.73 53	79.20 16	85.58 54	78.11 28	94.46 40	84.89 127

RE-MVS-def			85.50 6		86.19 52	79.18 10	87.23 9	86.27 20	77.51 13	87.65 23	90.73 53	81.38 7		78.11 28	94.46 40	84.89 127

UA-Net		81.56 39	82.28 47	79.40 51	88.91 28	69.16 90	84.67 40	80.01 171	75.34 18	79.80 138	94.91 2	69.79 104	80.25 162	72.63 79	94.46 40	88.78 44

ACMH	63.62 14	77.50 82	80.11 61	69.68 244	79.61 158	56.28 238	78.81 101	83.62 86	63.41 120	87.14 35	90.23 77	76.11 38	73.32 288	67.58 124	94.44 43	79.44 298
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ZNCC-MVS		83.12 24	83.68 26	81.45 27	89.14 24	73.28 55	86.32 26	85.97 25	67.39 71	84.02 82	90.39 68	74.73 53	86.46 16	80.73 7	94.43 44	84.60 147

SED-MVS		81.78 36	83.48 29	76.67 93	86.12 56	61.06 183	83.62 51	84.72 56	72.61 35	87.38 29	89.70 88	77.48 27	85.89 43	75.29 47	94.39 45	83.08 203

IU-MVS					86.12 56	60.90 187		80.38 163	45.49 381	81.31 119				75.64 46	94.39 45	84.65 141

DVP-MVS		81.15 44	83.12 37	75.24 117	86.16 54	60.78 189	83.77 49	80.58 159	72.48 37	85.83 52	90.41 65	78.57 19	85.69 49	75.86 43	94.39 45	79.24 301
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_SECOND				76.57 95	86.20 51	60.57 192	83.77 49	85.49 33					85.90 41	75.86 43	94.39 45	83.25 195

SMA-MVS		82.12 33	82.68 44	80.43 39	88.90 29	69.52 82	85.12 36	84.76 54	63.53 116	84.23 80	91.47 37	72.02 74	87.16 7	79.74 13	94.36 49	84.61 145
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
ACMMP_NAP		82.33 32	83.28 33	79.46 50	89.28 18	69.09 93	83.62 51	84.98 48	64.77 104	83.97 83	91.02 44	75.53 45	85.93 39	82.00 2	94.36 49	83.35 193

APD-MVS_3200maxsize		83.57 16	84.33 16	81.31 31	82.83 116	73.53 53	85.50 34	87.45 13	74.11 22	86.45 43	90.52 61	80.02 10	84.48 77	77.73 32	94.34 51	85.93 97

APDe-MVS		82.88 27	84.14 18	79.08 55	84.80 82	66.72 117	86.54 23	85.11 43	72.00 45	86.65 39	91.75 31	78.20 23	87.04 10	77.93 30	94.32 52	83.47 185
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		83.62 15	83.93 21	82.69 11	89.78 10	77.51 25	87.01 17	84.19 78	70.23 55	84.49 76	90.67 56	75.15 48	86.37 19	79.58 14	94.26 53	84.18 163

region2R		83.54 17	83.86 24	82.58 14	89.82 9	77.53 21	87.06 16	84.23 77	70.19 57	83.86 85	90.72 55	75.20 47	86.27 24	79.41 18	94.25 54	83.95 169

HFP-MVS		83.39 21	84.03 20	81.48 26	89.25 20	75.69 35	87.01 17	84.27 74	70.23 55	84.47 77	90.43 63	76.79 30	85.94 37	79.58 14	94.23 55	82.82 215

TestfortrainingZip a		82.48 31	83.93 21	78.11 77	86.27 48	64.11 152	86.10 28	85.02 46	72.46 39	86.32 44	90.03 80	76.75 31	85.37 56	78.23 26	94.22 56	84.86 130

test_241102_TWO								84.80 51	72.61 35	84.93 68	89.70 88	77.73 25	85.89 43	75.29 47	94.22 56	83.25 195

aaatest				78.47 70	86.27 48	64.31 146	86.10 28	84.54 64	64.93 103	85.54 58	88.38 123		86.37 19	74.09 63	94.20 58	84.73 138

aaEdge-Enhanced		81.36 41	82.39 46	78.28 73	84.42 90	64.31 146	82.78 60	85.02 46	71.25 48	84.81 72	88.38 123	76.53 34	85.81 45	74.09 63	94.20 58	84.73 138

XVS		83.51 18	83.73 25	82.85 8	89.43 15	77.61 19	86.80 20	84.66 60	72.71 32	82.87 95	90.39 68	73.86 60	86.31 22	78.84 23	94.03 60	84.64 142

X-MVStestdata		76.81 87	74.79 111	82.85 8	89.43 15	77.61 19	86.80 20	84.66 60	72.71 32	82.87 95	9.95 552	73.86 60	86.31 22	78.84 23	94.03 60	84.64 142

DPE-MVS		82.00 35	83.02 38	78.95 60	85.36 71	67.25 109	82.91 59	84.98 48	73.52 28	85.43 62	90.03 80	76.37 35	86.97 12	74.56 57	94.02 62	82.62 223
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
GST-MVS		82.79 28	83.27 34	81.34 30	88.99 26	73.29 54	85.94 32	85.13 42	68.58 66	84.14 81	90.21 78	73.37 64	86.41 17	79.09 22	93.98 63	84.30 162

9.14			80.22 60		80.68 144		80.35 83	87.69 11	59.90 148	83.00 92	88.20 128	74.57 55	81.75 132	73.75 69	93.78 64

SF-MVS		80.72 50	81.80 49	77.48 84	82.03 127	64.40 144	83.41 55	88.46 5	65.28 94	84.29 79	89.18 99	73.73 63	83.22 99	76.01 42	93.77 65	84.81 136

IS-MVSNet		75.10 106	75.42 106	74.15 130	79.23 165	48.05 325	79.43 94	78.04 215	70.09 58	79.17 147	88.02 134	53.04 305	83.60 90	58.05 247	93.76 66	90.79 17

test-260524					85.04 77	63.52 157		84.79 52		83.97 83		74.92 52	85.60 52	74.59 56	93.74 67

MED-MVS		81.77 37	82.86 41	78.51 67	86.27 48	64.31 146	86.10 28	84.54 64	72.46 39	85.54 58	90.03 80	72.97 67	86.37 19	74.09 63	93.74 67	84.86 130

ALIKED-MNN		63.44 336	63.42 339	63.48 352	73.99 278	70.97 69	71.80 223	66.48 364	32.46 506	71.87 325	81.60 302	36.54 432	58.50 441	42.45 404	93.63 69	60.97 511

PMVS	70.70 6	81.70 38	83.15 36	77.36 87	90.35 5	82.82 2	82.15 64	79.22 191	74.08 23	87.16 34	91.97 22	84.80 2	76.97 228	64.98 151	93.61 70	72.28 411
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
SR-MVS		84.51 8	85.27 7	82.25 18	88.52 33	77.71 18	86.81 19	85.25 41	77.42 16	86.15 47	90.24 76	81.69 5	85.94 37	77.77 31	93.58 71	83.09 202

OPM-MVS		80.99 48	81.63 53	79.07 56	86.86 43	69.39 85	79.41 96	84.00 83	65.64 86	85.54 58	89.28 94	76.32 37	83.47 95	74.03 67	93.57 72	84.35 159
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVG-ACMP-BASELINE		80.54 51	81.06 55	78.98 59	87.01 38	72.91 56	80.23 86	85.56 32	66.56 80	85.64 54	89.57 90	69.12 108	80.55 157	72.51 81	93.37 73	83.48 184

FIs		72.56 164	73.80 135	68.84 268	78.74 180	37.74 464	71.02 236	79.83 174	56.12 195	80.88 128	89.45 92	58.18 254	78.28 201	56.63 262	93.36 74	90.51 19

WR-MVS		71.20 193	72.48 172	67.36 294	84.98 78	35.70 482	64.43 374	68.66 348	65.05 99	81.49 117	86.43 181	57.57 266	76.48 238	50.36 330	93.32 75	89.90 22

CLD-MVS		72.88 155	72.36 176	74.43 124	77.03 207	54.30 259	68.77 289	83.43 89	52.12 271	76.79 202	74.44 412	69.54 106	83.91 83	55.88 271	93.25 76	85.09 122
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CPTT-MVS		81.51 40	81.76 50	80.76 37	89.20 22	78.75 13	86.48 24	82.03 122	68.80 62	80.92 125	88.52 119	72.00 75	82.39 117	74.80 50	93.04 77	81.14 259

APD-MVS		81.13 45	81.73 51	79.36 52	84.47 87	70.53 74	83.85 47	83.70 85	69.43 61	83.67 87	88.96 108	75.89 40	86.41 17	72.62 80	92.95 78	81.14 259
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZD-MVS					83.91 95	69.36 86		81.09 145	58.91 159	82.73 99	89.11 102	75.77 41	86.63 13	72.73 78	92.93 79

OurMVSNet-221017-0		78.57 69	78.53 75	78.67 63	80.48 146	64.16 150	80.24 85	82.06 121	61.89 132	88.77 15	93.32 5	57.15 271	82.60 112	70.08 100	92.80 80	89.25 30

Anonymous20231211		75.54 99	77.19 89	70.59 212	77.67 196	45.70 372	74.73 163	80.19 166	68.80 62	82.95 94	92.91 10	66.26 146	76.76 235	58.41 243	92.77 81	89.30 27

test_prior2							75.57 150		58.92 158	76.53 213	86.78 163	67.83 128		69.81 103	92.76 82

CDPH-MVS		77.33 83	77.06 91	78.14 75	84.21 92	63.98 154	76.07 145	83.45 88	54.20 235	77.68 175	87.18 146	69.98 100	85.37 56	68.01 119	92.72 83	85.08 123

EPP-MVSNet		73.86 125	73.38 147	75.31 114	78.19 185	53.35 268	80.45 79	77.32 225	65.11 98	76.47 216	86.80 160	49.47 332	83.77 87	53.89 302	92.72 83	88.81 43

RoMa-HiRes		73.61 128	73.51 143	73.92 133	82.27 124	81.71 3	77.59 114	64.83 380	51.32 287	88.72 16	83.92 240	60.47 219	61.70 423	60.01 219	92.44 85	78.34 315

OMC-MVS		79.41 62	78.79 70	81.28 32	80.62 145	70.71 73	80.91 75	84.76 54	62.54 128	81.77 111	86.65 172	71.46 82	83.53 93	67.95 121	92.44 85	89.60 24

tt0805		76.12 93	78.43 76	69.20 254	81.32 137	41.37 418	76.72 128	77.64 220	63.78 113	82.06 105	87.88 138	79.78 11	79.05 179	64.33 161	92.40 87	87.17 67

DP-MVS		78.44 73	79.29 67	75.90 105	81.86 130	65.33 134	79.05 99	84.63 62	74.83 21	80.41 133	86.27 184	71.68 76	83.45 96	62.45 185	92.40 87	78.92 308

nrg030		74.87 114	75.99 100	71.52 197	74.90 248	49.88 303	74.10 176	82.58 109	54.55 224	83.50 89	89.21 97	71.51 81	75.74 247	61.24 199	92.34 89	88.94 39

SD-MVS		80.28 56	81.55 54	76.47 98	83.57 100	67.83 102	83.39 56	85.35 40	64.42 106	86.14 48	87.07 150	74.02 59	80.97 148	77.70 33	92.32 90	80.62 277
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
DKM		69.82 224	69.29 234	71.40 201	80.33 148	80.76 8	73.05 190	60.16 415	47.00 359	85.42 63	79.91 336	48.29 348	58.24 444	57.18 255	92.25 91	75.19 373

Anonymous20240521		63.55 334	66.07 299	55.99 448	66.18 435	44.04 392	68.77 289	68.80 345	46.99 360	72.57 311	85.84 200	39.87 409	50.22 479	53.40 310	92.23 92	73.71 392

LTVRE_ROB	75.46 1	84.22 9	84.98 11	81.94 23	84.82 80	75.40 36	91.60 3	87.80 8	73.52 28	88.90 14	93.06 8	71.39 85	81.53 134	81.53 4	92.15 93	88.91 40
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
tt0320-xc		71.50 186	73.63 140	65.08 330	79.77 156	40.46 435	64.80 364	68.86 342	67.08 73	76.84 199	93.24 6	70.33 95	66.77 393	49.76 334	92.02 94	88.02 53

ACMMP++															91.96 95

v7n		79.37 63	80.41 59	76.28 100	78.67 181	55.81 244	79.22 98	82.51 112	70.72 53	87.54 26	92.44 16	68.00 124	81.34 136	72.84 77	91.72 96	91.69 10

VDDNet		71.60 184	73.13 154	67.02 304	86.29 47	41.11 421	69.97 254	66.50 363	68.72 64	74.74 256	91.70 32	59.90 228	75.81 244	48.58 352	91.72 96	84.15 165

UniMVSNet_ETH3D		76.74 88	79.02 68	69.92 240	89.27 19	43.81 394	74.47 169	71.70 295	72.33 43	85.50 61	93.65 3	77.98 24	76.88 232	54.60 292	91.64 98	89.08 34

wuyk23d		61.97 361	66.25 294	49.12 489	58.19 511	60.77 191	66.32 338	52.97 466	55.93 203	90.62 5	86.91 154	73.07 65	35.98 542	20.63 546	91.63 99	50.62 528

CNVR-MVS		78.49 71	78.59 73	78.16 74	85.86 65	67.40 107	78.12 112	81.50 131	63.92 110	77.51 178	86.56 176	68.43 117	84.82 72	73.83 68	91.61 100	82.26 234

train_agg		76.38 90	76.55 94	75.86 106	85.47 69	69.32 87	76.42 135	78.69 202	54.00 240	76.97 191	86.74 165	66.60 142	81.10 142	72.50 82	91.56 101	77.15 341

Gipuma		69.55 229	72.83 163	59.70 413	63.63 467	53.97 262	80.08 88	75.93 246	64.24 108	73.49 292	88.93 109	57.89 264	62.46 417	59.75 225	91.55 102	62.67 500
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ALIKED-LG		64.85 315	64.54 323	65.79 324	74.03 277	74.67 42	73.55 181	67.52 357	36.17 484	78.83 152	83.08 268	34.08 441	59.10 435	42.05 411	91.51 103	63.61 496

SixPastTwentyTwo		75.77 94	76.34 95	74.06 131	81.69 132	54.84 255	76.47 131	75.49 250	64.10 109	87.73 22	92.24 19	50.45 325	81.30 138	67.41 127	91.46 104	86.04 94

sc_t1		72.50 168	74.23 126	67.33 295	80.05 152	46.99 350	66.58 334	69.48 328	66.28 82	77.62 177	91.83 29	70.98 90	68.62 366	53.86 304	91.40 105	86.37 86

test9_res														72.12 86	91.37 106	77.40 333

3Dnovator+	73.19 2	81.08 46	80.48 58	82.87 7	81.41 136	72.03 58	84.38 43	86.23 23	77.28 17	80.65 129	90.18 79	59.80 231	87.58 5	73.06 74	91.34 107	89.01 36

ALIKED-NN		61.86 363	61.18 368	63.92 343	71.72 327	71.04 66	69.24 271	66.41 365	29.80 520	64.25 434	81.10 309	35.56 437	58.35 442	41.25 416	91.30 108	62.35 505

DeepPCF-MVS	71.07 5	78.48 72	77.14 90	82.52 16	84.39 91	77.04 29	76.35 138	84.05 81	56.66 190	80.27 135	85.31 208	68.56 112	87.03 11	67.39 129	91.26 109	83.50 181

LS3D		80.99 48	80.85 56	81.41 28	78.37 182	71.37 63	87.45 8	85.87 28	77.48 15	81.98 106	89.95 85	69.14 107	85.26 61	66.15 139	91.24 110	87.61 58

