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.

mamv4		90.28 1	88.75 1	94.85 1	93.34 1	96.17 1	82.69 55	91.63 1	86.34 1	97.97 1	94.77 3	66.57 117	95.38 1	87.74 1	97.72 1	93.00 7

Effi-MVS+-dtu		75.43 92	72.28 150	84.91 3	77.05 179	83.58 2	78.47 96	77.70 183	57.68 152	74.89 190	78.13 282	64.80 134	84.26 75	56.46 196	85.32 209	86.88 62

PMVS	70.70 6	81.70 33	83.15 32	77.36 76	90.35 6	82.82 3	82.15 57	79.22 155	74.08 21	87.16 29	91.97 20	84.80 2	76.97 198	64.98 120	93.61 60	72.28 306
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
mPP-MVS		84.01 11	84.39 12	82.88 7	90.65 4	81.38 4	87.08 12	82.79 85	72.41 37	85.11 56	90.85 45	76.65 28	84.89 64	79.30 17	94.63 33	82.35 177

TDRefinement		86.32 3	86.33 3	86.29 2	88.64 32	81.19 5	88.84 4	90.72 2	78.27 9	87.95 15	92.53 14	79.37 13	84.79 67	74.51 48	96.15 3	92.88 8

CP-MVS		84.12 9	84.55 11	82.80 11	89.42 18	79.74 6	88.19 5	84.43 59	71.96 41	84.70 62	90.56 53	77.12 25	86.18 28	79.24 18	95.36 13	82.49 175

SR-MVS-dyc-post		84.75 4	85.26 6	83.21 4	86.19 50	79.18 7	87.23 8	86.27 21	77.51 11	87.65 19	90.73 48	79.20 14	85.58 48	78.11 24	94.46 36	84.89 95

RE-MVS-def			85.50 4		86.19 50	79.18 7	87.23 8	86.27 21	77.51 11	87.65 19	90.73 48	81.38 7		78.11 24	94.46 36	84.89 95

MP-MVS		83.19 19	83.54 24	82.14 20	90.54 5	79.00 9	86.42 22	83.59 75	71.31 42	81.26 101	90.96 40	74.57 47	84.69 68	78.41 22	94.78 28	82.74 169
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CPTT-MVS		81.51 35	81.76 44	80.76 35	89.20 23	78.75 10	86.48 21	82.03 99	68.80 56	80.92 106	88.52 109	72.00 65	82.39 102	74.80 44	93.04 67	81.14 196

HPM-MVS++		79.89 52	79.80 58	80.18 40	89.02 26	78.44 11	83.49 46	80.18 139	64.71 93	78.11 138	88.39 112	65.46 128	83.14 91	77.64 30	91.20 95	78.94 238

MTAPA		83.19 19	83.87 19	81.13 31	91.16 3	78.16 12	84.87 30	80.63 129	72.08 39	84.93 57	90.79 46	74.65 46	84.42 73	80.98 5	94.75 29	80.82 206

FOURS1					89.19 24	77.84 13	91.64 1	89.11 3	84.05 3	91.57 3

SR-MVS		84.51 6	85.27 5	82.25 19	88.52 34	77.71 14	86.81 16	85.25 38	77.42 14	86.15 38	90.24 71	81.69 5	85.94 34	77.77 27	93.58 61	83.09 157

XVS		83.51 16	83.73 21	82.85 9	89.43 16	77.61 15	86.80 17	84.66 54	72.71 30	82.87 81	90.39 63	73.86 52	86.31 21	78.84 20	94.03 53	84.64 105

X-MVStestdata		76.81 79	74.79 101	82.85 9	89.43 16	77.61 15	86.80 17	84.66 54	72.71 30	82.87 81	9.95 408	73.86 52	86.31 21	78.84 20	94.03 53	84.64 105

region2R		83.54 15	83.86 20	82.58 15	89.82 10	77.53 17	87.06 13	84.23 65	70.19 51	83.86 71	90.72 50	75.20 40	86.27 23	79.41 15	94.25 50	83.95 130

RPSCF		75.76 86	74.37 107	79.93 41	74.81 217	77.53 17	77.53 108	79.30 154	59.44 136	78.88 128	89.80 80	71.26 71	73.09 242	57.45 186	80.89 262	89.17 31

ACMMPR		83.62 13	83.93 18	82.69 12	89.78 11	77.51 19	87.01 14	84.19 66	70.23 49	84.49 64	90.67 51	75.15 41	86.37 20	79.58 11	94.26 49	84.18 125

MSP-MVS		80.49 46	79.67 59	82.96 6	89.70 12	77.46 20	87.16 11	85.10 41	64.94 91	81.05 104	88.38 113	57.10 213	87.10 9	79.75 8	83.87 230	84.31 122
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
PGM-MVS		83.07 22	83.25 31	82.54 16	89.57 14	77.21 21	82.04 60	85.40 35	67.96 62	84.91 60	90.88 43	75.59 36	86.57 16	78.16 23	94.71 31	83.82 132

DeepPCF-MVS	71.07 5	78.48 66	77.14 82	82.52 17	84.39 83	77.04 22	76.35 124	84.05 69	56.66 164	80.27 114	85.31 177	68.56 92	87.03 12	67.39 100	91.26 93	83.50 140

ACMMP		84.22 7	84.84 9	82.35 18	89.23 22	76.66 23	87.65 6	85.89 27	71.03 45	85.85 42	90.58 52	78.77 16	85.78 41	79.37 16	95.17 17	84.62 107
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
HPM-MVS_fast		84.59 5	85.10 7	83.06 5	88.60 33	75.83 24	86.27 24	86.89 16	73.69 24	86.17 37	91.70 27	78.23 19	85.20 58	79.45 13	94.91 25	88.15 47

HFP-MVS		83.39 18	84.03 17	81.48 24	89.25 21	75.69 25	87.01 14	84.27 62	70.23 49	84.47 65	90.43 58	76.79 26	85.94 34	79.58 11	94.23 51	82.82 166

LTVRE_ROB	75.46 1	84.22 7	84.98 8	81.94 21	84.82 73	75.40 26	91.60 3	87.80 8	73.52 25	88.90 12	93.06 7	71.39 70	81.53 116	81.53 4	92.15 81	88.91 38
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
CNLPA		73.44 117	73.03 136	74.66 104	78.27 163	75.29 27	75.99 131	78.49 170	65.39 80	75.67 180	83.22 212	61.23 166	66.77 308	53.70 225	85.33 208	81.92 186

PM-MVS		64.49 241	63.61 251	67.14 236	76.68 191	75.15 28	68.49 232	42.85 390	51.17 236	77.85 141	80.51 242	45.76 275	66.31 311	52.83 231	76.35 305	59.96 379

XVG-OURS		79.51 54	79.82 57	78.58 62	86.11 57	74.96 29	76.33 126	84.95 45	66.89 65	82.75 84	88.99 99	66.82 110	78.37 177	74.80 44	90.76 115	82.40 176

XVG-OURS-SEG-HR		79.62 53	79.99 56	78.49 63	86.46 47	74.79 30	77.15 114	85.39 36	66.73 68	80.39 113	88.85 102	74.43 50	78.33 179	74.73 46	85.79 201	82.35 177

EGC-MVSNET		64.77 237	61.17 270	75.60 97	86.90 43	74.47 31	84.04 36	68.62 267	0.60 410	1.13 412	91.61 30	65.32 130	74.15 234	64.01 127	88.28 157	78.17 248

HPM-MVS		84.12 9	84.63 10	82.60 14	88.21 36	74.40 32	85.24 28	87.21 14	70.69 48	85.14 55	90.42 59	78.99 15	86.62 15	80.83 6	94.93 24	86.79 63
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
COLMAP_ROB	72.78 3	83.75 12	84.11 16	82.68 13	82.97 103	74.39 33	87.18 10	88.18 7	78.98 7	86.11 40	91.47 32	79.70 12	85.76 42	66.91 108	95.46 12	87.89 48
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
anonymousdsp		78.60 62	77.80 75	81.00 32	78.01 168	74.34 34	80.09 80	76.12 198	50.51 242	89.19 11	90.88 43	71.45 69	77.78 191	73.38 57	90.60 117	90.90 17

ACMM	69.25 9	82.11 30	83.31 28	78.49 63	88.17 37	73.96 35	83.11 51	84.52 58	66.40 71	87.45 23	89.16 94	81.02 8	80.52 139	74.27 51	95.73 8	80.98 202
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mvs_tets		78.93 59	78.67 67	79.72 44	84.81 74	73.93 36	80.65 70	76.50 196	51.98 223	87.40 24	91.86 24	76.09 33	78.53 169	68.58 84	90.20 121	86.69 65

MVS_111021_LR		72.10 149	71.82 155	72.95 139	79.53 143	73.90 37	70.45 203	66.64 275	56.87 160	76.81 159	81.76 227	68.78 90	71.76 261	61.81 146	83.74 232	73.18 294

jajsoiax		78.51 64	78.16 73	79.59 46	84.65 77	73.83 38	80.42 73	76.12 198	51.33 233	87.19 28	91.51 31	73.79 54	78.44 173	68.27 87	90.13 125	86.49 67

ITE_SJBPF				80.35 39	76.94 184	73.60 39		80.48 132	66.87 66	83.64 74	86.18 161	70.25 80	79.90 149	61.12 155	88.95 152	87.56 53

PatchMatch-RL		58.68 293	57.72 297	61.57 285	76.21 197	73.59 40	61.83 303	49.00 371	47.30 274	61.08 333	68.97 356	50.16 254	59.01 341	36.06 354	68.84 363	52.10 389

APD-MVS_3200maxsize		83.57 14	84.33 13	81.31 29	82.83 106	73.53 41	85.50 27	87.45 13	74.11 20	86.45 35	90.52 56	80.02 10	84.48 71	77.73 28	94.34 47	85.93 75

GST-MVS		82.79 25	83.27 30	81.34 28	88.99 27	73.29 42	85.94 25	85.13 39	68.58 60	84.14 68	90.21 73	73.37 56	86.41 18	79.09 19	93.98 56	84.30 124

ZNCC-MVS		83.12 21	83.68 22	81.45 25	89.14 25	73.28 43	86.32 23	85.97 26	67.39 63	84.02 69	90.39 63	74.73 45	86.46 17	80.73 7	94.43 40	84.60 110

XVG-ACMP-BASELINE		80.54 45	81.06 49	78.98 56	87.01 39	72.91 44	80.23 79	85.56 30	66.56 70	85.64 45	89.57 83	69.12 89	80.55 138	72.51 65	93.37 63	83.48 143

h-mvs33		73.08 126	71.61 159	77.48 74	83.89 89	72.89 45	70.47 202	71.12 249	54.28 194	77.89 139	83.41 200	49.04 261	80.98 129	63.62 135	90.77 114	78.58 242

3Dnovator+	73.19 2	81.08 40	80.48 52	82.87 8	81.41 124	72.03 46	84.38 35	86.23 24	77.28 15	80.65 110	90.18 74	59.80 184	87.58 6	73.06 59	91.34 92	89.01 34

F-COLMAP		75.29 93	73.99 114	79.18 51	81.73 120	71.90 47	81.86 62	82.98 82	59.86 134	72.27 230	84.00 193	64.56 136	83.07 94	51.48 236	87.19 183	82.56 174

hse-mvs2		72.32 146	70.66 171	77.31 78	83.10 100	71.77 48	69.19 219	71.45 239	54.28 194	77.89 139	78.26 278	49.04 261	79.23 157	63.62 135	89.13 148	80.92 203

AUN-MVS		70.22 167	67.88 203	77.22 79	82.96 104	71.61 49	69.08 220	71.39 240	49.17 257	71.70 236	78.07 283	37.62 328	79.21 158	61.81 146	89.15 146	80.82 206

FPMVS		59.43 288	60.07 279	57.51 314	77.62 176	71.52 50	62.33 302	50.92 362	57.40 157	69.40 267	80.00 253	39.14 318	61.92 332	37.47 340	66.36 372	39.09 402

LS3D		80.99 42	80.85 50	81.41 26	78.37 162	71.37 51	87.45 7	85.87 28	77.48 13	81.98 90	89.95 78	69.14 88	85.26 54	66.15 110	91.24 94	87.61 52

新几何1				69.99 190	88.37 35	71.34 52		62.08 307	43.85 299	74.99 189	86.11 166	52.85 238	70.57 271	50.99 241	83.23 239	68.05 342

test_djsdf		78.88 60	78.27 71	80.70 36	81.42 123	71.24 53	83.98 37	75.72 203	52.27 218	87.37 27	92.25 17	68.04 99	80.56 136	72.28 67	91.15 97	90.32 21

