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 47	56.89 29	92.77 2	86.30 90	77.83 1	77.88 35	92.13 45	60.24 7	94.78 19	78.97 48	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	53.77 104	88.08 54	88.36 52	76.17 2	79.40 28	91.09 68	55.43 27	90.09 110	85.01 13	80.40 82	91.99 48

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 16	93.77 1	91.10 11	75.95 3	77.10 39	93.09 30	54.15 38	95.57 12	85.80 10	85.87 38	93.31 11

MM		82.69 2	83.29 3	80.89 22	84.38 86	55.40 59	92.16 10	89.85 22	75.28 4	82.41 11	93.86 8	54.30 35	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 49	54.69 84	92.20 9	86.66 82	74.48 5	82.63 10	93.80 10	50.83 61	93.70 28	90.11 2	86.44 33	93.01 21

CLD-MVS		75.60 74	75.39 67	76.24 122	80.69 188	52.40 141	90.69 23	86.20 92	74.40 6	65.01 156	88.93 121	42.05 163	90.58 96	76.57 67	73.96 157	85.73 209
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPNet		78.36 30	78.49 25	77.97 82	85.49 65	52.04 149	89.36 39	84.07 151	73.22 7	77.03 40	91.72 58	49.32 74	90.17 109	73.46 94	82.77 60	91.69 55
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 96	91.60 14	86.47 86	73.13 8	79.89 26	93.10 28	49.88 70	92.98 33	84.09 18	84.75 50	93.08 19

UBG		78.86 24	78.86 22	78.86 57	87.80 40	55.43 55	87.67 64	91.21 10	72.83 9	72.10 83	88.40 132	58.53 16	89.08 137	73.21 98	77.98 107	92.08 41

testing11		79.18 22	78.85 23	80.16 33	88.33 30	56.99 26	88.31 52	92.06 1	72.82 10	70.62 106	88.37 133	57.69 17	92.30 50	75.25 78	76.24 129	91.20 73

VPNet		72.07 132	71.42 127	74.04 188	78.64 228	47.17 275	89.91 31	87.97 57	72.56 11	64.66 159	85.04 186	41.83 168	88.33 172	61.17 178	60.97 268	86.62 191

testing222		77.70 40	77.22 42	79.14 48	86.95 45	54.89 78	87.18 79	91.96 2	72.29 12	71.17 97	88.70 126	55.19 28	91.24 76	65.18 152	76.32 127	91.29 71

casdiffmvs		77.36 44	76.85 46	78.88 56	80.40 195	54.66 87	87.06 82	85.88 98	72.11 13	71.57 89	88.63 131	50.89 60	90.35 101	76.00 69	79.11 98	91.63 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing99		78.45 26	77.78 34	80.45 28	88.28 33	56.81 32	87.95 59	91.49 6	71.72 14	70.84 100	88.09 141	57.29 19	92.63 44	69.24 117	75.13 146	91.91 49

casdiffmvs_mvg		77.75 39	77.28 40	79.16 47	80.42 194	54.44 91	87.76 61	85.46 105	71.67 15	71.38 92	88.35 135	51.58 50	91.22 77	79.02 47	79.89 92	91.83 53
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		72.51 124	72.12 115	73.69 202	85.05 73	44.46 308	83.51 187	86.13 95	71.61 16	64.64 160	87.97 146	55.00 33	89.48 125	59.07 195	56.05 314	87.13 179

testing91		78.30 32	77.54 37	80.61 23	88.16 35	57.12 25	87.94 60	91.07 14	71.43 17	70.75 101	88.04 145	55.82 26	92.65 42	69.61 113	75.00 150	92.05 44

WTY-MVS		77.47 43	77.52 38	77.30 97	88.33 30	46.25 289	88.46 50	90.32 18	71.40 18	72.32 81	91.72 58	53.44 41	92.37 49	66.28 137	75.42 140	93.28 13

baseline		76.86 52	76.24 54	78.71 62	80.47 193	54.20 98	83.90 176	84.88 129	71.38 19	71.51 90	89.15 119	50.51 62	90.55 97	75.71 71	78.65 101	91.39 66

ETVMVS		75.80 72	75.44 65	76.89 112	86.23 54	50.38 185	85.55 118	91.42 7	71.30 20	68.80 117	87.94 147	56.42 23	89.24 132	56.54 226	74.75 153	91.07 77

gm-plane-assit					83.24 112	54.21 96			70.91 21		88.23 139		95.25 14	66.37 135

PS-MVSNAJ		80.06 17	79.52 18	81.68 14	85.58 63	60.97 3	91.69 12	87.02 74	70.62 22	80.75 22	93.22 27	37.77 208	92.50 46	82.75 24	86.25 35	91.57 60

DeepPCF-MVS	69.37 1	80.65 13	81.56 11	77.94 85	85.46 66	49.56 205	90.99 21	86.66 82	70.58 23	80.07 25	95.30 1	56.18 24	90.97 87	82.57 26	86.22 36	93.28 13

diffmvs		75.11 84	74.65 79	76.46 119	78.52 230	53.35 117	83.28 197	79.94 228	70.51 24	71.64 88	88.72 125	46.02 104	86.08 249	77.52 62	75.75 137	89.96 109
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 36	70.31 25	77.64 38	93.87 7	52.58 46	93.91 26	84.17 16	87.92 16	92.39 33

xiu_mvs_v2_base		79.86 18	79.31 19	81.53 15	85.03 75	60.73 4	91.65 13	86.86 77	70.30 26	80.77 21	93.07 32	37.63 213	92.28 52	82.73 25	85.71 39	91.57 60

baseline2		75.15 83	74.54 81	76.98 109	81.67 158	51.74 157	83.84 178	91.94 3	69.97 27	58.98 234	86.02 174	59.73 9	91.73 64	68.37 123	70.40 191	87.48 171

CHOSEN 1792x2688		76.24 59	74.03 88	82.88 1	83.09 117	62.84 2	85.73 111	85.39 108	69.79 28	64.87 158	83.49 205	41.52 172	93.69 29	70.55 107	81.82 69	92.12 40

balanced_conf03		80.28 16	79.73 15	81.90 11	86.47 51	59.34 6	80.45 264	89.51 24	69.76 29	71.05 98	86.66 168	58.68 15	93.24 31	84.64 15	90.40 6	93.14 18