DKM-HiRes		70.49 208	69.89 221	72.31 185	81.51 134	80.92 7	73.23 188	58.80 425	49.23 323	84.44 78	81.39 304	49.91 328	61.22 426	59.28 230	91.22 111	74.79 376

HPM-MVS++		79.89 58	79.80 64	80.18 42	89.02 25	78.44 14	83.49 54	80.18 167	64.71 105	78.11 166	88.39 122	65.46 157	83.14 100	77.64 34	91.20 112	78.94 307

KD-MVS_self_test		66.38 293	67.51 270	62.97 365	61.76 480	34.39 491	58.11 453	75.30 251	50.84 295	77.12 190	85.42 203	56.84 276	69.44 357	51.07 323	91.16 113	85.08 123

test_djsdf		78.88 66	78.27 77	80.70 38	81.42 135	71.24 65	83.98 45	75.72 248	52.27 267	87.37 31	92.25 18	68.04 123	80.56 155	72.28 84	91.15 114	90.32 20

DeepC-MVS_fast	69.89 7	77.17 84	76.33 96	79.70 47	83.90 96	67.94 99	80.06 89	83.75 84	56.73 189	74.88 255	85.32 207	65.54 155	87.79 2	65.61 147	91.14 115	83.35 193
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testf1		75.66 97	76.57 92	72.95 160	67.07 420	67.62 103	76.10 143	80.68 154	64.95 100	86.58 41	90.94 46	71.20 87	71.68 324	60.46 209	91.13 116	79.56 294

APD_test2		75.66 97	76.57 92	72.95 160	67.07 420	67.62 103	76.10 143	80.68 154	64.95 100	86.58 41	90.94 46	71.20 87	71.68 324	60.46 209	91.13 116	79.56 294

ambc				70.10 234	77.74 194	50.21 293	74.28 174	77.93 218		79.26 145	88.29 127	54.11 299	79.77 169	64.43 159	91.10 118	80.30 284

tt0320		71.34 191	73.47 144	64.97 332	79.92 154	40.81 426	65.22 356	69.07 336	66.72 78	76.15 223	93.36 4	70.35 94	66.90 386	49.31 342	91.09 119	87.21 63

原ACMM1				73.90 134	85.90 62	65.15 138		81.67 128	50.97 292	74.25 272	86.16 189	61.60 201	83.54 92	56.75 261	91.08 120	73.00 397

114514_t		73.40 137	73.33 151	73.64 138	84.15 94	57.11 234	78.20 110	80.02 170	43.76 410	72.55 312	86.07 196	64.00 171	83.35 98	60.14 216	91.03 121	80.45 281

RoMa-SfM		70.84 201	70.47 215	71.95 192	80.95 141	81.09 6	76.44 134	62.08 401	46.25 369	87.14 35	80.63 320	55.60 287	58.69 439	54.19 299	90.98 122	76.07 361

HQP_MVS		78.77 67	78.78 71	78.72 62	85.18 72	65.18 136	82.74 61	85.49 33	65.45 89	78.23 163	89.11 102	60.83 214	86.15 30	71.09 90	90.94 123	84.82 134

plane_prior5								85.49 33					86.15 30	71.09 90	90.94 123	84.82 134

agg_prior2														70.70 95	90.93 125	78.55 313

PHI-MVS		74.92 110	74.36 123	76.61 94	76.40 226	62.32 168	80.38 81	83.15 92	54.16 237	73.23 297	80.75 317	62.19 193	83.86 84	68.02 118	90.92 126	83.65 178

AllTest		77.66 78	77.43 84	78.35 71	79.19 168	70.81 70	78.60 103	88.64 3	65.37 92	80.09 136	88.17 129	70.33 95	78.43 195	55.60 275	90.90 127	85.81 99

TestCases				78.35 71	79.19 168	70.81 70		88.64 3	65.37 92	80.09 136	88.17 129	70.33 95	78.43 195	55.60 275	90.90 127	85.81 99

NCCC		78.25 74	78.04 80	78.89 61	85.61 67	69.45 83	79.80 93	80.99 149	65.77 85	75.55 232	86.25 186	67.42 129	85.42 55	70.10 99	90.88 129	81.81 248

VPNet		65.58 305	67.56 269	59.65 415	79.72 157	30.17 514	60.27 425	62.14 399	54.19 236	71.24 343	86.63 173	58.80 247	67.62 377	44.17 391	90.87 130	81.18 258

DVP-MVS++		81.24 42	82.74 43	76.76 92	83.14 106	60.90 187	91.64 1	85.49 33	74.03 24	84.93 68	90.38 70	66.82 137	85.90 41	77.43 35	90.78 131	83.49 182

PC_three_1452									46.98 361	81.83 110	86.28 183	66.55 144	84.47 78	63.31 178	90.78 131	83.49 182

h-mvs33		73.08 144	71.61 194	77.48 84	83.89 97	72.89 57	70.47 245	71.12 313	54.28 231	77.89 167	83.41 249	49.04 338	80.98 147	63.62 173	90.77 133	78.58 312

XVG-OURS		79.51 60	79.82 63	78.58 65	86.11 59	74.96 40	76.33 140	84.95 50	66.89 74	82.75 98	88.99 107	66.82 137	78.37 198	74.80 50	90.76 134	82.40 229

PS-MVSNAJss		77.54 79	77.35 88	78.13 76	84.88 79	66.37 122	78.55 104	79.59 183	53.48 252	86.29 45	92.43 17	62.39 188	80.25 162	67.90 122	90.61 135	87.77 55

anonymousdsp		78.60 68	77.80 81	81.00 34	78.01 190	74.34 46	80.09 87	76.12 243	50.51 301	89.19 10	90.88 48	71.45 83	77.78 212	73.38 71	90.60 136	90.90 16

DenseAffine		67.25 278	66.08 297	70.76 209	80.22 150	77.51 25	70.65 243	58.59 427	45.98 374	81.51 116	76.48 389	41.58 395	62.36 418	49.23 343	90.48 137	72.40 408

pmmvs6		71.82 180	73.66 138	66.31 317	75.94 235	42.01 412	66.99 326	72.53 287	63.45 118	76.43 217	92.78 12	72.95 68	69.69 353	51.41 320	90.46 138	87.22 62

test12				76.51 96	82.28 123	60.94 186		81.64 129		73.60 289		64.88 164	85.19 66		90.42 139	83.38 191

ELoFTR		57.63 410	59.55 387	51.85 470	66.16 436	61.46 176	69.66 260	43.94 514	30.20 519	82.28 103	77.47 381	33.76 444	42.30 528	42.10 408	90.40 140	51.81 526

usedtu_dtu_shiyan2		62.25 357	62.27 356	62.18 375	77.08 205	52.84 271	62.56 395	56.33 448	52.43 266	64.22 435	83.26 258	48.47 347	58.06 448	25.75 533	90.34 141	75.64 364

VDD-MVS		70.81 203	71.44 198	68.91 266	79.07 173	46.51 360	67.82 307	70.83 317	61.23 136	74.07 277	88.69 114	59.86 229	75.62 249	51.11 322	90.28 142	84.61 145

mvs_tets		78.93 65	78.67 72	79.72 46	84.81 81	73.93 48	80.65 77	76.50 236	51.98 274	87.40 28	91.86 28	76.09 39	78.53 189	68.58 112	90.20 143	86.69 75

EPNet		69.10 240	67.32 275	74.46 121	68.33 393	61.27 180	77.56 116	63.57 390	60.95 140	56.62 494	82.75 270	51.53 316	81.24 139	54.36 297	90.20 143	80.88 268
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPA-MVSNet		68.71 248	70.37 216	63.72 348	76.13 230	38.06 460	64.10 378	71.48 301	56.60 192	74.10 275	88.31 126	64.78 166	69.72 352	47.69 363	90.15 145	83.37 192

TAPA-MVS	65.27 12	75.16 105	74.29 125	77.77 82	74.86 249	68.08 97	77.89 113	84.04 82	55.15 211	76.19 222	83.39 250	66.91 135	80.11 166	60.04 218	90.14 146	85.13 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
jajsoiax		78.51 70	78.16 79	79.59 48	84.65 84	73.83 50	80.42 80	76.12 243	51.33 285	87.19 33	91.51 36	73.79 62	78.44 194	68.27 115	90.13 147	86.49 83

PMatch-Up-SfM		68.45 253	66.90 286	73.11 153	77.17 203	76.10 32	71.60 226	62.67 396	47.32 355	87.78 19	82.41 279	24.19 519	66.58 396	58.86 236	90.11 148	76.66 348

Casviewmamba		77.76 77	78.57 74	75.31 114	76.72 220	53.06 269	76.28 141	85.90 26	62.98 125	81.96 107	88.90 110	75.35 46	82.88 107	68.97 109	90.11 148	89.98 21

Anonymous20240529		72.56 164	73.79 136	68.86 267	76.89 218	45.21 376	68.80 288	77.25 227	67.16 72	76.89 195	90.44 62	65.95 150	74.19 276	50.75 325	90.00 150	87.18 66

AdaColmap		74.22 118	74.56 115	73.20 149	81.95 128	60.97 185	79.43 94	80.90 150	65.57 87	72.54 313	81.76 296	70.98 90	85.26 61	47.88 361	90.00 150	73.37 393

SymmetryMVS		74.00 121	72.85 161	77.43 86	85.17 74	70.01 80	79.92 91	68.48 350	58.60 161	75.21 245	84.02 235	52.85 306	81.82 128	61.45 195	89.99 152	80.47 280

DP-MVS Recon		73.57 130	72.69 165	76.23 101	82.85 115	63.39 159	74.32 171	82.96 99	57.75 171	70.35 352	81.98 289	64.34 170	84.41 80	49.69 335	89.95 153	80.89 267

test1111		64.62 320	65.19 312	62.93 366	79.01 174	29.91 516	65.45 352	54.41 456	54.09 238	71.47 341	88.48 120	37.02 429	74.29 275	46.83 370	89.94 154	84.58 148

plane_prior						65.18 136	80.06 89		61.88 133						89.91 155

PMatch-SfM		67.96 263	66.40 292	72.63 177	78.06 188	75.26 38	71.85 219	59.63 417	46.07 371	86.78 37	82.02 286	26.32 504	66.37 398	57.00 259	89.87 156	76.27 357

cl____		68.26 261	68.26 255	68.29 278	64.98 452	43.67 396	65.89 343	74.67 258	50.04 309	76.86 197	82.42 278	48.74 342	75.38 250	60.92 204	89.81 157	85.80 103

DIV-MVS_self_test		68.27 259	68.26 255	68.29 278	64.98 452	43.67 396	65.89 343	74.67 258	50.04 309	76.86 197	82.43 277	48.74 342	75.38 250	60.94 203	89.81 157	85.81 99

OPU-MVS				78.65 64	83.44 104	66.85 115	83.62 51				86.12 192	66.82 137	86.01 35	61.72 193	89.79 159	83.08 203

LFMVS		67.06 284	67.89 264	64.56 335	78.02 189	38.25 457	70.81 241	59.60 418	65.18 96	71.06 345	86.56 176	43.85 371	75.22 255	46.35 374	89.63 160	80.21 287

TSAR-MVS + GP.		73.08 144	71.60 195	77.54 83	78.99 177	70.73 72	74.96 156	69.38 329	60.73 143	74.39 269	78.44 368	57.72 265	82.78 109	60.16 214	89.60 161	79.11 303

EC-MVSNet		77.08 85	77.39 87	76.14 103	76.86 219	56.87 236	80.32 84	87.52 12	63.45 118	74.66 260	84.52 221	69.87 102	84.94 68	69.76 104	89.59 162	86.60 76

MIMVSNet1		66.57 291	69.23 238	58.59 428	81.26 139	37.73 465	64.06 379	57.62 430	57.02 182	78.40 161	90.75 52	62.65 182	58.10 447	41.77 413	89.58 163	79.95 289

mmtdpeth		68.76 246	70.55 214	63.40 357	67.06 423	56.26 239	68.73 292	71.22 311	55.47 208	70.09 358	88.64 117	65.29 160	56.89 452	58.94 234	89.50 164	77.04 347

TransMVSNet (Re)		69.62 227	71.63 192	63.57 350	76.51 224	35.93 480	65.75 347	71.29 307	61.05 138	75.02 250	89.90 86	65.88 152	70.41 342	49.79 333	89.48 165	84.38 158

ACMMP++_ref															89.47 166

test2506		61.23 372	60.85 375	62.38 373	78.80 178	27.88 524	67.33 317	37.42 543	54.23 233	67.55 400	88.68 115	17.87 546	74.39 272	46.33 375	89.41 167	84.86 130

ECVR-MVS		64.82 316	65.22 311	63.60 349	78.80 178	31.14 509	66.97 327	56.47 445	54.23 233	69.94 362	88.68 115	37.23 428	74.81 265	45.28 387	89.41 167	84.86 130

SPE-MVS-test		74.89 113	74.23 126	76.86 91	77.01 209	62.94 164	78.98 100	84.61 63	58.62 160	70.17 357	80.80 316	66.74 141	81.96 126	61.74 192	89.40 169	85.69 106

PCF-MVS	63.80 13	72.70 161	71.69 189	75.72 107	78.10 186	60.01 199	73.04 191	81.50 131	45.34 384	79.66 140	84.35 225	65.15 161	82.65 111	48.70 350	89.38 170	84.50 155
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SIFT-NCMNet		56.27 425	55.94 429	57.26 440	62.54 473	64.28 149	59.61 433	41.26 535	43.43 417	78.50 160	69.35 479	32.26 460	45.98 506	27.16 526	89.34 171	61.53 509

gbinet_0.2-2-1-0.02		62.58 353	61.83 358	64.86 333	67.07 420	41.37 418	61.56 406	67.91 354	49.27 321	66.62 409	67.23 502	41.53 396	74.46 270	45.94 379	89.31 172	78.74 309

HQP3-MVS								84.12 79							89.16 173

HQP-MVS		75.24 104	75.01 110	75.94 104	82.37 120	58.80 217	77.32 120	84.12 79	59.08 153	71.58 334	85.96 198	58.09 258	85.30 59	67.38 131	89.16 173	83.73 177

AUN-MVS		70.22 214	67.88 265	77.22 90	82.96 114	71.61 61	69.08 276	71.39 303	49.17 325	71.70 328	78.07 375	37.62 427	79.21 177	61.81 190	89.15 175	80.82 269

v10		75.69 96	76.20 97	74.16 129	74.44 266	48.69 312	75.84 149	82.93 100	59.02 157	85.92 50	89.17 100	58.56 251	82.74 110	70.73 94	89.14 176	91.05 13

MM		78.15 76	77.68 82	79.55 49	80.10 151	65.47 132	80.94 74	78.74 201	71.22 49	72.40 315	88.70 113	60.51 218	87.70 3	77.40 37	89.13 177	85.48 110

hse-mvs2		72.32 170	70.66 213	77.31 89	83.10 110	71.77 60	69.19 273	71.45 302	54.28 231	77.89 167	78.26 370	49.04 338	79.23 176	63.62 173	89.13 177	80.92 266

Elysia		77.52 80	77.43 84	77.78 80	79.01 174	60.26 195	76.55 129	84.34 70	67.82 69	78.73 153	87.94 136	58.68 249	83.79 85	74.70 54	89.10 179	89.28 28

StellarMVS		77.52 80	77.43 84	77.78 80	79.01 174	60.26 195	76.55 129	84.34 70	67.82 69	78.73 153	87.94 136	58.68 249	83.79 85	74.70 54	89.10 179	89.28 28

MCST-MVS		73.42 132	73.34 150	73.63 139	81.28 138	59.17 208	74.80 161	83.13 93	45.50 379	72.84 306	83.78 245	65.15 161	80.99 146	64.54 158	89.09 181	80.73 273

MGCNet		75.45 100	74.66 114	77.83 79	75.58 240	61.53 175	78.29 107	77.18 229	63.15 124	69.97 361	87.20 145	57.54 267	87.05 9	74.05 66	88.96 182	84.89 127

