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.

DELS-MVS		82.32 5	82.50 5	81.79 12	86.80 48	56.89 29	92.77 2	86.30 95	77.83 1	77.88 40	92.13 51	60.24 7	94.78 19	78.97 55	89.61 8	93.69 8
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
patch_mono-2		80.84 12	81.59 10	78.62 66	90.34 9	53.77 105	88.08 56	88.36 55	76.17 2	79.40 33	91.09 75	55.43 29	90.09 119	85.01 16	80.40 84	91.99 49

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 16	93.77 1	91.10 12	75.95 3	77.10 44	93.09 33	54.15 40	95.57 12	85.80 13	85.87 38	93.31 11

MM		82.69 2	83.29 3	80.89 22	84.38 87	55.40 59	92.16 10	89.85 23	75.28 4	82.41 11	93.86 11	54.30 37	93.98 23	90.29 1	87.13 21	93.30 12

MVS_0304		82.10 7	82.64 4	80.47 27	86.63 50	54.69 85	92.20 9	86.66 86	74.48 5	82.63 10	93.80 13	50.83 63	93.70 28	90.11 2	86.44 33	93.01 21

CLD-MVS		75.60 82	75.39 75	76.24 134	80.69 200	52.40 144	90.69 23	86.20 97	74.40 6	65.01 171	88.93 131	42.05 178	90.58 105	76.57 75	73.96 167	85.73 236
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
myMVS_eth3d28		77.77 39	77.94 31	77.27 105	87.58 42	52.89 135	86.06 107	91.33 10	74.15 7	68.16 136	88.24 151	58.17 18	88.31 191	69.88 126	77.87 114	90.61 94

EPNet		78.36 30	78.49 25	77.97 84	85.49 66	52.04 154	89.36 39	84.07 165	73.22 8	77.03 45	91.72 65	49.32 77	90.17 118	73.46 106	82.77 60	91.69 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		80.90 11	81.17 12	80.09 37	87.62 41	54.21 97	91.60 14	86.47 91	73.13 9	79.89 29	93.10 31	49.88 73	92.98 34	84.09 23	84.75 50	93.08 19

UBG		78.86 24	78.86 22	78.86 57	87.80 40	55.43 55	87.67 66	91.21 11	72.83 10	72.10 93	88.40 144	58.53 17	89.08 150	73.21 110	77.98 113	92.08 41

testing11		79.18 22	78.85 23	80.16 33	88.33 30	56.99 26	88.31 54	92.06 1	72.82 11	70.62 118	88.37 145	57.69 19	92.30 52	75.25 88	76.24 137	91.20 76

VPNet		72.07 148	71.42 142	74.04 213	78.64 246	47.17 303	89.91 31	87.97 61	72.56 12	64.66 175	85.04 207	41.83 183	88.33 189	61.17 200	60.97 299	86.62 217

testing222		77.70 41	77.22 44	79.14 48	86.95 46	54.89 79	87.18 82	91.96 2	72.29 13	71.17 109	88.70 136	55.19 30	91.24 80	65.18 167	76.32 135	91.29 73

NormalMVS		77.09 50	77.02 47	77.32 102	81.66 167	52.32 147	89.31 40	82.11 201	72.20 14	73.23 75	91.05 76	46.52 105	91.00 91	76.23 76	80.83 77	88.64 158

SymmetryMVS		77.43 45	77.09 46	78.44 73	82.56 140	52.32 147	89.31 40	84.15 163	72.20 14	73.23 75	91.05 76	46.52 105	91.00 91	76.23 76	78.55 107	92.00 48

casdiffmvs		77.36 46	76.85 51	78.88 56	80.40 210	54.66 88	87.06 85	85.88 103	72.11 16	71.57 100	88.63 141	50.89 62	90.35 110	76.00 79	79.11 102	91.63 59
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SSC-MVS3.2		68.13 236	66.89 228	71.85 280	82.26 145	43.97 346	82.09 247	89.29 28	71.74 17	61.12 232	79.83 287	34.60 287	87.45 225	41.23 350	59.85 305	84.14 261

testing99		78.45 26	77.78 35	80.45 28	88.28 33	56.81 32	87.95 61	91.49 6	71.72 18	70.84 112	88.09 154	57.29 21	92.63 45	69.24 131	75.13 155	91.91 50

viewmanbaseed2359cas		76.71 60	76.16 62	78.37 77	81.16 184	55.05 73	86.96 88	85.32 117	71.71 19	72.25 92	88.50 143	46.86 98	88.96 159	74.55 93	78.08 112	91.08 81

casdiffmvs_mvg		77.75 40	77.28 42	79.16 47	80.42 209	54.44 92	87.76 63	85.46 110	71.67 20	71.38 104	88.35 147	51.58 52	91.22 81	79.02 54	79.89 94	91.83 54
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline1		72.51 138	72.12 130	73.69 227	85.05 74	44.46 338	83.51 202	86.13 100	71.61 21	64.64 176	87.97 160	55.00 35	89.48 136	59.07 219	56.05 347	87.13 202

testing91		78.30 32	77.54 38	80.61 23	88.16 35	57.12 25	87.94 62	91.07 15	71.43 22	70.75 113	88.04 159	55.82 28	92.65 43	69.61 127	75.00 159	92.05 44

WTY-MVS		77.47 44	77.52 39	77.30 103	88.33 30	46.25 318	88.46 52	90.32 19	71.40 23	72.32 90	91.72 65	53.44 43	92.37 51	66.28 152	75.42 149	93.28 13

baseline		76.86 56	76.24 61	78.71 62	80.47 205	54.20 99	83.90 191	84.88 140	71.38 24	71.51 102	89.15 129	50.51 65	90.55 106	75.71 81	78.65 105	91.39 68

ETVMVS		75.80 80	75.44 73	76.89 119	86.23 55	50.38 201	85.55 125	91.42 7	71.30 25	68.80 130	87.94 161	56.42 25	89.24 144	56.54 251	74.75 163	91.07 82

gm-plane-assit					83.24 113	54.21 97			70.91 26		88.23 152		95.25 14	66.37 150

PS-MVSNAJ		80.06 17	79.52 18	81.68 14	85.58 64	60.97 3	91.69 12	87.02 78	70.62 27	80.75 23	93.22 30	37.77 225	92.50 47	82.75 31	86.25 35	91.57 62

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 87	85.46 67	49.56 222	90.99 21	86.66 86	70.58 28	80.07 28	95.30 1	56.18 26	90.97 96	82.57 33	86.22 36	93.28 13

diffmvs		75.11 92	74.65 89	76.46 130	78.52 248	53.35 118	83.28 212	79.94 249	70.51 29	71.64 99	88.72 135	46.02 114	86.08 272	77.52 69	75.75 145	89.96 121
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CNVR-MVS		81.76 9	81.90 8	81.33 18	90.04 10	57.70 14	91.71 11	88.87 39	70.31 30	77.64 43	93.87 10	52.58 48	93.91 26	84.17 21	87.92 16	92.39 33

xiu_mvs_v2_base		79.86 18	79.31 19	81.53 15	85.03 76	60.73 4	91.65 13	86.86 81	70.30 31	80.77 22	93.07 35	37.63 231	92.28 54	82.73 32	85.71 39	91.57 62

fmvsm_s_conf0.5_n_9		76.66 61	76.94 50	75.85 149	79.54 221	48.30 268	82.63 231	71.84 369	70.25 32	80.63 25	94.53 2	50.78 64	87.42 227	88.32 5	73.92 169	91.82 55

baseline2		75.15 91	74.54 91	76.98 116	81.67 166	51.74 167	83.84 193	91.94 3	69.97 33	58.98 261	86.02 192	59.73 9	91.73 67	68.37 137	70.40 214	87.48 191

diffmvs_AUTHOR		74.80 98	74.30 94	76.29 132	77.34 271	53.19 124	83.17 217	79.50 261	69.93 34	71.55 101	88.57 142	45.85 117	86.03 274	77.17 72	75.64 146	89.67 126

CHOSEN 1792x2688		76.24 66	74.03 99	82.88 1	83.09 118	62.84 2	85.73 118	85.39 113	69.79 35	64.87 173	83.49 233	41.52 187	93.69 29	70.55 119	81.82 69	92.12 40

fmvsm_s_conf0.5_n_6		76.17 68	76.84 52	74.15 210	77.42 270	46.46 311	85.53 127	77.86 300	69.78 36	79.78 30	92.90 38	46.80 99	84.81 300	84.67 19	76.86 127	91.17 78

balanced_conf03		80.28 16	79.73 15	81.90 11	86.47 52	59.34 6	80.45 289	89.51 26	69.76 37	71.05 110	86.66 182	58.68 16	93.24 31	84.64 20	90.40 6	93.14 18

CANet_DTU		73.71 116	73.14 109	75.40 167	82.61 139	50.05 210	84.67 165	79.36 267	69.72 38	75.39 52	90.03 112	29.41 337	85.93 281	67.99 141	79.11 102	90.22 106

TSAR-MVS + MP.		78.31 31	78.26 26	78.48 70	81.33 182	56.31 42	81.59 266	86.41 92	69.61 39	81.72 17	88.16 153	55.09 33	88.04 201	74.12 98	86.31 34	91.09 80
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
dmvs_re		67.61 245	66.00 250	72.42 258	81.86 158	43.45 352	64.67 398	80.00 246	69.56 40	60.07 242	85.00 208	34.71 285	87.63 218	51.48 292	66.68 243	86.17 227

DPM-MVS		82.39 4	82.36 7	82.49 5	80.12 214	59.50 5	92.24 8	90.72 16	69.37 41	83.22 8	94.47 3	63.81 5	93.18 33	74.02 99	93.25 2	94.80 1

lupinMVS		78.38 29	78.11 29	79.19 45	83.02 122	55.24 63	91.57 15	84.82 141	69.12 42	76.67 46	92.02 56	44.82 139	90.23 116	80.83 47	80.09 88	92.08 41

fmvsm_l_conf0.5_n_9		77.10 49	77.48 40	75.98 146	77.54 267	47.77 290	86.35 100	73.46 360	68.69 43	81.07 21	94.40 4	49.06 78	88.89 164	87.39 8	79.32 100	91.27 74

PAPM		76.76 58	76.07 64	78.81 58	80.20 212	59.11 7	86.86 92	86.23 96	68.60 44	70.18 122	88.84 134	51.57 53	87.16 235	65.48 160	86.68 30	90.15 111

DeepC-MVS_fast	67.50 3	78.00 36	77.63 36	79.13 49	88.52 27	55.12 69	89.95 28	85.98 102	68.31 45	71.33 105	92.75 40	45.52 124	90.37 109	71.15 117	85.14 46	91.91 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
jason		77.01 52	76.45 57	78.69 63	79.69 219	54.74 81	90.56 24	83.99 168	68.26 46	74.10 64	90.91 86	42.14 176	89.99 121	79.30 52	79.12 101	91.36 70
jason: jason.
ETV-MVS		77.17 48	76.74 53	78.48 70	81.80 159	54.55 90	86.13 105	85.33 116	68.20 47	73.10 77	90.52 95	45.23 129	90.66 102	79.37 51	80.95 74	90.22 106

fmvsm_s_conf0.5_n_8		76.50 63	76.68 55	75.94 147	78.67 242	47.92 284	85.18 139	74.71 341	68.09 48	80.67 24	94.26 5	47.09 95	89.26 143	86.62 10	74.85 161	90.65 92

h-mvs33		73.95 109	72.89 113	77.15 109	80.17 213	50.37 202	84.68 163	83.33 178	68.08 49	71.97 95	88.65 140	42.50 170	91.15 84	78.82 56	57.78 333	89.91 123

hse-mvs2		71.44 164	70.68 152	73.73 226	76.34 289	47.44 298	79.45 309	79.47 263	68.08 49	71.97 95	86.01 194	42.50 170	86.93 243	78.82 56	53.46 371	86.83 213

MVS_Test		75.85 76	74.93 84	78.62 66	84.08 93	55.20 67	83.99 187	85.17 126	68.07 51	73.38 72	82.76 244	50.44 66	89.00 155	65.90 156	80.61 80	91.64 58

ET-MVSNet_ETH3D		75.23 89	74.08 97	78.67 64	84.52 84	55.59 51	88.92 46	89.21 31	68.06 52	53.13 341	90.22 105	49.71 74	87.62 220	72.12 113	70.82 205	92.82 25

reproduce_monomvs		69.71 200	68.52 193	73.29 238	86.43 53	48.21 271	83.91 190	86.17 99	68.02 53	54.91 322	77.46 313	42.96 167	88.86 165	68.44 136	48.38 384	82.80 295

tpmrst		71.04 173	69.77 174	74.86 189	83.19 115	55.86 50	75.64 332	78.73 283	67.88 54	64.99 172	73.73 356	49.96 72	79.56 359	65.92 155	67.85 237	89.14 145

dcpmvs_2		79.33 21	78.94 21	80.49 25	89.75 12	56.54 36	84.83 158	83.68 172	67.85 55	69.36 124	90.24 103	60.20 8	92.10 60	84.14 22	80.40 84	92.82 25

PVSNet_Blended		76.53 62	76.54 56	76.50 129	85.91 57	51.83 162	88.89 47	84.24 160	67.82 56	69.09 128	89.33 126	46.70 102	88.13 197	75.43 84	81.48 73	89.55 130

tpm		68.36 229	67.48 219	70.97 294	79.93 217	51.34 177	76.58 329	78.75 282	67.73 57	63.54 205	74.86 346	48.33 80	72.36 408	53.93 274	63.71 273	89.21 142

NCCC		79.57 20	79.23 20	80.59 24	89.50 15	56.99 26	91.38 16	88.17 57	67.71 58	73.81 67	92.75 40	46.88 97	93.28 30	78.79 58	84.07 55	91.50 66

sasdasda		78.17 33	77.86 33	79.12 50	84.30 88	54.22 95	87.71 64	84.57 151	67.70 59	77.70 41	92.11 54	50.90 59	89.95 123	78.18 65	77.54 118	93.20 15

canonicalmvs		78.17 33	77.86 33	79.12 50	84.30 88	54.22 95	87.71 64	84.57 151	67.70 59	77.70 41	92.11 54	50.90 59	89.95 123	78.18 65	77.54 118	93.20 15