CANet_DTU		73.71 104	73.14 97	75.40 151	82.61 137	50.05 194	84.67 153	79.36 244	69.72 30	75.39 46	90.03 102	29.41 308	85.93 257	67.99 126	79.11 98	90.22 98

TSAR-MVS + MP.		78.31 31	78.26 26	78.48 70	81.33 172	56.31 42	81.59 244	86.41 87	69.61 31	81.72 17	88.16 140	55.09 31	88.04 183	74.12 87	86.31 34	91.09 76
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
dmvs_re		67.61 219	66.00 223	72.42 231	81.86 150	43.45 321	64.67 364	80.00 225	69.56 32	60.07 215	85.00 187	34.71 264	87.63 199	51.48 262	66.68 215	86.17 200

DPM-MVS		82.39 4	82.36 7	82.49 5	80.12 198	59.50 5	92.24 8	90.72 15	69.37 33	83.22 8	94.47 2	63.81 5	93.18 32	74.02 88	93.25 2	94.80 1

lupinMVS		78.38 29	78.11 29	79.19 45	83.02 120	55.24 63	91.57 15	84.82 130	69.12 34	76.67 41	92.02 50	44.82 125	90.23 107	80.83 40	80.09 86	92.08 41

PAPM		76.76 54	76.07 56	78.81 58	80.20 196	59.11 7	86.86 88	86.23 91	68.60 35	70.18 109	88.84 124	51.57 51	87.16 213	65.48 145	86.68 30	90.15 103

DeepC-MVS_fast	67.50 3	78.00 36	77.63 35	79.13 49	88.52 27	55.12 69	89.95 28	85.98 97	68.31 36	71.33 93	92.75 36	45.52 111	90.37 100	71.15 105	85.14 46	91.91 49
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 48	76.45 50	78.69 63	79.69 203	54.74 80	90.56 24	83.99 154	68.26 37	74.10 58	90.91 76	42.14 161	89.99 112	79.30 45	79.12 97	91.36 68
jason: jason.
ETV-MVS		77.17 46	76.74 47	78.48 70	81.80 151	54.55 89	86.13 100	85.33 111	68.20 38	73.10 68	90.52 85	45.23 115	90.66 93	79.37 44	80.95 74	90.22 98

h-mvs33		73.95 97	72.89 100	77.15 102	80.17 197	50.37 186	84.68 151	83.33 164	68.08 39	71.97 84	88.65 130	42.50 155	91.15 80	78.82 49	57.78 301	89.91 111

hse-mvs2		71.44 147	70.68 137	73.73 201	76.34 263	47.44 270	79.45 280	79.47 240	68.08 39	71.97 84	86.01 176	42.50 155	86.93 221	78.82 49	53.46 338	86.83 187

MVS_Test		75.85 68	74.93 75	78.62 66	84.08 92	55.20 67	83.99 173	85.17 120	68.07 41	73.38 65	82.76 216	50.44 63	89.00 142	65.90 141	80.61 78	91.64 56

ET-MVSNet_ETH3D		75.23 81	74.08 86	78.67 64	84.52 83	55.59 51	88.92 44	89.21 28	68.06 42	53.13 309	90.22 95	49.71 71	87.62 201	72.12 101	70.82 186	92.82 25

reproduce_monomvs		69.71 178	68.52 172	73.29 212	86.43 52	48.21 250	83.91 175	86.17 94	68.02 43	54.91 291	77.46 281	42.96 152	88.86 150	68.44 122	48.38 351	82.80 263

tpmrst		71.04 154	69.77 156	74.86 170	83.19 114	55.86 50	75.64 302	78.73 258	67.88 44	64.99 157	73.73 323	49.96 69	79.56 329	65.92 140	67.85 209	89.14 130

dcpmvs_2		79.33 21	78.94 21	80.49 25	89.75 12	56.54 36	84.83 146	83.68 158	67.85 45	69.36 111	90.24 93	60.20 8	92.10 58	84.14 17	80.40 82	92.82 25

PVSNet_Blended		76.53 56	76.54 49	76.50 118	85.91 56	51.83 155	88.89 45	84.24 148	67.82 46	69.09 115	89.33 116	46.70 95	88.13 179	75.43 74	81.48 73	89.55 117

tpm		68.36 204	67.48 196	70.97 265	79.93 201	51.34 167	76.58 299	78.75 257	67.73 47	63.54 183	74.86 313	48.33 76	72.36 373	53.93 244	63.71 243	89.21 127

NCCC		79.57 20	79.23 20	80.59 24	89.50 15	56.99 26	91.38 16	88.17 54	67.71 48	73.81 60	92.75 36	46.88 92	93.28 30	78.79 51	84.07 55	91.50 64

sasdasda		78.17 33	77.86 32	79.12 50	84.30 87	54.22 94	87.71 62	84.57 139	67.70 49	77.70 36	92.11 48	50.90 57	89.95 113	78.18 58	77.54 111	93.20 15

canonicalmvs		78.17 33	77.86 32	79.12 50	84.30 87	54.22 94	87.71 62	84.57 139	67.70 49	77.70 36	92.11 48	50.90 57	89.95 113	78.18 58	77.54 111	93.20 15

3Dnovator	64.70 6	74.46 89	72.48 104	80.41 29	82.84 130	55.40 59	83.08 203	88.61 47	67.61 51	59.85 217	88.66 127	34.57 266	93.97 24	58.42 203	88.70 12	91.85 52

VNet		77.99 37	77.92 31	78.19 78	87.43 42	50.12 193	90.93 22	91.41 8	67.48 52	75.12 47	90.15 99	46.77 94	91.00 84	73.52 93	78.46 103	93.44 9