N_pmnet		52.06 333	51.11 341	54.92 325	59.64 370	71.03 54	37.42 398	61.62 311	33.68 369	57.12 354	72.10 328	37.94 324	31.03 404	29.13 386	71.35 347	62.70 369

SteuartSystems-ACMMP		83.07 22	83.64 23	81.35 27	85.14 69	71.00 55	85.53 26	84.78 47	70.91 46	85.64 45	90.41 60	75.55 38	87.69 5	79.75 8	95.08 20	85.36 86
Skip Steuart: Steuart Systems R&D Blog.
AllTest		77.66 72	77.43 78	78.35 65	79.19 150	70.81 56	78.60 94	88.64 4	65.37 81	80.09 115	88.17 117	70.33 78	78.43 174	55.60 203	90.90 108	85.81 77

TestCases				78.35 65	79.19 150	70.81 56		88.64 4	65.37 81	80.09 115	88.17 117	70.33 78	78.43 174	55.60 203	90.90 108	85.81 77

TSAR-MVS + GP.		73.08 126	71.60 160	77.54 73	78.99 157	70.73 58	74.96 139	69.38 261	60.73 127	74.39 201	78.44 276	57.72 207	82.78 97	60.16 164	89.60 135	79.11 236

OMC-MVS		79.41 56	78.79 65	81.28 30	80.62 133	70.71 59	80.91 68	84.76 48	62.54 115	81.77 93	86.65 146	71.46 68	83.53 85	67.95 93	92.44 75	89.60 24

APD-MVS		81.13 39	81.73 45	79.36 50	84.47 80	70.53 60	83.85 39	83.70 73	69.43 55	83.67 73	88.96 100	75.89 34	86.41 18	72.62 64	92.95 68	81.14 196
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LPG-MVS_test		83.47 17	84.33 13	80.90 33	87.00 40	70.41 61	82.04 60	86.35 18	69.77 53	87.75 16	91.13 36	81.83 3	86.20 26	77.13 36	95.96 6	86.08 70

LGP-MVS_train				80.90 33	87.00 40	70.41 61		86.35 18	69.77 53	87.75 16	91.13 36	81.83 3	86.20 26	77.13 36	95.96 6	86.08 70

APD_test1		75.04 99	75.38 99	74.02 116	69.89 287	70.15 63	76.46 120	79.71 145	65.50 77	82.99 79	88.60 108	66.94 107	72.35 253	59.77 170	88.54 155	79.56 228

test_prior4						70.14 64	77.57 105

DeepC-MVS	72.44 4	81.00 41	80.83 51	81.50 23	86.70 45	70.03 65	82.06 58	87.00 15	59.89 133	80.91 107	90.53 54	72.19 61	88.56 2	73.67 56	94.52 35	85.92 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		82.12 29	82.68 39	80.43 37	88.90 30	69.52 66	85.12 29	84.76 48	63.53 104	84.23 67	91.47 32	72.02 64	87.16 8	79.74 10	94.36 45	84.61 108
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
NCCC		78.25 68	78.04 74	78.89 58	85.61 63	69.45 67	79.80 84	80.99 122	65.77 74	75.55 182	86.25 160	67.42 103	85.42 49	70.10 76	90.88 110	81.81 187

ACMP	69.50 8	82.64 26	83.38 27	80.40 38	86.50 46	69.44 68	82.30 56	86.08 25	66.80 67	86.70 31	89.99 76	81.64 6	85.95 33	74.35 50	96.11 4	85.81 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OPM-MVS		80.99 42	81.63 47	79.07 53	86.86 44	69.39 69	79.41 87	84.00 71	65.64 75	85.54 49	89.28 87	76.32 31	83.47 86	74.03 53	93.57 62	84.35 121
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ZD-MVS					83.91 87	69.36 70		81.09 119	58.91 143	82.73 85	89.11 95	75.77 35	86.63 14	72.73 62	92.93 69

TEST9					85.47 64	69.32 71	76.42 122	78.69 166	53.73 208	76.97 151	86.74 140	66.84 109	81.10 124

train_agg		76.38 82	76.55 86	75.86 94	85.47 64	69.32 71	76.42 122	78.69 166	54.00 203	76.97 151	86.74 140	66.60 115	81.10 124	72.50 66	91.56 88	77.15 261

UA-Net		81.56 34	82.28 41	79.40 49	88.91 29	69.16 73	84.67 33	80.01 142	75.34 16	79.80 117	94.91 2	69.79 85	80.25 143	72.63 63	94.46 36	88.78 42

test222					87.30 38	69.15 74	67.85 238	59.59 317	41.06 323	73.05 221	85.72 174	48.03 270			80.65 266	66.92 347

ACMMP_NAP		82.33 28	83.28 29	79.46 48	89.28 19	69.09 75	83.62 43	84.98 43	64.77 92	83.97 70	91.02 39	75.53 39	85.93 36	82.00 3	94.36 45	83.35 150

PLC	62.01 16	71.79 152	70.28 173	76.33 88	80.31 136	68.63 76	78.18 102	81.24 114	54.57 189	67.09 296	80.63 241	59.44 185	81.74 115	46.91 279	84.17 227	78.63 240
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MP-MVS-pluss		82.54 27	83.46 26	79.76 42	88.88 31	68.44 77	81.57 63	86.33 20	63.17 110	85.38 53	91.26 35	76.33 30	84.67 69	83.30 2	94.96 23	86.17 69
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TAPA-MVS	65.27 12	75.16 96	74.29 109	77.77 72	74.86 216	68.08 78	77.89 104	84.04 70	55.15 178	76.19 177	83.39 201	66.91 108	80.11 147	60.04 167	90.14 124	85.13 90
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS_fast	69.89 7	77.17 76	76.33 88	79.70 45	83.90 88	67.94 79	80.06 82	83.75 72	56.73 163	74.88 191	85.32 176	65.54 126	87.79 3	65.61 117	91.14 98	83.35 150
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_8					85.09 70	67.89 80	76.26 127	78.66 168	54.00 203	76.89 155	86.72 142	66.60 115	80.89 134

SD-MVS		80.28 50	81.55 48	76.47 87	83.57 90	67.83 81	83.39 48	85.35 37	64.42 94	86.14 39	87.07 131	74.02 51	80.97 130	77.70 29	92.32 79	80.62 214
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
testf1		75.66 88	76.57 84	72.95 139	67.07 321	67.62 82	76.10 128	80.68 127	64.95 89	86.58 33	90.94 41	71.20 72	71.68 263	60.46 160	91.13 99	79.56 228

APD_test2		75.66 88	76.57 84	72.95 139	67.07 321	67.62 82	76.10 128	80.68 127	64.95 89	86.58 33	90.94 41	71.20 72	71.68 263	60.46 160	91.13 99	79.56 228

TSAR-MVS + MP.		79.05 58	78.81 64	79.74 43	88.94 28	67.52 84	86.61 19	81.38 111	51.71 225	77.15 149	91.42 34	65.49 127	87.20 7	79.44 14	87.17 184	84.51 116
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		78.49 65	78.59 68	78.16 67	85.86 61	67.40 85	78.12 103	81.50 107	63.92 98	77.51 146	86.56 150	68.43 95	84.82 66	73.83 54	91.61 87	82.26 181

DPE-MVS		82.00 31	83.02 34	78.95 57	85.36 66	67.25 86	82.91 52	84.98 43	73.52 25	85.43 52	90.03 75	76.37 29	86.97 13	74.56 47	94.02 55	82.62 172
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
save fliter					87.00 40	67.23 87	79.24 88	77.94 181	56.65 165

MSC_two_6792asdad				79.02 54	83.14 96	67.03 88		80.75 124					86.24 24	77.27 34	94.85 26	83.78 134

No_MVS				79.02 54	83.14 96	67.03 88		80.75 124					86.24 24	77.27 34	94.85 26	83.78 134

OPU-MVS				78.65 61	83.44 94	66.85 90	83.62 43				86.12 165	66.82 110	86.01 32	61.72 149	89.79 133	83.08 158

APDe-MVS		82.88 24	84.14 15	79.08 52	84.80 75	66.72 91	86.54 20	85.11 40	72.00 40	86.65 32	91.75 26	78.20 20	87.04 11	77.93 26	94.32 48	83.47 144
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
test_part2					85.90 58	66.44 92				84.61 63

PS-MVSNAJss		77.54 73	77.35 80	78.13 69	84.88 72	66.37 93	78.55 95	79.59 149	53.48 210	86.29 36	92.43 16	62.39 152	80.25 143	67.90 94	90.61 116	87.77 49

test_fmvsmconf0.01_n		73.91 110	73.64 121	74.71 103	69.79 291	66.25 94	75.90 132	79.90 143	46.03 282	76.48 171	85.02 180	67.96 101	73.97 235	74.47 49	87.22 181	83.90 131

plane_prior7					85.18 67	66.21 95

test_fmvsmconf0.1_n		73.26 123	72.82 140	74.56 105	69.10 297	66.18 96	74.65 150	79.34 153	45.58 284	75.54 183	83.91 194	67.19 105	73.88 238	73.26 58	86.86 186	83.63 139

test_fmvsmconf_n		72.91 135	72.40 148	74.46 106	68.62 301	66.12 97	74.21 155	78.80 163	45.64 283	74.62 197	83.25 209	66.80 113	73.86 239	72.97 60	86.66 192	83.39 147

agg_prior					84.44 82	66.02 98		78.62 169		76.95 153			80.34 141

test_fmvsm_n_1920		69.63 175	68.45 193	73.16 132	70.56 276	65.86 99	70.26 205	78.35 172	37.69 349	74.29 202	78.89 272	61.10 170	68.10 290	65.87 115	79.07 282	85.53 84

plane_prior3						65.67 100			63.82 100	78.23 135

MM		78.15 70	77.68 76	79.55 47	80.10 137	65.47 101	80.94 67	78.74 165	71.22 43	72.40 229	88.70 104	60.51 175	87.70 4	77.40 33	89.13 148	85.48 85

MVS_111021_HR		72.98 133	72.97 138	72.99 137	80.82 131	65.47 101	68.81 224	72.77 225	57.67 153	75.76 179	82.38 219	71.01 74	77.17 196	61.38 151	86.15 195	76.32 267

DP-MVS		78.44 67	79.29 62	75.90 93	81.86 119	65.33 103	79.05 90	84.63 56	74.83 19	80.41 112	86.27 158	71.68 66	83.45 87	62.45 145	92.40 76	78.92 239

plane_prior6					84.18 85	65.31 104						60.83 173

HQP_MVS		78.77 61	78.78 66	78.72 59	85.18 67	65.18 105	82.74 53	85.49 31	65.45 78	78.23 135	89.11 95	60.83 173	86.15 29	71.09 70	90.94 104	84.82 100

plane_prior						65.18 105	80.06 82		61.88 120						89.91 130

原ACMM1				73.90 117	85.90 58	65.15 107		81.67 105	50.97 237	74.25 203	86.16 163	61.60 160	83.54 84	56.75 191	91.08 102	73.00 296

MAR-MVS		67.72 205	66.16 223	72.40 157	74.45 225	64.99 108	74.87 140	77.50 186	48.67 261	65.78 302	68.58 363	57.01 215	77.79 190	46.68 282	81.92 248	74.42 285
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
CS-MVS		76.51 81	76.00 91	78.06 70	77.02 181	64.77 109	80.78 69	82.66 90	60.39 129	74.15 204	83.30 207	69.65 86	82.07 109	69.27 82	86.75 190	87.36 55

Vis-MVSNet		74.85 106	74.56 104	75.72 95	81.63 122	64.64 110	76.35 124	79.06 157	62.85 113	73.33 217	88.41 111	62.54 150	79.59 154	63.94 132	82.92 240	82.94 162
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Fast-Effi-MVS+-dtu		70.00 170	68.74 190	73.77 119	73.47 239	64.53 111	71.36 188	78.14 178	55.81 173	68.84 278	74.71 308	65.36 129	75.75 210	52.00 233	79.00 283	81.03 199

SF-MVS		80.72 44	81.80 43	77.48 74	82.03 116	64.40 112	83.41 47	88.46 6	65.28 83	84.29 66	89.18 92	73.73 55	83.22 90	76.01 38	93.77 58	84.81 102