ITE_SJBPF				80.35 41	76.94 211	73.60 51		80.48 160	66.87 75	83.64 88	86.18 187	70.25 98	79.90 168	61.12 202	88.95 183	87.56 59

ArgMatch-SfM		64.74 319	63.70 335	67.83 286	77.62 198	76.78 30	67.30 319	58.21 428	36.64 481	81.94 108	73.41 427	38.67 419	56.92 451	50.66 327	88.89 184	69.81 436

ANet_high		67.08 282	69.94 220	58.51 429	57.55 514	27.09 527	58.43 450	76.80 234	63.56 115	82.40 102	91.93 25	59.82 230	64.98 408	50.10 332	88.86 185	83.46 186

test_0402		78.17 75	79.48 66	74.24 127	83.50 101	59.15 209	72.52 197	74.60 260	75.34 18	88.69 17	91.81 30	75.06 49	82.37 118	65.10 149	88.68 186	81.20 257

ArgMatch-Sym		63.94 332	63.05 347	66.61 312	76.68 221	75.81 34	65.98 341	57.57 431	35.60 489	80.60 130	69.62 474	43.62 375	55.74 454	49.14 344	88.61 187	68.29 452

APD_test1		75.04 108	75.38 107	74.02 132	69.89 367	70.15 77	76.46 132	79.71 177	65.50 88	82.99 93	88.60 118	66.94 134	72.35 305	59.77 223	88.54 188	79.56 294

mvs5depth		66.35 295	67.98 262	61.47 388	62.43 476	51.05 283	69.38 266	69.24 331	56.74 188	73.62 287	89.06 105	46.96 354	58.63 440	55.87 272	88.49 189	74.73 378

viewdifsd2359ckpt09		72.87 156	72.43 174	74.17 128	74.45 264	51.70 276	76.39 137	84.50 67	49.48 318	75.34 242	83.23 260	63.12 176	82.43 116	56.99 260	88.41 190	88.37 51

casdiffmvs_mvg		75.26 103	76.18 98	72.52 179	72.87 309	49.47 305	72.94 194	84.71 58	59.49 151	80.90 127	88.81 112	70.07 99	79.71 170	67.40 128	88.39 191	88.40 49
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EGC-MVSNET		64.77 318	61.17 369	75.60 110	86.90 42	74.47 43	84.04 44	68.62 349	0.60 555	1.13 559	91.61 35	65.32 159	74.15 277	64.01 163	88.28 192	78.17 321

FE-MVSNET2		68.70 249	69.85 223	65.22 327	74.82 251	37.95 462	67.28 321	73.47 270	53.40 253	77.65 176	87.72 141	59.72 232	73.17 290	46.39 373	88.23 193	84.56 149

IterMVS-LS		73.01 148	73.12 155	72.66 175	73.79 284	49.90 298	71.63 225	78.44 207	58.22 165	80.51 132	86.63 173	58.15 256	79.62 171	62.51 183	88.20 194	88.48 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSLP-MVS++		74.48 117	75.78 101	70.59 212	84.66 83	62.40 166	78.65 102	84.24 76	60.55 144	77.71 174	81.98 289	63.12 176	77.64 214	62.95 181	88.14 195	71.73 417

CL-MVSNet_self_test		62.44 355	63.40 341	59.55 417	72.34 318	32.38 501	56.39 463	64.84 379	51.21 288	67.46 401	81.01 312	50.75 323	63.51 415	38.47 441	88.12 196	82.75 217

casdiffseed414692147		74.13 119	74.76 113	72.25 188	73.89 282	49.89 302	75.54 151	82.35 115	58.57 163	77.77 171	87.76 140	69.09 109	78.46 192	59.77 223	88.10 197	88.41 48

FMVSNet1		71.06 195	72.48 172	66.81 306	77.65 197	40.68 429	71.96 211	73.03 274	61.14 137	79.45 144	90.36 73	60.44 220	75.20 257	50.20 331	88.05 198	84.54 150

pm-mvs1		68.40 254	69.85 223	64.04 342	73.10 300	39.94 439	64.61 370	70.50 319	55.52 207	73.97 281	89.33 93	63.91 173	68.38 368	49.68 336	88.02 199	83.81 173

TinyColmap		67.98 262	69.28 235	64.08 340	67.98 403	46.82 352	70.04 252	75.26 252	53.05 255	77.36 182	86.79 161	59.39 237	72.59 300	45.64 382	88.01 200	72.83 401

v8		75.07 107	75.64 103	73.35 145	73.42 290	47.46 339	75.20 153	81.45 133	60.05 147	85.64 54	89.26 95	58.08 260	81.80 131	69.71 106	87.97 201	90.79 17

BP-MVS1		71.60 184	70.06 218	76.20 102	74.07 276	55.22 250	74.29 173	73.44 271	57.29 179	73.87 286	84.65 216	32.57 455	83.49 94	72.43 83	87.94 202	89.89 23

tttt0517		69.46 230	67.79 267	74.46 121	75.34 241	52.72 272	75.05 155	63.27 394	54.69 219	78.87 151	84.37 224	26.63 502	81.15 140	63.95 166	87.93 203	89.51 25

SIFT-PointCN		56.55 422	55.82 430	58.75 424	62.59 472	63.48 158	59.22 434	45.58 505	42.97 425	74.44 268	69.65 473	25.00 514	47.28 501	35.25 478	87.73 204	65.49 480

new-patchmatchnet		52.89 454	55.76 432	44.26 512	59.94 497	6.31 560	37.36 539	50.76 478	41.10 441	64.28 433	79.82 338	44.77 364	48.43 494	36.24 469	87.61 205	78.03 324

tfpnnormal		66.48 292	67.93 263	62.16 376	73.40 291	36.65 471	63.45 386	64.99 377	55.97 201	72.82 307	87.80 139	57.06 274	69.10 361	48.31 356	87.54 206	80.72 274

Anonymous202405211		66.02 299	66.89 287	63.43 356	74.22 270	38.14 458	59.00 438	66.13 367	63.33 121	69.76 366	85.95 199	51.88 312	70.50 339	44.23 390	87.52 207	81.64 253

c3_l		69.82 224	69.89 221	69.61 246	66.24 433	43.48 399	68.12 304	79.61 182	51.43 280	77.72 173	80.18 331	54.61 295	78.15 206	63.62 173	87.50 208	87.20 65

v144192		72.99 150	73.06 157	72.77 171	74.58 263	47.48 338	71.90 215	80.44 162	51.57 278	81.46 118	84.11 232	58.04 262	82.12 123	67.98 120	87.47 209	88.70 45

Patchmtry		60.91 378	63.01 349	54.62 455	66.10 437	26.27 533	67.47 312	56.40 446	54.05 239	72.04 324	86.66 170	33.19 448	60.17 430	43.69 392	87.45 210	77.42 332

hybridcas		73.97 122	75.17 108	70.38 216	73.56 285	47.22 344	72.99 193	82.30 116	56.94 183	79.54 141	88.05 133	72.64 69	76.88 232	63.11 180	87.43 211	87.04 69

SIFT-PCN-Cal		56.03 427	55.47 434	57.69 436	63.19 469	62.93 165	58.63 445	43.46 519	42.37 430	75.62 230	69.51 477	25.32 512	44.67 519	33.77 493	87.41 212	65.45 482

v1921920		72.96 153	72.98 159	72.89 166	74.67 255	47.58 336	71.92 214	80.69 153	51.70 277	81.69 115	83.89 242	56.58 279	82.25 121	68.34 114	87.36 213	88.82 42

CSCG		74.12 120	74.39 121	73.33 146	79.35 162	61.66 174	77.45 119	81.98 123	62.47 130	79.06 149	80.19 330	61.83 197	78.79 185	59.83 222	87.35 214	79.54 297

MGCFI-Net		71.70 182	73.10 156	67.49 292	73.23 294	43.08 404	72.06 207	82.43 113	54.58 222	75.97 224	82.00 287	72.42 70	75.22 255	57.84 249	87.34 215	84.18 163

v1192		73.40 137	73.42 145	73.32 147	74.65 258	48.67 313	72.21 203	81.73 127	52.76 260	81.85 109	84.56 219	57.12 272	82.24 122	68.58 112	87.33 216	89.06 35

LCM-MVSNet-Re		69.10 240	71.57 196	61.70 383	70.37 357	34.30 492	61.45 408	79.62 180	56.81 186	89.59 8	88.16 131	68.44 116	72.94 292	42.30 405	87.33 216	77.85 328

sasdasda		72.29 172	73.38 147	69.04 258	74.23 268	47.37 340	73.93 178	83.18 90	54.36 229	76.61 208	81.64 300	72.03 72	75.34 252	57.12 256	87.28 218	84.40 156

canonicalmvs		72.29 172	73.38 147	69.04 258	74.23 268	47.37 340	73.93 178	83.18 90	54.36 229	76.61 208	81.64 300	72.03 72	75.34 252	57.12 256	87.28 218	84.40 156

baseline		73.10 143	73.96 133	70.51 214	71.46 332	46.39 364	72.08 206	84.40 69	55.95 202	76.62 207	86.46 180	67.20 131	78.03 207	64.22 162	87.27 220	87.11 68

SIFT-CM-Cal		57.90 407	56.75 417	61.34 391	65.62 441	67.48 106	60.91 415	44.69 509	44.05 405	73.16 299	71.09 455	30.69 481	50.23 478	33.27 497	87.25 221	66.31 473

wanda-best-256-512		61.16 374	60.55 378	62.98 362	66.67 427	39.85 441	58.66 443	68.87 340	46.67 363	64.46 428	67.75 494	41.94 390	71.84 319	42.67 401	87.24 222	77.26 337

FE-blended-shiyan7		61.16 374	60.55 378	62.98 362	66.67 427	39.85 441	58.66 443	68.87 340	46.67 363	64.46 428	67.75 494	41.94 390	71.84 319	42.67 401	87.24 222	77.26 337

blended_shiyan6		62.20 358	61.77 359	63.47 353	67.98 403	40.64 433	60.46 423	69.15 332	47.24 357	66.43 411	70.57 458	43.73 374	71.93 317	43.16 398	87.24 222	77.85 328

usedtu_blend_shiyan5		63.30 340	63.13 345	63.78 345	66.67 427	41.75 416	68.57 295	73.64 267	57.20 181	64.46 428	67.75 494	41.94 390	72.34 306	40.72 424	87.24 222	77.26 337

test_fmvsmconf0.01_n		73.91 123	73.64 139	74.71 118	69.79 371	66.25 123	75.90 147	79.90 173	46.03 373	76.48 215	85.02 211	67.96 126	73.97 279	74.47 60	87.22 226	83.90 171

alignmvs		70.54 207	71.00 205	69.15 256	73.50 287	48.04 326	69.85 257	79.62 180	53.94 243	76.54 212	82.00 287	59.00 243	74.68 266	57.32 254	87.21 227	84.72 140

LoFTR		61.29 371	62.50 354	57.67 438	69.07 382	65.66 131	68.96 278	48.59 491	43.15 422	86.65 39	79.95 335	32.68 454	53.14 465	46.21 376	87.20 228	54.22 524

blended_shiyan8		62.19 359	61.77 359	63.46 354	68.01 401	40.65 432	60.47 422	69.13 335	47.24 357	66.44 410	70.55 459	43.75 373	71.91 318	43.18 397	87.19 229	77.81 330

F-COLMAP		75.29 102	73.99 132	79.18 54	81.73 131	71.90 59	81.86 68	82.98 98	59.86 150	72.27 316	84.00 237	64.56 168	83.07 103	51.48 318	87.19 229	82.56 225

TSAR-MVS + MP.		79.05 64	78.81 69	79.74 45	88.94 27	67.52 105	86.61 22	81.38 136	51.71 276	77.15 189	91.42 39	65.49 156	87.20 6	79.44 17	87.17 231	84.51 154
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
v1240		73.06 146	73.14 153	72.84 169	74.74 254	47.27 343	71.88 216	81.11 143	51.80 275	82.28 103	84.21 226	56.22 283	82.34 119	68.82 111	87.17 231	88.91 40

SIFT-UM-Cal		57.67 409	56.99 414	59.70 413	64.92 454	66.46 120	59.84 431	46.03 503	44.18 404	76.77 203	71.89 446	29.03 495	48.71 488	33.08 499	87.13 233	63.93 495

GDP-MVS		70.84 201	69.24 237	75.62 109	76.44 225	55.65 246	74.62 168	82.78 104	49.63 313	72.10 321	83.79 244	31.86 466	82.84 108	64.93 152	87.01 234	88.39 50

test_fmvsmconf0.1_n		73.26 141	72.82 164	74.56 120	69.10 381	66.18 125	74.65 167	79.34 187	45.58 378	75.54 233	83.91 241	67.19 132	73.88 282	73.26 72	86.86 235	83.63 179

v1144		73.29 140	73.39 146	73.01 157	74.12 273	48.11 323	72.01 209	81.08 146	53.83 244	81.77 111	84.68 214	58.07 261	81.91 127	68.10 116	86.86 235	88.99 38

casdiffmvs		73.06 146	73.84 134	70.72 210	71.32 335	46.71 355	70.93 238	84.26 75	55.62 205	77.46 181	87.10 147	67.09 133	77.81 210	63.95 166	86.83 237	87.64 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SIFT-ConvMatch		58.61 401	57.61 408	61.63 384	65.55 443	67.97 98	62.24 398	42.52 525	44.40 401	77.28 184	73.28 430	30.00 487	50.42 475	36.36 466	86.82 238	66.50 471

Vis-MVSNet (Re-imp)		62.74 350	63.21 344	61.34 391	72.19 321	31.56 506	67.31 318	53.87 458	53.60 250	69.88 363	83.37 252	40.52 405	70.98 334	41.40 415	86.78 239	81.48 255

CS-MVS		76.51 89	76.00 99	78.06 78	77.02 208	64.77 141	80.78 76	82.66 107	60.39 145	74.15 273	83.30 256	69.65 105	82.07 124	69.27 108	86.75 240	87.36 61

K. test v3		73.67 126	73.61 141	73.87 135	79.78 155	55.62 248	74.69 165	62.04 404	66.16 84	84.76 73	93.23 7	49.47 332	80.97 148	65.66 146	86.67 241	85.02 126

test_fmvsmconf_n		72.91 154	72.40 175	74.46 121	68.62 386	66.12 126	74.21 175	78.80 199	45.64 377	74.62 262	83.25 259	66.80 140	73.86 283	72.97 75	86.66 242	83.39 190

thisisatest0530		67.05 285	65.16 313	72.73 174	73.10 300	50.55 287	71.26 234	63.91 388	50.22 306	74.46 267	80.75 317	26.81 501	80.25 162	59.43 227	86.50 243	87.37 60

lessismore_v0				72.75 172	79.60 159	56.83 237		57.37 434		83.80 86	89.01 106	47.45 352	78.74 186	64.39 160	86.49 244	82.69 221

MVSMamba_PlusPlus		76.88 86	78.21 78	72.88 167	80.83 142	48.71 311	83.28 57	82.79 102	72.78 31	79.17 147	91.94 24	56.47 281	83.95 82	70.51 98	86.15 245	85.99 96

MVS_111021_HR		72.98 151	72.97 160	72.99 158	80.82 143	65.47 132	68.81 286	72.77 283	57.67 173	75.76 226	82.38 280	71.01 89	77.17 222	61.38 197	86.15 245	76.32 356

LF4IMVS		67.50 269	67.31 276	68.08 281	58.86 506	61.93 170	71.43 228	75.90 247	44.67 398	72.42 314	80.20 329	57.16 270	70.44 340	58.99 233	86.12 247	71.88 414

FMVSNet2		67.48 270	68.21 258	65.29 326	73.14 297	38.94 450	68.81 286	71.21 312	54.81 214	76.73 204	86.48 179	48.63 344	74.60 267	47.98 360	86.11 248	82.35 230