WBMVS		73.93 98	73.39 91	75.55 145	87.82 39	55.21 65	89.37 37	87.29 70	67.27 53	63.70 178	80.30 253	60.32 6	86.47 234	61.58 174	62.85 258	84.97 221

dmvs_testset		57.65 315	58.21 296	55.97 367	74.62 293	9.82 428	63.75 367	63.34 378	67.23 54	48.89 334	83.68 204	39.12 197	76.14 354	23.43 390	59.80 274	81.96 270

fmvsm_l_conf0.5_n_3		75.73 73	75.78 58	75.61 141	76.03 273	48.33 246	85.34 120	72.92 331	67.16 55	78.55 32	93.85 9	46.22 99	87.53 204	85.61 11	76.30 128	90.98 80

IB-MVS	68.87 2	74.01 96	72.03 119	79.94 38	83.04 119	55.50 53	90.24 25	88.65 43	67.14 56	61.38 204	81.74 241	53.21 42	94.28 21	60.45 188	62.41 261	90.03 107
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
MVSTER		73.25 112	72.33 107	76.01 132	85.54 64	53.76 105	83.52 183	87.16 72	67.06 57	63.88 176	81.66 242	52.77 44	90.44 98	64.66 156	64.69 235	83.84 244

test_fmvsmconf_n		74.41 90	74.05 87	75.49 149	74.16 300	48.38 242	82.66 211	72.57 332	67.05 58	75.11 48	92.88 35	46.35 98	87.81 188	83.93 19	71.71 177	90.28 96

DeepC-MVS	67.15 4	76.90 51	76.27 53	78.80 59	80.70 187	55.02 73	86.39 94	86.71 80	66.96 59	67.91 124	89.97 103	48.03 79	91.41 71	75.60 73	84.14 54	89.96 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		70.00 172	70.24 151	69.30 287	77.93 240	38.55 357	83.99 173	87.72 64	66.86 60	57.66 260	84.17 194	52.28 47	85.31 264	52.72 257	68.80 201	84.02 235

test_fmvsmconf0.1_n		73.69 105	73.15 95	75.34 153	70.71 338	48.26 248	82.15 225	71.83 337	66.75 61	74.47 56	92.59 40	44.89 122	87.78 193	83.59 20	71.35 181	89.97 108

SDMVSNet		71.89 136	70.62 139	75.70 139	81.70 155	51.61 159	73.89 316	88.72 42	66.58 62	61.64 202	82.38 229	37.63 213	89.48 125	77.44 63	65.60 229	86.01 201

sd_testset		67.79 216	65.95 225	73.32 209	81.70 155	46.33 287	68.99 349	80.30 221	66.58 62	61.64 202	82.38 229	30.45 303	87.63 199	55.86 232	65.60 229	86.01 201

PC_three_1452									66.58 62	87.27 2	93.70 12	66.82 4	94.95 17	89.74 4	91.98 4	93.98 5

test_fmvsm_n_1920		75.56 75	75.54 63	75.61 141	74.60 294	49.51 210	81.82 235	74.08 318	66.52 65	80.40 23	93.46 19	46.95 91	89.72 120	86.69 7	75.30 141	87.61 169

PVSNet	62.49 8	69.27 188	67.81 188	73.64 203	84.41 85	51.85 154	84.63 154	77.80 274	66.42 66	59.80 218	84.95 188	22.14 359	80.44 317	55.03 236	75.11 147	88.62 143

CS-MVS		76.77 53	76.70 48	76.99 108	83.55 102	48.75 230	88.60 48	85.18 119	66.38 67	72.47 79	91.62 62	45.53 110	90.99 86	74.48 83	82.51 62	91.23 72

UniMVSNet_NR-MVSNet		68.82 195	68.29 177	70.40 273	75.71 279	42.59 333	84.23 164	86.78 78	66.31 68	58.51 244	82.45 226	51.57 51	84.64 277	53.11 248	55.96 315	83.96 241

HY-MVS	67.03 5	73.90 99	73.14 97	76.18 127	84.70 79	47.36 271	75.56 303	86.36 89	66.27 69	70.66 104	83.91 197	51.05 55	89.31 130	67.10 131	72.61 170	91.88 51

IU-MVS					89.48 17	57.49 17		91.38 9	66.22 70	88.26 1				82.83 23	87.60 18	92.44 32

fmvsm_s_conf0.5_n_3		74.97 86	75.42 66	73.62 205	76.99 256	46.67 279	83.13 201	71.14 345	66.20 71	82.13 13	93.76 11	47.49 85	84.00 282	81.95 30	76.02 130	90.19 102

EI-MVSNet-Vis-set		73.19 113	72.60 102	74.99 168	82.56 138	49.80 201	82.55 216	89.00 31	66.17 72	65.89 144	88.98 120	43.83 134	92.29 51	65.38 151	69.01 200	82.87 262

alignmvs		78.08 35	77.98 30	78.39 74	83.53 103	53.22 122	89.77 32	85.45 106	66.11 73	76.59 43	91.99 52	54.07 39	89.05 139	77.34 64	77.00 116	92.89 23

TESTMET0.1,1		72.86 117	72.33 107	74.46 175	81.98 145	50.77 173	85.13 131	85.47 104	66.09 74	67.30 127	83.69 202	37.27 223	83.57 289	65.06 154	78.97 100	89.05 132

MSP-MVS		82.30 6	83.47 1	78.80 59	82.99 122	52.71 135	85.04 136	88.63 45	66.08 75	86.77 3	92.75 36	72.05 1	91.46 70	83.35 21	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		73.89 100	72.30 109	78.66 65	82.36 141	56.58 33	75.56 303	85.30 113	66.06 76	70.50 108	76.88 293	57.02 20	89.06 138	68.27 125	68.74 202	90.33 94

NR-MVSNet		67.25 230	65.99 224	71.04 264	73.27 309	43.91 316	85.32 124	84.75 134	66.05 77	53.65 307	82.11 236	45.05 117	85.97 255	47.55 287	56.18 312	83.24 253

HPM-MVS++		80.50 14	80.71 14	79.88 39	87.34 43	55.20 67	89.93 29	87.55 68	66.04 78	79.46 27	93.00 34	53.10 43	91.76 63	80.40 41	89.56 9	92.68 29