3Dnovator	64.70 6	74.46 100	72.48 118	80.41 29	82.84 132	55.40 59	83.08 220	88.61 50	67.61 61	59.85 244	88.66 137	34.57 288	93.97 24	58.42 228	88.70 12	91.85 53

VNet		77.99 37	77.92 32	78.19 80	87.43 43	50.12 209	90.93 22	91.41 8	67.48 62	75.12 53	90.15 109	46.77 101	91.00 91	73.52 104	78.46 108	93.44 9

WBMVS		73.93 110	73.39 103	75.55 161	87.82 39	55.21 65	89.37 37	87.29 74	67.27 63	63.70 198	80.30 281	60.32 6	86.47 256	61.58 196	62.85 289	84.97 250

dmvs_testset		57.65 348	58.21 329	55.97 403	74.62 321	9.82 464	63.75 401	63.34 411	67.23 64	48.89 367	83.68 232	39.12 212	76.14 388	23.43 426	59.80 306	81.96 303

fmvsm_l_conf0.5_n_3		75.73 81	75.78 66	75.61 157	76.03 299	48.33 266	85.34 129	72.92 363	67.16 65	78.55 37	93.85 12	46.22 108	87.53 223	85.61 14	76.30 136	90.98 85

IB-MVS	68.87 2	74.01 108	72.03 134	79.94 38	83.04 121	55.50 53	90.24 25	88.65 46	67.14 66	61.38 229	81.74 269	53.21 44	94.28 21	60.45 210	62.41 292	90.03 119
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
fmvsm_s_conf0.5_n_7		73.10 127	73.89 101	70.72 297	74.17 329	46.03 321	83.28 212	74.19 345	67.10 67	73.94 66	91.73 64	43.42 160	77.61 377	83.92 25	73.26 174	88.53 165

fmvsm_s_conf0.5_n_5		75.02 93	75.07 80	74.88 188	74.33 327	47.83 287	83.99 187	73.54 355	67.10 67	76.32 49	92.43 47	45.42 126	86.35 262	82.98 29	79.50 99	90.47 99

fmvsm_s_conf0.5_n_4		74.92 96	74.88 85	75.03 183	75.96 302	47.53 293	85.84 111	73.19 362	67.07 69	79.43 32	92.60 44	46.12 110	88.03 202	84.70 18	69.01 223	89.53 132

MVSTER		73.25 125	72.33 122	76.01 144	85.54 65	53.76 106	83.52 198	87.16 76	67.06 70	63.88 193	81.66 270	52.77 46	90.44 107	64.66 172	64.69 265	83.84 274

test_fmvsmconf_n		74.41 101	74.05 98	75.49 165	74.16 330	48.38 262	82.66 229	72.57 364	67.05 71	75.11 54	92.88 39	46.35 107	87.81 207	83.93 24	71.71 194	90.28 104

DeepC-MVS	67.15 4	76.90 55	76.27 60	78.80 59	80.70 199	55.02 74	86.39 98	86.71 84	66.96 72	67.91 138	89.97 113	48.03 83	91.41 74	75.60 83	84.14 54	89.96 121
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		70.00 194	70.24 167	69.30 317	77.93 260	38.55 391	83.99 187	87.72 68	66.86 73	57.66 289	84.17 220	52.28 49	85.31 289	52.72 287	68.80 228	84.02 265

test_fmvsmconf0.1_n		73.69 117	73.15 107	75.34 169	70.71 370	48.26 269	82.15 244	71.83 370	66.75 74	74.47 62	92.59 45	44.89 136	87.78 212	83.59 26	71.35 200	89.97 120

SDMVSNet		71.89 153	70.62 154	75.70 155	81.70 163	51.61 169	73.89 347	88.72 45	66.58 75	61.64 227	82.38 257	37.63 231	89.48 136	77.44 70	65.60 258	86.01 228

sd_testset		67.79 242	65.95 252	73.32 235	81.70 163	46.33 316	68.99 383	80.30 240	66.58 75	61.64 227	82.38 257	30.45 332	87.63 218	55.86 258	65.60 258	86.01 228

PC_three_1452									66.58 75	87.27 2	93.70 15	66.82 4	94.95 17	89.74 4	91.98 4	93.98 5

test_fmvsm_n_1920		75.56 83	75.54 71	75.61 157	74.60 322	49.51 227	81.82 255	74.08 347	66.52 78	80.40 26	93.46 22	46.95 96	89.72 131	86.69 9	75.30 150	87.61 189

SD_0403		65.51 288	65.18 271	66.48 350	78.37 252	29.94 427	74.64 343	78.55 288	66.47 79	54.87 323	84.35 218	38.20 221	82.47 325	38.90 357	72.30 189	87.05 203

PVSNet	62.49 8	69.27 211	67.81 211	73.64 228	84.41 86	51.85 161	84.63 166	77.80 301	66.42 80	59.80 245	84.95 209	22.14 390	80.44 347	55.03 266	75.11 156	88.62 161

CS-MVS		76.77 57	76.70 54	76.99 115	83.55 103	48.75 249	88.60 50	85.18 125	66.38 81	72.47 88	91.62 69	45.53 123	90.99 95	74.48 94	82.51 62	91.23 75

UniMVSNet_NR-MVSNet		68.82 219	68.29 198	70.40 303	75.71 306	42.59 364	84.23 178	86.78 82	66.31 82	58.51 273	82.45 254	51.57 53	84.64 303	53.11 278	55.96 348	83.96 271

HY-MVS	67.03 5	73.90 111	73.14 109	76.18 139	84.70 80	47.36 299	75.56 333	86.36 94	66.27 83	70.66 116	83.91 224	51.05 57	89.31 141	67.10 146	72.61 184	91.88 52

IU-MVS					89.48 17	57.49 17		91.38 9	66.22 84	88.26 1				82.83 30	87.60 18	92.44 32

fmvsm_s_conf0.5_n_3		74.97 95	75.42 74	73.62 230	76.99 280	46.67 307	83.13 218	71.14 378	66.20 85	82.13 13	93.76 14	47.49 89	84.00 309	81.95 37	76.02 138	90.19 110

testing3-2		72.30 143	72.35 121	72.15 265	83.07 119	47.64 291	85.46 128	89.81 24	66.17 86	61.96 224	84.88 211	58.93 12	82.27 326	55.87 257	64.97 261	86.54 218

EI-MVSNet-Vis-set		73.19 126	72.60 116	74.99 186	82.56 140	49.80 217	82.55 235	89.00 34	66.17 86	65.89 158	88.98 130	43.83 148	92.29 53	65.38 166	69.01 223	82.87 294

alignmvs		78.08 35	77.98 30	78.39 75	83.53 104	53.22 123	89.77 32	85.45 111	66.11 88	76.59 48	91.99 58	54.07 41	89.05 152	77.34 71	77.00 123	92.89 23

TESTMET0.1,1		72.86 131	72.33 122	74.46 197	81.98 153	50.77 187	85.13 141	85.47 109	66.09 89	67.30 141	83.69 230	37.27 241	83.57 316	65.06 169	78.97 104	89.05 147

MSP-MVS		82.30 6	83.47 1	78.80 59	82.99 124	52.71 138	85.04 147	88.63 48	66.08 90	86.77 3	92.75 40	72.05 1	91.46 73	83.35 27	93.53 1	92.23 37
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
CostFormer		73.89 112	72.30 124	78.66 65	82.36 144	56.58 33	75.56 333	85.30 119	66.06 91	70.50 120	76.88 326	57.02 22	89.06 151	68.27 139	68.74 229	90.33 102

NR-MVSNet		67.25 257	65.99 251	71.04 293	73.27 339	43.91 347	85.32 133	84.75 145	66.05 92	53.65 339	82.11 264	45.05 131	85.97 279	47.55 317	56.18 345	83.24 284

HPM-MVS++		80.50 14	80.71 14	79.88 39	87.34 44	55.20 67	89.93 29	87.55 72	66.04 93	79.46 31	93.00 37	53.10 45	91.76 65	80.40 48	89.56 9	92.68 29

SPE-MVS-test		77.20 47	77.25 43	77.05 110	84.60 82	49.04 239	89.42 36	85.83 105	65.90 94	72.85 81	91.98 60	45.10 130	91.27 78	75.02 90	84.56 51	90.84 88

test_fmvsmconf0.01_n		71.97 151	70.95 150	75.04 182	66.21 399	47.87 285	80.35 292	70.08 386	65.85 95	72.69 83	91.68 67	39.99 205	87.67 216	82.03 36	69.66 219	89.58 129

MGCFI-Net		74.07 107	74.64 90	72.34 261	82.90 128	43.33 356	80.04 298	79.96 248	65.61 96	74.93 55	91.85 61	48.01 84	80.86 339	71.41 115	77.10 121	92.84 24

UWE-MVS		72.17 147	72.15 128	72.21 263	82.26 145	44.29 342	86.83 93	89.58 25	65.58 97	65.82 159	85.06 204	45.02 132	84.35 305	54.07 272	75.18 152	87.99 180

viewmambaseed2359dif		73.51 121	72.78 114	75.71 154	76.93 282	51.89 160	82.81 226	79.66 256	65.46 98	70.29 121	88.05 157	45.55 122	85.85 282	73.49 105	72.76 182	89.39 136

HQP-NCC					79.02 234		88.00 57		65.45 99	64.48 181

ACMP_Plane					79.02 234		88.00 57		65.45 99	64.48 181

HQP-MVS		72.34 141	71.44 141	75.03 183	79.02 234	51.56 171	88.00 57	83.68 172	65.45 99	64.48 181	85.13 202	37.35 238	88.62 172	66.70 147	73.12 176	84.91 252

PVSNet_BlendedMVS		73.42 122	73.30 105	73.76 224	85.91 57	51.83 162	86.18 104	84.24 160	65.40 102	69.09 128	80.86 277	46.70 102	88.13 197	75.43 84	65.92 257	81.33 318

MS-PatchMatch		72.34 141	71.26 144	75.61 157	82.38 143	55.55 52	88.00 57	89.95 22	65.38 103	56.51 311	80.74 279	32.28 313	92.89 35	57.95 237	88.10 15	78.39 353

v2v482		69.55 207	67.64 213	75.26 178	72.32 352	53.83 103	84.93 154	81.94 206	65.37 104	60.80 235	79.25 294	41.62 184	88.98 158	63.03 185	59.51 308	82.98 292

VDD-MVS		76.08 71	74.97 83	79.44 41	84.27 91	53.33 120	91.13 20	85.88 103	65.33 105	72.37 89	89.34 124	32.52 310	92.76 41	77.90 68	75.96 141	92.22 39

TranMVSNet+NR-MVSNet		66.94 267	65.61 261	70.93 295	73.45 335	43.38 354	83.02 223	84.25 158	65.31 106	58.33 280	81.90 268	39.92 207	85.52 285	49.43 304	54.89 357	83.89 273

EI-MVSNet-UG-set		72.37 140	71.73 135	74.29 206	81.60 171	49.29 234	81.85 253	88.64 47	65.29 107	65.05 169	88.29 150	43.18 162	91.83 64	63.74 180	67.97 235	81.75 306

MVS_111021_HR		76.39 65	75.38 76	79.42 42	85.33 70	56.47 38	88.15 55	84.97 137	65.15 108	66.06 155	89.88 114	43.79 150	92.16 57	75.03 89	80.03 91	89.64 128

miper_enhance_ethall		69.77 199	68.90 189	72.38 259	78.93 237	49.91 213	83.29 211	78.85 277	64.90 109	59.37 254	79.46 291	52.77 46	85.16 294	63.78 179	58.72 315	82.08 301

MG-MVS		78.42 28	76.99 49	82.73 2	93.17 1	64.46 1	89.93 29	88.51 53	64.83 110	73.52 70	88.09 154	48.07 82	92.19 56	62.24 190	84.53 52	91.53 64

EIA-MVS		75.92 74	75.18 79	78.13 81	85.14 73	51.60 170	87.17 83	85.32 117	64.69 111	68.56 132	90.53 94	45.79 118	91.58 70	67.21 145	82.18 66	91.20 76

plane_prior						49.57 219	87.43 72		64.57 112						72.84 180

BP-MVS1		76.09 70	75.55 70	77.71 91	79.49 222	52.27 151	84.70 161	90.49 18	64.44 113	69.86 123	90.31 102	55.05 34	91.35 75	70.07 124	75.58 148	89.53 132

FC-MVSNet-test		67.49 249	67.91 204	66.21 351	76.06 297	33.06 413	80.82 283	87.18 75	64.44 113	54.81 324	82.87 241	50.40 67	82.60 324	48.05 315	66.55 247	82.98 292

MonoMVSNet		66.80 270	64.41 279	73.96 216	76.21 294	48.07 277	76.56 330	78.26 294	64.34 115	54.32 331	74.02 353	37.21 244	86.36 261	64.85 170	53.96 364	87.45 193

WR-MVS		67.58 246	66.76 233	70.04 310	75.92 304	45.06 336	86.23 103	85.28 121	64.31 116	58.50 275	81.00 274	44.80 141	82.00 331	49.21 307	55.57 353	83.06 289

fmvsm_s_conf0.5_n_2		72.02 149	71.72 136	72.92 244	76.79 284	45.90 322	84.48 170	66.11 402	64.26 117	76.12 50	93.40 23	36.26 263	86.04 273	81.47 42	66.54 248	86.82 214

v1144		68.81 220	66.82 231	74.80 191	72.34 351	53.46 111	84.68 163	81.77 213	64.25 118	60.28 240	77.91 306	40.23 200	88.95 160	60.37 211	59.52 307	81.97 302