MatchFormer		53.09 451	55.03 441	47.30 496	59.31 502	57.25 233	67.30 319	37.25 545	27.23 527	82.61 100	74.56 409	26.23 506	42.89 526	34.73 484	86.00 249	41.75 542

EPNet_dtu		58.93 397	58.52 396	60.16 411	67.91 405	47.70 334	69.97 254	58.02 429	49.73 312	47.28 533	73.02 432	38.14 421	62.34 419	36.57 465	85.99 250	70.43 431
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SIFT-UMatch		58.13 404	57.37 412	60.42 406	65.49 445	67.10 112	61.52 407	43.57 517	44.20 403	76.80 201	72.60 434	29.70 490	47.95 497	36.61 463	85.82 251	66.20 475

XVG-OURS-SEG-HR		79.62 59	79.99 62	78.49 68	86.46 46	74.79 41	77.15 124	85.39 38	66.73 77	80.39 134	88.85 111	74.43 58	78.33 200	74.73 52	85.79 252	82.35 230

BridgeMVS		73.59 129	74.06 130	72.17 190	77.48 200	47.72 333	81.43 71	82.20 119	54.38 228	79.19 146	87.68 142	54.41 296	83.57 91	63.98 165	85.78 253	85.22 115

API-MVS		70.97 199	71.51 197	69.37 249	75.20 243	55.94 241	80.99 73	76.84 233	62.48 129	71.24 343	77.51 380	61.51 203	80.96 151	52.04 313	85.76 254	71.22 423

LuminaMVS		71.15 194	70.79 210	72.24 189	77.20 202	58.34 224	72.18 204	76.20 241	54.91 213	77.74 172	81.93 292	49.17 337	76.31 240	62.12 189	85.66 255	82.07 238

fmvsm_s_conf0.5_n_9		74.56 116	74.30 124	75.34 113	77.17 203	64.87 140	72.62 196	76.17 242	54.54 225	78.32 162	86.14 190	65.14 163	75.72 248	73.10 73	85.55 256	85.42 111

v2v482		72.55 166	72.58 169	72.43 181	72.92 308	46.72 354	71.41 229	79.13 192	55.27 209	81.17 122	85.25 209	55.41 289	81.13 141	67.25 135	85.46 257	89.43 26

GBi-Net		68.30 256	68.79 244	66.81 306	73.14 297	40.68 429	71.96 211	73.03 274	54.81 214	74.72 257	90.36 73	48.63 344	75.20 257	47.12 365	85.37 258	84.54 150

test1		68.30 256	68.79 244	66.81 306	73.14 297	40.68 429	71.96 211	73.03 274	54.81 214	74.72 257	90.36 73	48.63 344	75.20 257	47.12 365	85.37 258	84.54 150

FMVSNet3		65.00 313	65.16 313	64.52 336	69.47 375	37.56 467	66.63 332	70.38 320	51.55 279	74.72 257	83.27 257	37.89 425	74.44 271	47.12 365	85.37 258	81.57 254

CNLPA		73.44 131	73.03 158	74.66 119	78.27 183	75.29 37	75.99 146	78.49 206	65.39 91	75.67 229	83.22 264	61.23 207	66.77 393	53.70 305	85.33 261	81.92 245

Effi-MVS+-dtu		75.43 101	72.28 178	84.91 2	77.05 206	83.58 1	78.47 105	77.70 219	57.68 172	74.89 254	78.13 374	64.80 165	84.26 81	56.46 266	85.32 262	86.88 71

UGNet		70.20 215	69.05 240	73.65 137	76.24 228	63.64 155	75.87 148	72.53 287	61.48 135	60.93 469	86.14 190	52.37 310	77.12 227	50.67 326	85.21 263	80.17 288
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
VNet		64.01 331	65.15 315	60.57 401	73.28 293	35.61 483	57.60 455	67.08 359	54.61 221	66.76 407	83.37 252	56.28 282	66.87 389	42.19 407	85.20 264	79.23 302

TAMVS		65.31 307	63.75 333	69.97 239	82.23 125	59.76 202	66.78 331	63.37 393	45.20 390	69.79 365	79.37 351	47.42 353	72.17 309	34.48 486	85.15 265	77.99 326

test_yl		65.11 309	65.09 318	65.18 328	70.59 347	40.86 424	63.22 391	72.79 281	57.91 169	68.88 382	79.07 362	42.85 385	74.89 263	45.50 384	84.97 266	79.81 290

DCV-MVSNet		65.11 309	65.09 318	65.18 328	70.59 347	40.86 424	63.22 391	72.79 281	57.91 169	68.88 382	79.07 362	42.85 385	74.89 263	45.50 384	84.97 266	79.81 290

USDC		62.80 347	63.10 346	61.89 379	65.19 448	43.30 402	67.42 313	74.20 265	35.80 488	72.25 317	84.48 222	45.67 358	71.95 316	37.95 448	84.97 266	70.42 432

ETV-MVS		72.72 160	72.16 181	74.38 126	76.90 217	55.95 240	73.34 186	84.67 59	62.04 131	72.19 319	70.81 456	65.90 151	85.24 63	58.64 238	84.96 269	81.95 244

DPM-MVS		69.98 220	69.22 239	72.26 186	82.69 118	58.82 216	70.53 244	81.23 140	47.79 349	64.16 437	80.21 328	51.32 318	83.12 101	60.14 216	84.95 270	74.83 375

viewdifsd2359ckpt11		69.22 235	69.68 227	67.83 286	68.17 397	46.57 358	66.42 336	68.93 338	50.60 299	77.47 180	83.95 238	68.16 119	73.84 284	58.49 240	84.92 271	83.10 200

viewmsd2359difaftdt		69.22 235	69.68 227	67.83 286	68.17 397	46.57 358	66.42 336	68.93 338	50.60 299	77.48 179	83.94 239	68.16 119	73.84 284	58.49 240	84.92 271	83.10 200

mamba_0408		70.32 211	69.35 231	73.24 148	76.92 212	55.22 250	56.61 461	79.27 189	52.14 269	73.08 301	83.14 266	60.53 216	82.50 113	57.51 251	84.91 273	81.99 241

SSM_04072		67.23 279	69.35 231	60.89 398	76.92 212	55.22 250	56.61 461	79.27 189	52.14 269	73.08 301	83.14 266	60.53 216	45.46 511	57.51 251	84.91 273	81.99 241

SSM_0407		72.15 174	71.85 186	73.06 156	76.92 212	55.22 250	73.59 180	79.83 174	53.69 246	73.08 301	84.18 227	62.26 191	81.98 125	58.21 244	84.91 273	81.99 241

SIFT-NN-CMatch		57.48 412	56.23 422	61.21 394	63.66 466	67.89 100	60.78 418	40.90 538	41.97 433	71.65 330	71.96 444	32.11 461	49.35 483	38.19 445	84.88 276	66.37 472

viewmacassd2359aftdt		71.41 189	72.29 177	68.78 269	71.32 335	44.81 380	70.11 251	81.51 130	52.64 262	74.95 252	86.79 161	66.02 148	74.50 269	62.43 186	84.86 277	87.03 70

SIFT-NCM-Cal		58.68 399	57.65 406	61.77 382	67.58 412	68.99 94	62.62 394	43.04 522	44.65 399	75.91 225	72.23 438	33.66 445	49.28 485	34.36 487	84.76 278	67.03 464

TestfortrainingZip				73.58 141	79.21 166	57.65 230	86.10 28	81.22 141	72.34 42	72.08 323	83.19 265	58.95 244	83.71 88		84.76 278	79.38 300

SDMVSNet		66.36 294	67.85 266	61.88 380	73.04 303	46.14 367	58.54 448	71.36 304	51.42 281	68.93 378	82.72 272	65.62 154	62.22 421	54.41 295	84.67 280	77.28 334

sd_testset		63.55 334	65.38 309	58.07 432	73.04 303	38.83 452	57.41 456	65.44 374	51.42 281	68.93 378	82.72 272	63.76 174	58.11 446	41.05 418	84.67 280	77.28 334

eth_miper_zixun_eth		69.42 231	68.73 248	71.50 199	67.99 402	46.42 362	67.58 309	78.81 197	50.72 296	78.13 165	80.34 326	50.15 327	80.34 160	60.18 213	84.65 282	87.74 56

miper_lstm_enhance		61.97 361	61.63 364	62.98 362	60.04 493	45.74 370	47.53 512	70.95 314	44.04 406	73.06 304	78.84 365	39.72 411	60.33 429	55.82 274	84.64 283	82.88 211

SSM_0404		72.51 167	72.15 182	73.60 140	78.20 184	55.86 243	74.41 170	79.83 174	53.69 246	73.98 280	84.18 227	62.26 191	82.50 113	58.21 244	84.60 284	82.43 228

cl22		67.14 280	66.51 291	69.03 260	63.20 468	43.46 400	66.88 330	76.25 240	49.22 324	74.48 266	77.88 376	45.49 360	77.40 217	60.64 208	84.59 285	86.24 87

miper_ehance_all_eth		68.36 255	68.16 261	68.98 262	65.14 451	43.34 401	67.07 325	78.92 196	49.11 326	76.21 221	77.72 377	53.48 302	77.92 209	61.16 201	84.59 285	85.68 107

miper_enhance_ethall		65.86 301	65.05 321	68.28 280	61.62 482	42.62 409	64.74 366	77.97 216	42.52 428	73.42 294	72.79 433	49.66 330	77.68 213	58.12 246	84.59 285	84.54 150

XFeat-MNN		48.68 484	49.35 482	46.65 501	44.49 549	46.89 351	46.91 515	43.80 516	27.16 528	75.21 245	60.05 527	22.65 527	46.52 503	39.33 431	84.57 288	46.53 535

balanced_ft_v1		71.65 183	72.22 180	69.92 240	74.26 267	45.74 370	81.54 70	79.66 178	53.65 248	79.77 139	86.74 165	51.20 320	80.64 154	58.70 237	84.47 289	83.40 189

SIFT-NN-PointCN		57.17 417	56.12 425	60.35 409	62.47 475	65.79 129	59.98 428	44.36 513	42.73 426	72.13 320	71.16 454	30.84 478	48.08 496	36.92 460	84.45 290	67.17 463

KinetiMVS		72.61 163	72.54 170	72.82 170	71.47 331	55.27 249	68.54 296	76.50 236	61.70 134	74.95 252	86.08 194	59.17 241	76.95 229	69.96 101	84.45 290	86.24 87

CDS-MVSNet		64.33 327	62.66 353	69.35 251	80.44 147	58.28 225	65.26 355	65.66 371	44.36 402	67.30 403	75.54 399	43.27 377	71.77 321	37.68 450	84.44 292	78.01 325
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
testing3-2		56.85 419	57.62 407	54.53 456	75.84 236	22.23 545	51.26 499	49.10 488	61.04 139	63.74 445	79.73 340	22.29 528	59.44 433	31.16 508	84.43 293	81.92 245

E5new		73.42 132	74.46 117	70.29 222	74.61 259	47.14 345	71.85 219	83.01 94	56.07 196	77.28 184	86.81 156	71.54 79	77.15 223	64.59 154	84.39 294	86.59 77

E6new		73.42 132	74.46 117	70.29 222	74.60 261	47.14 345	71.86 217	82.99 96	56.07 196	77.28 184	86.81 156	71.55 77	77.14 225	64.59 154	84.39 294	86.59 77

E6		73.42 132	74.46 117	70.29 222	74.60 261	47.14 345	71.86 217	82.99 96	56.07 196	77.28 184	86.81 156	71.55 77	77.14 225	64.59 154	84.39 294	86.59 77

E5		73.42 132	74.46 117	70.29 222	74.61 259	47.14 345	71.85 219	83.01 94	56.07 196	77.28 184	86.81 156	71.54 79	77.15 223	64.59 154	84.39 294	86.59 77

CANet		73.00 149	71.84 187	76.48 97	75.82 237	61.28 179	74.81 159	80.37 164	63.17 122	62.43 457	80.50 323	61.10 211	85.16 67	64.00 164	84.34 298	83.01 207

PLC	62.01 16	71.79 181	70.28 217	76.33 99	80.31 149	68.63 95	78.18 111	81.24 139	54.57 223	67.09 405	80.63 320	59.44 236	81.74 133	46.91 368	84.17 299	78.63 310
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PVSNet_BlendedMVS		65.38 306	64.30 326	68.61 272	69.81 368	49.36 306	65.60 350	78.96 194	45.50 379	59.98 473	78.61 366	51.82 313	78.20 203	44.30 388	84.11 300	78.27 318

cascas		64.59 321	62.77 352	70.05 236	75.27 242	50.02 295	61.79 402	71.61 297	42.46 429	63.68 446	68.89 486	49.33 334	80.35 159	47.82 362	84.05 301	79.78 292

SIFT-NN-UMatch		57.27 416	56.18 423	60.54 403	62.85 471	66.67 118	61.19 412	41.27 534	43.01 424	70.01 360	72.44 437	32.76 452	49.32 484	38.19 445	83.87 302	65.63 479

MSP-MVS		80.49 52	79.67 65	82.96 5	89.70 11	77.46 27	87.16 12	85.10 44	64.94 102	81.05 123	88.38 123	57.10 273	87.10 8	79.75 11	83.87 302	84.31 160
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
test20.03		55.74 430	57.51 410	50.42 478	59.89 498	32.09 503	50.63 500	49.01 489	50.11 307	65.07 422	83.23 260	45.61 359	48.11 495	30.22 511	83.82 304	71.07 427

fmvsm_s_conf0.5_n_6		70.08 217	69.97 219	70.39 215	72.99 306	58.93 215	68.84 282	76.40 239	49.08 327	68.75 386	81.65 299	57.34 269	71.97 315	70.91 92	83.81 305	80.26 285

SIFT-MNN		59.60 390	58.57 395	62.71 369	68.39 389	69.16 90	63.67 384	48.13 494	45.22 389	73.92 283	73.85 421	30.71 480	50.57 474	39.45 429	83.78 306	68.40 450

D2MVS		62.58 353	61.05 371	67.20 298	63.85 462	47.92 327	56.29 464	69.58 326	39.32 457	70.07 359	78.19 372	34.93 439	72.68 294	53.44 308	83.74 307	81.00 264

MVS_111021_LR		72.10 175	71.82 188	72.95 160	79.53 160	73.90 49	70.45 246	66.64 362	56.87 184	76.81 200	81.76 296	68.78 110	71.76 322	61.81 190	83.74 307	73.18 395

PDCNetPlus		38.77 508	39.67 513	36.07 527	38.82 555	27.82 525	36.52 541	51.55 475	22.53 543	37.81 549	50.69 539	7.16 558	32.98 544	28.21 523	83.73 309	47.40 532

patch_mono-2		62.73 351	64.08 330	58.68 427	70.36 358	55.87 242	60.84 417	64.11 387	41.23 440	64.04 438	78.22 371	60.00 225	48.80 487	54.17 300	83.71 310	71.37 420

dcpmvs_2		71.02 198	72.65 166	66.16 318	76.06 234	50.49 288	71.97 210	79.36 186	50.34 303	82.81 97	83.63 246	64.38 169	67.27 382	61.54 194	83.71 310	80.71 275

fmvsm_s_conf0.5_n_10		72.30 171	72.02 183	73.15 152	70.76 343	59.05 212	73.40 185	79.63 179	48.80 333	75.39 241	84.03 234	59.60 235	75.18 260	72.85 76	83.68 312	85.21 118

fmvsm_l_mol_unc0.5_1		67.37 274	67.71 268	66.34 316	68.12 400	43.59 398	61.82 401	58.96 424	48.28 337	80.57 131	88.00 135	54.81 291	72.39 304	65.22 148	83.61 313	83.05 205

test_fmvsmvis_n_1920		72.36 169	72.49 171	71.96 191	71.29 337	64.06 153	72.79 195	81.82 125	40.23 452	81.25 121	81.04 311	70.62 93	68.69 363	69.74 105	83.60 314	83.14 199

thres600view7		61.82 364	61.38 367	63.12 360	71.81 326	34.93 487	64.64 368	56.99 439	54.78 218	70.33 353	79.74 339	32.07 463	72.42 303	38.61 439	83.46 315	82.02 239

viewmanbaseed2359cas		70.24 212	70.83 208	68.48 274	69.99 366	44.55 386	69.48 263	81.01 148	50.87 293	73.61 288	84.84 213	64.00 171	74.31 274	60.24 211	83.43 316	86.56 81