SPE-MVS-test		77.20 45	77.25 41	77.05 103	84.60 81	49.04 220	89.42 36	85.83 100	65.90 79	72.85 72	91.98 54	45.10 116	91.27 74	75.02 80	84.56 51	90.84 83

test_fmvsmconf0.01_n		71.97 135	70.95 135	75.04 165	66.21 363	47.87 263	80.35 267	70.08 353	65.85 80	72.69 74	91.68 60	39.99 190	87.67 197	82.03 29	69.66 196	89.58 116

MGCFI-Net		74.07 95	74.64 80	72.34 234	82.90 126	43.33 325	80.04 273	79.96 227	65.61 81	74.93 49	91.85 55	48.01 80	80.86 309	71.41 103	77.10 114	92.84 24

UWE-MVS		72.17 131	72.15 113	72.21 236	82.26 142	44.29 312	86.83 89	89.58 23	65.58 82	65.82 145	85.06 185	45.02 118	84.35 279	54.07 242	75.18 143	87.99 161

HQP-NCC					79.02 217		88.00 55		65.45 83	64.48 165

ACMP_Plane					79.02 217		88.00 55		65.45 83	64.48 165

HQP-MVS		72.34 126	71.44 126	75.03 166	79.02 217	51.56 161	88.00 55	83.68 158	65.45 83	64.48 165	85.13 183	37.35 220	88.62 157	66.70 132	73.12 163	84.91 223

PVSNet_BlendedMVS		73.42 109	73.30 93	73.76 199	85.91 56	51.83 155	86.18 99	84.24 148	65.40 86	69.09 115	80.86 249	46.70 95	88.13 179	75.43 74	65.92 228	81.33 285

MS-PatchMatch		72.34 126	71.26 129	75.61 141	82.38 140	55.55 52	88.00 55	89.95 21	65.38 87	56.51 280	80.74 251	32.28 288	92.89 34	57.95 212	88.10 15	78.39 320

v2v482		69.55 184	67.64 190	75.26 162	72.32 322	53.83 102	84.93 143	81.94 188	65.37 88	60.80 209	79.25 263	41.62 169	88.98 145	63.03 163	59.51 276	82.98 260

VDD-MVS		76.08 63	74.97 74	79.44 41	84.27 90	53.33 119	91.13 20	85.88 98	65.33 89	72.37 80	89.34 114	32.52 285	92.76 40	77.90 61	75.96 133	92.22 39

TranMVSNet+NR-MVSNet		66.94 240	65.61 234	70.93 266	73.45 305	43.38 323	83.02 206	84.25 146	65.31 90	58.33 251	81.90 240	39.92 192	85.52 260	49.43 274	54.89 324	83.89 243

EI-MVSNet-UG-set		72.37 125	71.73 120	74.29 182	81.60 161	49.29 215	81.85 233	88.64 44	65.29 91	65.05 154	88.29 138	43.18 147	91.83 62	63.74 159	67.97 207	81.75 273

MVS_111021_HR		76.39 58	75.38 68	79.42 42	85.33 69	56.47 38	88.15 53	84.97 126	65.15 92	66.06 141	89.88 104	43.79 136	92.16 55	75.03 79	80.03 89	89.64 115

miper_enhance_ethall		69.77 177	68.90 169	72.38 232	78.93 220	49.91 197	83.29 196	78.85 252	64.90 93	59.37 227	79.46 260	52.77 44	85.16 269	63.78 158	58.72 283	82.08 268

MG-MVS		78.42 28	76.99 45	82.73 2	93.17 1	64.46 1	89.93 29	88.51 50	64.83 94	73.52 63	88.09 141	48.07 78	92.19 54	62.24 168	84.53 52	91.53 62

EIA-MVS		75.92 66	75.18 71	78.13 79	85.14 72	51.60 160	87.17 80	85.32 112	64.69 95	68.56 119	90.53 84	45.79 107	91.58 67	67.21 130	82.18 66	91.20 73

plane_prior						49.57 203	87.43 70		64.57 96						72.84 167

BP-MVS1		76.09 62	75.55 62	77.71 88	79.49 205	52.27 146	84.70 149	90.49 17	64.44 97	69.86 110	90.31 92	55.05 32	91.35 72	70.07 111	75.58 139	89.53 119

FC-MVSNet-test		67.49 223	67.91 182	66.21 319	76.06 271	33.06 378	80.82 260	87.18 71	64.44 97	54.81 292	82.87 213	50.40 64	82.60 296	48.05 285	66.55 219	82.98 260

MonoMVSNet		66.80 243	64.41 251	73.96 191	76.21 268	48.07 256	76.56 300	78.26 268	64.34 99	54.32 299	74.02 320	37.21 226	86.36 239	64.85 155	53.96 331	87.45 173

WR-MVS		67.58 220	66.76 206	70.04 280	75.92 277	45.06 306	86.23 98	85.28 115	64.31 100	58.50 246	81.00 246	44.80 127	82.00 301	49.21 277	55.57 320	83.06 258

fmvsm_s_conf0.5_n_2		72.02 133	71.72 121	72.92 217	76.79 259	45.90 292	84.48 157	66.11 369	64.26 101	76.12 44	93.40 20	36.26 245	86.04 250	81.47 35	66.54 220	86.82 188

v1144		68.81 196	66.82 204	74.80 171	72.34 321	53.46 110	84.68 151	81.77 195	64.25 102	60.28 213	77.91 274	40.23 185	88.95 146	60.37 189	59.52 275	81.97 269

test1111		71.06 153	70.42 144	72.97 216	79.48 206	41.49 343	84.82 147	82.74 178	64.20 103	62.98 187	87.43 156	35.20 257	87.92 185	58.54 200	78.42 104	89.49 120

fmvsm_s_conf0.5_n		74.48 88	74.12 85	75.56 144	76.96 257	47.85 264	85.32 124	69.80 356	64.16 104	78.74 29	93.48 18	45.51 112	89.29 131	86.48 8	66.62 217	89.55 117

testdata1							77.55 294		64.14 105

fmvsm_s_conf0.1_n_2		71.45 146	71.01 133	72.78 221	75.37 283	45.82 296	84.18 166	64.59 374	64.02 106	75.67 45	93.02 33	34.99 262	85.99 252	81.18 39	66.04 227	86.52 193

test2506		72.91 116	72.43 106	74.32 181	80.12 198	44.18 315	83.19 199	84.77 133	64.02 106	65.97 142	87.43 156	47.67 84	88.72 154	59.08 194	79.66 94	90.08 105