UWE-MVS-28		67.43 251	67.98 203	65.75 353	75.66 307	34.74 403	80.00 301	88.17 57	64.21 119	57.27 299	84.14 221	45.68 121	78.82 362	44.33 337	72.40 186	83.70 276

test1111		71.06 172	70.42 160	72.97 243	79.48 223	41.49 376	84.82 159	82.74 192	64.20 120	62.98 209	87.43 170	35.20 277	87.92 204	58.54 225	78.42 109	89.49 134

fmvsm_s_conf0.5_n		74.48 99	74.12 96	75.56 160	76.96 281	47.85 286	85.32 133	69.80 389	64.16 121	78.74 34	93.48 21	45.51 125	89.29 142	86.48 11	66.62 245	89.55 130

testdata1							77.55 323		64.14 122

fmvsm_s_conf0.1_n_2		71.45 163	71.01 148	72.78 248	75.37 311	45.82 326	84.18 180	64.59 407	64.02 123	75.67 51	93.02 36	34.99 282	85.99 276	81.18 46	66.04 256	86.52 220

test2506		72.91 130	72.43 120	74.32 205	80.12 214	44.18 345	83.19 215	84.77 144	64.02 123	65.97 156	87.43 170	47.67 88	88.72 169	59.08 218	79.66 96	90.08 117

ECVR-MVS		71.81 155	71.00 149	74.26 207	80.12 214	43.49 351	84.69 162	82.16 198	64.02 123	64.64 176	87.43 170	35.04 280	89.21 147	61.24 199	79.66 96	90.08 117

plane_prior3						48.95 241			64.01 126	62.15 220

VPA-MVSNet		71.12 169	70.66 153	72.49 256	78.75 240	44.43 340	87.64 67	90.02 20	63.97 127	65.02 170	81.58 272	42.14 176	87.42 227	63.42 182	63.38 280	85.63 240

PVSNet_0	57.04 13	61.19 321	57.24 334	73.02 241	77.45 269	50.31 206	79.43 310	77.36 311	63.96 128	47.51 378	72.45 372	25.03 368	83.78 313	52.76 286	19.22 450	84.96 251

V42		67.66 244	65.60 262	73.86 220	70.69 372	53.63 108	81.50 269	78.61 286	63.85 129	59.49 253	77.49 312	37.98 222	87.65 217	62.33 188	58.43 318	80.29 333

AstraMVS		70.12 188	68.56 191	74.81 190	76.48 287	47.48 295	84.35 174	82.58 195	63.80 130	62.09 222	84.54 212	31.39 326	89.96 122	68.24 140	63.58 275	87.00 204

mvs_anonymous		72.29 144	70.74 151	76.94 118	82.85 131	54.72 83	78.43 317	81.54 216	63.77 131	61.69 226	79.32 293	51.11 56	85.31 289	62.15 192	75.79 143	90.79 90

PAPR		75.20 90	74.13 95	78.41 74	88.31 32	55.10 71	84.31 176	85.66 107	63.76 132	67.55 140	90.73 91	43.48 158	89.40 138	66.36 151	77.03 122	90.73 91

PVSNet_Blended_VisFu		73.40 123	72.44 119	76.30 131	81.32 183	54.70 84	85.81 112	78.82 279	63.70 133	64.53 180	85.38 200	47.11 94	87.38 230	67.75 142	77.55 117	86.81 215

v148		68.24 234	66.35 241	73.88 219	71.76 357	51.47 174	84.23 178	81.90 210	63.69 134	58.94 262	76.44 331	43.72 153	87.78 212	60.63 204	55.86 350	82.39 299

UniMVSNet (Re)		67.71 243	66.80 232	70.45 301	74.44 323	42.93 360	82.42 241	84.90 139	63.69 134	59.63 248	80.99 275	47.18 92	85.23 292	51.17 295	56.75 339	83.19 286

HQP_MVS		70.96 175	69.91 173	74.12 211	77.95 258	49.57 219	85.76 114	82.59 193	63.60 136	62.15 220	83.28 238	36.04 268	88.30 192	65.46 161	72.34 187	84.49 256

plane_prior2							85.76 114		63.60 136

DU-MVS		66.84 269	65.74 258	70.16 306	73.27 339	42.59 364	81.50 269	82.92 190	63.53 138	58.51 273	82.11 264	40.75 193	84.64 303	53.11 278	55.96 348	83.24 284

fmvsm_l_conf0.5_n		75.95 73	76.16 62	75.31 171	76.01 301	48.44 261	84.98 150	71.08 379	63.50 139	81.70 18	93.52 20	50.00 69	87.18 234	87.80 6	76.87 126	90.32 103

EC-MVSNet		75.30 85	75.20 77	75.62 156	80.98 188	49.00 240	87.43 72	84.68 148	63.49 140	70.97 111	90.15 109	42.86 169	91.14 85	74.33 96	81.90 68	86.71 216

fmvsm_s_conf0.5_n_a		73.68 118	73.15 107	75.29 174	75.45 310	48.05 278	83.88 192	68.84 394	63.43 141	78.60 35	93.37 26	45.32 127	88.92 163	85.39 15	64.04 269	88.89 150

fmvsm_s_conf0.1_n		73.80 113	73.26 106	75.43 166	73.28 338	47.80 288	84.57 169	69.43 391	63.34 142	78.40 38	93.29 28	44.73 142	89.22 146	85.99 12	66.28 254	89.26 139

GA-MVS		69.04 214	66.70 235	76.06 142	75.11 313	52.36 145	83.12 219	80.23 241	63.32 143	60.65 237	79.22 295	30.98 329	88.37 185	61.25 198	66.41 249	87.46 192

CDS-MVSNet		70.48 184	69.43 178	73.64 228	77.56 266	48.83 246	83.51 202	77.45 308	63.27 144	62.33 216	85.54 199	43.85 147	83.29 321	57.38 247	74.00 166	88.79 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LFMVS		78.52 25	77.14 45	82.67 3	89.58 13	58.90 8	91.27 19	88.05 60	63.22 145	74.63 58	90.83 89	41.38 188	94.40 20	75.42 86	79.90 93	94.72 2

v1192		67.96 238	65.74 258	74.63 194	71.79 356	53.43 116	84.06 185	80.99 229	63.19 146	59.56 250	77.46 313	37.50 237	88.65 171	58.20 232	58.93 314	81.79 305

fmvsm_l_conf0.5_n_a		75.88 75	76.07 64	75.31 171	76.08 296	48.34 264	85.24 135	70.62 382	63.13 147	81.45 19	93.62 19	49.98 71	87.40 229	87.76 7	76.77 128	90.20 108

Fast-Effi-MVS+		72.73 133	71.15 147	77.48 97	82.75 134	54.76 80	86.77 94	80.64 233	63.05 148	65.93 157	84.01 222	44.42 144	89.03 153	56.45 255	76.36 134	88.64 158

MAR-MVS		76.76 58	75.60 69	80.21 31	90.87 7	54.68 86	89.14 44	89.11 32	62.95 149	70.54 119	92.33 49	41.05 189	94.95 17	57.90 239	86.55 32	91.00 84
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
SteuartSystems-ACMMP		77.08 51	76.33 59	79.34 43	80.98 188	55.31 61	89.76 33	86.91 80	62.94 150	71.65 98	91.56 71	42.33 172	92.56 46	77.14 73	83.69 57	90.15 111
Skip Steuart: Steuart Systems R&D Blog.
icg_test_0407_2		71.26 166	69.99 171	75.09 181	82.26 145	50.87 183	79.65 305	85.16 128	62.91 151	63.68 199	86.07 188	35.56 272	84.32 306	64.03 175	70.55 209	90.09 113

IMVS_0407		71.97 151	70.10 169	77.57 94	82.26 145	50.87 183	80.69 287	85.16 128	62.91 151	63.68 199	86.07 188	35.56 272	91.75 66	64.03 175	70.55 209	90.09 113

IMVS_0404		69.11 212	67.25 225	74.68 193	82.26 145	50.87 183	76.74 327	85.16 128	62.91 151	50.76 359	86.07 188	26.76 354	83.06 323	64.03 175	70.55 209	90.09 113

IMVS_0403		72.39 139	70.59 155	77.79 88	82.26 145	50.87 183	81.76 256	85.16 128	62.91 151	64.87 173	86.07 188	37.71 230	92.40 50	64.03 175	70.55 209	90.09 113

v144192		67.86 239	65.76 257	74.16 209	71.68 358	53.09 128	84.14 182	80.83 231	62.85 155	59.21 259	77.28 317	39.30 210	88.00 203	58.67 224	57.88 331	81.40 315

test_fmvsmvis_n_1920		71.29 165	70.38 161	74.00 215	71.04 368	48.79 248	79.19 312	64.62 406	62.75 156	66.73 144	91.99 58	40.94 191	88.35 187	83.00 28	73.18 175	84.85 254

nrg030		72.27 146	71.56 138	74.42 199	75.93 303	50.60 191	86.97 87	83.21 183	62.75 156	67.15 143	84.38 216	50.07 68	86.66 250	71.19 116	62.37 293	85.99 230

guyue		70.53 182	69.12 186	74.76 192	77.61 263	47.53 293	84.86 157	85.17 126	62.70 158	62.18 218	83.74 227	34.72 284	89.86 125	64.69 171	66.38 250	86.87 207

miper_ehance_all_eth		68.70 225	67.58 214	72.08 267	76.91 283	49.48 228	82.47 239	78.45 291	62.68 159	58.28 281	77.88 307	50.90 59	85.01 297	61.91 193	58.72 315	81.75 306

XXY-MVS		70.18 186	69.28 184	72.89 247	77.64 262	42.88 361	85.06 145	87.50 73	62.58 160	62.66 214	82.34 261	43.64 155	89.83 127	58.42 228	63.70 274	85.96 232

thisisatest0515		73.64 119	72.20 126	77.97 84	81.63 169	53.01 131	86.69 95	88.81 42	62.53 161	64.06 188	85.65 196	52.15 51	92.50 47	58.43 226	69.84 217	88.39 170

fmvsm_s_conf0.1_n_a		72.82 132	72.05 132	75.12 180	70.95 369	47.97 281	82.72 228	68.43 396	62.52 162	78.17 39	93.08 34	44.21 145	88.86 165	84.82 17	63.54 276	88.54 164

cl22		68.85 217	67.69 212	72.35 260	78.07 256	49.98 212	82.45 240	78.48 290	62.50 163	58.46 277	77.95 305	49.99 70	85.17 293	62.55 187	58.72 315	81.90 304

v1921920		67.45 250	65.23 270	74.10 212	71.51 361	52.90 134	83.75 196	80.44 237	62.48 164	59.12 260	77.13 318	36.98 250	87.90 205	57.53 244	58.14 325	81.49 310

GDP-MVS		75.27 87	74.38 92	77.95 86	79.04 233	52.86 136	85.22 136	86.19 98	62.43 165	70.66 116	90.40 100	53.51 42	91.60 69	69.25 130	72.68 183	89.39 136

thres200		68.71 223	67.27 224	73.02 241	84.73 79	46.76 306	85.03 148	87.73 67	62.34 166	59.87 243	83.45 234	43.15 163	88.32 190	31.25 398	67.91 236	83.98 269

Effi-MVS+-dtu		66.24 280	64.96 275	70.08 308	75.17 312	49.64 218	82.01 248	74.48 343	62.15 167	57.83 284	76.08 339	30.59 331	83.79 312	65.40 165	60.93 300	76.81 369

TAMVS		69.51 208	68.16 201	73.56 232	76.30 292	48.71 252	82.57 233	77.17 313	62.10 168	61.32 230	84.23 219	41.90 181	83.46 318	54.80 269	73.09 178	88.50 167

VortexMVS		68.49 227	66.84 230	73.46 234	81.10 187	48.75 249	84.63 166	84.73 146	62.05 169	57.22 301	77.08 321	34.54 290	89.20 148	63.08 183	57.12 337	82.43 298

eth_miper_zixun_eth		66.98 266	65.28 269	72.06 268	75.61 308	50.40 198	81.00 278	76.97 319	62.00 170	56.99 303	76.97 322	44.84 138	85.58 284	58.75 223	54.42 361	80.21 334

c3_l		67.97 237	66.66 236	71.91 278	76.20 295	49.31 233	82.13 246	78.00 298	61.99 171	57.64 290	76.94 323	49.41 75	84.93 298	60.62 205	57.01 338	81.49 310

v1240		66.99 265	64.68 276	73.93 217	71.38 365	52.66 139	83.39 209	79.98 247	61.97 172	58.44 279	77.11 319	35.25 276	87.81 207	56.46 254	58.15 323	81.33 318

OPM-MVS		70.75 179	69.58 177	74.26 207	75.55 309	51.34 177	86.05 108	83.29 182	61.94 173	62.95 210	85.77 195	34.15 293	88.44 183	65.44 164	71.07 202	82.99 290
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_prior2							89.04 45		61.88 174	73.55 69	91.46 74	48.01 84		74.73 91	85.46 42

EPNet_dtu		66.25 279	66.71 234	64.87 362	78.66 245	34.12 408	82.80 227	75.51 333	61.75 175	64.47 184	86.90 177	37.06 248	72.46 407	43.65 342	69.63 221	88.02 179
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		68.45 228	65.44 266	77.47 98	84.91 77	56.17 43	71.89 371	81.91 209	61.72 176	60.85 234	72.49 370	36.21 264	87.06 238	47.32 319	71.62 195	89.17 144

RRT-MVS		73.29 124	71.37 143	79.07 52	84.63 81	54.16 100	78.16 318	86.64 88	61.67 177	60.17 241	82.35 260	40.63 197	92.26 55	70.19 123	77.87 114	90.81 89

PMMVS		72.98 128	72.05 132	75.78 151	83.57 102	48.60 253	84.08 183	82.85 191	61.62 178	68.24 135	90.33 101	28.35 341	87.78 212	72.71 111	76.69 129	90.95 86

save fliter					85.35 69	56.34 41	89.31 40	81.46 217	61.55 179

UA-Net		67.32 256	66.23 245	70.59 299	78.85 238	41.23 379	73.60 349	75.45 335	61.54 180	66.61 148	84.53 215	38.73 216	86.57 255	42.48 349	74.24 165	83.98 269

v8		67.25 257	64.99 274	74.04 213	72.89 345	53.31 121	82.37 242	80.11 245	61.54 180	54.29 332	76.02 340	42.89 168	88.41 184	58.43 226	56.36 340	80.39 332