旧先验1					84.55 86	60.36 194		63.69 389			87.05 151	54.65 294			83.34 317	69.66 439

SSC-MVS3.2		57.01 418	59.50 388	49.57 485	67.73 408	25.95 535	46.68 516	51.75 473	51.41 283	63.84 442	79.66 342	53.28 304	50.34 477	37.85 449	83.28 318	72.41 407

新几何1				69.99 237	88.37 34	71.34 64		62.08 401	43.85 407	74.99 251	86.11 193	52.85 306	70.57 338	50.99 324	83.23 319	68.05 458

fmvsm_l_conf0.5_n_9		70.73 204	71.08 203	69.67 245	70.44 355	58.80 217	70.21 250	75.11 255	48.15 343	73.50 291	82.69 274	65.69 153	68.05 374	70.87 93	83.02 320	82.16 235

Vis-MVSNet		74.85 115	74.56 115	75.72 107	81.63 133	64.64 142	76.35 138	79.06 193	62.85 126	73.33 295	88.41 121	62.54 186	79.59 173	63.94 168	82.92 321	82.94 208
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_s_conf0.5_n_8		72.87 156	72.85 161	72.93 163	72.25 319	59.01 214	72.35 200	80.13 169	56.32 193	75.74 227	84.12 230	60.14 224	75.05 261	71.71 87	82.90 322	84.75 137

guyue		66.95 287	66.74 290	67.56 291	70.12 365	51.14 282	65.05 360	68.68 347	49.98 311	74.64 261	80.83 315	50.77 322	70.34 343	57.72 250	82.89 323	81.21 256

ET-MVSNet_ETH3D		63.32 339	60.69 377	71.20 205	70.15 363	55.66 245	65.02 361	64.32 385	43.28 421	68.99 374	72.05 443	25.46 510	78.19 205	54.16 301	82.80 324	79.74 293

E4		72.74 159	73.54 142	70.35 219	74.85 250	46.82 352	69.53 261	82.80 101	55.60 206	76.23 220	86.50 178	69.87 102	77.45 216	63.72 170	82.77 325	86.76 74

DELS-MVS		68.83 244	68.31 253	70.38 216	70.55 351	48.31 319	63.78 383	82.13 120	54.00 240	68.96 375	75.17 404	58.95 244	80.06 167	58.55 239	82.74 326	82.76 216
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
CMPMVS	48.73 20	61.54 370	60.89 374	63.52 351	61.08 484	51.55 278	68.07 305	68.00 353	33.88 498	65.87 415	81.25 306	37.91 424	67.71 375	49.32 341	82.60 327	71.31 422
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
VortexMVS		65.93 300	66.04 301	65.58 325	67.63 411	47.55 337	64.81 363	72.75 284	47.37 354	75.17 248	79.62 344	49.28 335	71.00 333	55.20 280	82.51 328	78.21 320

ppachtmachnet_test		60.26 385	59.61 386	62.20 374	67.70 409	44.33 389	58.18 452	60.96 408	40.75 448	65.80 416	72.57 436	41.23 398	63.92 412	46.87 369	82.42 329	78.33 316

SP-MNN		63.33 338	64.30 326	60.41 407	66.01 438	60.04 198	65.58 351	60.61 409	49.33 319	69.45 368	73.75 422	41.65 394	48.61 490	69.96 101	82.36 330	72.57 404

AstraMVS		67.11 281	66.84 289	67.92 282	70.75 344	51.36 280	64.77 365	67.06 360	49.03 329	75.40 238	82.05 285	51.26 319	70.65 336	58.89 235	82.32 331	81.77 250

v148		69.38 233	69.39 230	69.36 250	69.14 380	44.56 384	68.83 284	72.70 285	54.79 217	78.59 156	84.12 230	54.69 293	76.74 236	59.40 228	82.20 332	86.79 72

thisisatest0515		60.48 383	57.86 404	68.34 277	67.25 415	46.42 362	60.58 421	62.14 399	40.82 446	63.58 449	69.12 480	26.28 505	78.34 199	48.83 347	82.13 333	80.26 285

fmvsm_s_conf0.5_n_5		71.46 188	71.62 193	70.99 207	73.89 282	59.95 200	73.02 192	73.08 273	45.15 391	77.30 183	84.06 233	64.73 167	70.08 347	71.20 88	82.10 334	82.92 209

OpenMVS	62.51 15	68.76 246	68.75 246	68.78 269	70.56 349	53.91 263	78.29 107	77.35 224	48.85 332	70.22 354	83.52 248	52.65 309	76.93 230	55.31 279	81.99 335	75.49 366

MAR-MVS		67.72 267	66.16 296	72.40 182	74.45 264	64.99 139	74.87 157	77.50 222	48.67 335	65.78 417	68.58 490	57.01 275	77.79 211	46.68 371	81.92 336	74.42 385
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
Anonymous20231206		54.13 441	55.82 430	49.04 490	70.89 338	35.96 479	51.73 495	50.87 477	34.86 491	62.49 456	79.22 357	42.52 388	44.29 521	27.95 524	81.88 337	66.88 466

SIFT-NN-NCMNet		57.48 412	56.02 427	61.86 381	66.93 425	69.26 89	62.14 399	44.46 512	42.32 431	67.01 406	71.93 445	32.46 457	50.96 472	35.06 481	81.87 338	65.36 483

FE-MVS		68.29 258	66.96 284	72.26 186	74.16 272	54.24 260	77.55 117	73.42 272	57.65 175	72.66 310	84.91 212	32.02 465	81.49 135	48.43 354	81.85 339	81.04 261

GeoE		73.14 142	73.77 137	71.26 203	78.09 187	52.64 273	74.32 171	79.56 184	56.32 193	76.35 219	83.36 254	70.76 92	77.96 208	63.32 177	81.84 340	83.18 198

FA-MVS(test-final)		71.27 192	71.06 204	71.92 193	73.96 279	52.32 275	76.45 133	76.12 243	59.07 156	74.04 279	86.18 187	52.18 311	79.43 175	59.75 225	81.76 341	84.03 167

E3		71.98 177	72.60 167	70.13 232	74.09 274	46.61 356	69.15 274	82.56 110	54.40 226	75.31 244	85.35 204	68.51 113	77.34 218	62.30 187	81.75 342	86.44 84

E2		71.98 177	72.60 167	70.13 232	74.09 274	46.61 356	69.15 274	82.56 110	54.40 226	75.32 243	85.35 204	68.51 113	77.34 218	62.30 187	81.74 343	86.44 84

thres100view900		61.17 373	61.09 370	61.39 389	72.14 322	35.01 486	65.42 353	56.99 439	55.23 210	70.71 348	79.90 337	32.07 463	72.09 311	35.61 475	81.73 344	77.08 344

tfpn200view9		60.35 384	59.97 383	61.51 386	70.78 341	35.35 484	63.27 389	57.47 432	53.00 257	68.31 392	77.09 384	32.45 458	72.09 311	35.61 475	81.73 344	77.08 344

thres400		60.77 381	59.97 383	63.15 359	70.78 341	35.35 484	63.27 389	57.47 432	53.00 257	68.31 392	77.09 384	32.45 458	72.09 311	35.61 475	81.73 344	82.02 239

MG-MVS		70.47 209	71.34 199	67.85 284	79.26 164	40.42 436	74.67 166	75.15 254	58.41 164	68.74 387	88.14 132	56.08 284	83.69 89	59.90 220	81.71 347	79.43 299

usedtu_dtu_shiyan1		61.16 374	60.92 372	61.90 377	69.70 373	36.41 475	58.57 446	68.86 342	44.94 395	65.02 423	75.67 395	43.00 382	70.28 344	40.83 421	81.68 348	78.99 305

FE-MVSNET3		61.16 374	60.92 372	61.90 377	69.70 373	36.41 475	58.57 446	68.86 342	44.94 395	65.02 423	75.67 395	43.00 382	70.28 344	40.82 422	81.68 348	78.99 305

PAPM_NR		73.91 123	74.16 128	73.16 150	81.90 129	53.50 266	81.28 72	81.40 134	66.17 83	73.30 296	83.31 255	59.96 226	83.10 102	58.45 242	81.66 350	82.87 212

FMVSNet5		55.08 437	55.54 433	53.71 458	65.80 439	33.50 497	56.22 465	52.50 468	43.72 413	61.06 466	83.38 251	25.46 510	54.87 458	30.11 512	81.64 351	72.75 402

viewdifsd2359ckpt07		70.24 212	71.30 200	67.05 302	70.55 351	43.90 393	67.15 323	77.48 223	53.60 250	75.49 235	85.35 204	71.42 84	72.13 310	59.03 232	81.60 352	85.12 120

SP-LightGlue		66.16 298	66.97 283	63.75 346	68.62 386	66.76 116	68.82 285	62.15 398	57.30 178	70.52 350	75.63 397	43.02 381	48.82 486	75.09 49	81.55 353	75.66 363

FE-MVSNET		62.77 348	64.36 325	57.97 435	70.52 353	33.96 493	61.66 405	67.88 355	50.67 297	73.18 298	82.58 276	48.03 349	68.22 370	43.21 396	81.55 353	71.74 416

fmvsm_s_conf0.5_n_3		72.97 152	74.13 129	69.47 248	71.40 333	58.36 223	73.07 189	80.64 156	56.86 185	75.49 235	84.67 215	67.86 127	72.33 308	75.68 45	81.54 355	77.73 331

diffmvs_AUTHOR		68.27 259	68.59 250	67.32 296	63.76 464	45.37 373	65.31 354	77.19 228	49.25 322	72.68 309	82.19 283	59.62 234	71.17 330	65.75 145	81.53 356	85.42 111

PAPR		69.20 237	68.66 249	70.82 208	75.15 245	47.77 331	75.31 152	81.11 143	49.62 315	66.33 412	79.27 356	61.53 202	82.96 104	48.12 358	81.50 357	81.74 252

SP-SuperGlue		66.58 290	67.36 273	64.24 337	68.59 388	66.47 119	68.14 302	61.29 407	58.07 167	71.67 329	75.95 392	46.37 355	50.95 473	74.72 53	81.46 358	75.29 372

testdata				64.13 339	85.87 64	63.34 160		61.80 405	47.83 348	76.42 218	86.60 175	48.83 341	62.31 420	54.46 294	81.26 359	66.74 469

onestephybrid01		68.67 251	68.21 258	70.07 235	64.40 459	49.83 304	67.51 310	76.41 238	51.08 290	71.78 326	81.97 291	59.69 233	75.32 254	59.85 221	81.20 360	85.06 125

icg_test_0407_2		63.88 333	65.59 305	58.75 424	72.47 313	48.64 314	53.19 485	72.98 277	45.33 385	68.91 380	79.37 351	61.91 195	51.11 470	55.06 282	81.11 361	76.49 350

IMVS_0407		67.26 277	67.35 274	66.97 305	72.47 313	48.64 314	69.03 277	72.98 277	45.33 385	68.91 380	79.37 351	61.91 195	75.77 245	55.06 282	81.11 361	76.49 350

IMVS_0404		62.18 360	63.05 347	59.58 416	72.47 313	48.64 314	55.47 471	72.98 277	45.33 385	55.80 501	79.37 351	49.84 329	53.60 463	55.06 282	81.11 361	76.49 350

IMVS_0403		67.07 283	67.08 279	67.03 303	72.47 313	48.64 314	68.44 300	72.98 277	45.33 385	68.63 388	79.37 351	60.38 221	75.97 241	55.06 282	81.11 361	76.49 350

3Dnovator	65.95 11	71.50 186	71.22 202	72.34 183	73.16 296	63.09 162	78.37 106	78.32 209	57.67 173	72.22 318	84.61 218	54.77 292	78.47 191	60.82 205	81.07 365	75.45 367

viewdifsd2359ckpt13		69.89 222	69.74 226	70.32 221	70.82 340	48.73 310	72.39 199	81.39 135	48.20 341	72.73 308	82.73 271	62.61 183	76.50 237	55.87 272	80.93 366	85.73 105

viewcassd2359sk11		71.41 189	71.89 184	69.98 238	73.50 287	46.46 361	68.91 281	82.39 114	53.62 249	74.57 264	84.41 223	67.40 130	77.27 220	61.35 198	80.89 367	86.21 90

testing3		58.28 403	58.38 399	58.00 434	77.45 201	26.12 534	60.78 418	43.00 523	56.02 200	70.18 356	75.76 393	13.27 554	67.24 383	48.02 359	80.89 367	80.65 276

RPSCF		75.76 95	74.37 122	79.93 43	74.81 252	77.53 21	77.53 118	79.30 188	59.44 152	78.88 150	89.80 87	71.26 86	73.09 291	57.45 253	80.89 367	89.17 33

EG-PatchMatch MVS		70.70 205	70.88 207	70.16 230	82.64 119	58.80 217	71.48 227	73.64 267	54.98 212	76.55 211	81.77 295	61.10 211	78.94 182	54.87 288	80.84 370	72.74 403

V42		71.06 195	70.83 208	71.72 194	67.25 415	47.14 345	65.94 342	80.35 165	51.35 284	83.40 90	83.23 260	59.25 239	78.80 184	65.91 143	80.81 371	89.23 31

fmvsm_s_conf0.5_n_7		67.30 276	66.92 285	68.43 275	72.78 311	58.22 226	60.90 416	72.51 289	49.62 315	63.66 447	80.65 319	58.56 251	68.63 365	62.83 182	80.76 372	78.45 314

ttmdpeth		56.40 424	55.45 435	59.25 418	55.63 524	40.69 428	58.94 440	49.72 482	36.22 483	65.39 418	86.97 152	23.16 523	56.69 453	42.30 405	80.74 373	80.36 283

fmvsm_s_conf0.1_n_2		69.14 239	68.42 252	71.28 202	68.30 394	57.60 231	65.06 359	69.91 323	48.24 339	74.56 265	82.84 269	55.55 288	69.73 351	70.66 96	80.69 374	86.52 82

test222					87.30 37	69.15 92	67.85 306	59.59 419	41.06 442	73.05 305	85.72 202	48.03 349			80.65 375	66.92 465

BH-untuned		69.39 232	69.46 229	69.18 255	77.96 191	56.88 235	68.47 299	77.53 221	56.77 187	77.79 170	79.63 343	60.30 223	80.20 165	46.04 378	80.65 375	70.47 430

pmmvs-eth3d		64.41 326	63.27 343	67.82 289	75.81 238	60.18 197	69.49 262	62.05 403	38.81 463	74.13 274	82.23 282	43.76 372	68.65 364	42.53 403	80.63 377	74.63 379

fmvsm_s_conf0.5_n_2		68.93 242	68.23 257	71.02 206	67.78 407	57.58 232	64.74 366	69.56 327	48.16 342	74.38 270	82.32 281	56.00 285	69.68 354	70.65 97	80.52 378	85.80 103

fmvsm_s_conf0.5_n_11		71.06 195	70.91 206	71.51 198	72.09 323	59.40 203	73.49 182	79.97 172	50.98 291	68.33 391	81.50 303	61.82 198	72.64 296	69.54 107	80.43 379	82.51 226

fmvsm_l_conf0.5_n_3		71.98 177	71.68 190	72.88 167	72.84 310	64.15 151	73.48 183	77.11 230	48.97 331	71.31 342	84.18 227	67.98 125	71.60 326	68.86 110	80.43 379	82.89 210