ECVR-MVS		71.81 138	71.00 134	74.26 183	80.12 198	43.49 320	84.69 150	82.16 183	64.02 106	64.64 160	87.43 156	35.04 260	89.21 135	61.24 177	79.66 94	90.08 105

plane_prior3						48.95 222			64.01 109	62.15 197

VPA-MVSNet		71.12 150	70.66 138	72.49 229	78.75 223	44.43 310	87.64 65	90.02 19	63.97 110	65.02 155	81.58 244	42.14 161	87.42 207	63.42 161	63.38 249	85.63 213

PVSNet_0	57.04 13	61.19 288	57.24 301	73.02 214	77.45 247	50.31 190	79.43 281	77.36 284	63.96 111	47.51 345	72.45 339	25.03 338	83.78 286	52.76 256	19.22 414	84.96 222

V42		67.66 218	65.60 235	73.86 195	70.69 340	53.63 107	81.50 247	78.61 261	63.85 112	59.49 226	77.49 280	37.98 205	87.65 198	62.33 166	58.43 286	80.29 300

mvs_anonymous		72.29 128	70.74 136	76.94 111	82.85 129	54.72 82	78.43 288	81.54 197	63.77 113	61.69 201	79.32 262	51.11 54	85.31 264	62.15 170	75.79 135	90.79 85

PAPR		75.20 82	74.13 84	78.41 73	88.31 32	55.10 71	84.31 162	85.66 102	63.76 114	67.55 126	90.73 81	43.48 144	89.40 127	66.36 136	77.03 115	90.73 86

PVSNet_Blended_VisFu		73.40 110	72.44 105	76.30 120	81.32 173	54.70 83	85.81 105	78.82 254	63.70 115	64.53 164	85.38 182	47.11 90	87.38 209	67.75 127	77.55 110	86.81 189

v148		68.24 209	66.35 214	73.88 194	71.76 326	51.47 164	84.23 164	81.90 192	63.69 116	58.94 235	76.44 298	43.72 139	87.78 193	60.63 182	55.86 317	82.39 266

UniMVSNet (Re)		67.71 217	66.80 205	70.45 271	74.44 295	42.93 329	82.42 222	84.90 128	63.69 116	59.63 221	80.99 247	47.18 88	85.23 267	51.17 265	56.75 306	83.19 255

HQP_MVS		70.96 156	69.91 155	74.12 186	77.95 238	49.57 203	85.76 107	82.59 179	63.60 118	62.15 197	83.28 210	36.04 250	88.30 174	65.46 146	72.34 172	84.49 227

plane_prior2							85.76 107		63.60 118

DU-MVS		66.84 242	65.74 231	70.16 276	73.27 309	42.59 333	81.50 247	82.92 176	63.53 120	58.51 244	82.11 236	40.75 178	84.64 277	53.11 248	55.96 315	83.24 253

fmvsm_l_conf0.5_n		75.95 65	76.16 55	75.31 155	76.01 275	48.44 241	84.98 139	71.08 346	63.50 121	81.70 18	93.52 17	50.00 66	87.18 212	87.80 5	76.87 119	90.32 95

EC-MVSNet		75.30 77	75.20 69	75.62 140	80.98 176	49.00 221	87.43 70	84.68 136	63.49 122	70.97 99	90.15 99	42.86 154	91.14 81	74.33 85	81.90 68	86.71 190

fmvsm_s_conf0.5_n_a		73.68 106	73.15 95	75.29 158	75.45 282	48.05 257	83.88 177	68.84 361	63.43 123	78.60 30	93.37 23	45.32 113	88.92 149	85.39 12	64.04 239	88.89 135

fmvsm_s_conf0.1_n		73.80 101	73.26 94	75.43 150	73.28 308	47.80 265	84.57 156	69.43 358	63.34 124	78.40 33	93.29 25	44.73 128	89.22 134	85.99 9	66.28 225	89.26 124

GA-MVS		69.04 190	66.70 208	76.06 130	75.11 285	52.36 142	83.12 202	80.23 222	63.32 125	60.65 211	79.22 264	30.98 300	88.37 168	61.25 176	66.41 221	87.46 172

CDS-MVSNet		70.48 164	69.43 160	73.64 203	77.56 245	48.83 227	83.51 187	77.45 281	63.27 126	62.33 194	85.54 181	43.85 133	83.29 294	57.38 222	74.00 156	88.79 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
LFMVS		78.52 25	77.14 43	82.67 3	89.58 13	58.90 8	91.27 19	88.05 56	63.22 127	74.63 52	90.83 79	41.38 173	94.40 20	75.42 76	79.90 91	94.72 2

v1192		67.96 212	65.74 231	74.63 172	71.79 325	53.43 115	84.06 171	80.99 210	63.19 128	59.56 223	77.46 281	37.50 219	88.65 156	58.20 207	58.93 282	81.79 272

fmvsm_l_conf0.5_n_a		75.88 67	76.07 56	75.31 155	76.08 270	48.34 244	85.24 126	70.62 349	63.13 129	81.45 19	93.62 16	49.98 68	87.40 208	87.76 6	76.77 120	90.20 100

Fast-Effi-MVS+		72.73 119	71.15 132	77.48 93	82.75 132	54.76 79	86.77 90	80.64 214	63.05 130	65.93 143	84.01 195	44.42 130	89.03 140	56.45 230	76.36 126	88.64 142