SMA-MVS		79.10 23	78.76 24	80.12 35	84.42 85	55.87 49	87.58 71	86.76 83	61.48 182	80.26 27	93.10 31	46.53 104	92.41 49	79.97 49	88.77 11	92.08 41
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
WB-MVSnew		69.36 210	68.24 199	72.72 250	79.26 228	49.40 231	85.72 119	88.85 40	61.33 183	64.59 179	82.38 257	34.57 288	87.53 223	46.82 324	70.63 206	81.22 322

DIV-MVS_self_test		67.43 251	65.93 253	71.94 276	76.33 290	48.01 280	82.57 233	79.11 273	61.31 184	56.73 305	76.92 324	46.09 112	86.43 259	57.98 235	56.31 342	81.39 316

cl____		67.43 251	65.93 253	71.95 275	76.33 290	48.02 279	82.58 232	79.12 272	61.30 185	56.72 306	76.92 324	46.12 110	86.44 258	57.98 235	56.31 342	81.38 317

MP-MVS-pluss		75.54 84	75.03 81	77.04 111	81.37 181	52.65 140	84.34 175	84.46 153	61.16 186	69.14 127	91.76 63	39.98 206	88.99 157	78.19 63	84.89 49	89.48 135
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvsmamba		69.38 209	67.52 218	74.95 187	82.86 130	52.22 152	67.36 390	76.75 320	61.14 187	49.43 363	82.04 266	37.26 242	84.14 307	73.93 100	76.91 124	88.50 167

v10		66.61 272	64.20 283	73.83 222	72.59 348	53.37 117	81.88 252	79.91 251	61.11 188	54.09 334	75.60 342	40.06 204	88.26 195	56.47 253	56.10 346	79.86 338

ACMMP_NAP		76.43 64	75.66 68	78.73 61	81.92 156	54.67 87	84.06 185	85.35 115	61.10 189	72.99 78	91.50 72	40.25 199	91.00 91	76.84 74	86.98 25	90.51 98

EI-MVSNet		69.70 204	68.70 190	72.68 251	75.00 316	48.90 244	79.54 306	87.16 76	61.05 190	63.88 193	83.74 227	45.87 115	90.44 107	57.42 246	64.68 266	78.70 346

IterMVS-LS		66.63 271	65.36 268	70.42 302	75.10 314	48.90 244	81.45 272	76.69 324	61.05 190	55.71 316	77.10 320	45.86 116	83.65 315	57.44 245	57.88 331	78.70 346
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CL-MVSNet_self_test		62.98 306	61.14 307	68.50 331	65.86 402	42.96 359	84.37 172	82.98 188	60.98 192	53.95 335	72.70 369	40.43 198	83.71 314	41.10 351	47.93 387	78.83 345

AUN-MVS		68.20 235	66.35 241	73.76 224	76.37 288	47.45 297	79.52 308	79.52 260	60.98 192	62.34 215	86.02 192	36.59 260	86.94 242	62.32 189	53.47 370	86.89 206

Syy-MVS		61.51 319	61.35 304	62.00 380	81.73 161	30.09 424	80.97 279	81.02 225	60.93 194	55.06 320	82.64 249	35.09 279	80.81 340	16.40 444	58.32 319	75.10 387

myMVS_eth3d		63.52 300	63.56 287	63.40 372	81.73 161	34.28 405	80.97 279	81.02 225	60.93 194	55.06 320	82.64 249	48.00 86	80.81 340	23.42 427	58.32 319	75.10 387

FMVSNet3		68.84 218	67.40 220	73.19 240	85.05 74	48.53 256	85.71 120	85.36 114	60.90 196	57.58 291	79.15 296	42.16 175	86.77 246	47.25 320	63.40 277	84.27 260

tfpn200view9		67.57 247	66.13 247	71.89 279	84.05 94	45.07 333	83.40 207	87.71 69	60.79 197	57.79 286	82.76 244	43.53 156	87.80 209	28.80 405	66.36 251	82.78 296

thres400		67.40 255	66.13 247	71.19 290	84.05 94	45.07 333	83.40 207	87.71 69	60.79 197	57.79 286	82.76 244	43.53 156	87.80 209	28.80 405	66.36 251	80.71 328

LCM-MVSNet-Re		58.82 339	56.54 338	65.68 354	79.31 227	29.09 433	61.39 413	45.79 433	60.73 199	37.65 420	72.47 371	31.42 325	81.08 336	49.66 302	70.41 213	86.87 207

Effi-MVS+		75.24 88	73.61 102	80.16 33	81.92 156	57.42 21	85.21 137	76.71 323	60.68 200	73.32 73	89.34 124	47.30 91	91.63 68	68.28 138	79.72 95	91.42 67

D2MVS		63.49 301	61.39 303	69.77 312	69.29 384	48.93 243	78.89 314	77.71 304	60.64 201	49.70 362	72.10 378	27.08 352	83.48 317	54.48 270	62.65 290	76.90 367

IterMVS		63.77 299	61.67 299	70.08 308	72.68 347	51.24 180	80.44 290	75.51 333	60.51 202	51.41 351	73.70 359	32.08 316	78.91 360	54.30 271	54.35 362	80.08 336
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dp		64.41 291	61.58 300	72.90 245	82.40 142	54.09 101	72.53 359	76.59 326	60.39 203	55.68 317	70.39 387	35.18 278	76.90 383	39.34 356	61.71 296	87.73 185

MVP-Stereo		70.97 174	70.44 157	72.59 253	76.03 299	51.36 176	85.02 149	86.99 79	60.31 204	56.53 310	78.92 298	40.11 203	90.00 120	60.00 214	90.01 7	76.41 376
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm2		70.82 177	68.44 195	77.98 83	80.78 197	56.11 44	74.21 346	81.28 222	60.24 205	68.04 137	75.27 344	52.26 50	88.50 182	55.82 260	68.03 234	89.33 138

CR-MVSNet		62.47 313	59.04 325	72.77 249	73.97 333	56.57 34	60.52 414	71.72 372	60.04 206	57.49 294	65.86 403	38.94 213	80.31 348	42.86 346	59.93 303	81.42 313

ab-mvs		70.65 180	69.11 187	75.29 174	80.87 194	46.23 319	73.48 351	85.24 124	59.99 207	66.65 146	80.94 276	43.13 165	88.69 170	63.58 181	68.07 233	90.95 86

9.14			78.19 28		85.67 62		88.32 53	88.84 41	59.89 208	74.58 60	92.62 43	46.80 99	92.66 42	81.40 45	85.62 41

GeoE		69.96 196	67.88 206	76.22 135	81.11 186	51.71 168	84.15 181	76.74 322	59.83 209	60.91 233	84.38 216	41.56 186	88.10 199	51.67 291	70.57 208	88.84 152

KinetiMVS		71.15 167	69.25 185	76.82 121	77.99 257	50.49 194	85.05 146	86.51 89	59.78 210	64.10 187	85.34 201	32.16 314	91.33 77	58.82 222	73.54 172	88.64 158

BH-w/o		70.02 193	68.51 194	74.56 195	82.77 133	50.39 199	86.60 97	78.14 296	59.77 211	59.65 247	85.57 198	39.27 211	87.30 231	49.86 301	74.94 160	85.99 230

ZNCC-MVS		75.82 79	75.02 82	78.23 79	83.88 99	53.80 104	86.91 91	86.05 101	59.71 212	67.85 139	90.55 93	42.23 174	91.02 89	72.66 112	85.29 45	89.87 124

1112_ss		70.05 192	69.37 180	72.10 266	80.77 198	42.78 362	85.12 144	76.75 320	59.69 213	61.19 231	92.12 52	47.48 90	83.84 311	53.04 280	68.21 232	89.66 127

miper_lstm_enhance		63.91 296	62.30 295	68.75 325	75.06 315	46.78 305	69.02 382	81.14 223	59.68 214	52.76 343	72.39 373	40.71 195	77.99 371	56.81 250	53.09 372	81.48 312

Baseline_NR-MVSNet		65.49 289	64.27 282	69.13 318	74.37 326	41.65 373	83.39 209	78.85 277	59.56 215	59.62 249	76.88 326	40.75 193	87.44 226	49.99 299	55.05 355	78.28 355

Fast-Effi-MVS+-dtu		66.53 274	64.10 284	73.84 221	72.41 350	52.30 150	84.73 160	75.66 332	59.51 216	56.34 312	79.11 297	28.11 343	85.85 282	57.74 243	63.29 281	83.35 280

UGNet		68.71 223	67.11 227	73.50 233	80.55 204	47.61 292	84.08 183	78.51 289	59.45 217	65.68 162	82.73 247	23.78 377	85.08 296	52.80 283	76.40 130	87.80 183
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
1314		71.11 170	69.41 179	76.22 135	79.32 226	50.49 194	80.23 295	85.14 134	59.44 218	58.93 263	88.89 133	33.83 298	89.60 135	61.49 197	77.42 120	88.57 163

MTAPA		72.73 133	71.22 145	77.27 105	81.54 175	53.57 109	67.06 392	81.31 220	59.41 219	68.39 133	90.96 82	36.07 267	89.01 154	73.80 103	82.45 64	89.23 141

thres600view7		66.46 275	65.12 272	70.47 300	83.41 106	43.80 349	82.15 244	87.78 64	59.37 220	56.02 314	82.21 262	43.73 151	86.90 244	26.51 417	64.94 262	80.71 328

sss		70.49 183	70.13 168	71.58 284	81.59 172	39.02 388	80.78 284	84.71 147	59.34 221	66.61 148	88.09 154	37.17 245	85.52 285	61.82 195	71.02 203	90.20 108

Vis-MVSNet (Re-imp)		65.52 287	65.63 260	65.17 360	77.49 268	30.54 420	75.49 336	77.73 303	59.34 221	52.26 348	86.69 181	49.38 76	80.53 346	37.07 365	75.28 151	84.42 258

MVS_111021_LR		69.07 213	67.91 204	72.54 254	77.27 273	49.56 222	79.77 303	73.96 350	59.33 223	60.73 236	87.82 162	30.19 334	81.53 332	69.94 125	72.19 190	86.53 219

PS-MVSNAJss		68.78 222	67.17 226	73.62 230	73.01 342	48.33 266	84.95 153	84.81 142	59.30 224	58.91 265	79.84 286	37.77 225	88.86 165	62.83 186	63.12 286	83.67 278

GST-MVS		74.87 97	73.90 100	77.77 89	83.30 111	53.45 113	85.75 116	85.29 120	59.22 225	66.50 151	89.85 115	40.94 191	90.76 99	70.94 118	83.35 58	89.10 146

MDTV_nov1_ep13			61.56 301		81.68 165	55.12 69	72.41 362	78.18 295	59.19 226	58.85 267	69.29 392	34.69 286	86.16 266	36.76 370	62.96 287

CSCG		80.41 15	79.72 16	82.49 5	89.12 25	57.67 15	89.29 43	91.54 5	59.19 226	71.82 97	90.05 111	59.72 10	96.04 10	78.37 61	88.40 14	93.75 7

test-LLR		69.65 205	69.01 188	71.60 282	78.67 242	48.17 272	85.13 141	79.72 254	59.18 228	63.13 207	82.58 251	36.91 252	80.24 349	60.56 206	75.17 153	86.39 224

test0.0.03 1		62.54 310	62.44 294	62.86 377	72.28 354	29.51 430	82.93 224	78.78 280	59.18 228	53.07 342	82.41 255	36.91 252	77.39 378	37.45 361	58.96 313	81.66 308

MIMVSNet		63.12 305	60.29 315	71.61 281	75.92 304	46.65 308	65.15 395	81.94 206	59.14 230	54.65 327	69.47 390	25.74 362	80.63 343	41.03 352	69.56 222	87.55 190

IS-MVSNet		68.80 221	67.55 216	72.54 254	78.50 249	43.43 353	81.03 277	79.35 268	59.12 231	57.27 299	86.71 180	46.05 113	87.70 215	44.32 339	75.60 147	86.49 221

thres100view900		66.87 268	65.42 267	71.24 288	83.29 112	43.15 358	81.67 262	87.78 64	59.04 232	55.92 315	82.18 263	43.73 151	87.80 209	28.80 405	66.36 251	82.78 296

3Dnovator+	62.71 7	72.29 144	70.50 156	77.65 93	83.40 109	51.29 179	87.32 76	86.40 93	59.01 233	58.49 276	88.32 149	32.40 311	91.27 78	57.04 248	82.15 67	90.38 101

UnsupCasMVSNet_eth		57.56 349	55.15 348	64.79 363	64.57 412	33.12 412	73.17 354	83.87 170	58.98 234	41.75 403	70.03 388	22.54 385	79.92 353	46.12 330	35.31 423	81.32 320

BH-RMVSNet		70.08 191	68.01 202	76.27 133	84.21 92	51.22 181	87.29 79	79.33 270	58.96 235	63.63 202	86.77 179	33.29 302	90.30 114	44.63 336	73.96 167	87.30 197

PatchmatchNet		67.07 264	63.63 286	77.40 100	83.10 116	58.03 11	72.11 369	77.77 302	58.85 236	59.37 254	70.83 383	37.84 224	84.93 298	42.96 345	69.83 218	89.26 139
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_vis1_n_1920		68.59 226	68.31 197	69.44 316	69.16 385	41.51 375	84.63 166	68.58 395	58.80 237	73.26 74	88.37 145	25.30 365	80.60 344	79.10 53	67.55 238	86.23 226

SF-MVS		77.64 42	77.42 41	78.32 78	83.75 101	52.47 143	86.63 96	87.80 63	58.78 238	74.63 58	92.38 48	47.75 87	91.35 75	78.18 65	86.85 27	91.15 79

Vis-MVSNet		70.61 181	69.34 181	74.42 199	80.95 193	48.49 258	86.03 109	77.51 307	58.74 239	65.55 163	87.78 163	34.37 291	85.95 280	52.53 288	80.61 80	88.80 153
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS		76.91 53	75.48 72	81.23 19	84.56 83	55.21 65	80.23 295	91.64 4	58.65 240	65.37 164	91.48 73	45.72 119	95.05 16	72.11 114	89.52 10	93.44 9