OurMVSNet-221017-0		78.57 63	78.53 69	78.67 60	80.48 134	64.16 113	80.24 78	82.06 98	61.89 119	88.77 13	93.32 5	57.15 211	82.60 100	70.08 77	92.80 70	89.25 28

test_fmvsmvis_n_1920		72.36 145	72.49 145	71.96 163	71.29 267	64.06 114	72.79 165	81.82 102	40.23 333	81.25 102	81.04 235	70.62 77	68.69 284	69.74 80	83.60 236	83.14 156

CDPH-MVS		77.33 75	77.06 83	78.14 68	84.21 84	63.98 115	76.07 130	83.45 76	54.20 198	77.68 145	87.18 127	69.98 82	85.37 50	68.01 91	92.72 73	85.08 92

UGNet		70.20 168	69.05 183	73.65 120	76.24 196	63.64 116	75.87 133	72.53 228	61.48 121	60.93 337	86.14 164	52.37 241	77.12 197	50.67 243	85.21 210	80.17 222
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
PVSNet_Blended_VisFu		70.04 169	68.88 186	73.53 126	82.71 107	63.62 117	74.81 142	81.95 101	48.53 262	67.16 295	79.18 267	51.42 248	78.38 176	54.39 218	79.72 278	78.60 241

DP-MVS Recon		73.57 116	72.69 142	76.23 90	82.85 105	63.39 118	74.32 152	82.96 83	57.75 151	70.35 254	81.98 223	64.34 138	84.41 74	49.69 250	89.95 128	80.89 204

testdata				64.13 258	85.87 60	63.34 119		61.80 310	47.83 269	76.42 174	86.60 149	48.83 264	62.31 330	54.46 216	81.26 260	66.74 351

LCM-MVSNet		86.90 2	88.67 2	81.57 22	91.50 2	63.30 120	84.80 32	87.77 10	86.18 2	96.26 2	96.06 1	90.32 1	84.49 70	68.08 89	97.05 2	96.93 1

3Dnovator	65.95 11	71.50 155	71.22 165	72.34 158	73.16 245	63.09 121	78.37 97	78.32 173	57.67 153	72.22 232	84.61 183	54.77 226	78.47 171	60.82 158	81.07 261	75.45 273

NP-MVS					83.34 95	63.07 122					85.97 169

CS-MVS-test		74.89 104	74.23 110	76.86 80	77.01 182	62.94 123	78.98 91	84.61 57	58.62 144	70.17 258	80.80 238	66.74 114	81.96 110	61.74 148	89.40 142	85.69 82

MSLP-MVS++		74.48 107	75.78 93	70.59 176	84.66 76	62.40 124	78.65 93	84.24 64	60.55 128	77.71 144	81.98 223	63.12 143	77.64 193	62.95 142	88.14 159	71.73 311

ACMH+	66.64 10	81.20 37	82.48 40	77.35 77	81.16 128	62.39 125	80.51 71	87.80 8	73.02 27	87.57 21	91.08 38	80.28 9	82.44 101	64.82 121	96.10 5	87.21 57

PHI-MVS		74.92 101	74.36 108	76.61 83	76.40 194	62.32 126	80.38 74	83.15 80	54.16 200	73.23 219	80.75 239	62.19 155	83.86 79	68.02 90	90.92 107	83.65 138

fmvsm_l_conf0.5_n		67.48 208	66.88 219	69.28 201	67.41 316	62.04 127	70.69 200	69.85 258	39.46 336	69.59 265	81.09 234	58.15 198	68.73 283	67.51 97	78.16 295	77.07 265

LF4IMVS		67.50 207	67.31 211	68.08 224	58.86 373	61.93 128	71.43 186	75.90 202	44.67 296	72.42 228	80.20 248	57.16 210	70.44 273	58.99 177	86.12 198	71.88 309

xiu_mvs_v1_base_debu		67.87 202	67.07 214	70.26 182	79.13 152	61.90 129	67.34 245	71.25 245	47.98 266	67.70 288	74.19 316	61.31 163	72.62 247	56.51 193	78.26 292	76.27 268

xiu_mvs_v1_base		67.87 202	67.07 214	70.26 182	79.13 152	61.90 129	67.34 245	71.25 245	47.98 266	67.70 288	74.19 316	61.31 163	72.62 247	56.51 193	78.26 292	76.27 268

xiu_mvs_v1_base_debi		67.87 202	67.07 214	70.26 182	79.13 152	61.90 129	67.34 245	71.25 245	47.98 266	67.70 288	74.19 316	61.31 163	72.62 247	56.51 193	78.26 292	76.27 268

CSCG		74.12 109	74.39 106	73.33 128	79.35 145	61.66 132	77.45 109	81.98 100	62.47 117	79.06 127	80.19 249	61.83 157	78.79 166	59.83 169	87.35 173	79.54 231

MVS_0304		75.45 91	74.66 103	77.83 71	75.58 207	61.53 133	78.29 98	77.18 191	63.15 112	69.97 260	87.20 126	57.54 209	87.05 10	74.05 52	88.96 151	84.89 95

test_one_0601					85.84 62	61.45 134		85.63 29	75.27 18	85.62 48	90.38 65	76.72 27

fmvsm_l_conf0.5_n_a		66.66 218	65.97 227	68.72 216	67.09 319	61.38 135	70.03 207	69.15 263	38.59 343	68.41 281	80.36 245	56.56 219	68.32 288	66.10 111	77.45 299	76.46 266

CANet		73.00 131	71.84 154	76.48 86	75.82 204	61.28 136	74.81 142	80.37 136	63.17 110	62.43 327	80.50 243	61.10 170	85.16 60	64.00 128	84.34 226	83.01 161

EPNet		69.10 185	67.32 210	74.46 106	68.33 305	61.27 137	77.56 106	63.57 300	60.95 125	56.62 361	82.75 213	51.53 247	81.24 121	54.36 219	90.20 121	80.88 205
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.1_n_a		67.37 212	66.36 221	70.37 180	70.86 269	61.17 138	74.00 157	57.18 328	40.77 328	68.83 279	80.88 237	63.11 144	67.61 295	66.94 107	74.72 319	82.33 180

fmvsm_s_conf0.5_n_a		67.00 217	65.95 228	70.17 185	69.72 292	61.16 139	73.34 161	56.83 331	40.96 325	68.36 282	80.08 252	62.84 145	67.57 296	66.90 109	74.50 323	81.78 188

SED-MVS		81.78 32	83.48 25	76.67 82	86.12 54	61.06 140	83.62 43	84.72 50	72.61 33	87.38 25	89.70 81	77.48 23	85.89 39	75.29 42	94.39 41	83.08 158

test_241102_ONE					86.12 54	61.06 140		84.72 50	72.64 32	87.38 25	89.47 84	77.48 23	85.74 43

AdaColmap		74.22 108	74.56 104	73.20 131	81.95 117	60.97 142	79.43 85	80.90 123	65.57 76	72.54 227	81.76 227	70.98 75	85.26 54	47.88 272	90.00 126	73.37 292

test12				76.51 85	82.28 113	60.94 143		81.64 106		73.60 212		64.88 133	85.19 59		90.42 119	83.38 148

DVP-MVS++		81.24 36	82.74 38	76.76 81	83.14 96	60.90 144	91.64 1	85.49 31	74.03 22	84.93 57	90.38 65	66.82 110	85.90 37	77.43 31	90.78 112	83.49 141

IU-MVS					86.12 54	60.90 144		80.38 135	45.49 287	81.31 100				75.64 41	94.39 41	84.65 104

DVP-MVS		81.15 38	83.12 33	75.24 102	86.16 52	60.78 146	83.77 41	80.58 131	72.48 35	85.83 43	90.41 60	78.57 17	85.69 44	75.86 39	94.39 41	79.24 234
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					86.16 52	60.78 146	83.81 40	85.10 41	72.48 35	85.27 54	89.96 77	78.57 17

wuyk23d		61.97 266	66.25 222	49.12 356	58.19 378	60.77 148	66.32 261	52.97 355	55.93 172	90.62 6	86.91 134	73.07 57	35.98 402	20.63 405	91.63 86	50.62 391

test_0728_SECOND				76.57 84	86.20 49	60.57 149	83.77 41	85.49 31					85.90 37	75.86 39	94.39 41	83.25 152

MVP-Stereo		61.56 271	59.22 284	68.58 218	79.28 146	60.44 150	69.20 218	71.57 235	43.58 305	56.42 362	78.37 277	39.57 316	76.46 206	34.86 359	60.16 387	68.86 338
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
旧先验1					84.55 79	60.36 151		63.69 299			87.05 132	54.65 228			83.34 238	69.66 330

pmmvs-eth3d		64.41 244	63.27 255	67.82 228	75.81 205	60.18 152	69.49 213	62.05 308	38.81 342	74.13 205	82.23 220	43.76 289	68.65 285	42.53 305	80.63 268	74.63 280

PCF-MVS	63.80 13	72.70 140	71.69 156	75.72 95	78.10 165	60.01 153	73.04 163	81.50 107	45.34 290	79.66 118	84.35 189	65.15 131	82.65 99	48.70 261	89.38 143	84.50 117
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_prior				75.27 101	82.15 115	59.85 154		84.33 61					83.39 88			82.58 173

TAMVS		65.31 230	63.75 249	69.97 191	82.23 114	59.76 155	66.78 257	63.37 301	45.20 291	69.79 263	79.37 263	47.42 273	72.17 254	34.48 360	85.15 212	77.99 253

jason		64.47 242	62.84 259	69.34 200	76.91 185	59.20 156	67.15 250	65.67 281	35.29 360	65.16 305	76.74 293	44.67 283	70.68 269	54.74 212	79.28 281	78.14 249
jason: jason.
MVSFormer		69.93 172	69.03 184	72.63 153	74.93 213	59.19 157	83.98 37	75.72 203	52.27 218	63.53 322	76.74 293	43.19 292	80.56 136	72.28 67	78.67 287	78.14 249

lupinMVS		63.36 252	61.49 268	68.97 209	74.93 213	59.19 157	65.80 268	64.52 294	34.68 365	63.53 322	74.25 314	43.19 292	70.62 270	53.88 224	78.67 287	77.10 262

MCST-MVS		73.42 118	73.34 128	73.63 122	81.28 126	59.17 159	74.80 144	83.13 81	45.50 285	72.84 222	83.78 197	65.15 131	80.99 128	64.54 122	89.09 150	80.73 210

fmvsm_s_conf0.1_n		66.60 219	65.54 229	69.77 193	68.99 298	59.15 160	72.12 170	56.74 333	40.72 330	68.25 285	80.14 251	61.18 169	66.92 302	67.34 104	74.40 324	83.23 154

test_0402		78.17 69	79.48 60	74.24 112	83.50 91	59.15 160	72.52 166	74.60 212	75.34 16	88.69 14	91.81 25	75.06 42	82.37 103	65.10 118	88.68 154	81.20 194

fmvsm_s_conf0.5_n		66.34 224	65.27 232	69.57 196	68.20 306	59.14 162	71.66 183	56.48 334	40.92 326	67.78 287	79.46 260	61.23 166	66.90 303	67.39 100	74.32 327	82.66 171

EI-MVSNet-Vis-set		72.78 138	71.87 153	75.54 98	74.77 218	59.02 163	72.24 168	71.56 236	63.92 98	78.59 130	71.59 333	66.22 120	78.60 168	67.58 95	80.32 269	89.00 35

DPM-MVS		69.98 171	69.22 182	72.26 160	82.69 108	58.82 164	70.53 201	81.23 115	47.79 270	64.16 312	80.21 247	51.32 249	83.12 92	60.14 165	84.95 217	74.83 279

HQP5-MVS						58.80 165

EG-PatchMatch MVS		70.70 163	70.88 168	70.16 186	82.64 109	58.80 165	71.48 185	73.64 217	54.98 179	76.55 168	81.77 226	61.10 170	78.94 163	54.87 210	80.84 264	72.74 301

HQP-MVS		75.24 95	75.01 100	75.94 92	82.37 110	58.80 165	77.32 110	84.12 67	59.08 137	71.58 238	85.96 170	58.09 200	85.30 52	67.38 102	89.16 144	83.73 137

EI-MVSNet-UG-set		72.63 141	71.68 157	75.47 99	74.67 220	58.64 168	72.02 173	71.50 237	63.53 104	78.58 132	71.39 337	65.98 121	78.53 169	67.30 105	80.18 271	89.23 29