MAR-MVS		76.76 54	75.60 61	80.21 31	90.87 7	54.68 85	89.14 42	89.11 29	62.95 131	70.54 107	92.33 43	41.05 174	94.95 17	57.90 214	86.55 32	91.00 79
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 47	76.33 52	79.34 43	80.98 176	55.31 61	89.76 33	86.91 76	62.94 132	71.65 87	91.56 64	42.33 157	92.56 45	77.14 65	83.69 57	90.15 103
Skip Steuart: Steuart Systems R&D Blog.
v144192		67.86 213	65.76 230	74.16 185	71.68 327	53.09 126	84.14 168	80.83 212	62.85 133	59.21 232	77.28 285	39.30 195	88.00 184	58.67 199	57.88 299	81.40 282

test_fmvsmvis_n_1920		71.29 148	70.38 145	74.00 190	71.04 336	48.79 229	79.19 283	64.62 373	62.75 134	66.73 130	91.99 52	40.94 176	88.35 170	83.00 22	73.18 162	84.85 225

nrg030		72.27 130	71.56 123	74.42 177	75.93 276	50.60 177	86.97 84	83.21 169	62.75 134	67.15 129	84.38 191	50.07 65	86.66 228	71.19 104	62.37 262	85.99 203

miper_ehance_all_eth		68.70 201	67.58 191	72.08 239	76.91 258	49.48 211	82.47 220	78.45 265	62.68 136	58.28 252	77.88 275	50.90 57	85.01 272	61.91 171	58.72 283	81.75 273

XXY-MVS		70.18 166	69.28 166	72.89 220	77.64 242	42.88 330	85.06 135	87.50 69	62.58 137	62.66 192	82.34 233	43.64 141	89.83 116	58.42 203	63.70 244	85.96 205

thisisatest0515		73.64 107	72.20 111	77.97 82	81.63 159	53.01 129	86.69 91	88.81 39	62.53 138	64.06 171	85.65 178	52.15 49	92.50 46	58.43 201	69.84 194	88.39 151

fmvsm_s_conf0.1_n_a		72.82 118	72.05 117	75.12 164	70.95 337	47.97 260	82.72 210	68.43 363	62.52 139	78.17 34	93.08 31	44.21 131	88.86 150	84.82 14	63.54 245	88.54 146

cl22		68.85 193	67.69 189	72.35 233	78.07 237	49.98 196	82.45 221	78.48 264	62.50 140	58.46 248	77.95 273	49.99 67	85.17 268	62.55 165	58.72 283	81.90 271

v1921920		67.45 224	65.23 243	74.10 187	71.51 330	52.90 132	83.75 181	80.44 218	62.48 141	59.12 233	77.13 286	36.98 232	87.90 186	57.53 219	58.14 293	81.49 277

GDP-MVS		75.27 79	74.38 82	77.95 84	79.04 216	52.86 133	85.22 127	86.19 93	62.43 142	70.66 104	90.40 90	53.51 40	91.60 66	69.25 116	72.68 169	89.39 122

thres200		68.71 199	67.27 200	73.02 214	84.73 78	46.76 278	85.03 137	87.73 63	62.34 143	59.87 216	83.45 206	43.15 148	88.32 173	31.25 362	67.91 208	83.98 239

Effi-MVS+-dtu		66.24 251	64.96 247	70.08 278	75.17 284	49.64 202	82.01 228	74.48 315	62.15 144	57.83 255	76.08 306	30.59 302	83.79 285	65.40 150	60.93 269	76.81 335

TAMVS		69.51 185	68.16 180	73.56 207	76.30 266	48.71 232	82.57 214	77.17 286	62.10 145	61.32 205	84.23 193	41.90 166	83.46 291	54.80 239	73.09 165	88.50 148

eth_miper_zixun_eth		66.98 239	65.28 242	72.06 240	75.61 280	50.40 183	81.00 255	76.97 292	62.00 146	56.99 272	76.97 289	44.84 124	85.58 259	58.75 198	54.42 328	80.21 301

c3_l		67.97 211	66.66 209	71.91 250	76.20 269	49.31 214	82.13 227	78.00 272	61.99 147	57.64 261	76.94 290	49.41 72	84.93 273	60.62 183	57.01 305	81.49 277

v1240		66.99 238	64.68 248	73.93 192	71.38 333	52.66 136	83.39 194	79.98 226	61.97 148	58.44 250	77.11 287	35.25 256	87.81 188	56.46 229	58.15 291	81.33 285

OPM-MVS		70.75 160	69.58 159	74.26 183	75.55 281	51.34 167	86.05 102	83.29 168	61.94 149	62.95 188	85.77 177	34.15 270	88.44 166	65.44 149	71.07 183	82.99 259
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_prior2							89.04 43		61.88 150	73.55 62	91.46 67	48.01 80		74.73 81	85.46 42

EPNet_dtu		66.25 250	66.71 207	64.87 329	78.66 227	34.12 373	82.80 209	75.51 306	61.75 151	64.47 168	86.90 163	37.06 230	72.46 372	43.65 311	69.63 198	88.02 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		68.45 203	65.44 239	77.47 94	84.91 76	56.17 43	71.89 337	81.91 191	61.72 152	60.85 208	72.49 337	36.21 246	87.06 216	47.32 289	71.62 178	89.17 129

RRT-MVS		73.29 111	71.37 128	79.07 52	84.63 80	54.16 99	78.16 289	86.64 84	61.67 153	60.17 214	82.35 232	40.63 182	92.26 53	70.19 110	77.87 108	90.81 84

PMMVS		72.98 114	72.05 117	75.78 136	83.57 101	48.60 233	84.08 169	82.85 177	61.62 154	68.24 122	90.33 91	28.35 312	87.78 193	72.71 99	76.69 121	90.95 81

save fliter					85.35 68	56.34 41	89.31 40	81.46 198	61.55 155

UA-Net		67.32 229	66.23 218	70.59 269	78.85 221	41.23 346	73.60 318	75.45 308	61.54 156	66.61 134	84.53 190	38.73 201	86.57 233	42.48 318	74.24 155	83.98 239

v8		67.25 230	64.99 246	74.04 188	72.89 315	53.31 120	82.37 223	80.11 224	61.54 156	54.29 300	76.02 307	42.89 153	88.41 167	58.43 201	56.36 307	80.39 299