CDPH-MVS		76.05 72	75.19 78	78.62 66	86.51 51	54.98 76	87.32 76	84.59 150	58.62 241	70.75 113	90.85 88	43.10 166	90.63 104	70.50 121	84.51 53	90.24 105

GBi-Net		67.09 262	65.47 264	71.96 272	82.71 135	46.36 313	83.52 198	83.31 179	58.55 242	57.58 291	76.23 335	36.72 257	86.20 263	47.25 320	63.40 277	83.32 281

test1		67.09 262	65.47 264	71.96 272	82.71 135	46.36 313	83.52 198	83.31 179	58.55 242	57.58 291	76.23 335	36.72 257	86.20 263	47.25 320	63.40 277	83.32 281

FMVSNet2		67.57 247	65.79 256	72.90 245	82.71 135	47.97 281	85.15 140	84.93 138	58.55 242	56.71 307	78.26 304	36.72 257	86.67 249	46.15 329	62.94 288	84.07 264

HyFIR lowres test		69.94 197	67.58 214	77.04 111	77.11 279	57.29 22	81.49 271	79.11 273	58.27 245	58.86 266	80.41 280	42.33 172	86.96 241	61.91 193	68.68 230	86.87 207

MSLP-MVS++		74.21 104	72.25 125	80.11 36	81.45 179	56.47 38	86.32 101	79.65 258	58.19 246	66.36 152	92.29 50	36.11 265	90.66 102	67.39 143	82.49 63	93.18 17

PHI-MVS		77.49 43	77.00 48	78.95 53	85.33 70	50.69 189	88.57 51	88.59 51	58.14 247	73.60 68	93.31 27	43.14 164	93.79 27	73.81 102	88.53 13	92.37 34

XVS		72.92 129	71.62 137	76.81 122	83.41 106	52.48 141	84.88 155	83.20 184	58.03 248	63.91 191	89.63 119	35.50 274	89.78 128	65.50 158	80.50 82	88.16 173

X-MVStestdata		65.85 284	62.20 296	76.81 122	83.41 106	52.48 141	84.88 155	83.20 184	58.03 248	63.91 191	4.82 462	35.50 274	89.78 128	65.50 158	80.50 82	88.16 173

DVP-MVS++		82.44 3	82.38 6	82.62 4	91.77 4	57.49 17	84.98 150	88.88 37	58.00 250	83.60 6	93.39 24	67.21 2	96.39 4	81.64 40	91.98 4	93.98 5

test_0728_THIRD									58.00 250	81.91 15	93.64 17	56.54 23	96.44 2	81.64 40	86.86 26	92.23 37

test_yl		75.85 76	74.83 87	78.91 54	88.08 37	51.94 157	91.30 17	89.28 29	57.91 252	71.19 107	89.20 127	42.03 179	92.77 39	69.41 128	75.07 157	92.01 46

DCV-MVSNet		75.85 76	74.83 87	78.91 54	88.08 37	51.94 157	91.30 17	89.28 29	57.91 252	71.19 107	89.20 127	42.03 179	92.77 39	69.41 128	75.07 157	92.01 46

MP-MVS		74.99 94	74.33 93	76.95 117	82.89 129	53.05 130	85.63 121	83.50 177	57.86 254	67.25 142	90.24 103	43.38 161	88.85 168	76.03 78	82.23 65	88.96 148
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		76.91 53	76.40 58	78.45 72	85.68 60	55.42 56	87.59 69	84.00 166	57.84 255	72.99 78	90.98 80	44.99 133	88.58 175	78.19 63	85.32 44	91.34 72

test_8					85.72 59	55.31 61	87.60 68	83.88 169	57.84 255	72.84 82	90.99 79	44.99 133	88.34 188

TEST9					85.68 60	55.42 56	87.59 69	84.00 166	57.72 257	72.99 78	90.98 80	44.87 137	88.58 175

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 19	92.34 5	89.99 21	57.71 258	81.91 15	93.64 17	55.17 31	96.44 2	81.68 38	87.13 21	92.72 28
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					89.40 20	57.45 19	92.32 7	88.63 48	57.71 258	83.14 9	93.96 9	55.17 31

BH-untuned		68.28 232	66.40 240	73.91 218	81.62 170	50.01 211	85.56 124	77.39 309	57.63 260	57.47 296	83.69 230	36.36 262	87.08 237	44.81 334	73.08 179	84.65 255

thisisatest0530		70.47 185	68.56 191	76.20 137	79.78 218	51.52 173	83.49 204	88.58 52	57.62 261	58.60 272	82.79 243	51.03 58	91.48 72	52.84 282	62.36 294	85.59 241

test_241102_ONE					89.48 17	56.89 29		88.94 35	57.53 262	84.61 4	93.29 28	58.81 13	96.45 1

API-MVS		74.17 105	72.07 131	80.49 25	90.02 11	58.55 9	87.30 78	84.27 157	57.51 263	65.77 161	87.77 164	41.61 185	95.97 11	51.71 290	82.63 61	86.94 205

SED-MVS		81.92 8	81.75 9	82.44 7	89.48 17	56.89 29	92.48 3	88.94 35	57.50 264	84.61 4	94.09 6	58.81 13	96.37 6	82.28 34	87.60 18	94.06 3

test_241102_TWO								88.76 44	57.50 264	83.60 6	94.09 6	56.14 27	96.37 6	82.28 34	87.43 20	92.55 30

Patchmatch-RL test		58.72 340	54.32 353	71.92 277	63.91 414	44.25 343	61.73 410	55.19 424	57.38 266	49.31 365	54.24 434	37.60 233	80.89 337	62.19 191	47.28 392	90.63 93

Test_1112_low_res		67.18 259	66.23 245	70.02 311	78.75 240	41.02 380	83.43 205	73.69 352	57.29 267	58.45 278	82.39 256	45.30 128	80.88 338	50.50 297	66.26 255	88.16 173

FA-MVS(test-final)		69.00 216	66.60 238	76.19 138	83.48 105	47.96 283	74.73 340	82.07 204	57.27 268	62.18 218	78.47 302	36.09 266	92.89 35	53.76 276	71.32 201	87.73 185

OpenMVS	61.00 11	69.99 195	67.55 216	77.30 103	78.37 252	54.07 102	84.36 173	85.76 106	57.22 269	56.71 307	87.67 166	30.79 330	92.83 37	43.04 344	84.06 56	85.01 249

test_one_0601					89.39 22	57.29 22		88.09 59	57.21 270	82.06 14	93.39 24	54.94 36

TR-MVS		69.71 200	67.85 210	75.27 177	82.94 126	48.48 259	87.40 75	80.86 230	57.15 271	64.61 178	87.08 175	32.67 309	89.64 134	46.38 327	71.55 197	87.68 187

ZD-MVS					89.55 14	53.46 111		84.38 154	57.02 272	73.97 65	91.03 78	44.57 143	91.17 83	75.41 87	81.78 71

TransMVSNet (Re)		62.82 308	60.76 310	69.02 319	73.98 332	41.61 374	86.36 99	79.30 271	56.90 273	52.53 344	76.44 331	41.85 182	87.60 221	38.83 358	40.61 411	77.86 359

USDC		54.36 365	51.23 369	63.76 367	64.29 413	37.71 396	62.84 407	73.48 358	56.85 274	35.47 425	71.94 379	9.23 435	78.43 363	38.43 359	48.57 383	75.13 386

region2R		73.75 115	72.55 117	77.33 101	83.90 98	52.98 132	85.54 126	84.09 164	56.83 275	65.10 168	90.45 96	37.34 240	90.24 115	68.89 134	80.83 77	88.77 155

HFP-MVS		74.37 102	73.13 111	78.10 82	84.30 88	53.68 107	85.58 122	84.36 155	56.82 276	65.78 160	90.56 92	40.70 196	90.90 97	69.18 132	80.88 75	89.71 125

ACMMPR		73.76 114	72.61 115	77.24 108	83.92 97	52.96 133	85.58 122	84.29 156	56.82 276	65.12 167	90.45 96	37.24 243	90.18 117	69.18 132	80.84 76	88.58 162

SD-MVS		76.18 67	74.85 86	80.18 32	85.39 68	56.90 28	85.75 116	82.45 197	56.79 278	74.48 61	91.81 62	43.72 153	90.75 100	74.61 92	78.65 105	92.91 22
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
SCA		63.84 297	60.01 318	75.32 170	78.58 247	57.92 12	61.61 411	77.53 306	56.71 279	57.75 288	70.77 384	31.97 317	79.91 355	48.80 309	56.36 340	88.13 176

cascas		69.01 215	66.13 247	77.66 92	79.36 224	55.41 58	86.99 86	83.75 171	56.69 280	58.92 264	81.35 273	24.31 375	92.10 60	53.23 277	70.61 207	85.46 242

ACMMP		70.81 178	69.29 183	75.39 168	81.52 177	51.92 159	83.43 205	83.03 187	56.67 281	58.80 268	88.91 132	31.92 319	88.58 175	65.89 157	73.39 173	85.67 237
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
QAPM		71.88 154	69.33 182	79.52 40	82.20 151	54.30 94	86.30 102	88.77 43	56.61 282	59.72 246	87.48 168	33.90 296	95.36 13	47.48 318	81.49 72	88.90 149

TSAR-MVS + GP.		77.82 38	77.59 37	78.49 69	85.25 72	50.27 208	90.02 26	90.57 17	56.58 283	74.26 63	91.60 70	54.26 38	92.16 57	75.87 80	79.91 92	93.05 20

PGM-MVS		72.60 135	71.20 146	76.80 124	82.95 125	52.82 137	83.07 221	82.14 199	56.51 284	63.18 206	89.81 116	35.68 271	89.76 130	67.30 144	80.19 87	87.83 182

PCF-MVS	61.03 10	70.10 190	68.40 196	75.22 179	77.15 278	51.99 156	79.30 311	82.12 200	56.47 285	61.88 225	86.48 186	43.98 146	87.24 233	55.37 265	72.79 181	86.43 223
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		71.99 150	70.31 163	77.01 113	90.65 8	53.44 114	89.37 37	82.97 189	56.33 286	63.56 204	89.47 121	34.02 294	92.15 59	54.05 273	72.41 185	85.43 243

EPP-MVSNet		71.14 168	70.07 170	74.33 204	79.18 230	46.52 310	83.81 194	86.49 90	56.32 287	57.95 282	84.90 210	54.23 39	89.14 149	58.14 233	69.65 220	87.33 195

HPM-MVS		72.60 135	71.50 139	75.89 148	82.02 152	51.42 175	80.70 286	83.05 186	56.12 288	64.03 189	89.53 120	37.55 234	88.37 185	70.48 122	80.04 90	87.88 181
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APDe-MVS		78.44 27	78.20 27	79.19 45	88.56 26	54.55 90	89.76 33	87.77 66	55.91 289	78.56 36	92.49 46	48.20 81	92.65 43	79.49 50	83.04 59	90.39 100
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
xiu_mvs_v1_base_debu		71.60 160	70.29 164	75.55 161	77.26 274	53.15 125	85.34 129	79.37 264	55.83 290	72.54 84	90.19 106	22.38 386	86.66 250	73.28 107	76.39 131	86.85 210

xiu_mvs_v1_base		71.60 160	70.29 164	75.55 161	77.26 274	53.15 125	85.34 129	79.37 264	55.83 290	72.54 84	90.19 106	22.38 386	86.66 250	73.28 107	76.39 131	86.85 210

xiu_mvs_v1_base_debi		71.60 160	70.29 164	75.55 161	77.26 274	53.15 125	85.34 129	79.37 264	55.83 290	72.54 84	90.19 106	22.38 386	86.66 250	73.28 107	76.39 131	86.85 210

mPP-MVS		71.79 157	70.38 161	76.04 143	82.65 138	52.06 153	84.45 171	81.78 212	55.59 293	62.05 223	89.68 118	33.48 300	88.28 194	65.45 163	78.24 111	87.77 184

DPE-MVS		79.82 19	79.66 17	80.29 30	89.27 24	55.08 72	88.70 49	87.92 62	55.55 294	81.21 20	93.69 16	56.51 24	94.27 22	78.36 62	85.70 40	91.51 65
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
pm-mvs1		64.12 294	62.56 293	68.78 324	71.68 358	38.87 389	82.89 225	81.57 215	55.54 295	53.89 336	77.82 308	37.73 228	86.74 247	48.46 313	53.49 369	80.72 327

mamba_0408		66.33 277	62.87 288	76.70 127	80.45 206	51.81 164	46.11 435	78.90 275	55.46 296	63.82 195	84.54 212	31.91 320	91.03 87	55.68 261	68.97 225	87.25 198

SSM_04072		64.04 295	62.87 288	67.56 336	80.45 206	51.81 164	46.11 435	78.90 275	55.46 296	63.82 195	84.54 212	31.91 320	63.62 421	55.68 261	68.97 225	87.25 198

ACMP	61.11 9	66.24 280	64.33 281	72.00 271	74.89 318	49.12 235	83.18 216	79.83 252	55.41 298	52.29 346	82.68 248	25.83 361	86.10 269	60.89 201	63.94 272	80.78 326
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_cas_vis1_n_1920		67.10 261	66.60 238	68.59 329	65.17 407	43.23 357	83.23 214	69.84 388	55.34 299	70.67 115	87.71 165	24.70 372	76.66 385	78.57 60	64.20 268	85.89 234

CP-MVS		72.59 137	71.46 140	76.00 145	82.93 127	52.32 147	86.93 90	82.48 196	55.15 300	63.65 201	90.44 99	35.03 281	88.53 181	68.69 135	77.83 116	87.15 201

pmmvs4		63.34 303	61.07 308	70.16 306	70.14 375	50.53 193	79.97 302	71.41 377	55.08 301	54.12 333	78.58 300	32.79 308	82.09 330	50.33 298	57.22 336	77.86 359

KD-MVS_2432*1600		59.04 336	56.44 340	66.86 344	79.07 231	45.87 324	72.13 367	80.42 238	55.03 302	48.15 370	71.01 381	36.73 255	78.05 369	35.21 378	30.18 436	76.67 370

miper_refine_blended		59.04 336	56.44 340	66.86 344	79.07 231	45.87 324	72.13 367	80.42 238	55.03 302	48.15 370	71.01 381	36.73 255	78.05 369	35.21 378	30.18 436	76.67 370