CDS-MVSNet		64.33 245	62.66 261	69.35 199	80.44 135	58.28 169	65.26 275	65.66 282	44.36 297	67.30 294	75.54 300	43.27 291	71.77 260	37.68 337	84.44 225	78.01 252
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		67.68 206	66.07 225	72.49 155	73.34 242	58.20 170	63.80 290	65.55 284	48.10 265	76.91 154	82.64 216	45.20 279	78.84 164	61.20 153	77.89 297	80.44 218

mvsany_test3		43.76 366	41.01 370	52.01 340	48.09 406	57.74 171	42.47 389	23.85 412	23.30 402	64.80 307	62.17 384	27.12 380	40.59 396	29.17 384	48.11 402	57.69 384

pmmvs4		60.78 277	59.04 286	66.00 247	73.06 251	57.67 172	64.53 284	60.22 314	36.91 354	65.96 299	77.27 289	39.66 315	68.54 286	38.87 327	74.89 318	71.80 310

114514_t		73.40 119	73.33 129	73.64 121	84.15 86	57.11 173	78.20 101	80.02 141	43.76 302	72.55 226	86.07 168	64.00 139	83.35 89	60.14 165	91.03 103	80.45 217

BH-untuned		69.39 181	69.46 177	69.18 203	77.96 169	56.88 174	68.47 233	77.53 185	56.77 162	77.79 142	79.63 258	60.30 178	80.20 146	46.04 286	80.65 266	70.47 322

EC-MVSNet		77.08 77	77.39 79	76.14 91	76.86 189	56.87 175	80.32 77	87.52 12	63.45 106	74.66 196	84.52 186	69.87 84	84.94 62	69.76 79	89.59 136	86.60 66

lessismore_v0				72.75 148	79.60 142	56.83 176		57.37 324		83.80 72	89.01 98	47.45 272	78.74 167	64.39 124	86.49 194	82.69 170

ACMH	63.62 14	77.50 74	80.11 55	69.68 194	79.61 141	56.28 177	78.81 92	83.62 74	63.41 108	87.14 30	90.23 72	76.11 32	73.32 240	67.58 95	94.44 39	79.44 232
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ETV-MVS		72.72 139	72.16 152	74.38 111	76.90 187	55.95 178	73.34 161	84.67 53	62.04 118	72.19 233	70.81 338	65.90 123	85.24 56	58.64 178	84.96 216	81.95 185

API-MVS		70.97 161	71.51 162	69.37 197	75.20 210	55.94 179	80.99 66	76.84 193	62.48 116	71.24 246	77.51 288	61.51 162	80.96 133	52.04 232	85.76 203	71.22 316

patch_mono-2		62.73 262	64.08 246	58.68 307	70.36 282	55.87 180	60.84 311	64.11 297	41.23 321	64.04 313	78.22 279	60.00 179	48.80 364	54.17 221	83.71 234	71.37 313

v7n		79.37 57	80.41 53	76.28 89	78.67 161	55.81 181	79.22 89	82.51 93	70.72 47	87.54 22	92.44 15	68.00 100	81.34 118	72.84 61	91.72 83	91.69 11

ET-MVSNet_ETH3D		63.32 253	60.69 276	71.20 172	70.15 285	55.66 182	65.02 278	64.32 295	43.28 311	68.99 271	72.05 331	25.46 388	78.19 184	54.16 222	82.80 241	79.74 227

EIA-MVS		68.59 193	67.16 213	72.90 143	75.18 211	55.64 183	69.39 215	81.29 112	52.44 217	64.53 308	70.69 339	60.33 177	82.30 105	54.27 220	76.31 306	80.75 209

K. test v3		73.67 113	73.61 122	73.87 118	79.78 139	55.62 184	74.69 148	62.04 309	66.16 73	84.76 61	93.23 6	49.47 257	80.97 130	65.66 116	86.67 191	85.02 94

JIA-IIPM		54.03 318	51.62 336	61.25 290	59.14 372	55.21 185	59.10 321	47.72 374	50.85 238	50.31 388	85.81 173	20.10 401	63.97 322	36.16 352	55.41 398	64.55 364

SixPastTwentyTwo		75.77 85	76.34 87	74.06 115	81.69 121	54.84 186	76.47 119	75.49 205	64.10 97	87.73 18	92.24 18	50.45 253	81.30 120	67.41 98	91.46 90	86.04 72

BH-w/o		64.81 236	64.29 244	66.36 243	76.08 201	54.71 187	65.61 271	75.23 208	50.10 248	71.05 249	71.86 332	54.33 231	79.02 161	38.20 334	76.14 307	65.36 357

MSDG		67.47 210	67.48 209	67.46 231	70.70 272	54.69 188	66.90 255	78.17 176	60.88 126	70.41 253	74.76 306	61.22 168	73.18 241	47.38 275	76.87 302	74.49 283

Patchmatch-RL test		59.95 284	59.12 285	62.44 278	72.46 257	54.61 189	59.63 319	47.51 376	41.05 324	74.58 198	74.30 313	31.06 364	65.31 316	51.61 235	79.85 274	67.39 344

CLD-MVS		72.88 136	72.36 149	74.43 109	77.03 180	54.30 190	68.77 227	83.43 77	52.12 220	76.79 160	74.44 311	69.54 87	83.91 78	55.88 201	93.25 66	85.09 91
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
FE-MVS		68.29 198	66.96 217	72.26 160	74.16 231	54.24 191	77.55 107	73.42 219	57.65 155	72.66 224	84.91 181	32.02 355	81.49 117	48.43 265	81.85 250	81.04 198

HyFIR lowres test		63.01 257	60.47 277	70.61 175	83.04 101	54.10 192	59.93 318	72.24 232	33.67 370	69.00 270	75.63 299	38.69 320	76.93 199	36.60 347	75.45 314	80.81 208

Gipuma		69.55 178	72.83 139	59.70 300	63.63 347	53.97 193	80.08 81	75.93 201	64.24 96	73.49 214	88.93 101	57.89 206	62.46 328	59.75 171	91.55 89	62.67 370
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
OpenMVS	62.51 15	68.76 190	68.75 189	68.78 215	70.56 276	53.91 194	78.29 98	77.35 187	48.85 260	70.22 256	83.52 199	52.65 239	76.93 199	55.31 207	81.99 247	75.49 272

BH-RMVSNet		68.69 192	68.20 199	70.14 187	76.40 194	53.90 195	64.62 282	73.48 218	58.01 148	73.91 211	81.78 225	59.09 189	78.22 181	48.59 262	77.96 296	78.31 245

mvsmamba		68.87 187	67.30 212	73.57 124	76.58 192	53.70 196	84.43 34	74.25 214	45.38 289	76.63 163	84.55 185	35.85 335	85.27 53	49.54 253	78.49 289	81.75 189

PAPM_NR		73.91 110	74.16 111	73.16 132	81.90 118	53.50 197	81.28 65	81.40 110	66.17 72	73.30 218	83.31 206	59.96 180	83.10 93	58.45 180	81.66 257	82.87 164

PMMVS		44.69 361	43.95 369	46.92 363	50.05 403	53.47 198	48.08 376	42.40 392	22.36 403	44.01 402	53.05 398	42.60 297	45.49 376	31.69 371	61.36 385	41.79 400

EPP-MVSNet		73.86 112	73.38 125	75.31 100	78.19 164	53.35 199	80.45 72	77.32 188	65.11 87	76.47 172	86.80 136	49.47 257	83.77 80	53.89 223	92.72 73	88.81 41

IterMVS		63.12 256	62.48 262	65.02 253	66.34 326	52.86 200	63.81 289	62.25 304	46.57 278	71.51 243	80.40 244	44.60 284	66.82 307	51.38 238	75.47 313	75.38 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tttt0517		69.46 179	67.79 205	74.46 106	75.34 208	52.72 201	75.05 138	63.27 302	54.69 185	78.87 129	84.37 188	26.63 382	81.15 122	63.95 130	87.93 165	89.51 25

GeoE		73.14 124	73.77 119	71.26 171	78.09 166	52.64 202	74.32 152	79.56 150	56.32 167	76.35 175	83.36 205	70.76 76	77.96 187	63.32 139	81.84 251	83.18 155

QAPM		69.18 184	69.26 180	68.94 210	71.61 263	52.58 203	80.37 75	78.79 164	49.63 252	73.51 213	85.14 179	53.66 234	79.12 159	55.11 208	75.54 312	75.11 278

FA-MVS(test-final)		71.27 156	71.06 166	71.92 164	73.96 233	52.32 204	76.45 121	76.12 198	59.07 140	74.04 209	86.18 161	52.18 242	79.43 156	59.75 171	81.76 252	84.03 128

CHOSEN 280x420		41.62 368	39.89 373	46.80 364	61.81 353	51.59 205	33.56 402	35.74 405	27.48 389	37.64 407	53.53 396	23.24 395	42.09 391	27.39 388	58.64 391	46.72 395

CMPMVS	48.73 20	61.54 272	60.89 273	63.52 266	61.08 358	51.55 206	68.07 237	68.00 270	33.88 367	65.87 300	81.25 232	37.91 325	67.71 292	49.32 256	82.60 243	71.31 315
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PS-MVSNAJ		64.27 246	63.73 250	65.90 248	77.82 171	51.42 207	63.33 295	72.33 230	45.09 293	61.60 329	68.04 365	62.39 152	73.95 236	49.07 257	73.87 330	72.34 304

xiu_mvs_v2_base		64.43 243	63.96 247	65.85 249	77.72 173	51.32 208	63.63 292	72.31 231	45.06 294	61.70 328	69.66 351	62.56 148	73.93 237	49.06 258	73.91 329	72.31 305

test_vis1_rt		46.70 355	45.24 363	51.06 345	44.58 409	51.04 209	39.91 394	67.56 271	21.84 405	51.94 380	50.79 401	33.83 341	39.77 397	35.25 358	61.50 384	62.38 373

CHOSEN 1792x2688		58.09 296	56.30 307	63.45 267	79.95 138	50.93 210	54.07 357	65.59 283	28.56 386	61.53 330	74.33 312	41.09 305	66.52 310	33.91 363	67.69 370	72.92 297

TR-MVS		64.59 239	63.54 252	67.73 229	75.75 206	50.83 211	63.39 294	70.29 256	49.33 255	71.55 242	74.55 309	50.94 250	78.46 172	40.43 319	75.69 310	73.89 289

thisisatest0530		67.05 216	65.16 235	72.73 150	73.10 249	50.55 212	71.26 192	63.91 298	50.22 246	74.46 200	80.75 239	26.81 381	80.25 143	59.43 173	86.50 193	87.37 54

dcpmvs_2		71.02 160	72.65 143	66.16 245	76.06 202	50.49 213	71.97 175	79.36 152	50.34 243	82.81 83	83.63 198	64.38 137	67.27 299	61.54 150	83.71 234	80.71 212

test_fmvs1_n		52.70 328	52.01 335	54.76 326	53.83 398	50.36 214	55.80 345	65.90 279	24.96 397	65.39 303	60.64 389	27.69 379	48.46 366	45.88 288	67.99 367	65.46 356

Effi-MVS+		72.10 149	72.28 150	71.58 166	74.21 230	50.33 215	74.72 147	82.73 88	62.62 114	70.77 250	76.83 292	69.96 83	80.97 130	60.20 162	78.43 290	83.45 146

IB-MVS	49.67 18	59.69 286	56.96 302	67.90 225	68.19 307	50.30 216	61.42 306	65.18 287	47.57 272	55.83 365	67.15 372	23.77 394	79.60 153	43.56 301	79.97 273	73.79 290
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
ambc				70.10 188	77.74 172	50.21 217	74.28 154	77.93 182		79.26 122	88.29 115	54.11 233	79.77 150	64.43 123	91.10 101	80.30 219

test_vis3_rt		51.94 336	51.04 342	54.65 327	46.32 408	50.13 218	44.34 387	78.17 176	23.62 401	68.95 273	62.81 381	21.41 398	38.52 400	41.49 312	72.22 342	75.30 277

cascas		64.59 239	62.77 260	70.05 189	75.27 209	50.02 219	61.79 304	71.61 234	42.46 313	63.68 319	68.89 359	49.33 259	80.35 140	47.82 273	84.05 229	79.78 226

test_vis1_n		51.27 339	50.41 349	53.83 329	56.99 381	50.01 220	56.75 337	60.53 313	25.68 395	59.74 345	57.86 393	29.40 375	47.41 371	43.10 303	63.66 378	64.08 366

test_fmvs2		54.80 313	54.11 322	56.88 318	51.76 401	49.95 221	56.70 338	65.80 280	26.22 393	69.42 266	65.25 375	31.82 356	49.98 361	49.63 252	70.36 354	70.71 321

mvsany_test1		37.88 370	35.74 375	44.28 374	47.28 407	49.90 222	36.54 400	24.37 411	19.56 406	45.76 395	53.46 397	32.99 346	37.97 401	26.17 389	35.52 404	44.99 399