SMA-MVS		79.10 23	78.76 24	80.12 35	84.42 84	55.87 49	87.58 69	86.76 79	61.48 158	80.26 24	93.10 28	46.53 97	92.41 48	79.97 42	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		69.36 187	68.24 178	72.72 223	79.26 211	49.40 212	85.72 112	88.85 37	61.33 159	64.59 163	82.38 229	34.57 266	87.53 204	46.82 294	70.63 187	81.22 289

DIV-MVS_self_test		67.43 225	65.93 226	71.94 248	76.33 264	48.01 259	82.57 214	79.11 250	61.31 160	56.73 274	76.92 291	46.09 102	86.43 237	57.98 210	56.31 309	81.39 283

cl____		67.43 225	65.93 226	71.95 247	76.33 264	48.02 258	82.58 213	79.12 249	61.30 161	56.72 275	76.92 291	46.12 101	86.44 236	57.98 210	56.31 309	81.38 284

MP-MVS-pluss		75.54 76	75.03 72	77.04 104	81.37 171	52.65 137	84.34 161	84.46 141	61.16 162	69.14 114	91.76 57	39.98 191	88.99 144	78.19 56	84.89 49	89.48 121
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvsmamba		69.38 186	67.52 195	74.95 169	82.86 128	52.22 147	67.36 356	76.75 293	61.14 163	49.43 330	82.04 238	37.26 224	84.14 280	73.93 89	76.91 117	88.50 148

v10		66.61 245	64.20 254	73.83 197	72.59 318	53.37 116	81.88 232	79.91 230	61.11 164	54.09 302	75.60 309	40.06 189	88.26 177	56.47 228	56.10 313	79.86 305

ACMMP_NAP		76.43 57	75.66 60	78.73 61	81.92 148	54.67 86	84.06 171	85.35 110	61.10 165	72.99 69	91.50 65	40.25 184	91.00 84	76.84 66	86.98 25	90.51 91

EI-MVSNet		69.70 181	68.70 170	72.68 224	75.00 288	48.90 225	79.54 277	87.16 72	61.05 166	63.88 176	83.74 200	45.87 105	90.44 98	57.42 221	64.68 236	78.70 313

IterMVS-LS		66.63 244	65.36 241	70.42 272	75.10 286	48.90 225	81.45 250	76.69 297	61.05 166	55.71 285	77.10 288	45.86 106	83.65 288	57.44 220	57.88 299	78.70 313
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CL-MVSNet_self_test		62.98 273	61.14 274	68.50 301	65.86 366	42.96 328	84.37 159	82.98 174	60.98 168	53.95 303	72.70 336	40.43 183	83.71 287	41.10 319	47.93 354	78.83 312

AUN-MVS		68.20 210	66.35 214	73.76 199	76.37 262	47.45 269	79.52 279	79.52 238	60.98 168	62.34 193	86.02 174	36.59 242	86.94 220	62.32 167	53.47 337	86.89 181

Syy-MVS		61.51 286	61.35 271	62.00 344	81.73 153	30.09 389	80.97 256	81.02 206	60.93 170	55.06 289	82.64 221	35.09 259	80.81 310	16.40 408	58.32 287	75.10 353

myMVS_eth3d		63.52 267	63.56 258	63.40 336	81.73 153	34.28 370	80.97 256	81.02 206	60.93 170	55.06 289	82.64 221	48.00 82	80.81 310	23.42 391	58.32 287	75.10 353

FMVSNet3		68.84 194	67.40 197	73.19 213	85.05 73	48.53 236	85.71 113	85.36 109	60.90 172	57.58 262	79.15 265	42.16 160	86.77 224	47.25 290	63.40 246	84.27 231

tfpn200view9		67.57 221	66.13 220	71.89 251	84.05 93	45.07 303	83.40 192	87.71 65	60.79 173	57.79 257	82.76 216	43.53 142	87.80 190	28.80 369	66.36 222	82.78 264

thres400		67.40 228	66.13 220	71.19 261	84.05 93	45.07 303	83.40 192	87.71 65	60.79 173	57.79 257	82.76 216	43.53 142	87.80 190	28.80 369	66.36 222	80.71 295

LCM-MVSNet-Re		58.82 306	56.54 305	65.68 321	79.31 210	29.09 397	61.39 379	45.79 397	60.73 175	37.65 384	72.47 338	31.42 297	81.08 306	49.66 272	70.41 190	86.87 182

Effi-MVS+		75.24 80	73.61 90	80.16 33	81.92 148	57.42 21	85.21 128	76.71 296	60.68 176	73.32 66	89.34 114	47.30 87	91.63 65	68.28 124	79.72 93	91.42 65

D2MVS		63.49 268	61.39 270	69.77 282	69.29 348	48.93 224	78.89 285	77.71 277	60.64 177	49.70 329	72.10 345	27.08 323	83.48 290	54.48 240	62.65 259	76.90 334

IterMVS		63.77 266	61.67 266	70.08 278	72.68 317	51.24 170	80.44 265	75.51 306	60.51 178	51.41 319	73.70 326	32.08 290	78.91 330	54.30 241	54.35 329	80.08 303
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dp		64.41 259	61.58 267	72.90 218	82.40 139	54.09 100	72.53 327	76.59 299	60.39 179	55.68 286	70.39 354	35.18 258	76.90 351	39.34 324	61.71 265	87.73 166

MVP-Stereo		70.97 155	70.44 141	72.59 226	76.03 273	51.36 166	85.02 138	86.99 75	60.31 180	56.53 279	78.92 267	40.11 188	90.00 111	60.00 192	90.01 7	76.41 342
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpm2		70.82 158	68.44 174	77.98 81	80.78 185	56.11 44	74.21 315	81.28 203	60.24 181	68.04 123	75.27 311	52.26 48	88.50 165	55.82 234	68.03 206	89.33 123

CR-MVSNet		62.47 280	59.04 292	72.77 222	73.97 303	56.57 34	60.52 380	71.72 339	60.04 182	57.49 265	65.86 369	38.94 198	80.31 318	42.86 315	59.93 272	81.42 280

ab-mvs		70.65 161	69.11 167	75.29 158	80.87 182	46.23 290	73.48 320	85.24 118	59.99 183	66.65 132	80.94 248	43.13 150	88.69 155	63.58 160	68.07 205	90.95 81