MDTV_nov1_ep13_2view						43.62 350	71.13 374		54.95 304	59.29 258		36.76 254		46.33 328		87.32 196

Anonymous202405211		70.11 189	67.88 206	76.79 125	87.20 45	47.24 302	89.49 35	77.38 310	54.88 305	66.14 153	86.84 178	20.93 395	91.54 71	56.45 255	71.62 195	91.59 60

OMC-MVS		65.97 283	65.06 273	68.71 326	72.97 343	42.58 366	78.61 315	75.35 336	54.72 306	59.31 256	86.25 187	33.30 301	77.88 373	57.99 234	67.05 241	85.66 238

LPG-MVS_test		66.44 276	64.58 277	72.02 269	74.42 324	48.60 253	83.07 221	80.64 233	54.69 307	53.75 337	83.83 225	25.73 363	86.98 239	60.33 212	64.71 263	80.48 330

LGP-MVS_train				72.02 269	74.42 324	48.60 253		80.64 233	54.69 307	53.75 337	83.83 225	25.73 363	86.98 239	60.33 212	64.71 263	80.48 330

tfpnnormal		61.47 320	59.09 324	68.62 328	76.29 293	41.69 372	81.14 276	85.16 128	54.48 309	51.32 352	73.63 360	32.32 312	86.89 245	21.78 431	55.71 352	77.29 365

mmtdpeth		57.93 347	54.78 351	67.39 339	72.32 352	43.38 354	72.72 357	68.93 393	54.45 310	56.85 304	62.43 414	17.02 416	83.46 318	57.95 237	30.31 435	75.31 383

tttt0517		68.33 231	66.29 243	74.46 197	78.08 255	49.06 236	80.88 282	89.08 33	54.40 311	54.75 326	80.77 278	51.31 55	90.33 111	49.35 305	58.01 327	83.99 267

pmmvs5		62.80 309	61.18 306	67.66 335	69.53 382	42.37 369	82.65 230	75.19 337	54.30 312	52.03 349	78.51 301	31.64 324	80.67 342	48.60 311	58.15 323	79.95 337

SSM_0407		69.71 200	67.38 221	76.69 128	80.45 206	51.81 164	81.36 273	80.18 242	54.07 313	63.82 195	85.05 205	33.09 303	91.01 90	59.40 215	68.97 225	87.25 198

SSM_0404		70.13 187	67.87 209	76.88 120	80.22 211	52.00 155	81.71 261	80.18 242	54.07 313	65.36 165	85.05 205	33.09 303	91.03 87	59.40 215	71.80 193	87.63 188

APD-MVS		76.15 69	75.68 67	77.54 96	88.52 27	53.44 114	87.26 81	85.03 136	53.79 315	74.91 56	91.68 67	43.80 149	90.31 112	74.36 95	81.82 69	88.87 151
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
114514_t		69.87 198	67.88 206	75.85 149	88.38 29	52.35 146	86.94 89	83.68 172	53.70 316	55.68 317	85.60 197	30.07 335	91.20 82	55.84 259	71.02 203	83.99 267

testing3		59.97 326	60.19 316	59.32 392	77.60 264	30.01 426	81.75 258	81.79 211	53.54 317	50.34 360	79.94 283	48.99 79	76.91 381	17.19 442	50.59 379	71.03 415

PAPM_NR		71.80 156	69.98 172	77.26 107	81.54 175	53.34 119	78.60 316	85.25 123	53.46 318	60.53 239	88.66 137	45.69 120	89.24 144	56.49 252	79.62 98	89.19 143

test-mter		68.36 229	67.29 222	71.60 282	78.67 242	48.17 272	85.13 141	79.72 254	53.38 319	63.13 207	82.58 251	27.23 351	80.24 349	60.56 206	75.17 153	86.39 224

jajsoiax		63.21 304	60.84 309	70.32 304	68.33 392	44.45 339	81.23 274	81.05 224	53.37 320	50.96 356	77.81 309	17.49 414	85.49 287	59.31 217	58.05 326	81.02 324

testgi		54.25 366	52.57 365	59.29 393	62.76 419	21.65 448	72.21 365	70.47 383	53.25 321	41.94 401	77.33 316	14.28 424	77.95 372	29.18 404	51.72 377	78.28 355

tpm cat1		66.28 278	62.78 290	76.77 126	81.40 180	57.14 24	70.03 378	77.19 312	53.00 322	58.76 269	70.73 386	46.17 109	86.73 248	43.27 343	64.46 267	86.44 222

mvs_tets		62.96 307	60.55 311	70.19 305	68.22 395	44.24 344	80.90 281	80.74 232	52.99 323	50.82 358	77.56 310	16.74 418	85.44 288	59.04 220	57.94 328	80.89 325

test20.03		55.22 362	54.07 355	58.68 395	63.14 418	25.00 439	77.69 322	74.78 340	52.64 324	43.43 395	72.39 373	26.21 358	74.76 395	29.31 403	47.05 395	76.28 377

VDDNet		74.37 102	72.13 129	81.09 20	79.58 220	56.52 37	90.02 26	86.70 85	52.61 325	71.23 106	87.20 173	31.75 323	93.96 25	74.30 97	75.77 144	92.79 27

v7n		62.50 312	59.27 323	72.20 264	67.25 398	49.83 216	77.87 321	80.12 244	52.50 326	48.80 368	73.07 364	32.10 315	87.90 205	46.83 323	54.92 356	78.86 344

FMVSNet1		64.57 290	62.11 297	71.96 272	77.32 272	46.36 313	83.52 198	83.31 179	52.43 327	54.42 329	76.23 335	27.80 347	86.20 263	42.59 348	61.34 298	83.32 281

K. test v3		54.04 367	49.42 380	67.92 334	68.55 389	42.57 367	75.51 335	63.07 412	52.07 328	39.21 414	64.59 409	19.34 402	82.21 327	37.11 364	25.31 441	78.97 343

原ACMM1				76.13 140	84.89 78	54.59 89		85.26 122	51.98 329	66.70 145	87.07 176	40.15 202	89.70 132	51.23 294	85.06 48	84.10 263

tpmvs		62.45 314	59.42 321	71.53 285	83.93 96	54.32 93	70.03 378	77.61 305	51.91 330	53.48 340	68.29 396	37.91 223	86.66 250	33.36 388	58.27 321	73.62 398

PEN-MVS		58.35 345	57.15 335	61.94 381	67.55 397	34.39 404	77.01 324	78.35 293	51.87 331	47.72 374	76.73 328	33.91 295	73.75 400	34.03 385	47.17 393	77.68 361

EG-PatchMatch MVS		62.40 315	59.59 319	70.81 296	73.29 337	49.05 237	85.81 112	84.78 143	51.85 332	44.19 391	73.48 362	15.52 423	89.85 126	40.16 354	67.24 240	73.54 399

UniMVSNet_ETH3D		62.51 311	60.49 312	68.57 330	68.30 393	40.88 382	73.89 347	79.93 250	51.81 333	54.77 325	79.61 290	24.80 370	81.10 335	49.93 300	61.35 297	83.73 275

CP-MVSNet		58.54 344	57.57 333	61.46 385	68.50 390	33.96 409	76.90 326	78.60 287	51.67 334	47.83 373	76.60 330	34.99 282	72.79 405	35.45 375	47.58 389	77.64 363

WR-MVS_H		58.91 338	58.04 330	61.54 384	69.07 386	33.83 410	76.91 325	81.99 205	51.40 335	48.17 369	74.67 347	40.23 200	74.15 396	31.78 395	48.10 385	76.64 373

lecture		74.14 106	73.05 112	77.44 99	81.66 167	50.39 199	87.43 72	84.22 162	51.38 336	72.10 93	90.95 85	38.31 220	93.23 32	70.51 120	80.83 77	88.69 156

PS-CasMVS		58.12 346	57.03 337	61.37 386	68.24 394	33.80 411	76.73 328	78.01 297	51.20 337	47.54 377	76.20 338	32.85 306	72.76 406	35.17 380	47.37 391	77.55 364

DTE-MVSNet		57.03 351	55.73 346	60.95 389	65.94 401	32.57 416	75.71 331	77.09 315	51.16 338	46.65 384	76.34 333	32.84 307	73.22 404	30.94 399	44.87 402	77.06 366

LuminaMVS		66.60 273	64.37 280	73.27 239	70.06 378	49.57 219	80.77 285	81.76 214	50.81 339	60.56 238	78.41 303	24.50 373	87.26 232	64.24 173	68.25 231	82.99 290

HPM-MVS_fast		67.86 239	66.28 244	72.61 252	80.67 201	48.34 264	81.18 275	75.95 331	50.81 339	59.55 251	88.05 157	27.86 346	85.98 277	58.83 221	73.58 171	83.51 279

MVSMamba_PlusPlus		75.28 86	73.39 103	80.96 21	80.85 195	58.25 10	74.47 344	87.61 71	50.53 341	65.24 166	83.41 235	57.38 20	92.83 37	73.92 101	87.13 21	91.80 56

MVSFormer		73.53 120	72.19 127	77.57 94	83.02 122	55.24 63	81.63 263	81.44 218	50.28 342	76.67 46	90.91 86	44.82 139	86.11 267	60.83 202	80.09 88	91.36 70

test_djsdf		63.84 297	61.56 301	70.70 298	68.78 387	44.69 337	81.63 263	81.44 218	50.28 342	52.27 347	76.26 334	26.72 355	86.11 267	60.83 202	55.84 351	81.29 321

FMVSNet5		58.61 341	56.45 339	65.10 361	77.20 277	39.74 384	74.77 339	77.12 314	50.27 344	43.28 397	67.71 397	26.15 360	76.90 383	36.78 369	54.78 358	78.65 348

FE-MVS		64.15 293	60.43 314	75.30 173	80.85 195	49.86 215	68.28 387	78.37 292	50.26 345	59.31 256	73.79 355	26.19 359	91.92 63	40.19 353	66.67 244	84.12 262

Anonymous20231206		59.08 335	57.59 332	63.55 369	68.77 388	32.14 418	80.26 294	79.78 253	50.00 346	49.39 364	72.39 373	26.64 356	78.36 364	33.12 391	57.94 328	80.14 335

ACMH	53.70 16	59.78 327	55.94 345	71.28 287	76.59 286	48.35 263	80.15 297	76.11 329	49.74 347	41.91 402	73.45 363	16.50 420	90.31 112	31.42 396	57.63 334	75.17 385
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs-eth3d		55.97 359	52.78 363	65.54 356	61.02 423	46.44 312	75.36 337	67.72 398	49.61 348	43.65 394	67.58 398	21.63 392	77.04 379	44.11 340	44.33 403	73.15 403

AdaColmap		67.86 239	65.48 263	75.00 185	88.15 36	54.99 75	86.10 106	76.63 325	49.30 349	57.80 285	86.65 183	29.39 338	88.94 162	45.10 333	70.21 215	81.06 323

无先验							85.19 138	78.00 298	49.08 350				85.13 295	52.78 284		87.45 193

ppachtmachnet_test		58.56 342	54.34 352	71.24 288	71.42 363	54.74 81	81.84 254	72.27 366	49.02 351	45.86 388	68.99 394	26.27 357	83.30 320	30.12 400	43.23 406	75.69 379

SR-MVS		70.92 176	69.73 175	74.50 196	83.38 110	50.48 196	84.27 177	79.35 268	48.96 352	66.57 150	90.45 96	33.65 299	87.11 236	66.42 149	74.56 164	85.91 233

tt0805		63.39 302	61.31 305	69.64 313	69.36 383	38.87 389	78.00 319	85.48 108	48.82 353	55.66 319	81.66 270	24.38 374	86.37 260	49.04 308	59.36 311	83.68 277

reproduce-ours		71.77 158	70.43 158	75.78 151	81.96 154	49.54 225	82.54 236	81.01 227	48.77 354	69.21 125	90.96 82	37.13 246	89.40 138	66.28 152	76.01 139	88.39 170

our_new_method		71.77 158	70.43 158	75.78 151	81.96 154	49.54 225	82.54 236	81.01 227	48.77 354	69.21 125	90.96 82	37.13 246	89.40 138	66.28 152	76.01 139	88.39 170

our_test_3		59.11 334	55.08 350	71.18 291	71.42 363	53.29 122	81.96 249	74.52 342	48.32 356	42.08 400	69.28 393	28.14 342	82.15 328	34.35 384	45.68 401	78.11 358

kuosan		50.20 387	50.09 374	50.52 411	73.09 341	29.09 433	65.25 394	74.89 339	48.27 357	41.34 405	60.85 422	43.45 159	67.48 418	18.59 440	25.07 442	55.01 436

APD-MVS_3200maxsize		69.62 206	68.23 200	73.80 223	81.58 173	48.22 270	81.91 251	79.50 261	48.21 358	64.24 186	89.75 117	31.91 320	87.55 222	63.08 183	73.85 170	85.64 239

CHOSEN 280x420		57.53 350	56.38 342	60.97 388	74.01 331	48.10 276	46.30 434	54.31 426	48.18 359	50.88 357	77.43 315	38.37 219	59.16 432	54.83 267	63.14 285	75.66 380

reproduce_model		71.07 171	69.67 176	75.28 176	81.51 178	48.82 247	81.73 259	80.57 236	47.81 360	68.26 134	90.78 90	36.49 261	88.60 174	65.12 168	74.76 162	88.42 169

FOURS1					83.24 113	49.90 214	84.98 150	78.76 281	47.71 361	73.42 71

ACMM	58.35 12	64.35 292	62.01 298	71.38 286	74.21 328	48.51 257	82.25 243	79.66 256	47.61 362	54.54 328	80.11 282	25.26 366	86.00 275	51.26 293	63.16 284	79.64 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		54.37 364	50.10 373	67.21 340	70.70 371	41.46 377	74.73 340	64.69 405	47.56 363	39.12 415	69.49 389	18.49 409	84.69 302	31.87 394	34.20 429	75.48 381