EI-MVSNet-Vis-set		72.78 158	71.87 185	75.54 111	74.77 253	59.02 213	72.24 202	71.56 299	63.92 110	78.59 156	71.59 448	66.22 147	78.60 188	67.58 124	80.32 381	89.00 37

diffmvs		67.42 273	67.50 271	67.20 298	62.26 478	45.21 376	64.87 362	77.04 231	48.21 340	71.74 327	79.70 341	58.40 253	71.17 330	64.99 150	80.27 382	85.22 115
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SIFT-NN		56.62 421	55.34 438	60.47 404	67.01 424	67.25 109	61.74 403	45.38 508	42.69 427	64.49 427	71.36 453	28.48 496	47.55 498	36.68 462	80.23 383	66.63 470

MVStest1		55.38 434	54.97 442	56.58 445	43.72 550	40.07 438	59.13 436	47.09 499	34.83 492	76.53 213	84.65 216	13.55 553	53.30 464	55.04 286	80.23 383	76.38 355

EI-MVSNet-UG-set		72.63 162	71.68 190	75.47 112	74.67 255	58.64 221	72.02 208	71.50 300	63.53 116	78.58 158	71.39 452	65.98 149	78.53 189	67.30 134	80.18 385	89.23 31

E3new		70.94 200	71.30 200	69.86 242	72.98 307	46.34 365	68.74 291	82.28 117	53.01 256	73.95 282	83.57 247	66.41 145	77.21 221	60.68 207	80.06 386	86.03 95

MDA-MVSNet-bldmvs		62.34 356	61.73 361	64.16 338	61.64 481	49.90 298	48.11 510	57.24 437	53.31 254	80.95 124	79.39 350	49.00 340	61.55 424	45.92 380	80.05 387	81.03 262

reproduce_monomvs		58.94 396	58.14 401	61.35 390	59.70 500	40.98 423	60.24 426	63.51 391	45.85 376	68.95 376	75.31 403	18.27 544	65.82 401	51.47 319	79.97 388	77.26 337

IB-MVS	49.67 18	59.69 389	56.96 415	67.90 283	68.19 396	50.30 291	61.42 409	65.18 376	47.57 351	55.83 499	67.15 503	23.77 520	79.60 172	43.56 394	79.97 388	73.79 391
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
WBMVS		53.38 447	54.14 448	51.11 475	70.16 362	26.66 529	50.52 502	51.64 474	39.32 457	63.08 453	77.16 383	23.53 521	55.56 455	31.99 503	79.88 390	71.11 426

Patchmatch-RL test		59.95 387	59.12 390	62.44 372	72.46 317	54.61 258	59.63 432	47.51 497	41.05 443	74.58 263	74.30 414	31.06 475	65.31 405	51.61 317	79.85 391	67.39 460

EI-MVSNet		69.61 228	69.01 242	71.41 200	73.94 280	49.90 298	71.31 232	71.32 305	58.22 165	75.40 238	70.44 460	58.16 255	75.85 242	62.51 183	79.81 392	88.48 46

MVSTER		63.29 341	61.60 365	68.36 276	59.77 499	46.21 366	60.62 420	71.32 305	41.83 435	75.40 238	79.12 360	30.25 484	75.85 242	56.30 267	79.81 392	83.03 206

viewmamba		69.26 234	69.34 233	69.03 260	64.17 461	47.67 335	67.23 322	76.95 232	52.82 259	73.15 300	83.23 260	62.99 179	74.06 278	63.71 171	79.80 394	85.36 113

ab-mvs		64.11 329	65.13 316	61.05 395	71.99 324	38.03 461	67.59 308	68.79 346	49.08 327	65.32 420	86.26 185	58.02 263	66.85 391	39.33 431	79.79 395	78.27 318

dtuonlycased		61.79 365	62.24 357	60.43 405	73.00 305	39.07 447	61.74 403	60.61 409	33.09 504	74.10 275	80.34 326	59.20 240	60.39 428	38.34 442	79.76 396	81.83 247

PVSNet_Blended_VisFu		70.04 218	68.88 243	73.53 144	82.71 117	63.62 156	74.81 159	81.95 124	48.53 336	67.16 404	79.18 359	51.42 317	78.38 197	54.39 296	79.72 397	78.60 311

thres200		57.55 411	57.02 413	59.17 419	67.89 406	34.93 487	58.91 441	57.25 436	50.24 305	64.01 439	71.46 450	32.49 456	71.39 328	31.31 506	79.57 398	71.19 425

SP-DiffGlue		64.90 314	65.69 304	62.51 371	69.18 377	64.39 145	69.79 258	60.46 412	52.50 263	75.70 228	72.08 440	44.17 369	48.59 491	67.84 123	79.52 399	74.54 381

testgi		54.00 445	56.86 416	45.45 506	58.20 510	25.81 536	49.05 506	49.50 485	45.43 382	67.84 395	81.17 307	51.81 315	43.20 525	29.30 516	79.41 400	67.34 462

jason		64.47 324	62.84 350	69.34 252	76.91 215	59.20 205	67.15 323	65.67 370	35.29 490	65.16 421	76.74 387	44.67 365	70.68 335	54.74 290	79.28 401	78.14 322
jason: jason.
hybridnocas07		66.30 297	66.22 295	66.51 313	60.68 488	44.53 387	64.01 380	74.60 260	48.26 338	70.21 355	81.74 298	56.61 277	71.06 332	60.70 206	79.20 402	83.94 170

test_fmvsm_n_1920		69.63 226	68.45 251	73.16 150	70.56 349	65.86 128	70.26 249	78.35 208	37.69 472	74.29 271	78.89 364	61.10 211	68.10 372	65.87 144	79.07 403	85.53 109

Fast-Effi-MVS+-dtu		70.00 219	68.74 247	73.77 136	73.47 289	64.53 143	71.36 230	78.14 214	55.81 204	68.84 384	74.71 408	65.36 158	75.75 246	52.00 314	79.00 404	81.03 262

fmvsm_s_conf0.5_n_4		70.18 216	69.83 225	71.24 204	71.65 328	58.59 222	69.29 269	71.66 296	48.69 334	71.62 331	82.11 284	59.94 227	70.03 348	74.52 58	78.96 405	85.10 121

FBQ-MVS		59.22 393	57.87 403	63.30 358	73.18 295	39.68 443	68.92 279	63.38 392	45.87 375	60.72 470	69.03 481	27.40 499	73.66 287	33.33 496	78.95 406	76.57 349

EU-MVSNet		60.82 379	60.80 376	60.86 399	68.37 391	41.16 420	72.27 201	68.27 352	26.96 529	69.08 372	75.71 394	32.09 462	67.44 380	55.59 277	78.90 407	73.97 387

MVS_Test		69.84 223	70.71 212	67.24 297	67.49 413	43.25 403	69.87 256	81.22 141	52.69 261	71.57 337	86.68 169	62.09 194	74.51 268	66.05 141	78.74 408	83.96 168

Fast-Effi-MVS+		68.81 245	68.30 254	70.35 219	74.66 257	48.61 318	66.06 340	78.32 209	50.62 298	71.48 340	75.54 399	68.75 111	79.59 173	50.55 329	78.73 409	82.86 213

MVSFormer		69.93 221	69.03 241	72.63 177	74.93 246	59.19 206	83.98 45	75.72 248	52.27 267	63.53 450	76.74 387	43.19 378	80.56 155	72.28 84	78.67 410	78.14 322

lupinMVS		63.36 337	61.49 366	68.97 263	74.93 246	59.19 206	65.80 346	64.52 384	34.68 496	63.53 450	74.25 416	43.19 378	70.62 337	53.88 303	78.67 410	77.10 343

SP-NN		62.65 352	63.58 337	59.87 412	64.90 455	59.38 204	64.50 373	60.00 416	50.42 302	66.09 413	73.43 426	43.16 380	46.39 504	71.17 89	78.53 412	73.85 390

mvsmamba		68.87 243	67.30 277	73.57 142	76.58 223	53.70 265	84.43 42	74.25 263	45.38 383	76.63 206	84.55 220	35.85 435	85.27 60	49.54 338	78.49 413	81.75 251

Effi-MVS+		72.10 175	72.28 178	71.58 195	74.21 271	50.33 290	74.72 164	82.73 105	62.62 127	70.77 347	76.83 386	69.96 101	80.97 148	60.20 212	78.43 414	83.45 188

hybrid		65.62 304	65.49 307	66.01 320	60.48 490	44.28 390	64.13 376	74.21 264	46.41 367	69.84 364	80.86 314	55.77 286	70.28 344	59.30 229	78.42 415	83.46 186

CANet_DTU		64.04 330	63.83 332	64.66 334	68.39 389	42.97 406	73.45 184	74.50 262	52.05 273	54.78 506	75.44 402	43.99 370	70.42 341	53.49 307	78.41 416	80.59 278

myMVS_eth3d28		51.35 466	51.99 463	49.44 486	69.21 376	22.51 543	49.82 505	49.11 487	49.00 330	55.03 504	70.31 463	22.73 526	52.88 466	24.33 539	78.39 417	72.92 398

xiu_mvs_v1_base_debu		67.87 264	67.07 280	70.26 226	79.13 170	61.90 171	67.34 314	71.25 308	47.98 345	67.70 397	74.19 418	61.31 204	72.62 297	56.51 263	78.26 418	76.27 357

xiu_mvs_v1_base		67.87 264	67.07 280	70.26 226	79.13 170	61.90 171	67.34 314	71.25 308	47.98 345	67.70 397	74.19 418	61.31 204	72.62 297	56.51 263	78.26 418	76.27 357

xiu_mvs_v1_base_debi		67.87 264	67.07 280	70.26 226	79.13 170	61.90 171	67.34 314	71.25 308	47.98 345	67.70 397	74.19 418	61.31 204	72.62 297	56.51 263	78.26 418	76.27 357

fmvsm_l_conf0.5_n		67.48 270	66.88 288	69.28 253	67.41 414	62.04 169	70.69 242	69.85 324	39.46 456	69.59 367	81.09 310	58.15 256	68.73 362	67.51 126	78.16 421	77.07 346

BH-RMVSNet		68.69 250	68.20 260	70.14 231	76.40 226	53.90 264	64.62 369	73.48 269	58.01 168	73.91 284	81.78 294	59.09 242	78.22 202	48.59 351	77.96 422	78.31 317

RRT-MVS		70.33 210	70.73 211	69.14 257	71.93 325	45.24 375	75.10 154	75.08 256	60.85 142	78.62 155	87.36 144	49.54 331	78.64 187	60.16 214	77.90 423	83.55 180

IterMVS-SCA-FT		67.68 268	66.07 299	72.49 180	73.34 292	58.20 227	63.80 382	65.55 373	48.10 344	76.91 194	82.64 275	45.20 361	78.84 183	61.20 200	77.89 424	80.44 282

PVSNet_Blended		62.90 346	61.64 363	66.69 309	69.81 368	49.36 306	61.23 411	78.96 194	42.04 432	59.98 473	68.86 487	51.82 313	78.20 203	44.30 388	77.77 425	72.52 405

SD_0403		61.63 368	62.83 351	58.03 433	72.21 320	32.43 500	69.33 267	69.00 337	44.54 400	62.01 458	79.42 348	55.27 290	66.88 388	36.07 472	77.63 426	74.78 377

fmvsm_l_conf0.5_n_a		66.66 288	65.97 302	68.72 271	67.09 418	61.38 178	70.03 253	69.15 332	38.59 464	68.41 389	80.36 325	56.56 280	68.32 369	66.10 140	77.45 427	76.46 354

viewmambaseed2359dif		65.63 303	65.13 316	67.11 301	64.57 457	44.73 383	64.12 377	72.48 290	43.08 423	71.59 332	81.17 307	58.90 246	72.46 301	52.94 311	77.33 428	84.13 166

UWE-MVS		52.94 453	52.70 456	53.65 459	73.56 285	27.49 526	57.30 457	49.57 483	38.56 465	62.79 455	71.42 451	19.49 540	60.41 427	24.33 539	77.33 428	73.06 396

dtuplus		65.20 308	64.80 322	66.40 314	65.25 447	44.86 379	64.55 371	72.19 294	43.76 410	72.09 322	81.87 293	57.49 268	71.49 327	48.79 348	77.23 430	82.85 214

testing222		53.37 448	52.50 459	55.98 449	70.51 354	29.68 517	56.20 466	51.85 471	46.19 370	56.76 492	68.94 484	19.18 541	65.39 404	25.87 532	76.98 431	72.87 400

MSDG		67.47 272	67.48 272	67.46 293	70.70 345	54.69 257	66.90 329	78.17 212	60.88 141	70.41 351	74.76 406	61.22 209	73.18 289	47.38 364	76.87 432	74.49 383

testing91		55.74 430	55.29 439	57.08 441	70.63 346	30.85 511	54.94 477	56.31 449	50.34 303	57.08 488	70.10 468	24.50 516	65.86 400	36.98 459	76.75 433	74.53 382

XFeat-NN		44.60 500	44.89 502	43.74 514	46.61 546	44.56 384	41.07 530	40.59 539	23.40 541	66.73 408	54.97 532	20.65 532	40.41 536	33.52 495	76.49 434	46.25 536

E-PMN		45.17 495	45.36 498	44.60 510	50.07 539	42.75 407	38.66 536	42.29 529	46.39 368	39.55 546	51.15 537	26.00 507	45.37 513	37.68 450	76.41 435	45.69 538

PM-MVS		64.49 323	63.61 336	67.14 300	76.68 221	75.15 39	68.49 298	42.85 524	51.17 289	77.85 169	80.51 322	45.76 357	66.31 399	52.83 312	76.35 436	59.96 513

EIA-MVS		68.59 252	67.16 278	72.90 165	75.18 244	55.64 247	69.39 265	81.29 137	52.44 265	64.53 426	70.69 457	60.33 222	82.30 120	54.27 298	76.31 437	80.75 272

PRO-TEST		65.07 312	64.53 324	66.68 310	71.39 334	50.28 292	70.38 248	74.81 257	46.63 365	61.27 464	74.26 415	54.06 300	73.83 286	51.83 316	76.14 438	75.93 362

BH-w/o		64.81 317	64.29 328	66.36 315	76.08 233	54.71 256	65.61 349	75.23 253	50.10 308	71.05 346	71.86 447	54.33 297	79.02 180	38.20 444	76.14 438	65.36 483

testing99		55.16 436	54.56 446	56.98 443	70.13 364	30.58 513	54.55 480	54.11 457	49.53 317	56.76 492	70.14 467	22.76 525	65.79 402	36.99 458	76.04 440	74.57 380

MVS		60.62 382	59.97 383	62.58 370	68.13 399	47.28 342	68.59 293	73.96 266	32.19 507	59.94 475	68.86 487	50.48 324	77.64 214	41.85 412	75.74 441	62.83 498

TR-MVS		64.59 321	63.54 338	67.73 290	75.75 239	50.83 286	63.39 387	70.29 321	49.33 319	71.55 338	74.55 410	50.94 321	78.46 192	40.43 426	75.69 442	73.89 389

mvs_anonymous		65.08 311	65.49 307	63.83 344	63.79 463	37.60 466	66.52 335	69.82 325	43.44 416	73.46 293	86.08 194	58.79 248	71.75 323	51.90 315	75.63 443	82.15 236

QAPM		69.18 238	69.26 236	68.94 264	71.61 329	52.58 274	80.37 82	78.79 200	49.63 313	73.51 290	85.14 210	53.66 301	79.12 178	55.11 281	75.54 444	75.11 374