EI-MVSNet		69.61 177	69.01 185	71.41 170	73.94 234	49.90 222	71.31 190	71.32 242	58.22 146	75.40 186	70.44 340	58.16 197	75.85 207	62.51 143	79.81 275	88.48 44

MDA-MVSNet-bldmvs		62.34 265	61.73 263	64.16 257	61.64 355	49.90 222	48.11 375	57.24 327	53.31 211	80.95 105	79.39 262	49.00 263	61.55 333	45.92 287	80.05 272	81.03 199

IterMVS-LS		73.01 130	73.12 133	72.66 151	73.79 236	49.90 222	71.63 184	78.44 171	58.22 146	80.51 111	86.63 147	58.15 198	79.62 152	62.51 143	88.20 158	88.48 44
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
nrg030		74.87 105	75.99 92	71.52 168	74.90 215	49.88 226	74.10 156	82.58 92	54.55 190	83.50 75	89.21 90	71.51 67	75.74 211	61.24 152	92.34 78	88.94 37

casdiffmvs_mvg		75.26 94	76.18 90	72.52 154	72.87 255	49.47 227	72.94 164	84.71 52	59.49 135	80.90 108	88.81 103	70.07 81	79.71 151	67.40 99	88.39 156	88.40 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_BlendedMVS		65.38 229	64.30 243	68.61 217	69.81 288	49.36 228	65.60 272	78.96 158	45.50 285	59.98 340	78.61 274	51.82 244	78.20 182	44.30 295	84.11 228	78.27 246

PVSNet_Blended		62.90 259	61.64 265	66.69 241	69.81 288	49.36 228	61.23 308	78.96 158	42.04 314	59.98 340	68.86 360	51.82 244	78.20 182	44.30 295	77.77 298	72.52 302

test_fmvs1		51.51 338	50.86 345	53.48 332	49.72 404	49.35 230	54.11 356	64.96 289	24.64 399	63.66 320	59.61 392	28.33 378	48.45 367	45.38 293	67.30 371	62.66 371

MS-PatchMatch		55.59 308	54.89 317	57.68 313	69.18 294	49.05 231	61.00 310	62.93 303	35.98 357	58.36 350	68.93 358	36.71 332	66.59 309	37.62 339	63.30 379	57.39 385

MVSMamba_PlusPlus		76.88 78	78.21 72	72.88 145	80.83 129	48.71 232	83.28 49	82.79 85	72.78 28	79.17 124	91.94 21	56.47 220	83.95 76	70.51 74	86.15 195	85.99 73

bld_raw_conf03		72.88 136	72.76 141	73.22 130	76.77 190	48.71 232	83.28 49	82.79 85	48.38 263	79.17 124	86.44 154	52.61 240	84.97 61	59.29 175	86.15 195	85.99 73

v10		75.69 87	76.20 89	74.16 113	74.44 226	48.69 234	75.84 134	82.93 84	59.02 141	85.92 41	89.17 93	58.56 194	82.74 98	70.73 72	89.14 147	91.05 14

v1192		73.40 119	73.42 123	73.32 129	74.65 223	48.67 235	72.21 169	81.73 104	52.76 215	81.85 91	84.56 184	57.12 212	82.24 107	68.58 84	87.33 175	89.06 33

Fast-Effi-MVS+		68.81 189	68.30 195	70.35 181	74.66 222	48.61 236	66.06 263	78.32 173	50.62 241	71.48 244	75.54 300	68.75 91	79.59 154	50.55 245	78.73 286	82.86 165

DELS-MVS		68.83 188	68.31 194	70.38 179	70.55 278	48.31 237	63.78 291	82.13 97	54.00 203	68.96 272	75.17 304	58.95 191	80.06 148	58.55 179	82.74 242	82.76 167
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
pmmvs3		46.71 354	45.09 364	51.55 342	56.76 383	48.25 238	55.78 346	39.53 402	24.13 400	50.35 387	63.40 379	15.90 410	51.08 358	29.29 382	70.69 353	55.33 388

CR-MVSNet		58.96 290	58.49 291	60.36 297	66.37 324	48.24 239	70.93 196	56.40 336	32.87 373	61.35 331	86.66 144	33.19 344	63.22 327	48.50 264	70.17 356	69.62 331

RPMNet		65.77 227	65.08 241	67.84 227	66.37 324	48.24 239	70.93 196	86.27 21	54.66 186	61.35 331	86.77 139	33.29 343	85.67 46	55.93 200	70.17 356	69.62 331

v1144		73.29 122	73.39 124	73.01 136	74.12 232	48.11 241	72.01 174	81.08 120	53.83 207	81.77 93	84.68 182	58.07 203	81.91 111	68.10 88	86.86 186	88.99 36

test_fmvs3		56.78 301	55.99 310	59.12 304	53.96 397	48.09 242	58.76 325	66.22 277	27.54 388	76.66 162	68.69 362	25.32 390	51.31 357	53.42 229	73.38 333	77.97 254

IS-MVSNet		75.10 97	75.42 98	74.15 114	79.23 148	48.05 243	79.43 85	78.04 179	70.09 52	79.17 124	88.02 121	53.04 237	83.60 82	58.05 183	93.76 59	90.79 18

alignmvs		70.54 165	71.00 167	69.15 204	73.50 238	48.04 244	69.85 211	79.62 146	53.94 206	76.54 169	82.00 221	59.00 190	74.68 226	57.32 187	87.21 182	84.72 103

D2MVS		62.58 263	61.05 272	67.20 234	63.85 344	47.92 245	56.29 340	69.58 260	39.32 337	70.07 259	78.19 280	34.93 338	72.68 245	53.44 228	83.74 232	81.00 201

UniMVSNet (Re)		75.00 100	75.48 97	73.56 125	83.14 96	47.92 245	70.41 204	81.04 121	63.67 102	79.54 119	86.37 156	62.83 146	81.82 112	57.10 190	95.25 15	90.94 16

test_cas_vis1_n_1920		50.90 340	50.92 344	50.83 346	54.12 396	47.80 247	51.44 368	54.61 343	26.95 391	63.95 315	60.85 387	37.86 327	44.97 380	45.53 290	62.97 380	59.72 380

PAPR		69.20 183	68.66 192	70.82 173	75.15 212	47.77 248	75.31 136	81.11 117	49.62 253	66.33 298	79.27 264	61.53 161	82.96 95	48.12 269	81.50 259	81.74 190

CVMVSNet		59.21 289	58.44 292	61.51 286	73.94 234	47.76 249	71.31 190	64.56 293	26.91 392	60.34 339	70.44 340	36.24 334	67.65 293	53.57 226	68.66 364	69.12 336

balanced_conf03		73.59 115	74.06 112	72.17 162	77.48 177	47.72 250	81.43 64	82.20 96	54.38 191	79.19 123	87.68 124	54.41 230	83.57 83	63.98 129	85.78 202	85.22 87

EPNet_dtu		58.93 291	58.52 290	60.16 299	67.91 311	47.70 251	69.97 208	58.02 320	49.73 251	47.28 393	73.02 325	38.14 322	62.34 329	36.57 348	85.99 200	70.43 323
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		72.96 134	72.98 137	72.89 144	74.67 220	47.58 252	71.92 179	80.69 126	51.70 226	81.69 97	83.89 195	56.58 218	82.25 106	68.34 86	87.36 172	88.82 40

v144192		72.99 132	73.06 135	72.77 147	74.58 224	47.48 253	71.90 180	80.44 134	51.57 227	81.46 99	84.11 192	58.04 204	82.12 108	67.98 92	87.47 170	88.70 43

v8		75.07 98	75.64 95	73.35 127	73.42 240	47.46 254	75.20 137	81.45 109	60.05 131	85.64 45	89.26 88	58.08 202	81.80 113	69.71 81	87.97 164	90.79 18

sasdasda		72.29 147	73.38 125	69.04 205	74.23 227	47.37 255	73.93 158	83.18 78	54.36 192	76.61 165	81.64 229	72.03 62	75.34 215	57.12 188	87.28 177	84.40 118

canonicalmvs		72.29 147	73.38 125	69.04 205	74.23 227	47.37 255	73.93 158	83.18 78	54.36 192	76.61 165	81.64 229	72.03 62	75.34 215	57.12 188	87.28 177	84.40 118

MVS		60.62 279	59.97 280	62.58 277	68.13 308	47.28 257	68.59 229	73.96 216	32.19 374	59.94 342	68.86 360	50.48 252	77.64 193	41.85 310	75.74 309	62.83 368

v1240		73.06 128	73.14 131	72.84 146	74.74 219	47.27 258	71.88 181	81.11 117	51.80 224	82.28 88	84.21 190	56.22 223	82.34 104	68.82 83	87.17 184	88.91 38

V42		71.06 158	70.83 169	71.72 165	67.25 317	47.14 259	65.94 264	80.35 137	51.35 232	83.40 76	83.23 210	59.25 188	78.80 165	65.91 114	80.81 265	89.23 29

iter_conf05		77.90 71	79.33 61	73.61 123	80.83 129	46.85 260	82.06 58	86.72 17	72.78 28	85.44 51	91.94 21	56.47 220	83.95 76	70.51 74	87.24 180	90.02 22

TinyColmap		67.98 201	69.28 179	64.08 259	67.98 310	46.82 261	70.04 206	75.26 207	53.05 212	77.36 148	86.79 137	59.39 186	72.59 250	45.64 289	88.01 163	72.83 299

v2v482		72.55 144	72.58 144	72.43 156	72.92 254	46.72 262	71.41 187	79.13 156	55.27 176	81.17 103	85.25 178	55.41 225	81.13 123	67.25 106	85.46 204	89.43 26

casdiffmvs		73.06 128	73.84 116	70.72 174	71.32 266	46.71 263	70.93 196	84.26 63	55.62 174	77.46 147	87.10 128	67.09 106	77.81 189	63.95 130	86.83 188	87.64 51
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
VDD-MVS		70.81 162	71.44 163	68.91 212	79.07 155	46.51 264	67.82 239	70.83 253	61.23 122	74.07 207	88.69 105	59.86 182	75.62 212	51.11 239	90.28 120	84.61 108

eth_miper_zixun_eth		69.42 180	68.73 191	71.50 169	67.99 309	46.42 265	67.58 241	78.81 161	50.72 240	78.13 137	80.34 246	50.15 255	80.34 141	60.18 163	84.65 220	87.74 50

thisisatest0515		60.48 280	57.86 296	68.34 220	67.25 317	46.42 265	60.58 314	62.14 305	40.82 327	63.58 321	69.12 354	26.28 384	78.34 178	48.83 259	82.13 246	80.26 220

baseline		73.10 125	73.96 115	70.51 178	71.46 265	46.39 267	72.08 171	84.40 60	55.95 171	76.62 164	86.46 153	67.20 104	78.03 186	64.22 126	87.27 179	87.11 61

MVSTER		63.29 254	61.60 267	68.36 219	59.77 369	46.21 268	60.62 313	71.32 242	41.83 316	75.40 186	79.12 268	30.25 370	75.85 207	56.30 197	79.81 275	83.03 160

SDMVSNet		66.36 223	67.85 204	61.88 283	73.04 252	46.14 269	58.54 326	71.36 241	51.42 230	68.93 274	82.72 214	65.62 125	62.22 331	54.41 217	84.67 218	77.28 258

UniMVSNet_NR-MVSNet		74.90 103	75.65 94	72.64 152	83.04 101	45.79 270	69.26 217	78.81 161	66.66 69	81.74 95	86.88 135	63.26 142	81.07 126	56.21 198	94.98 21	91.05 14

DU-MVS		74.91 102	75.57 96	72.93 142	83.50 91	45.79 270	69.47 214	80.14 140	65.22 84	81.74 95	87.08 129	61.82 158	81.07 126	56.21 198	94.98 21	91.93 9

miper_lstm_enhance		61.97 266	61.63 266	62.98 272	60.04 363	45.74 272	47.53 377	70.95 250	44.04 298	73.06 220	78.84 273	39.72 314	60.33 336	55.82 202	84.64 221	82.88 163

Anonymous20231211		75.54 90	77.19 81	70.59 176	77.67 174	45.70 273	74.73 146	80.19 138	68.80 56	82.95 80	92.91 9	66.26 119	76.76 203	58.41 181	92.77 71	89.30 27