Anonymous20240529		69.71 200	67.28 223	77.00 114	83.78 100	50.36 203	88.87 48	85.10 135	47.22 364	64.03 189	83.37 236	27.93 345	92.10 60	57.78 242	67.44 239	88.53 165

ACMH+	54.58 15	58.55 343	55.24 347	68.50 331	74.68 320	45.80 327	80.27 293	70.21 385	47.15 365	42.77 399	75.48 343	16.73 419	85.98 277	35.10 382	54.78 358	73.72 397

XVG-OURS		61.88 317	59.34 322	69.49 314	65.37 404	46.27 317	64.80 397	73.49 356	47.04 366	57.41 298	82.85 242	25.15 367	78.18 365	53.00 281	64.98 260	84.01 266

TAPA-MVS	56.12 14	61.82 318	60.18 317	66.71 346	78.48 250	37.97 395	75.19 338	76.41 328	46.82 367	57.04 302	86.52 185	27.67 349	77.03 380	26.50 418	67.02 242	85.14 247
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UnsupCasMVSNet_bld		53.86 368	50.53 372	63.84 366	63.52 417	34.75 402	71.38 372	81.92 208	46.53 368	38.95 416	57.93 429	20.55 396	80.20 351	39.91 355	34.09 430	76.57 374

anonymousdsp		60.46 325	57.65 331	68.88 320	63.63 416	45.09 332	72.93 355	78.63 285	46.52 369	51.12 353	72.80 368	21.46 393	83.07 322	57.79 241	53.97 363	78.47 350

XVG-OURS-SEG-HR		62.02 316	59.54 320	69.46 315	65.30 405	45.88 323	65.06 396	73.57 354	46.45 370	57.42 297	83.35 237	26.95 353	78.09 367	53.77 275	64.03 270	84.42 258

SR-MVS-dyc-post		68.27 233	66.87 229	72.48 257	80.96 190	48.14 274	81.54 267	76.98 316	46.42 371	62.75 212	89.42 122	31.17 328	86.09 271	60.52 208	72.06 191	83.19 286

RE-MVS-def			66.66 236		80.96 190	48.14 274	81.54 267	76.98 316	46.42 371	62.75 212	89.42 122	29.28 339		60.52 208	72.06 191	83.19 286

OpenMVS_ROB	53.19 17	59.20 332	56.00 344	68.83 322	71.13 367	44.30 341	83.64 197	75.02 338	46.42 371	46.48 385	73.03 365	18.69 406	88.14 196	27.74 413	61.80 295	74.05 395

Elysia		65.59 285	62.65 291	74.42 199	69.85 379	49.46 229	80.04 298	82.11 201	46.32 374	58.74 270	79.64 288	20.30 397	88.57 178	55.48 263	71.37 198	85.22 245

StellarMVS		65.59 285	62.65 291	74.42 199	69.85 379	49.46 229	80.04 298	82.11 201	46.32 374	58.74 270	79.64 288	20.30 397	88.57 178	55.48 263	71.37 198	85.22 245

CPTT-MVS		67.15 260	65.84 255	71.07 292	80.96 190	50.32 205	81.94 250	74.10 346	46.18 376	57.91 283	87.64 167	29.57 336	81.31 334	64.10 174	70.18 216	81.56 309

new-patchmatchnet		48.21 390	46.55 392	53.18 407	57.73 429	18.19 456	70.24 376	71.02 381	45.70 377	33.70 429	60.23 423	18.00 410	69.86 415	27.97 412	34.35 427	71.49 413

新几何1				73.30 237	83.10 116	53.48 110		71.43 376	45.55 378	66.14 153	87.17 174	33.88 297	80.54 345	48.50 312	80.33 86	85.88 235

旧先验2							81.73 259		45.53 379	74.66 57			70.48 414	58.31 230

Anonymous20231211		66.08 282	63.67 285	73.31 236	83.07 119	48.75 249	86.01 110	84.67 149	45.27 380	56.54 309	76.67 329	28.06 344	88.95 160	52.78 284	59.95 302	82.23 300

XVG-ACMP-BASELINE		56.03 358	52.85 362	65.58 355	61.91 421	40.95 381	63.36 402	72.43 365	45.20 381	46.02 386	74.09 351	9.20 436	78.12 366	45.13 332	58.27 321	77.66 362

pmmvs6		59.64 328	57.15 335	67.09 341	66.01 400	36.86 399	80.50 288	78.64 284	45.05 382	49.05 366	73.94 354	27.28 350	86.10 269	43.96 341	49.94 381	78.31 354

mvs5depth		50.97 384	46.98 390	62.95 375	56.63 431	34.23 407	62.73 408	67.35 400	45.03 383	48.00 372	65.41 407	10.40 432	79.88 357	36.00 371	31.27 434	74.73 390

ADS-MVSNet2		55.21 363	51.44 368	66.51 349	80.60 202	49.56 222	55.03 426	65.44 403	44.72 384	51.00 354	61.19 420	22.83 382	75.41 393	28.54 408	53.63 366	74.57 392

ADS-MVSNet		56.17 357	51.95 367	68.84 321	80.60 202	53.07 129	55.03 426	70.02 387	44.72 384	51.00 354	61.19 420	22.83 382	78.88 361	28.54 408	53.63 366	74.57 392

testdata				67.08 342	77.59 265	45.46 330		69.20 392	44.47 386	71.50 103	88.34 148	31.21 327	70.76 413	52.20 289	75.88 142	85.03 248

MSDG		59.44 329	55.14 349	72.32 262	74.69 319	50.71 188	74.39 345	73.58 353	44.44 387	43.40 396	77.52 311	19.45 401	90.87 98	31.31 397	57.49 335	75.38 382

KD-MVS_self_test		49.24 388	46.85 391	56.44 401	54.32 433	22.87 442	57.39 421	73.36 361	44.36 388	37.98 419	59.30 427	18.97 405	71.17 411	33.48 387	42.44 407	75.26 384

YYNet1		53.82 369	49.96 375	65.41 358	70.09 377	48.95 241	72.30 363	71.66 374	44.25 389	31.89 435	63.07 413	23.73 378	73.95 398	33.26 389	39.40 415	73.34 400

MDA-MVSNet_test_wron		53.82 369	49.95 376	65.43 357	70.13 376	49.05 237	72.30 363	71.65 375	44.23 390	31.85 436	63.13 412	23.68 379	74.01 397	33.25 390	39.35 416	73.23 402

MDA-MVSNet-bldmvs		51.56 382	47.75 389	63.00 374	71.60 360	47.32 300	69.70 381	72.12 367	43.81 391	27.65 443	63.38 411	21.97 391	75.96 389	27.30 415	32.19 431	65.70 426

PLC	52.38 18	60.89 322	58.97 326	66.68 348	81.77 160	45.70 328	78.96 313	74.04 349	43.66 392	47.63 375	83.19 240	23.52 380	77.78 376	37.47 360	60.46 301	76.55 375
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
IterMVS-SCA-FT		59.12 333	58.81 327	60.08 390	70.68 373	45.07 333	80.42 291	74.25 344	43.54 393	50.02 361	73.73 356	31.97 317	56.74 436	51.06 296	53.60 368	78.42 352

MIMVSNet1		50.35 386	47.81 387	57.96 397	61.53 422	27.80 437	67.40 389	74.06 348	43.25 394	33.31 434	65.38 408	16.03 421	71.34 410	21.80 430	47.55 390	74.75 389

LTVRE_ROB	45.45 19	52.73 374	49.74 378	61.69 383	69.78 381	34.99 401	44.52 437	67.60 399	43.11 395	43.79 393	74.03 352	18.54 408	81.45 333	28.39 410	57.94 328	68.62 418
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
test_0402		56.45 355	53.03 359	66.69 347	76.78 285	50.31 206	81.76 256	69.61 390	42.79 396	43.88 392	72.13 376	22.82 384	86.46 257	16.57 443	50.94 378	63.31 430

test222					79.36 224	50.97 182	77.99 320	67.84 397	42.54 397	62.84 211	86.53 184	30.26 333			76.91 124	85.23 244

CNLPA		60.59 324	58.44 328	67.05 343	79.21 229	47.26 301	79.75 304	64.34 409	42.46 398	51.90 350	83.94 223	27.79 348	75.41 393	37.12 363	59.49 309	78.47 350

PatchMatch-RL		56.66 352	53.75 357	65.37 359	77.91 261	45.28 331	69.78 380	60.38 415	41.35 399	47.57 376	73.73 356	16.83 417	76.91 381	36.99 366	59.21 312	73.92 396

DP-MVS		59.24 331	56.12 343	68.63 327	88.24 34	50.35 204	82.51 238	64.43 408	41.10 400	46.70 383	78.77 299	24.75 371	88.57 178	22.26 429	56.29 344	66.96 421

F-COLMAP		55.96 360	53.65 358	62.87 376	72.76 346	42.77 363	74.70 342	70.37 384	40.03 401	41.11 408	79.36 292	17.77 412	73.70 401	32.80 392	53.96 364	72.15 407

dongtai		43.51 397	44.07 398	41.82 422	63.75 415	21.90 446	63.80 400	72.05 368	39.59 402	33.35 433	54.54 433	41.04 190	57.30 434	10.75 451	17.77 451	46.26 445

gg-mvs-nofinetune		67.43 251	64.53 278	76.13 140	85.95 56	47.79 289	64.38 399	88.28 56	39.34 403	66.62 147	41.27 443	58.69 15	89.00 155	49.64 303	86.62 31	91.59 60

TinyColmap		48.15 391	44.49 395	59.13 394	65.73 403	38.04 393	63.34 403	62.86 413	38.78 404	29.48 438	67.23 400	6.46 446	73.30 403	24.59 422	41.90 409	66.04 424

PatchT		56.60 353	52.97 360	67.48 337	72.94 344	46.16 320	57.30 422	73.78 351	38.77 405	54.37 330	57.26 431	37.52 235	78.06 368	32.02 393	52.79 373	78.23 357

sc_t1		53.51 372	49.92 377	64.29 364	70.33 374	39.55 387	72.93 355	59.60 418	38.74 406	47.16 380	66.47 401	17.59 413	76.50 386	36.83 368	39.62 414	76.82 368

OurMVSNet-221017-0		52.39 378	48.73 382	63.35 373	65.21 406	38.42 392	68.54 386	64.95 404	38.19 407	39.57 413	71.43 380	13.23 426	79.92 353	37.16 362	40.32 412	71.72 410

ANet_high		34.39 410	29.59 416	48.78 414	30.34 459	22.28 444	55.53 425	63.79 410	38.11 408	15.47 451	36.56 448	6.94 442	59.98 428	13.93 447	5.64 462	64.08 428

PM-MVS		46.92 393	43.76 400	56.41 402	52.18 437	32.26 417	63.21 405	38.18 445	37.99 409	40.78 409	66.20 402	5.09 450	65.42 420	48.19 314	41.99 408	71.54 412

Patchmtry		56.56 354	52.95 361	67.42 338	72.53 349	50.59 192	59.05 418	71.72 372	37.86 410	46.92 381	65.86 403	38.94 213	80.06 352	36.94 367	46.72 397	71.60 411

tt0320-xc		52.22 380	48.38 384	63.75 368	72.19 355	42.25 370	72.19 366	57.59 421	37.24 411	44.41 390	61.56 417	17.90 411	75.89 390	35.60 374	36.73 419	73.12 404

JIA-IIPM		52.33 379	47.77 388	66.03 352	71.20 366	46.92 304	40.00 445	76.48 327	37.10 412	46.73 382	37.02 445	32.96 305	77.88 373	35.97 372	52.45 375	73.29 401

CVMVSNet		60.85 323	60.44 313	62.07 378	75.00 316	32.73 415	79.54 306	73.49 356	36.98 413	56.28 313	83.74 227	29.28 339	69.53 416	46.48 326	63.23 282	83.94 272

ITE_SJBPF				51.84 408	58.03 428	31.94 419		53.57 429	36.67 414	41.32 406	75.23 345	11.17 430	51.57 441	25.81 419	48.04 386	72.02 409

Anonymous20240521		51.65 381	48.42 383	61.34 387	56.43 432	39.65 386	73.57 350	73.47 359	36.64 415	36.59 421	63.98 410	10.75 431	72.25 409	35.35 376	49.01 382	72.11 408

COLMAP_ROB	43.60 20	50.90 385	48.05 386	59.47 391	67.81 396	40.57 383	71.25 373	62.72 414	36.49 416	36.19 423	73.51 361	13.48 425	73.92 399	20.71 433	50.26 380	63.92 429
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tt0320		52.45 377	48.75 381	63.55 369	71.47 362	41.85 371	72.42 361	59.73 417	36.33 417	44.52 389	61.55 418	19.34 402	76.45 387	33.53 386	39.85 413	72.36 406

RPMNet		59.29 330	54.25 354	74.42 199	73.97 333	56.57 34	60.52 414	76.98 316	35.72 418	57.49 294	58.87 428	37.73 228	85.26 291	27.01 416	59.93 303	81.42 313

N_pmnet		41.25 400	39.77 403	45.66 418	68.50 390	0.82 470	72.51 360	0.38 469	35.61 419	35.26 426	61.51 419	20.07 400	67.74 417	23.51 425	40.63 410	68.42 419

AllTest		47.32 392	44.66 394	55.32 405	65.08 408	37.50 397	62.96 406	54.25 427	35.45 420	33.42 431	72.82 366	9.98 433	59.33 429	24.13 423	43.84 404	69.13 416

TestCases				55.32 405	65.08 408	37.50 397		54.25 427	35.45 420	33.42 431	72.82 366	9.98 433	59.33 429	24.13 423	43.84 404	69.13 416

LS3D		56.40 356	53.82 356	64.12 365	81.12 185	45.69 329	73.42 352	66.14 401	35.30 422	43.24 398	79.88 284	22.18 389	79.62 358	19.10 438	64.00 271	67.05 420

WB-MVS		37.41 407	36.37 407	40.54 425	54.23 434	10.43 463	65.29 393	43.75 436	34.86 423	27.81 442	54.63 432	24.94 369	63.21 422	6.81 458	15.00 453	47.98 444