IterMVS		63.12 343	62.48 355	65.02 331	66.34 432	52.86 270	63.81 381	62.25 397	46.57 366	71.51 339	80.40 324	44.60 366	66.82 392	51.38 321	75.47 445	75.38 369
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		63.01 344	60.47 380	70.61 211	83.04 111	54.10 261	59.93 430	72.24 293	33.67 501	69.00 373	75.63 397	38.69 418	76.93 230	36.60 464	75.45 446	80.81 271

EMVS		44.61 499	44.45 505	45.10 509	48.91 542	43.00 405	37.92 537	41.10 537	46.75 362	38.00 548	48.43 542	26.42 503	46.27 505	37.11 457	75.38 447	46.03 537

MIMVSNet		54.39 440	56.12 425	49.20 487	72.57 312	30.91 510	59.98 428	48.43 493	41.66 436	55.94 498	83.86 243	41.19 400	50.42 475	26.05 529	75.38 447	66.27 474

our_test_3		56.46 423	56.51 419	56.30 446	67.70 409	39.66 444	55.36 473	52.34 470	40.57 451	63.85 441	69.91 471	40.04 408	58.22 445	43.49 395	75.29 449	71.03 428

pmmvs4		60.78 380	59.04 391	66.00 321	73.06 302	57.67 229	64.53 372	60.22 413	36.91 479	65.96 414	77.27 382	39.66 412	68.54 367	38.87 436	74.89 450	71.80 415

fmvsm_s_conf0.1_n_a		67.37 274	66.36 293	70.37 218	70.86 339	61.17 181	74.00 177	57.18 438	40.77 447	68.83 385	80.88 313	63.11 178	67.61 378	66.94 136	74.72 451	82.33 233

GA-MVS		62.91 345	61.66 362	66.66 311	67.09 418	44.49 388	61.18 413	69.36 330	51.33 285	69.33 371	74.47 411	36.83 430	74.94 262	50.60 328	74.72 451	80.57 279

KD-MVS_2432*1600		52.05 461	51.58 465	53.44 461	52.11 536	31.20 507	44.88 524	64.83 380	41.53 437	64.37 431	70.03 469	15.61 550	64.20 409	36.25 467	74.61 453	64.93 489

miper_refine_blended		52.05 461	51.58 465	53.44 461	52.11 536	31.20 507	44.88 524	64.83 380	41.53 437	64.37 431	70.03 469	15.61 550	64.20 409	36.25 467	74.61 453	64.93 489

fmvsm_s_conf0.5_n_a		67.00 286	65.95 303	70.17 229	69.72 372	61.16 182	73.34 186	56.83 441	40.96 444	68.36 390	80.08 333	62.84 180	67.57 379	66.90 138	74.50 455	81.78 249

fmvsm_s_conf0.1_n		66.60 289	65.54 306	69.77 243	68.99 383	59.15 209	72.12 205	56.74 443	40.72 449	68.25 394	80.14 332	61.18 210	66.92 385	67.34 133	74.40 456	83.23 197

pmmvs5		52.49 458	52.58 458	52.21 468	54.99 527	32.38 501	55.45 472	53.84 459	32.15 509	55.49 502	74.81 405	38.08 422	57.37 450	34.02 489	74.40 456	66.88 466

PatchT		53.35 449	56.47 420	43.99 513	64.19 460	17.46 549	59.15 435	43.10 521	52.11 272	54.74 507	86.95 153	29.97 488	49.98 480	43.62 393	74.40 456	64.53 493

fmvsm_s_conf0.5_n		66.34 296	65.27 310	69.57 247	68.20 395	59.14 211	71.66 224	56.48 444	40.92 445	67.78 396	79.46 346	61.23 207	66.90 386	67.39 129	74.32 459	82.66 222

SSC-MVS		61.79 365	66.08 297	48.89 491	76.91 215	10.00 557	53.56 484	47.37 498	68.20 67	76.56 210	89.21 97	54.13 298	57.59 449	54.75 289	74.07 460	79.08 304

xiu_mvs_v2_base		64.43 325	63.96 331	65.85 323	77.72 195	51.32 281	63.63 385	72.31 292	45.06 394	61.70 459	69.66 472	62.56 184	73.93 281	49.06 346	73.91 461	72.31 410

PS-MVSNAJ		64.27 328	63.73 334	65.90 322	77.82 193	51.42 279	63.33 388	72.33 291	45.09 393	61.60 460	68.04 492	62.39 188	73.95 280	49.07 345	73.87 462	72.34 409

OpenMVS_ROB	54.93 17	63.23 342	63.28 342	63.07 361	69.81 368	45.34 374	68.52 297	67.14 358	43.74 412	70.61 349	79.22 357	47.90 351	72.66 295	48.75 349	73.84 463	71.21 424

dtuonly		50.13 475	51.25 468	46.77 500	53.07 535	30.10 515	52.41 493	49.25 486	28.98 522	53.76 512	72.59 435	39.83 410	41.82 532	37.58 453	73.80 464	68.37 451

ETVMVS		50.32 473	49.87 481	51.68 471	70.30 360	26.66 529	52.33 494	43.93 515	43.54 415	54.91 505	67.95 493	20.01 538	60.17 430	22.47 542	73.40 465	68.22 454

test_fmvs3		56.78 420	55.99 428	59.12 421	53.96 533	48.09 324	58.76 442	66.22 366	27.54 525	76.66 205	68.69 489	25.32 512	51.31 469	53.42 309	73.38 466	77.97 327

MDTV_nov1_ep13			54.05 450		65.54 444	29.30 519	59.00 438	55.22 450	35.96 487	52.44 515	75.98 391	30.77 479	59.62 432	38.21 443	73.33 467

PAPM		61.79 365	60.37 381	66.05 319	76.09 231	41.87 413	69.30 268	76.79 235	40.64 450	53.80 511	79.62 344	44.38 367	82.92 105	29.64 515	73.11 468	73.36 394

WB-MVS		60.04 386	64.19 329	47.59 494	76.09 231	10.22 556	52.44 492	46.74 500	65.17 97	74.07 277	87.48 143	53.48 302	55.28 457	49.36 340	72.84 469	77.28 334

testing11		53.13 450	52.26 461	55.75 450	70.44 355	31.73 505	54.75 478	52.40 469	44.81 397	52.36 517	68.40 491	21.83 529	65.74 403	32.64 502	72.73 470	69.78 437

Patchmatch-test		47.93 486	49.96 480	41.84 518	57.42 515	24.26 538	48.75 507	41.49 532	39.30 459	56.79 491	73.48 424	30.48 483	33.87 543	29.29 517	72.61 471	67.39 460

gg-mvs-nofinetune		55.75 429	56.75 417	52.72 465	62.87 470	28.04 523	68.92 279	41.36 533	71.09 50	50.80 522	92.63 14	20.74 531	66.86 390	29.97 513	72.41 472	63.25 497

Syy-MVS		54.13 441	55.45 435	50.18 479	68.77 384	23.59 539	55.02 474	44.55 510	43.80 408	58.05 485	64.07 511	46.22 356	58.83 437	46.16 377	72.36 473	68.12 456

myMVS_eth3d		50.36 472	50.52 477	49.88 480	68.77 384	22.69 541	55.02 474	44.55 510	43.80 408	58.05 485	64.07 511	14.16 552	58.83 437	33.90 492	72.36 473	68.12 456

test_vis3_rt		51.94 463	51.04 471	54.65 454	46.32 547	50.13 294	44.34 526	78.17 212	23.62 540	68.95 376	62.81 516	21.41 530	38.52 540	41.49 414	72.22 475	75.30 371

test-LLR		50.43 471	50.69 476	49.64 483	60.76 486	41.87 413	53.18 486	45.48 506	43.41 418	49.41 527	60.47 525	29.22 492	44.73 517	42.09 409	72.14 476	62.33 506

test-mter		48.56 485	48.20 488	49.64 483	60.76 486	41.87 413	53.18 486	45.48 506	31.91 513	49.41 527	60.47 525	18.34 543	44.73 517	42.09 409	72.14 476	62.33 506

1112_ss		59.48 391	58.99 392	60.96 397	77.84 192	42.39 411	61.42 409	68.45 351	37.96 470	59.93 476	67.46 498	45.11 363	65.07 407	40.89 420	71.81 478	75.41 368

WB-MVSnew		53.94 446	54.76 444	51.49 473	71.53 330	28.05 522	58.22 451	50.36 479	37.94 471	59.16 480	70.17 466	49.21 336	51.94 468	24.49 537	71.80 479	74.47 384

MASt3R-SfM		45.75 491	47.16 492	41.50 521	47.00 545	47.91 329	45.50 521	38.10 542	21.81 547	73.91 284	62.86 515	29.14 494	29.95 548	34.59 485	71.54 480	46.65 534

UBG		49.18 482	49.35 482	48.66 492	70.36 358	26.56 531	50.53 501	45.61 504	37.43 475	53.37 513	65.97 505	23.03 524	54.20 461	26.29 527	71.54 480	65.20 486

PatchmatchNet1														28.98 522	71.38 482	62.61 502
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
N_pmnet		52.06 460	51.11 470	54.92 452	59.64 501	71.03 67	37.42 538	61.62 406	33.68 500	57.12 487	72.10 439	37.94 423	31.03 545	29.13 521	71.35 483	62.70 499

XXY-MVS		55.19 435	57.40 411	48.56 493	64.45 458	34.84 489	51.54 496	53.59 460	38.99 462	63.79 444	79.43 347	56.59 278	45.57 509	36.92 460	71.29 484	65.25 485

MDA-MVSNet_test_wron		52.57 457	53.49 453	49.81 482	54.24 529	36.47 473	40.48 533	46.58 501	38.13 467	75.47 237	73.32 428	41.05 403	43.85 523	40.98 419	71.20 485	69.10 447

YYNet1		52.58 456	53.50 451	49.85 481	54.15 530	36.45 474	40.53 532	46.55 502	38.09 468	75.52 234	73.31 429	41.08 402	43.88 522	41.10 417	71.14 486	69.21 445

nomal-1		49.95 477	49.18 484	52.26 466	57.73 513	44.81 380	46.14 520	49.57 483	37.60 474	56.41 496	65.96 506	24.21 518	52.60 467	33.97 490	71.04 487	59.37 515

HY-MVS	49.31 19	57.96 406	57.59 409	59.10 422	66.85 426	36.17 477	65.13 358	65.39 375	39.24 460	54.69 508	78.14 373	44.28 368	67.18 384	33.75 494	70.79 488	73.95 388

Test_1112_low_res		58.78 398	58.69 394	59.04 423	79.41 161	38.13 459	57.62 454	66.98 361	34.74 494	59.62 479	77.56 379	42.92 384	63.65 414	38.66 438	70.73 489	75.35 370

pmmvs3		46.71 489	45.09 500	51.55 472	56.76 518	48.25 320	55.78 470	39.53 541	24.13 539	50.35 525	63.40 513	15.90 549	51.08 471	29.29 517	70.69 490	55.33 523

test_fmvs2		54.80 438	54.11 449	56.88 444	51.76 538	49.95 297	56.70 460	65.80 369	26.22 532	69.42 369	65.25 509	31.82 467	49.98 480	49.63 337	70.36 491	70.71 429

SCA		58.57 402	58.04 402	60.17 410	70.17 361	41.07 422	65.19 357	53.38 464	43.34 420	61.00 468	73.48 424	45.20 361	69.38 358	40.34 427	70.31 492	70.05 433

CR-MVSNet		58.96 395	58.49 397	60.36 408	66.37 430	48.24 321	70.93 238	56.40 446	32.87 505	61.35 462	86.66 170	33.19 448	63.22 416	48.50 353	70.17 493	69.62 440

RPMNet		65.77 302	65.08 320	67.84 285	66.37 430	48.24 321	70.93 238	86.27 20	54.66 220	61.35 462	86.77 164	33.29 447	85.67 51	55.93 270	70.17 493	69.62 440

test0.0.03 1		47.72 487	48.31 486	45.93 504	55.53 525	29.39 518	46.40 518	41.21 536	43.41 418	55.81 500	67.65 497	29.22 492	43.77 524	25.73 534	69.87 495	64.62 491

PVSNet	43.83 21	51.56 464	51.17 469	52.73 464	68.34 392	38.27 456	48.22 509	53.56 462	36.41 482	54.29 509	64.94 510	34.60 440	54.20 461	30.34 510	69.87 495	65.71 478

tpm2		56.12 426	54.64 445	60.55 402	66.24 433	36.01 478	68.14 302	56.77 442	33.60 502	58.25 484	75.52 401	30.25 484	74.33 273	33.27 497	69.76 497	71.32 421

CostFormer		57.35 415	56.14 424	60.97 396	63.76 464	38.43 454	67.50 311	60.22 413	37.14 478	59.12 481	76.34 390	32.78 451	71.99 314	39.12 435	69.27 498	72.47 406

MonoMVSNet		62.75 349	63.42 339	60.73 400	65.60 442	40.77 427	72.49 198	70.56 318	52.49 264	75.07 249	79.42 348	39.52 414	69.97 350	46.59 372	69.06 499	71.44 419

baseline1		57.82 408	58.36 400	56.19 447	69.17 379	30.76 512	62.94 393	55.21 451	46.04 372	63.83 443	78.47 367	41.20 399	63.68 413	39.44 430	68.99 500	74.13 386

PatchMatch-RL		58.68 399	57.72 405	61.57 385	76.21 229	73.59 52	61.83 400	49.00 490	47.30 356	61.08 465	68.97 483	50.16 326	59.01 436	36.06 473	68.84 501	52.10 525

CVMVSNet		59.21 394	58.44 398	61.51 386	73.94 280	47.76 332	71.31 232	64.56 383	26.91 531	60.34 472	70.44 460	36.24 434	67.65 376	53.57 306	68.66 502	69.12 446

dmvs_re		49.91 478	50.77 475	47.34 495	59.98 494	38.86 451	53.18 486	53.58 461	39.75 454	55.06 503	61.58 521	36.42 433	44.40 520	29.15 520	68.23 503	58.75 517

TESTMET0.1,1		45.17 495	44.93 501	45.89 505	56.02 521	38.31 455	53.18 486	41.94 531	27.85 524	44.86 540	56.47 531	17.93 545	41.50 534	38.08 447	68.06 504	57.85 518

test_fmvs1_n		52.70 455	52.01 462	54.76 453	53.83 534	50.36 289	55.80 469	65.90 368	24.96 536	65.39 418	60.64 524	27.69 498	48.46 492	45.88 381	67.99 505	65.46 481

PMMVS2		37.74 510	40.87 509	28.36 529	42.41 552	5.35 562	24.61 546	27.75 551	32.15 509	47.85 532	70.27 464	35.85 435	29.51 549	19.08 547	67.85 506	50.22 529

1314		59.83 388	58.86 393	62.74 368	65.71 440	44.78 382	68.59 293	72.63 286	33.54 503	61.05 467	67.29 501	43.62 375	71.26 329	49.49 339	67.84 507	72.19 412

CHOSEN 1792x2688		58.09 405	56.30 421	63.45 355	79.95 153	50.93 285	54.07 482	65.59 372	28.56 523	61.53 461	74.33 413	41.09 401	66.52 397	33.91 491	67.69 508	72.92 398

0.4-1-1-0.1		51.02 468	48.31 486	59.15 420	60.95 485	37.94 463	53.17 490	59.12 423	39.52 455	47.88 531	50.31 540	20.36 536	69.99 349	35.79 474	67.66 509	69.51 442

test_fmvs1		51.51 465	50.86 474	53.48 460	49.72 541	49.35 308	54.11 481	64.96 378	24.64 538	63.66 447	59.61 528	28.33 497	48.45 493	45.38 386	67.30 510	62.66 501

0.4-1-1-0.2		49.48 480	46.57 494	58.21 430	58.02 512	36.93 470	50.24 503	59.18 421	37.97 469	44.94 538	46.16 544	20.52 533	69.54 356	34.84 483	67.28 511	68.17 455