OpenMVS_ROB	54.93 17	63.23 255	63.28 254	63.07 271	69.81 288	45.34 274	68.52 231	67.14 272	43.74 303	70.61 252	79.22 265	47.90 271	72.66 246	48.75 260	73.84 331	71.21 317

Anonymous20240529		72.56 142	73.79 118	68.86 213	76.89 188	45.21 275	68.80 226	77.25 190	67.16 64	76.89 155	90.44 57	65.95 122	74.19 233	50.75 242	90.00 126	87.18 59

diffmvs		67.42 211	67.50 208	67.20 234	62.26 352	45.21 275	64.87 279	77.04 192	48.21 264	71.74 235	79.70 257	58.40 195	71.17 267	64.99 119	80.27 270	85.22 87
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_vis1_n_1920		52.96 325	53.50 324	51.32 344	59.15 371	44.90 277	56.13 343	64.29 296	30.56 384	59.87 344	60.68 388	40.16 311	47.47 370	48.25 268	62.46 381	61.58 376

1314		59.83 285	58.86 288	62.74 276	65.71 332	44.78 278	68.59 229	72.63 227	33.54 372	61.05 335	67.29 371	43.62 290	71.26 266	49.49 254	67.84 369	72.19 307

v148		69.38 182	69.39 178	69.36 198	69.14 296	44.56 279	68.83 223	72.70 226	54.79 183	78.59 130	84.12 191	54.69 227	76.74 204	59.40 174	82.20 245	86.79 63

GA-MVS		62.91 258	61.66 264	66.66 242	67.09 319	44.49 280	61.18 309	69.36 262	51.33 233	69.33 268	74.47 310	36.83 331	74.94 222	50.60 244	74.72 319	80.57 216

ppachtmachnet_test		60.26 282	59.61 283	62.20 280	67.70 313	44.33 281	58.18 330	60.96 312	40.75 329	65.80 301	72.57 327	41.23 302	63.92 323	46.87 280	82.42 244	78.33 244

baseline2		55.57 309	52.74 328	64.05 260	65.26 334	44.11 282	62.38 301	54.43 344	39.03 340	51.21 382	67.35 370	33.66 342	72.45 251	37.14 342	64.22 377	75.60 271

Anonymous20240521		63.55 250	66.07 225	55.99 321	66.18 329	44.04 283	68.77 227	68.80 264	46.99 275	72.57 225	85.84 172	39.87 313	50.22 360	53.40 230	92.23 80	73.71 291

UniMVSNet_ETH3D		76.74 80	79.02 63	69.92 192	89.27 20	43.81 284	74.47 151	71.70 233	72.33 38	85.50 50	93.65 4	77.98 21	76.88 201	54.60 214	91.64 85	89.08 32

NR-MVSNet		73.62 114	74.05 113	72.33 159	83.50 91	43.71 285	65.65 270	77.32 188	64.32 95	75.59 181	87.08 129	62.45 151	81.34 118	54.90 209	95.63 9	91.93 9

cl____		68.26 200	68.26 196	68.29 221	64.98 339	43.67 286	65.89 265	74.67 210	50.04 249	76.86 157	82.42 218	48.74 265	75.38 213	60.92 157	89.81 131	85.80 81

DIV-MVS_self_test		68.27 199	68.26 196	68.29 221	64.98 339	43.67 286	65.89 265	74.67 210	50.04 249	76.86 157	82.43 217	48.74 265	75.38 213	60.94 156	89.81 131	85.81 77

c3_l		69.82 174	69.89 175	69.61 195	66.24 327	43.48 288	68.12 236	79.61 148	51.43 229	77.72 143	80.18 250	54.61 229	78.15 185	63.62 135	87.50 169	87.20 58

cl22		67.14 213	66.51 220	69.03 207	63.20 348	43.46 289	66.88 256	76.25 197	49.22 256	74.48 199	77.88 284	45.49 278	77.40 195	60.64 159	84.59 222	86.24 68

miper_ehance_all_eth		68.36 195	68.16 200	68.98 208	65.14 338	43.34 290	67.07 251	78.92 160	49.11 258	76.21 176	77.72 285	53.48 235	77.92 188	61.16 154	84.59 222	85.68 83

USDC		62.80 260	63.10 257	61.89 282	65.19 335	43.30 291	67.42 244	74.20 215	35.80 359	72.25 231	84.48 187	45.67 276	71.95 259	37.95 336	84.97 213	70.42 324

MVS_Test		69.84 173	70.71 170	67.24 233	67.49 315	43.25 292	69.87 210	81.22 116	52.69 216	71.57 241	86.68 143	62.09 156	74.51 228	66.05 112	78.74 285	83.96 129

MGCFI-Net		71.70 153	73.10 134	67.49 230	73.23 244	43.08 293	72.06 172	82.43 94	54.58 188	75.97 178	82.00 221	72.42 60	75.22 217	57.84 185	87.34 174	84.18 125

EMVS		44.61 363	44.45 368	45.10 372	48.91 405	43.00 294	37.92 397	41.10 400	46.75 277	38.00 406	48.43 403	26.42 383	46.27 373	37.11 343	75.38 315	46.03 396

CANet_DTU		64.04 248	63.83 248	64.66 254	68.39 302	42.97 295	73.45 160	74.50 213	52.05 222	54.78 370	75.44 303	43.99 287	70.42 274	53.49 227	78.41 291	80.59 215

E-PMN		45.17 359	45.36 362	44.60 373	50.07 402	42.75 296	38.66 396	42.29 394	46.39 279	39.55 404	51.15 400	26.00 385	45.37 378	37.68 337	76.41 304	45.69 397

WR-MVS_H		80.22 51	82.17 42	74.39 110	89.46 15	42.69 297	78.24 100	82.24 95	78.21 10	89.57 10	92.10 19	68.05 98	85.59 47	66.04 113	95.62 10	94.88 5

miper_enhance_ethall		65.86 226	65.05 242	68.28 223	61.62 356	42.62 298	64.74 280	77.97 180	42.52 312	73.42 216	72.79 326	49.66 256	77.68 192	58.12 182	84.59 222	84.54 112

TranMVSNet+NR-MVSNet		76.13 83	77.66 77	71.56 167	84.61 78	42.57 299	70.98 195	78.29 175	68.67 59	83.04 77	89.26 88	72.99 58	80.75 135	55.58 206	95.47 11	91.35 12

1112_ss		59.48 287	58.99 287	60.96 293	77.84 170	42.39 300	61.42 306	68.45 268	37.96 347	59.93 343	67.46 368	45.11 281	65.07 318	40.89 317	71.81 345	75.41 274

pmmvs6		71.82 151	73.66 120	66.31 244	75.94 203	42.01 301	66.99 252	72.53 228	63.45 106	76.43 173	92.78 11	72.95 59	69.69 277	51.41 237	90.46 118	87.22 56

test-LLR		50.43 342	50.69 347	49.64 352	60.76 359	41.87 302	53.18 360	45.48 381	43.41 308	49.41 389	60.47 390	29.22 376	44.73 382	42.09 308	72.14 343	62.33 374

test-mter		48.56 350	48.20 355	49.64 352	60.76 359	41.87 302	53.18 360	45.48 381	31.91 379	49.41 389	60.47 390	18.34 405	44.73 382	42.09 308	72.14 343	62.33 374

PAPM		61.79 269	60.37 278	66.05 246	76.09 199	41.87 302	69.30 216	76.79 195	40.64 331	53.80 375	79.62 259	44.38 285	82.92 96	29.64 380	73.11 335	73.36 293

tt0805		76.12 84	78.43 70	69.20 202	81.32 125	41.37 305	76.72 118	77.64 184	63.78 101	82.06 89	87.88 122	79.78 11	79.05 160	64.33 125	92.40 76	87.17 60

EU-MVSNet		60.82 276	60.80 275	60.86 294	68.37 303	41.16 306	72.27 167	68.27 269	26.96 390	69.08 269	75.71 298	32.09 352	67.44 297	55.59 205	78.90 284	73.97 287

VDDNet		71.60 154	73.13 132	67.02 237	86.29 48	41.11 307	69.97 208	66.50 276	68.72 58	74.74 192	91.70 27	59.90 181	75.81 209	48.58 263	91.72 83	84.15 127

SCA		58.57 294	58.04 295	60.17 298	70.17 284	41.07 308	65.19 276	53.38 353	43.34 310	61.00 336	73.48 320	45.20 279	69.38 279	40.34 320	70.31 355	70.05 325

test_yl		65.11 231	65.09 239	65.18 251	70.59 274	40.86 309	63.22 298	72.79 223	57.91 149	68.88 276	79.07 270	42.85 295	74.89 223	45.50 291	84.97 213	79.81 224

DCV-MVSNet		65.11 231	65.09 239	65.18 251	70.59 274	40.86 309	63.22 298	72.79 223	57.91 149	68.88 276	79.07 270	42.85 295	74.89 223	45.50 291	84.97 213	79.81 224

GBi-Net		68.30 196	68.79 187	66.81 238	73.14 246	40.68 311	71.96 176	73.03 220	54.81 180	74.72 193	90.36 68	48.63 267	75.20 219	47.12 276	85.37 205	84.54 112

test1		68.30 196	68.79 187	66.81 238	73.14 246	40.68 311	71.96 176	73.03 220	54.81 180	74.72 193	90.36 68	48.63 267	75.20 219	47.12 276	85.37 205	84.54 112

FMVSNet1		71.06 158	72.48 146	66.81 238	77.65 175	40.68 311	71.96 176	73.03 220	61.14 123	79.45 121	90.36 68	60.44 176	75.20 219	50.20 247	88.05 161	84.54 112

ADS-MVSNet2		48.76 349	47.25 358	53.29 335	55.90 387	40.54 314	47.34 378	54.99 342	31.41 381	50.48 385	72.06 329	31.23 361	54.26 354	25.93 391	55.93 395	65.07 359

MG-MVS		70.47 166	71.34 164	67.85 226	79.26 147	40.42 315	74.67 149	75.15 209	58.41 145	68.74 280	88.14 120	56.08 224	83.69 81	59.90 168	81.71 256	79.43 233

PVSNet_0	36.71 22	41.12 369	40.78 372	42.14 378	59.97 365	40.13 316	40.97 391	42.24 395	30.81 383	44.86 399	49.41 402	40.70 308	45.12 379	23.15 400	34.96 405	41.16 401

pm-mvs1		68.40 194	69.85 176	64.04 261	73.10 249	39.94 317	64.61 283	70.50 254	55.52 175	73.97 210	89.33 86	63.91 140	68.38 287	49.68 251	88.02 162	83.81 133

tpm cat1		54.02 319	52.63 330	58.19 310	64.85 341	39.86 318	66.26 262	57.28 325	32.16 375	56.90 357	70.39 342	32.75 348	65.30 317	34.29 361	58.79 390	69.41 333

our_test_3		56.46 302	56.51 305	56.30 319	67.70 313	39.66 319	55.36 348	52.34 359	40.57 332	63.85 316	69.91 350	40.04 312	58.22 345	43.49 302	75.29 317	71.03 320

PS-CasMVS		80.41 48	82.86 37	73.07 135	89.93 7	39.21 320	77.15 114	81.28 113	79.74 6	90.87 5	92.73 12	75.03 43	84.93 63	63.83 133	95.19 16	95.07 3

PatchmatchNet		54.60 314	54.27 321	55.59 324	65.17 337	39.08 321	66.92 254	51.80 361	39.89 334	58.39 349	73.12 324	31.69 358	58.33 344	43.01 304	58.38 393	69.38 334
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CP-MVSNet		79.48 55	81.65 46	72.98 138	89.66 13	39.06 322	76.76 117	80.46 133	78.91 8	90.32 8	91.70 27	68.49 93	84.89 64	63.40 138	95.12 19	95.01 4

PEN-MVS		80.46 47	82.91 35	73.11 134	89.83 9	39.02 323	77.06 116	82.61 91	80.04 5	90.60 7	92.85 10	74.93 44	85.21 57	63.15 141	95.15 18	95.09 2

FMVSNet2		67.48 208	68.21 198	65.29 250	73.14 246	38.94 324	68.81 224	71.21 248	54.81 180	76.73 161	86.48 152	48.63 267	74.60 227	47.98 271	86.11 199	82.35 177

dmvs_re		49.91 347	50.77 346	47.34 361	59.98 364	38.86 325	53.18 360	53.58 350	39.75 335	55.06 368	61.58 386	36.42 333	44.40 384	29.15 385	68.23 365	58.75 382

sd_testset		63.55 250	65.38 231	58.07 311	73.04 252	38.83 326	57.41 334	65.44 285	51.42 230	68.93 274	82.72 214	63.76 141	58.11 346	41.05 315	84.67 218	77.28 258

test_f		43.79 365	45.63 360	38.24 386	42.29 412	38.58 327	34.76 401	47.68 375	22.22 404	67.34 293	63.15 380	31.82 356	30.60 405	39.19 325	62.28 382	45.53 398