Patchmatch-test		53.33 373	48.17 385	68.81 323	73.31 336	42.38 368	42.98 440	58.23 419	32.53 424	38.79 417	70.77 384	39.66 208	73.51 402	25.18 420	52.06 376	90.55 95

test_fmvs1		53.60 371	52.54 366	56.78 399	58.07 427	30.26 422	68.95 384	42.19 439	32.46 425	63.59 203	82.56 253	11.55 428	60.81 426	58.25 231	55.27 354	79.28 340

test_fmvs1_n		52.55 376	51.19 370	56.65 400	51.90 438	30.14 423	67.66 388	42.84 438	32.27 426	62.30 217	82.02 267	9.12 437	60.84 425	57.82 240	54.75 360	78.99 342

test_vis1_n		51.19 383	49.66 379	55.76 404	51.26 440	29.85 428	67.20 391	38.86 444	32.12 427	59.50 252	79.86 285	8.78 438	58.23 433	56.95 249	52.46 374	79.19 341

SSC-MVS		35.20 409	34.30 411	37.90 428	52.58 436	8.65 466	61.86 409	41.64 440	31.81 428	25.54 445	52.94 438	23.39 381	59.28 431	6.10 459	12.86 454	45.78 447

EU-MVSNet		52.63 375	50.72 371	58.37 396	62.69 420	28.13 436	72.60 358	75.97 330	30.94 429	40.76 410	72.11 377	20.16 399	70.80 412	35.11 381	46.11 399	76.19 378

CMPMVS	40.41 21	55.34 361	52.64 364	63.46 371	60.88 424	43.84 348	61.58 412	71.06 380	30.43 430	36.33 422	74.63 348	24.14 376	75.44 392	48.05 315	66.62 245	71.12 414
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TDRefinement		40.91 401	38.37 405	48.55 415	50.45 442	33.03 414	58.98 419	50.97 430	28.50 431	29.89 437	67.39 399	6.21 448	54.51 438	17.67 441	35.25 424	58.11 433

ttmdpeth		40.58 402	37.50 406	49.85 412	49.40 443	22.71 443	56.65 423	46.78 431	28.35 432	40.29 412	69.42 391	5.35 449	61.86 424	20.16 435	21.06 448	64.96 427

pmmvs3		45.53 396	41.55 402	57.44 398	48.97 445	39.68 385	70.06 377	57.66 420	28.32 433	34.06 428	57.29 430	8.50 439	66.85 419	34.86 383	34.26 428	65.80 425

mvsany_test1		43.38 398	42.57 401	45.82 417	50.96 441	26.10 438	55.80 424	27.74 457	27.15 434	47.41 379	74.39 350	18.67 407	44.95 448	44.66 335	36.31 421	66.40 423

RPSCF		45.77 395	44.13 397	50.68 409	57.67 430	29.66 429	54.92 428	45.25 435	26.69 435	45.92 387	75.92 341	17.43 415	45.70 447	27.44 414	45.95 400	76.67 370

test_fmvs2		45.89 394	44.32 396	50.62 410	45.85 449	24.70 440	58.87 420	37.84 447	25.22 436	52.46 345	74.56 349	7.07 441	54.69 437	49.28 306	47.70 388	72.48 405

mamv4		42.60 399	44.05 399	38.26 427	59.21 426	38.00 394	44.14 439	39.03 443	25.03 437	40.61 411	68.39 395	37.01 249	24.28 461	46.62 325	36.43 420	52.50 439

MVS-HIRNet		49.01 389	44.71 393	61.92 382	76.06 297	46.61 309	63.23 404	54.90 425	24.77 438	33.56 430	36.60 447	21.28 394	75.88 391	29.49 402	62.54 291	63.26 431

test_vis1_rt		40.29 403	38.64 404	45.25 419	48.91 446	30.09 424	59.44 417	27.07 458	24.52 439	38.48 418	51.67 439	6.71 444	49.44 442	44.33 337	46.59 398	56.23 434

new_pmnet		33.56 412	31.89 414	38.59 426	49.01 444	20.42 449	51.01 429	37.92 446	20.58 440	23.45 446	46.79 441	6.66 445	49.28 444	20.00 437	31.57 433	46.09 446

LF4IMVS		33.04 413	32.55 413	34.52 431	40.96 450	22.03 445	44.45 438	35.62 449	20.42 441	28.12 441	62.35 415	5.03 451	31.88 460	21.61 432	34.42 426	49.63 442

FPMVS		35.40 408	33.67 412	40.57 424	46.34 448	28.74 435	41.05 442	57.05 422	20.37 442	22.27 447	53.38 436	6.87 443	44.94 449	8.62 452	47.11 394	48.01 443

DSMNet-mixed		38.35 404	35.36 409	47.33 416	48.11 447	14.91 460	37.87 446	36.60 448	19.18 443	34.37 427	59.56 426	15.53 422	53.01 440	20.14 436	46.89 396	74.07 394

PMMVS2		26.71 418	22.98 423	37.87 429	36.89 453	8.51 467	42.51 441	29.32 456	19.09 444	13.01 453	37.54 444	2.23 458	53.11 439	14.54 446	11.71 455	51.99 441

test_fmvs3		37.95 406	35.75 408	44.55 420	35.50 455	18.92 452	48.32 431	34.00 452	18.36 445	41.31 407	61.58 416	2.29 457	48.06 446	42.72 347	37.71 418	66.66 422

MVStest1		38.35 404	34.53 410	49.82 413	51.43 439	30.41 421	50.39 430	55.25 423	17.56 446	26.45 444	65.85 405	11.72 427	57.00 435	14.79 445	17.31 452	62.05 432

mvsany_test3		28.00 415	25.98 417	34.05 432	28.97 460	15.31 458	34.54 449	18.17 463	16.24 447	29.30 439	53.37 437	2.79 455	33.38 459	30.01 401	20.41 449	53.45 438

PMVS	19.57 22	25.07 420	22.43 425	32.99 435	23.12 466	22.98 441	40.98 443	35.19 450	15.99 448	11.95 457	35.87 449	1.47 463	49.29 443	5.41 461	31.90 432	26.70 454
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		27.47 416	24.26 421	37.12 430	60.55 425	29.17 432	11.68 457	60.00 416	14.18 449	10.52 458	15.12 459	2.20 459	63.01 423	8.39 453	35.65 422	19.18 455
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis3_rt		24.79 421	22.95 424	30.31 437	28.59 461	18.92 452	37.43 447	17.27 465	12.90 450	21.28 448	29.92 454	1.02 464	36.35 453	28.28 411	29.82 438	35.65 448

LCM-MVSNet		28.07 414	23.85 422	40.71 423	27.46 464	18.93 451	30.82 452	46.19 432	12.76 451	16.40 449	34.70 450	1.90 460	48.69 445	20.25 434	24.22 443	54.51 437

test_f		27.12 417	24.85 418	33.93 433	26.17 465	15.25 459	30.24 453	22.38 462	12.53 452	28.23 440	49.43 440	2.59 456	34.34 458	25.12 421	26.99 439	52.20 440

APD_test1		26.46 419	24.41 420	32.62 436	37.58 452	21.74 447	40.50 444	30.39 454	11.45 453	16.33 450	43.76 442	1.63 462	41.62 450	11.24 449	26.82 440	34.51 450

E-PMN		19.16 425	18.40 429	21.44 441	36.19 454	13.63 461	47.59 432	30.89 453	10.73 454	5.91 461	16.59 457	3.66 453	39.77 451	5.95 460	8.14 457	10.92 457

DeepMVS_CX				13.10 443	21.34 467	8.99 465		10.02 467	10.59 455	7.53 460	30.55 453	1.82 461	14.55 462	6.83 457	7.52 458	15.75 456

EMVS		18.42 426	17.66 430	20.71 442	34.13 456	12.64 462	46.94 433	29.94 455	10.46 456	5.58 462	14.93 460	4.23 452	38.83 452	5.24 462	7.51 459	10.67 458

testf1		21.11 423	19.08 427	27.18 439	30.56 457	18.28 454	33.43 450	24.48 459	8.02 457	12.02 455	33.50 451	0.75 466	35.09 456	7.68 454	21.32 445	28.17 452

APD_test2		21.11 423	19.08 427	27.18 439	30.56 457	18.28 454	33.43 450	24.48 459	8.02 457	12.02 455	33.50 451	0.75 466	35.09 456	7.68 454	21.32 445	28.17 452

MVE	16.60 23	17.34 428	13.39 431	29.16 438	28.43 462	19.72 450	13.73 456	23.63 461	7.23 459	7.96 459	21.41 455	0.80 465	36.08 454	6.97 456	10.39 456	31.69 451
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		24.09 422	21.07 426	33.16 434	27.67 463	8.35 468	26.63 454	35.11 451	3.40 460	14.35 452	36.98 446	3.46 454	35.31 455	19.08 439	22.95 444	55.81 435

wuyk23d		9.11 430	8.77 434	10.15 444	40.18 451	16.76 457	20.28 455	1.01 468	2.58 461	2.66 463	0.98 463	0.23 468	12.49 463	4.08 463	6.90 460	1.19 460

tmp_tt		9.44 429	10.68 432	5.73 445	2.49 468	4.21 469	10.48 458	18.04 464	0.34 462	12.59 454	20.49 456	11.39 429	7.03 464	13.84 448	6.46 461	5.95 459

EGC-MVSNET		33.75 411	30.42 415	43.75 421	64.94 410	36.21 400	60.47 416	40.70 442	0.02 463	0.10 464	53.79 435	7.39 440	60.26 427	11.09 450	35.23 425	34.79 449

mmdepth		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

monomultidepth		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

test_blank		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

uanet_test		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

DCPMVS		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

cdsmvs_eth3d_5k		18.33 427	24.44 419	0.00 448	0.00 470	0.00 472	0.00 459	89.40 27	0.00 464	0.00 467	92.02 56	38.55 217	0.00 465	0.00 466	0.00 463	0.00 463

pcd_1.5k_mvsjas		3.15 434	4.20 437	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	37.77 225	0.00 465	0.00 466	0.00 463	0.00 463

sosnet-low-res		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

sosnet		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

uncertanet		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

Regformer		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

testmvs		6.14 432	8.18 435	0.01 446	0.01 469	0.00 472	73.40 353	0.00 470	0.00 464	0.02 465	0.15 464	0.00 469	0.00 465	0.02 464	0.00 463	0.02 461

test123		6.01 433	8.01 436	0.01 446	0.00 470	0.01 471	71.93 370	0.00 470	0.00 464	0.02 465	0.11 465	0.00 469	0.00 465	0.02 464	0.00 463	0.02 461

ab-mvs-re		7.68 431	10.24 433	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	92.12 52	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

uanet		0.00 435	0.00 438	0.00 448	0.00 470	0.00 472	0.00 459	0.00 470	0.00 464	0.00 467	0.00 466	0.00 469	0.00 465	0.00 466	0.00 463	0.00 463

WAC-MVS						34.28 405								22.56 428

MSC_two_6792asdad				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 43	86.80 28	92.34 35

No_MVS				81.53 15	91.77 4	56.03 46		91.10 12					96.22 8	81.46 43	86.80 28	92.34 35

eth-test2					0.00 470

eth-test					0.00 470

OPU-MVS				81.71 13	92.05 3	55.97 48	92.48 3				94.01 8	67.21 2	95.10 15	89.82 3	92.55 3	94.06 3

test_0728_SECOND				82.20 8	89.50 15	57.73 13	92.34 5	88.88 37					96.39 4	81.68 38	87.13 21	92.47 31

GSMVS																88.13 176

test_part2					89.33 23	55.48 54				82.27 12

sam_mvs1												38.86 215				88.13 176

sam_mvs												35.99 270

ambc				62.06 379	53.98 435	29.38 431	35.08 448	79.65 258		41.37 404	59.96 424	6.27 447	82.15 328	35.34 377	38.22 417	74.65 391

MTGPA								81.31 220

test_post1							70.84 375				14.72 461	34.33 292	83.86 310	48.80 309

test_post											16.22 458	37.52 235	84.72 301

patchmatchnet-post											59.74 425	38.41 218	79.91 355

GG-mvs-BLEND				77.77 89	86.68 49	50.61 190	68.67 385	88.45 54		68.73 131	87.45 169	59.15 11	90.67 101	54.83 267	87.67 17	92.03 45

MTMP							87.27 80	15.34 466

test9_res														78.72 59	85.44 43	91.39 68

agg_prior2														75.65 82	85.11 47	91.01 83

agg_prior					85.64 63	54.92 77		83.61 176		72.53 87			88.10 199

test_prior4						56.39 40	87.15 84

test_prior				78.39 75	86.35 54	54.91 78		85.45 111					89.70 132			90.55 95

新几何2							81.61 265

旧先验1					81.57 174	47.48 295		71.83 370			88.66 137	36.94 251			78.34 110	88.67 157

原ACMM2							83.77 195

testdata2													77.81 375	45.64 331

segment_acmp												44.97 135

test12				79.24 44	86.89 47	56.08 45		85.16 128		72.27 91		47.15 93	91.10 86		85.93 37	90.54 97

plane_prior7					77.95 258	48.46 260

plane_prior6					78.42 251	49.39 232						36.04 268

plane_prior5								82.59 193					88.30 192	65.46 161	72.34 187	84.49 256

plane_prior4											83.28 238

plane_prior1					78.31 254

n2								0.00 470

nn								0.00 470

door-mid								41.31 441

lessismore_v0				67.98 333	64.76 411	41.25 378		45.75 434		36.03 424	65.63 406	19.29 404	84.11 308	35.67 373	21.24 447	78.59 349

test11								84.25 158

door								43.27 437

HQP5-MVS						51.56 171

BP-MVS														66.70 147

HQP4-MVS										64.47 184			88.61 173			84.91 252

HQP3-MVS								83.68 172							73.12 176

HQP2-MVS												37.35 238

NP-MVS					78.76 239	50.43 197					85.12 203

ACMMP++_ref															63.20 283

ACMMP++															59.38 310

Test By Simon												39.38 209

High-resolution multi-view benchmark