0.3-1-1-0.015		49.68 479	46.67 493	58.69 426	58.94 505	37.51 468	51.35 498	59.18 421	38.35 466	44.62 542	47.14 543	18.49 542	69.68 354	35.13 480	66.84 512	68.87 448

tpm		50.60 470	52.42 460	45.14 508	65.18 449	26.29 532	60.30 424	43.50 518	37.41 476	57.01 489	79.09 361	30.20 486	42.32 527	32.77 501	66.36 513	66.81 468

FPMVS		59.43 392	60.07 382	57.51 439	77.62 198	71.52 62	62.33 397	50.92 476	57.40 177	69.40 370	80.00 334	39.14 416	61.92 422	37.47 454	66.36 513	39.09 544

GG-mvs-BLEND				52.24 467	60.64 489	29.21 520	69.73 259	42.41 526		45.47 536	52.33 536	20.43 535	68.16 371	25.52 535	65.42 515	59.36 516

UWE-MVS-28		44.18 501	44.37 506	43.61 515	60.10 492	16.96 550	52.62 491	33.27 549	36.79 480	48.86 529	69.47 478	19.96 539	45.65 508	13.40 549	64.83 516	68.23 453

tpmvs		55.84 428	55.45 435	57.01 442	60.33 491	33.20 498	65.89 343	59.29 420	47.52 353	56.04 497	73.60 423	31.05 476	68.06 373	40.64 425	64.64 517	69.77 438

WTY-MVS		49.39 481	50.31 479	46.62 502	61.22 483	32.00 504	46.61 517	49.77 481	33.87 499	54.12 510	69.55 476	41.96 389	45.40 512	31.28 507	64.42 518	62.47 503

baseline2		55.57 433	52.74 455	64.05 341	65.26 446	44.11 391	62.38 396	54.43 455	39.03 461	51.21 520	67.35 500	33.66 445	72.45 302	37.14 456	64.22 519	75.60 365

test_vis1_n		51.27 467	50.41 478	53.83 457	56.99 516	50.01 296	56.75 459	60.53 411	25.68 534	59.74 478	57.86 529	29.40 491	47.41 500	43.10 399	63.66 520	64.08 494

MS-PatchMatch		55.59 432	54.89 443	57.68 437	69.18 377	49.05 309	61.00 414	62.93 395	35.98 486	58.36 483	68.93 485	36.71 431	66.59 395	37.62 452	63.30 521	57.39 520

test_cas_vis1_n_1920		50.90 469	50.92 473	50.83 477	54.12 532	47.80 330	51.44 497	54.61 454	26.95 530	63.95 440	60.85 522	37.86 426	44.97 515	45.53 383	62.97 522	59.72 514

test_vis1_n_1920		52.96 452	53.50 451	51.32 474	59.15 503	44.90 378	56.13 467	64.29 386	30.56 518	59.87 477	60.68 523	40.16 407	47.47 499	48.25 357	62.46 523	61.58 508

test_f		43.79 503	45.63 496	38.24 526	42.29 553	38.58 453	34.76 543	47.68 496	22.22 545	67.34 402	63.15 514	31.82 467	30.60 547	39.19 434	62.28 524	45.53 539

sss		47.59 488	48.32 485	45.40 507	56.73 519	33.96 493	45.17 522	48.51 492	32.11 512	52.37 516	65.79 507	40.39 406	41.91 531	31.85 504	61.97 525	60.35 512

test_vis1_rt		46.70 490	45.24 499	51.06 476	44.58 548	51.04 284	39.91 534	67.56 356	21.84 546	51.94 518	50.79 538	33.83 443	39.77 537	35.25 478	61.50 526	62.38 504

PMMVS		44.69 497	43.95 507	46.92 498	50.05 540	53.47 267	48.08 511	42.40 527	22.36 544	44.01 544	53.05 535	42.60 387	45.49 510	31.69 505	61.36 527	41.79 541

blend_shiyan4		57.39 414	55.27 440	63.73 347	67.25 415	41.75 416	60.08 427	69.15 332	47.57 351	64.19 436	67.14 504	20.46 534	72.34 306	40.73 423	60.88 528	77.11 342

UnsupCasMVSNet_bld		50.01 476	51.03 472	46.95 497	58.61 507	32.64 499	48.31 508	53.27 465	34.27 497	60.47 471	71.53 449	41.40 397	47.07 502	30.68 509	60.78 529	61.13 510

MVP-Stereo		61.56 369	59.22 389	68.58 273	79.28 163	60.44 193	69.20 272	71.57 298	43.58 414	56.42 495	78.37 369	39.57 413	76.46 239	34.86 482	60.16 530	68.86 449
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DSMNet-mixed		43.18 505	44.66 504	38.75 524	54.75 528	28.88 521	57.06 458	27.42 552	13.47 549	47.27 534	77.67 378	38.83 417	39.29 539	25.32 536	60.12 531	48.08 530

UnsupCasMVSNet_eth		52.26 459	53.29 454	49.16 488	55.08 526	33.67 496	50.03 504	58.79 426	37.67 473	63.43 452	74.75 407	41.82 393	45.83 507	38.59 440	59.42 532	67.98 459

tpm cat1		54.02 444	52.63 457	58.19 431	64.85 456	39.86 440	66.26 339	57.28 435	32.16 508	56.90 490	70.39 462	32.75 453	65.30 406	34.29 488	58.79 533	69.41 443

CHOSEN 280x420		41.62 506	39.89 511	46.80 499	61.81 479	51.59 277	33.56 544	35.74 546	27.48 526	37.64 550	53.53 533	23.24 522	42.09 529	27.39 525	58.64 534	46.72 533

tpmrst		50.15 474	51.38 467	46.45 503	56.05 520	24.77 537	64.40 375	49.98 480	36.14 485	53.32 514	69.59 475	35.16 438	48.69 489	39.24 433	58.51 535	65.89 476

PatchmatchNet		54.60 439	54.27 447	55.59 451	65.17 450	39.08 446	66.92 328	51.80 472	39.89 453	58.39 482	73.12 431	31.69 469	58.33 443	43.01 400	58.38 536	69.38 444
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		45.53 493	47.29 490	40.24 522	62.29 477	26.82 528	56.02 468	37.41 544	29.74 521	43.69 545	81.27 305	33.96 442	55.48 456	24.46 538	56.79 537	38.43 545

ADS-MVSNet2		48.76 483	47.25 491	53.29 463	55.90 522	40.54 434	47.34 513	54.99 453	31.41 515	50.48 523	72.06 441	31.23 472	54.26 460	25.93 530	55.93 538	65.07 487

ADS-MVSNet		44.62 498	45.58 497	41.73 519	55.90 522	20.83 546	47.34 513	39.94 540	31.41 515	50.48 523	72.06 441	31.23 472	39.31 538	25.93 530	55.93 538	65.07 487

EPMVS		45.74 492	46.53 495	43.39 516	54.14 531	22.33 544	55.02 474	35.00 548	34.69 495	51.09 521	70.20 465	25.92 508	42.04 530	37.19 455	55.50 540	65.78 477

JIA-IIPM		54.03 443	51.62 464	61.25 393	59.14 504	55.21 254	59.10 437	47.72 495	50.85 294	50.31 526	85.81 201	20.10 537	63.97 411	36.16 470	55.41 541	64.55 492

dmvs_testset		45.26 494	47.51 489	38.49 525	59.96 496	14.71 552	58.50 449	43.39 520	41.30 439	51.79 519	56.48 530	39.44 415	49.91 482	21.42 544	55.35 542	50.85 527

new_pmnet		37.55 511	39.80 512	30.79 528	56.83 517	16.46 551	39.35 535	30.65 550	25.59 535	45.26 537	61.60 520	24.54 515	28.02 550	21.60 543	52.80 543	47.90 531

dp		44.09 502	44.88 503	41.72 520	58.53 509	23.18 540	54.70 479	42.38 528	34.80 493	44.25 543	65.61 508	24.48 517	44.80 516	29.77 514	49.42 544	57.18 521

mvsany_test3		43.76 504	41.01 508	52.01 469	48.09 543	57.74 228	42.47 528	23.85 555	23.30 542	64.80 425	62.17 519	27.12 500	40.59 535	29.17 519	48.11 545	57.69 519

MVE	27.91 23	36.69 512	35.64 515	39.84 523	43.37 551	35.85 481	19.49 547	24.61 553	24.68 537	39.05 547	62.63 518	38.67 419	27.10 551	21.04 545	47.25 546	56.56 522
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GLUNet-SfM		24.03 514	24.76 517	21.84 531	12.84 557	18.20 548	27.35 545	15.92 557	9.48 550	63.07 454	34.11 547	10.20 556	23.13 553	9.60 553	40.26 547	24.18 548

mvsany_test1		37.88 509	35.74 514	44.28 511	47.28 544	49.90 298	36.54 540	24.37 554	19.56 548	45.76 535	53.46 534	32.99 450	37.97 541	26.17 528	35.52 548	44.99 540

PVSNet_0	36.71 22	41.12 507	40.78 510	42.14 517	59.97 495	40.13 437	40.97 531	42.24 530	30.81 517	44.86 540	49.41 541	40.70 404	45.12 514	23.15 541	34.96 549	41.16 543

tmp_tt		11.98 518	14.73 521	3.72 537	2.28 561	4.62 563	19.44 548	14.50 558	0.47 556	21.55 552	9.58 553	25.78 509	4.57 557	11.61 551	27.37 550	1.96 553

test_method		19.26 516	19.12 520	19.71 532	9.09 559	1.91 564	7.79 549	53.44 463	1.42 554	10.27 556	35.80 546	17.42 547	25.11 552	12.44 550	24.38 551	32.10 546

dongtai		31.66 513	32.98 516	27.71 530	58.58 508	12.61 554	45.02 523	14.24 559	41.90 434	47.93 530	43.91 545	10.65 555	41.81 533	14.06 548	20.53 552	28.72 547

kuosan		22.02 515	23.52 519	17.54 533	41.56 554	11.24 555	41.99 529	13.39 560	26.13 533	28.87 551	30.75 548	9.72 557	21.94 554	4.77 555	14.49 553	19.43 549

VLMVS_CLIP		7.76 520	8.41 523	5.81 535	6.67 560	5.99 561	6.46 551	9.96 562	2.09 552	12.33 555	14.87 551	5.07 559	8.68 556	4.33 556	13.87 554	2.74 552

DeepMVS_CX				11.83 534	15.51 556	13.86 553		11.25 561	5.76 551	20.85 553	26.46 549	17.06 548	9.22 555	9.69 552	13.82 555	12.42 550

MVS_clip		7.93 519	9.12 522	4.36 536	9.81 558	6.92 559	6.89 550	1.72 563	1.89 553	16.36 554	21.19 550	4.56 560	2.56 558	6.56 554	13.13 556	3.60 551

MVS_baseline		2.33 525	2.94 528	0.51 539	2.02 562	0.19 567	1.06 552	0.36 566	0.07 560	6.71 557	7.92 554	1.17 562	0.00 562	0.96 557	6.20 557	1.34 555

VLMVS		1.59 526	1.75 529	1.12 538	1.56 563	1.00 565	0.99 553	0.58 564	0.08 559	2.81 558	3.50 555	2.79 561	0.76 559	0.70 558	2.74 558	1.60 554

test123		4.43 523	5.78 526	0.39 541	0.97 564	0.28 566	46.33 519	0.45 565	0.31 557	0.62 560	1.50 558	0.61 564	0.11 561	0.56 559	0.63 559	0.77 557

testmvs		4.06 524	5.28 527	0.41 540	0.64 565	0.16 568	42.54 527	0.31 567	0.26 558	0.50 561	1.40 559	0.77 563	0.17 560	0.56 559	0.55 560	0.90 556

mmdepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

monomultidepth		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

test_blank		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uanet_test		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

DCPMVS		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

cdsmvs_eth3d_5k		17.71 517	23.62 518	0.00 542	0.00 566	0.00 569	0.00 554	70.17 322	0.00 561	0.00 562	74.25 416	68.16 119	0.00 562	0.00 561	0.00 561	0.00 558

pcd_1.5k_mvsjas		5.20 522	6.93 525	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	62.39 188	0.00 562	0.00 561	0.00 561	0.00 558

sosnet-low-res		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

sosnet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uncertanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

Regformer		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

ab-mvs-re		5.62 521	7.50 524	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	67.46 498	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

uanet		0.00 527	0.00 530	0.00 542	0.00 566	0.00 569	0.00 554	0.00 568	0.00 561	0.00 562	0.00 560	0.00 565	0.00 562	0.00 561	0.00 561	0.00 558

Meshroom													0.00 562
: In preparation.
AliceVision / Meshro													0.00 562
: In preparation.
AliceVision_Meshroom													0.00 562
: In preparation.
PatchmatchNet2					0.00 566	8.37 558	35.35 542	35.51 547	32.14 511
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													30.98 546
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						22.69 541								36.10 471

FOURS1					89.19 23	77.84 17	91.64 1	89.11 2	84.05 2	91.57 2

test_one_0601					85.84 66	61.45 177		85.63 31	75.27 20	85.62 57	90.38 70	76.72 32

eth-test2					0.00 566

eth-test					0.00 566

test_241102_ONE					86.12 56	61.06 183		84.72 56	72.64 34	87.38 29	89.47 91	77.48 27	85.74 48

save fliter					87.00 39	67.23 111	79.24 97	77.94 217	56.65 191

test0726					86.16 54	60.78 189	83.81 48	85.10 44	72.48 37	85.27 65	89.96 84	78.57 19

GSMVS																70.05 433

test_part2					85.90 62	66.44 121				84.61 75

sam_mvs1												31.41 470				70.05 433

sam_mvs												31.21 474

MTGPA								80.63 157

test_post1							66.63 332				2.08 556	30.66 482	59.33 434	40.34 427

test_post											1.99 557	30.91 477	54.76 459

patchmatchnet-post											68.99 482	31.32 471	69.38 358

MTMP							84.83 38	19.26 556

gm-plane-assit					62.51 474	33.91 495			37.25 477		62.71 517		72.74 293	38.70 437

TEST9					85.47 69	69.32 87	76.42 135	78.69 202	53.73 245	76.97 191	86.74 165	66.84 136	81.10 142

test_8					85.09 76	67.89 100	76.26 142	78.66 204	54.00 240	76.89 195	86.72 168	66.60 142	80.89 152

agg_prior					84.44 89	66.02 127		78.62 205		76.95 193			80.34 160

test_prior4						70.14 78	77.57 115

test_prior				75.27 116	82.15 126	59.85 201		84.33 73					83.39 97			82.58 224

旧先验2							71.17 235		45.11 392	78.54 159			61.28 425	59.19 231

新几何2							71.33 231

无先验							74.82 158	70.94 315	47.75 350				76.85 234	54.47 293		72.09 413

原ACMM2							74.78 162

testdata2													67.30 381	48.34 355

segment_acmp												68.30 118

testdata1							68.34 301		57.24 180

plane_prior7					85.18 72	66.21 124

plane_prior6					84.18 93	65.31 135						60.83 214

plane_prior4											89.11 102

plane_prior3						65.67 130			63.82 112	78.23 163

plane_prior2							82.74 61		65.45 89

plane_prior1					84.46 88

n2								0.00 568

nn								0.00 568

door-mid								55.02 452

test11								82.71 106

door								52.91 467

HQP5-MVS						58.80 217

HQP-NCC					82.37 120		77.32 120		59.08 153	71.58 334

ACMP_Plane					82.37 120		77.32 120		59.08 153	71.58 334

BP-MVS														67.38 131

HQP4-MVS										71.59 332			85.31 58			83.74 176

HQP2-MVS												58.09 258

NP-MVS					83.34 105	63.07 163					85.97 197

MDTV_nov1_ep13_2view						18.41 547	53.74 483		31.57 514	44.89 539		29.90 489		32.93 500		71.48 418

Test By Simon												62.56 184

High-resolution multi-view benchmark