CostFormer		57.35 300	56.14 308	60.97 292	63.76 346	38.43 328	67.50 242	60.22 314	37.14 353	59.12 348	76.34 295	32.78 347	71.99 258	39.12 326	69.27 361	72.47 303

TESTMET0.1,1		45.17 359	44.93 365	45.89 368	56.02 386	38.31 329	53.18 360	41.94 396	27.85 387	44.86 399	56.47 395	17.93 406	41.50 395	38.08 335	68.06 366	57.85 383

PVSNet	43.83 21	51.56 337	51.17 340	52.73 336	68.34 304	38.27 330	48.22 374	53.56 351	36.41 355	54.29 373	64.94 376	34.60 339	54.20 355	30.34 375	69.87 358	65.71 355

LFMVS		67.06 215	67.89 202	64.56 255	78.02 167	38.25 331	70.81 199	59.60 316	65.18 85	71.06 248	86.56 150	43.85 288	75.22 217	46.35 283	89.63 134	80.21 221

Anonymous202405211		66.02 225	66.89 218	63.43 268	74.22 229	38.14 332	59.00 322	66.13 278	63.33 109	69.76 264	85.95 171	51.88 243	70.50 272	44.23 297	87.52 168	81.64 191

Test_1112_low_res		58.78 292	58.69 289	59.04 306	79.41 144	38.13 333	57.62 332	66.98 274	34.74 363	59.62 346	77.56 287	42.92 294	63.65 325	38.66 329	70.73 352	75.35 276

VPA-MVSNet		68.71 191	70.37 172	63.72 263	76.13 198	38.06 334	64.10 287	71.48 238	56.60 166	74.10 206	88.31 114	64.78 135	69.72 276	47.69 274	90.15 123	83.37 149

ab-mvs		64.11 247	65.13 238	61.05 291	71.99 261	38.03 335	67.59 240	68.79 265	49.08 259	65.32 304	86.26 159	58.02 205	66.85 306	39.33 323	79.79 277	78.27 246

FIs		72.56 142	73.80 117	68.84 214	78.74 160	37.74 336	71.02 194	79.83 144	56.12 168	80.88 109	89.45 85	58.18 196	78.28 180	56.63 192	93.36 64	90.51 20

MIMVSNet1		66.57 220	69.23 181	58.59 308	81.26 127	37.73 337	64.06 288	57.62 321	57.02 159	78.40 134	90.75 47	62.65 147	58.10 347	41.77 311	89.58 137	79.95 223

mvs_anonymous		65.08 233	65.49 230	63.83 262	63.79 345	37.60 338	66.52 260	69.82 259	43.44 307	73.46 215	86.08 167	58.79 193	71.75 262	51.90 234	75.63 311	82.15 182

FMVSNet3		65.00 234	65.16 235	64.52 256	69.47 293	37.56 339	66.63 258	70.38 255	51.55 228	74.72 193	83.27 208	37.89 326	74.44 229	47.12 276	85.37 205	81.57 192

DTE-MVSNet		80.35 49	82.89 36	72.74 149	89.84 8	37.34 340	77.16 113	81.81 103	80.45 4	90.92 4	92.95 8	74.57 47	86.12 31	63.65 134	94.68 32	94.76 6

tfpnnormal		66.48 221	67.93 201	62.16 281	73.40 241	36.65 341	63.45 293	64.99 288	55.97 170	72.82 223	87.80 123	57.06 214	69.10 282	48.31 267	87.54 167	80.72 211

FC-MVSNet-test		73.32 121	74.78 102	68.93 211	79.21 149	36.57 342	71.82 182	79.54 151	57.63 156	82.57 86	90.38 65	59.38 187	78.99 162	57.91 184	94.56 34	91.23 13

MDA-MVSNet_test_wron		52.57 330	53.49 326	49.81 351	54.24 393	36.47 343	40.48 393	46.58 379	38.13 345	75.47 185	73.32 322	41.05 307	43.85 387	40.98 316	71.20 349	69.10 337

YYNet1		52.58 329	53.50 324	49.85 350	54.15 394	36.45 344	40.53 392	46.55 380	38.09 346	75.52 184	73.31 323	41.08 306	43.88 386	41.10 314	71.14 350	69.21 335

HY-MVS	49.31 19	57.96 297	57.59 298	59.10 305	66.85 323	36.17 345	65.13 277	65.39 286	39.24 339	54.69 372	78.14 281	44.28 286	67.18 301	33.75 365	70.79 351	73.95 288

tpm2		56.12 303	54.64 319	60.55 296	66.24 327	36.01 346	68.14 235	56.77 332	33.60 371	58.25 351	75.52 302	30.25 370	74.33 231	33.27 366	69.76 360	71.32 314

Anonymous20231206		54.13 316	55.82 311	49.04 357	70.89 268	35.96 347	51.73 366	50.87 363	34.86 361	62.49 326	79.22 265	42.52 298	44.29 385	27.95 387	81.88 249	66.88 348

TransMVSNet (Re)		69.62 176	71.63 158	63.57 265	76.51 193	35.93 348	65.75 269	71.29 244	61.05 124	75.02 188	89.90 79	65.88 124	70.41 275	49.79 249	89.48 138	84.38 120

MVE	27.91 23	36.69 373	35.64 376	39.84 383	43.37 410	35.85 349	19.49 404	24.61 410	24.68 398	39.05 405	62.63 383	38.67 321	27.10 408	21.04 404	47.25 403	56.56 387
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
WR-MVS		71.20 157	72.48 146	67.36 232	84.98 71	35.70 350	64.43 285	68.66 266	65.05 88	81.49 98	86.43 155	57.57 208	76.48 205	50.36 246	93.32 65	89.90 23

VNet		64.01 249	65.15 237	60.57 295	73.28 243	35.61 351	57.60 333	67.08 273	54.61 187	66.76 297	83.37 203	56.28 222	66.87 304	42.19 307	85.20 211	79.23 235

tfpn200view9		60.35 281	59.97 280	61.51 286	70.78 270	35.35 352	63.27 296	57.47 322	53.00 213	68.31 283	77.09 290	32.45 350	72.09 255	35.61 355	81.73 253	77.08 263

thres400		60.77 278	59.97 280	63.15 269	70.78 270	35.35 352	63.27 296	57.47 322	53.00 213	68.31 283	77.09 290	32.45 350	72.09 255	35.61 355	81.73 253	82.02 183

thres100view900		61.17 274	61.09 271	61.39 288	72.14 260	35.01 354	65.42 274	56.99 329	55.23 177	70.71 251	79.90 254	32.07 353	72.09 255	35.61 355	81.73 253	77.08 263

thres600view7		61.82 268	61.38 269	63.12 270	71.81 262	34.93 355	64.64 281	56.99 329	54.78 184	70.33 255	79.74 256	32.07 353	72.42 252	38.61 330	83.46 237	82.02 183

thres200		57.55 299	57.02 301	59.17 303	67.89 312	34.93 355	58.91 324	57.25 326	50.24 245	64.01 314	71.46 335	32.49 349	71.39 265	31.31 372	79.57 279	71.19 318

XXY-MVS		55.19 310	57.40 300	48.56 359	64.45 342	34.84 357	51.54 367	53.59 349	38.99 341	63.79 318	79.43 261	56.59 217	45.57 375	36.92 346	71.29 348	65.25 358

Baseline_NR-MVSNet		70.62 164	73.19 130	62.92 275	76.97 183	34.44 358	68.84 222	70.88 252	60.25 130	79.50 120	90.53 54	61.82 158	69.11 281	54.67 213	95.27 14	85.22 87

KD-MVS_self_test		66.38 222	67.51 207	62.97 273	61.76 354	34.39 359	58.11 331	75.30 206	50.84 239	77.12 150	85.42 175	56.84 216	69.44 278	51.07 240	91.16 96	85.08 92

LCM-MVSNet-Re		69.10 185	71.57 161	61.70 284	70.37 281	34.30 360	61.45 305	79.62 146	56.81 161	89.59 9	88.16 119	68.44 94	72.94 243	42.30 306	87.33 175	77.85 255

sss		47.59 353	48.32 353	45.40 370	56.73 384	33.96 361	45.17 383	48.51 372	32.11 378	52.37 378	65.79 373	40.39 310	41.91 393	31.85 370	61.97 383	60.35 378

gm-plane-assit					62.51 350	33.91 362			37.25 352		62.71 382		72.74 244	38.70 328

UnsupCasMVSNet_eth		52.26 332	53.29 327	49.16 355	55.08 390	33.67 363	50.03 370	58.79 319	37.67 350	63.43 324	74.75 307	41.82 300	45.83 374	38.59 331	59.42 389	67.98 343

FMVSNet5		55.08 312	55.54 313	53.71 330	65.80 331	33.50 364	56.22 341	52.50 357	43.72 304	61.06 334	83.38 202	25.46 388	54.87 352	30.11 377	81.64 258	72.75 300

tpmvs		55.84 304	55.45 314	57.01 316	60.33 362	33.20 365	65.89 265	59.29 318	47.52 273	56.04 363	73.60 319	31.05 365	68.06 291	40.64 318	64.64 375	69.77 329

UnsupCasMVSNet_bld		50.01 346	51.03 343	46.95 362	58.61 374	32.64 366	48.31 373	53.27 354	34.27 366	60.47 338	71.53 334	41.40 301	47.07 372	30.68 374	60.78 386	61.13 377

CL-MVSNet_self_test		62.44 264	63.40 253	59.55 302	72.34 258	32.38 367	56.39 339	64.84 290	51.21 235	67.46 292	81.01 236	50.75 251	63.51 326	38.47 332	88.12 160	82.75 168

pmmvs5		52.49 331	52.58 331	52.21 339	54.99 391	32.38 367	55.45 347	53.84 348	32.15 376	55.49 367	74.81 305	38.08 323	57.37 349	34.02 362	74.40 324	66.88 348

test20.03		55.74 306	57.51 299	50.42 347	59.89 368	32.09 369	50.63 369	49.01 370	50.11 247	65.07 306	83.23 210	45.61 277	48.11 369	30.22 376	83.82 231	71.07 319

WTY-MVS		49.39 348	50.31 350	46.62 365	61.22 357	32.00 370	46.61 380	49.77 367	33.87 368	54.12 374	69.55 353	41.96 299	45.40 377	31.28 373	64.42 376	62.47 372

testing11		53.13 324	52.26 334	55.75 323	70.44 280	31.73 371	54.75 353	52.40 358	44.81 295	52.36 379	68.40 364	21.83 397	65.74 314	32.64 369	72.73 337	69.78 328

Vis-MVSNet (Re-imp)		62.74 261	63.21 256	61.34 289	72.19 259	31.56 372	67.31 249	53.87 347	53.60 209	69.88 262	83.37 203	40.52 309	70.98 268	41.40 313	86.78 189	81.48 193

KD-MVS_2432*1600		52.05 334	51.58 337	53.44 333	52.11 399	31.20 373	44.88 385	64.83 291	41.53 318	64.37 309	70.03 348	15.61 411	64.20 320	36.25 349	74.61 321	64.93 361

miper_refine_blended		52.05 334	51.58 337	53.44 333	52.11 399	31.20 373	44.88 385	64.83 291	41.53 318	64.37 309	70.03 348	15.61 411	64.20 320	36.25 349	74.61 321	64.93 361

ECVR-MVS		64.82 235	65.22 233	63.60 264	78.80 158	31.14 375	66.97 253	56.47 335	54.23 196	69.94 261	88.68 106	37.23 329	74.81 225	45.28 294	89.41 140	84.86 98

MIMVSNet		54.39 315	56.12 309	49.20 354	72.57 256	30.91 376	59.98 317	48.43 373	41.66 317	55.94 364	83.86 196	41.19 304	50.42 359	26.05 390	75.38 315	66.27 352

testing91		55.74 306	55.29 316	57.08 315	70.63 273	30.85 377	54.94 352	56.31 338	50.34 243	57.08 355	70.10 347	24.50 392	65.86 312	36.98 345	76.75 303	74.53 282

baseline1		57.82 298	58.36 294	56.19 320	69.17 295	30.76 378	62.94 300	55.21 340	46.04 281	63.83 317	78.47 275	41.20 303	63.68 324	39.44 322	68.99 362	74.13 286

testing99		55.16 311	54.56 320	56.98 317	70.13 286	30.58 379	54.55 355	54.11 346	49.53 254	56.76 359	70.14 346	22.76 396	65.79 313	36.99 344	76.04 308	74.57 281

VPNet		65.58 228	67.56 206	59.65 301	79.72 140	30.17 380	60.27 316	62.14 305	54.19 199	71.24 246	86.63 147	58.80 192	67.62 294	44.17 298	90.87 111	81.18 195

test1111		64.62 238	65.19 234	62.93 274	79.01 156	29.91 381	65.45 273	54.41 345	54.09 201	71.47 245	88.48 110	37.02 330	74.29 232	46.83 281	89.94 129	84.58 111

testing222		53.37 322	52.50 332	55.98 322	70.51 279	29.68 382	56.20 342	51.85 360	46.19 280	56.76 359	68.94 357	19.18 404	65.39 315	25.87 393	76.98 301	72.87 298

test0.0.03 1		47.72 352	48.31 354	45.93 367	55.53 389	29.39 383	46.40 381	41.21 399	43.41 308	55.81 366	67.65 367	29.22 376	43.77 388	25.73 394	69.87 358	64.62 363

MDTV_nov1_ep13			54.05 323		65.54 333	29.30 384	59.00 322	55.22 339	35.96 358	52.44 377	75.98 296	30.77 367	59.62 339	38.21 333	73.33 334

GG-mvs-BLEND				52.24 338	60.64 361	29.21 385	69.73 212	42.41 391		45.47 396	52.33 399	20.43 400	68.16 289	25.52 395	65.42 374	59.36 381

DSMNet-mixed		43.18 367	44.66 367	38.75 384	54.75 392	28.88 386	57.06 336	27.42 409	13.47 407	47.27 394	77.67 286	38.83 319	39.29 399	25.32 396	60.12 388	48.08 393

WB-MVSnew		53.94 321	54.76 318	51.49 343	71.53 264	28.05 387	58.22 329	50.36 365	37.94 348	59.16 347	70.17 345	49.21 260	51.94 356	24.49 397	71.80 346	74.47 284

gg-mvs-nofinetune		55.75 305	56.75 304	52.72 337	62.87 349	28.04 388	68.92 221	41.36 398	71.09 44	50.80 384	92.63 13	20.74 399	66.86 305	29.97 378	72.41 339	63.25 367

test2506		61.23 273	60.85 274	62.38 279	78.80 158	27.88 389	67.33 248	37.42 403	54.23 196	67.55 291	88.68 106	17.87 407	74.39 230	46.33 284	89.41 140	84.86 98

UWE-MVS		52.94 326	52.70 329	53.65 331	73.56 237	27.49 390	57.30 335	49.57 368	38.56 344	62.79 325	71.42 336	19.49 403	60.41 335	24.33 399	77.33 300	73.06 295

ANet_high		67.08 214	69.94 174	58.51 309	57.55 379	27.09 391	58.43 328	76.80 194	63.56 103	82.40 87	91.93 23	59.82 183	64.98 319	50.10 248	88.86 153	83.46 145

MVS-HIRNet		45.53 357	47.29 357	40.24 382	62.29 351	26.82 392	56.02 344	37.41 404	29.74 385	43.69 403	81.27 231	33.96 340	55.48 350	24.46 398	56.79 394	38.43 403

ETVMVS		50.32 344	49.87 352	51.68 341	70.30 283	26.66 393	52.33 365	43.93 385	43.54 306	54.91 369	67.95 366	20.01 402	60.17 337	22.47 401	73.40 332	68.22 339

tpm		50.60 341	52.42 333	45.14 371	65.18 336	26.29 394	60.30 315	43.50 386	37.41 351	57.01 356	79.09 269	30.20 372	42.32 390	32.77 368	66.36 372	66.81 350

Patchmtry		60.91 275	63.01 258	54.62 328	66.10 330	26.27 395	67.47 243	56.40 336	54.05 202	72.04 234	86.66 144	33.19 344	60.17 337	43.69 299	87.45 171	77.42 256

testing3		58.28 295	58.38 293	58.00 312	77.45 178	26.12 396	60.78 312	43.00 389	56.02 169	70.18 257	75.76 297	13.27 414	67.24 300	48.02 270	80.89 262	80.65 213

testgi		54.00 320	56.86 303	45.45 369	58.20 377	25.81 397	49.05 371	49.50 369	45.43 288	67.84 286	81.17 233	51.81 246	43.20 389	29.30 381	79.41 280	67.34 346

tpmrst		50.15 345	51.38 339	46.45 366	56.05 385	24.77 398	64.40 286	49.98 366	36.14 356	53.32 376	69.59 352	35.16 337	48.69 365	39.24 324	58.51 392	65.89 353

Patchmatch-test		47.93 351	49.96 351	41.84 379	57.42 380	24.26 399	48.75 372	41.49 397	39.30 338	56.79 358	73.48 320	30.48 369	33.87 403	29.29 382	72.61 338	67.39 344

Syy-MVS		54.13 316	55.45 314	50.18 348	68.77 299	23.59 400	55.02 349	44.55 383	43.80 300	58.05 352	64.07 377	46.22 274	58.83 342	46.16 285	72.36 340	68.12 340

dp		44.09 364	44.88 366	41.72 381	58.53 376	23.18 401	54.70 354	42.38 393	34.80 362	44.25 401	65.61 374	24.48 393	44.80 381	29.77 379	49.42 401	57.18 386

WAC-MVS						22.69 402								36.10 353

myMVS_eth3d		50.36 343	50.52 348	49.88 349	68.77 299	22.69 402	55.02 349	44.55 383	43.80 300	58.05 352	64.07 377	14.16 413	58.83 342	33.90 364	72.36 340	68.12 340

EPMVS		45.74 356	46.53 359	43.39 377	54.14 395	22.33 404	55.02 349	35.00 406	34.69 364	51.09 383	70.20 344	25.92 386	42.04 392	37.19 341	55.50 397	65.78 354

ADS-MVSNet		44.62 362	45.58 361	41.73 380	55.90 387	20.83 405	47.34 378	39.94 401	31.41 381	50.48 385	72.06 329	31.23 361	39.31 398	25.93 391	55.93 395	65.07 359

MDTV_nov1_ep13_2view						18.41 406	53.74 358		31.57 380	44.89 398		29.90 374		32.93 367		71.48 312

PatchT		53.35 323	56.47 306	43.99 376	64.19 343	17.46 407	59.15 320	43.10 388	52.11 221	54.74 371	86.95 133	29.97 373	49.98 361	43.62 300	74.40 324	64.53 365

new_pmnet		37.55 372	39.80 374	30.79 387	56.83 382	16.46 408	39.35 395	30.65 407	25.59 396	45.26 397	61.60 385	24.54 391	28.02 407	21.60 402	52.80 400	47.90 394

dmvs_testset		45.26 358	47.51 356	38.49 385	59.96 366	14.71 409	58.50 327	43.39 387	41.30 320	51.79 381	56.48 394	39.44 317	49.91 363	21.42 403	55.35 399	50.85 390

DeepMVS_CX				11.83 392	15.51 414	13.86 410		11.25 417	5.76 408	20.85 410	26.46 407	17.06 409	9.22 411	9.69 410	13.82 410	12.42 407

dongtai		31.66 374	32.98 377	27.71 389	58.58 375	12.61 411	45.02 384	14.24 415	41.90 315	47.93 391	43.91 404	10.65 415	41.81 394	14.06 407	20.53 408	28.72 405

kuosan		22.02 375	23.52 379	17.54 391	41.56 413	11.24 412	41.99 390	13.39 416	26.13 394	28.87 408	30.75 406	9.72 416	21.94 410	4.77 411	14.49 409	19.43 406

WB-MVS		60.04 283	64.19 245	47.59 360	76.09 199	10.22 413	52.44 364	46.74 378	65.17 86	74.07 207	87.48 125	53.48 235	55.28 351	49.36 255	72.84 336	77.28 258

SSC-MVS		61.79 269	66.08 224	48.89 358	76.91 185	10.00 414	53.56 359	47.37 377	68.20 61	76.56 167	89.21 90	54.13 232	57.59 348	54.75 211	74.07 328	79.08 237

new-patchmatchnet		52.89 327	55.76 312	44.26 375	59.94 367	6.31 415	37.36 399	50.76 364	41.10 322	64.28 311	79.82 255	44.77 282	48.43 368	36.24 351	87.61 166	78.03 251

PMMVS2		37.74 371	40.87 371	28.36 388	42.41 411	5.35 416	24.61 403	27.75 408	32.15 376	47.85 392	70.27 343	35.85 335	29.51 406	19.08 406	67.85 368	50.22 392

tmp_tt		11.98 378	14.73 381	3.72 393	2.28 416	4.62 417	19.44 405	14.50 414	0.47 411	21.55 409	9.58 409	25.78 387	4.57 412	11.61 409	27.37 406	1.96 408

test_method		19.26 376	19.12 380	19.71 390	9.09 415	1.91 418	7.79 406	53.44 352	1.42 409	10.27 411	35.80 405	17.42 408	25.11 409	12.44 408	24.38 407	32.10 404

test123		4.43 381	5.78 384	0.39 395	0.97 417	0.28 419	46.33 382	0.45 418	0.31 412	0.62 413	1.50 412	0.61 418	0.11 414	0.56 412	0.63 411	0.77 410

testmvs		4.06 382	5.28 385	0.41 394	0.64 418	0.16 420	42.54 388	0.31 419	0.26 413	0.50 414	1.40 413	0.77 417	0.17 413	0.56 412	0.55 412	0.90 409

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

cdsmvs_eth3d_5k		17.71 377	23.62 378	0.00 396	0.00 419	0.00 421	0.00 407	70.17 257	0.00 414	0.00 415	74.25 314	68.16 97	0.00 415	0.00 414	0.00 413	0.00 411

pcd_1.5k_mvsjas		5.20 380	6.93 383	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	62.39 152	0.00 415	0.00 414	0.00 413	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

ab-mvs-re		5.62 379	7.50 382	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	67.46 368	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 413	0.00 411

PC_three_1452									46.98 276	81.83 92	86.28 157	66.55 118	84.47 72	63.31 140	90.78 112	83.49 141

eth-test2					0.00 419

eth-test					0.00 419

test_241102_TWO								84.80 46	72.61 33	84.93 57	89.70 81	77.73 22	85.89 39	75.29 42	94.22 52	83.25 152

9.14			80.22 54		80.68 132		80.35 76	87.69 11	59.90 132	83.00 78	88.20 116	74.57 47	81.75 114	73.75 55	93.78 57

test_0728_THIRD									74.03 22	85.83 43	90.41 60	75.58 37	85.69 44	77.43 31	94.74 30	84.31 122

GSMVS																70.05 325

sam_mvs1												31.41 359				70.05 325

sam_mvs												31.21 363

MTGPA								80.63 129

test_post1							66.63 258				2.08 410	30.66 368	59.33 340	40.34 320

test_post											1.99 411	30.91 366	54.76 353

patchmatchnet-post											68.99 355	31.32 360	69.38 279

MTMP							84.83 31	19.26 413

test9_res														72.12 69	91.37 91	77.40 257

agg_prior2														70.70 73	90.93 106	78.55 243

test_prior2							75.57 135		58.92 142	76.53 170	86.78 138	67.83 102		69.81 78	92.76 72

旧先验2							71.17 193		45.11 292	78.54 133			61.28 334	59.19 176

新几何2							71.33 189

无先验							74.82 141	70.94 251	47.75 271				76.85 202	54.47 215		72.09 308

原ACMM2							74.78 145

testdata2													67.30 298	48.34 266

segment_acmp												68.30 96

testdata1							68.34 234		57.24 158

plane_prior5								85.49 31					86.15 29	71.09 70	90.94 104	84.82 100

plane_prior4											89.11 95

plane_prior2							82.74 53		65.45 78

plane_prior1					84.46 81

n2								0.00 420

nn								0.00 420

door-mid								55.02 341

test11								82.71 89

door								52.91 356

HQP-NCC					82.37 110		77.32 110		59.08 137	71.58 238

ACMP_Plane					82.37 110		77.32 110		59.08 137	71.58 238

BP-MVS														67.38 102

HQP4-MVS										71.59 237			85.31 51			83.74 136

HQP3-MVS								84.12 67							89.16 144

HQP2-MVS												58.09 200

ACMMP++_ref															89.47 139

ACMMP++															91.96 82

Test By Simon												62.56 148

High-resolution multi-view benchmark