9.14			78.19 28		85.67 61		88.32 51	88.84 38	59.89 184	74.58 54	92.62 39	46.80 93	92.66 41	81.40 38	85.62 41

GeoE		69.96 174	67.88 184	76.22 123	81.11 175	51.71 158	84.15 167	76.74 295	59.83 185	60.91 207	84.38 191	41.56 171	88.10 181	51.67 261	70.57 189	88.84 137

BH-w/o		70.02 171	68.51 173	74.56 173	82.77 131	50.39 184	86.60 93	78.14 270	59.77 186	59.65 220	85.57 180	39.27 196	87.30 210	49.86 271	74.94 151	85.99 203

ZNCC-MVS		75.82 71	75.02 73	78.23 77	83.88 98	53.80 103	86.91 87	86.05 96	59.71 187	67.85 125	90.55 83	42.23 159	91.02 83	72.66 100	85.29 45	89.87 112

1112_ss		70.05 170	69.37 162	72.10 238	80.77 186	42.78 331	85.12 134	76.75 293	59.69 188	61.19 206	92.12 46	47.48 86	83.84 284	53.04 250	68.21 204	89.66 114

miper_lstm_enhance		63.91 263	62.30 262	68.75 295	75.06 287	46.78 277	69.02 348	81.14 204	59.68 189	52.76 311	72.39 340	40.71 180	77.99 340	56.81 225	53.09 339	81.48 279

Baseline_NR-MVSNet		65.49 257	64.27 253	69.13 288	74.37 298	41.65 340	83.39 194	78.85 252	59.56 190	59.62 222	76.88 293	40.75 178	87.44 206	49.99 269	55.05 322	78.28 322

Fast-Effi-MVS+-dtu		66.53 246	64.10 255	73.84 196	72.41 320	52.30 145	84.73 148	75.66 305	59.51 191	56.34 281	79.11 266	28.11 314	85.85 258	57.74 218	63.29 250	83.35 249

UGNet		68.71 199	67.11 202	73.50 208	80.55 192	47.61 267	84.08 169	78.51 263	59.45 192	65.68 148	82.73 219	23.78 346	85.08 271	52.80 253	76.40 122	87.80 164
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		71.11 151	69.41 161	76.22 123	79.32 209	50.49 180	80.23 270	85.14 123	59.44 193	58.93 236	88.89 123	33.83 275	89.60 124	61.49 175	77.42 113	88.57 145

MTAPA		72.73 119	71.22 130	77.27 99	81.54 165	53.57 108	67.06 358	81.31 201	59.41 194	68.39 120	90.96 73	36.07 249	89.01 141	73.80 92	82.45 64	89.23 126

thres600view7		66.46 247	65.12 244	70.47 270	83.41 105	43.80 318	82.15 225	87.78 60	59.37 195	56.02 283	82.21 234	43.73 137	86.90 222	26.51 381	64.94 232	80.71 295

sss		70.49 163	70.13 152	71.58 255	81.59 162	39.02 354	80.78 261	84.71 135	59.34 196	66.61 134	88.09 141	37.17 227	85.52 260	61.82 173	71.02 184	90.20 100

Vis-MVSNet (Re-imp)		65.52 256	65.63 233	65.17 327	77.49 246	30.54 385	75.49 306	77.73 276	59.34 196	52.26 316	86.69 167	49.38 73	80.53 316	37.07 332	75.28 142	84.42 229

MVS_111021_LR		69.07 189	67.91 182	72.54 227	77.27 249	49.56 205	79.77 275	73.96 321	59.33 198	60.73 210	87.82 148	30.19 305	81.53 302	69.94 112	72.19 174	86.53 192

PS-MVSNAJss		68.78 198	67.17 201	73.62 205	73.01 312	48.33 246	84.95 142	84.81 131	59.30 199	58.91 238	79.84 258	37.77 208	88.86 150	62.83 164	63.12 255	83.67 247

GST-MVS		74.87 87	73.90 89	77.77 86	83.30 110	53.45 112	85.75 109	85.29 114	59.22 200	66.50 137	89.85 105	40.94 176	90.76 90	70.94 106	83.35 58	89.10 131

MDTV_nov1_ep13			61.56 268		81.68 157	55.12 69	72.41 329	78.18 269	59.19 201	58.85 240	69.29 359	34.69 265	86.16 243	36.76 336	62.96 256

CSCG		80.41 15	79.72 16	82.49 5	89.12 25	57.67 15	89.29 41	91.54 5	59.19 201	71.82 86	90.05 101	59.72 10	96.04 10	78.37 54	88.40 14	93.75 7

test-LLR		69.65 182	69.01 168	71.60 253	78.67 225	48.17 251	85.13 131	79.72 233	59.18 203	63.13 185	82.58 223	36.91 234	80.24 319	60.56 184	75.17 144	86.39 197

test0.0.03 1		62.54 277	62.44 261	62.86 341	72.28 324	29.51 394	82.93 207	78.78 255	59.18 203	53.07 310	82.41 227	36.91 234	77.39 346	37.45 328	58.96 281	81.66 275

MIMVSNet		63.12 272	60.29 282	71.61 252	75.92 277	46.65 280	65.15 361	81.94 188	59.14 205	54.65 295	69.47 357	25.74 332	80.63 313	41.03 320	69.56 199	87.55 170

IS-MVSNet		68.80 197	67.55 193	72.54 227	78.50 231	43.43 322	81.03 254	79.35 245	59.12 206	57.27 270	86.71 166	46.05 103	87.70 196	44.32 308	75.60 138	86.49 194

thres100view900		66.87 241	65.42 240	71.24 259	83.29 111	43.15 327	81.67 240	87.78 60	59.04 207	55.92 284	82.18 235	43.73 137	87.80 190	28.80 369	66.36 222	82.78 264

3Dnovator+	62.71 7	72.29 128	70.50 140	77.65 90	83.40 108	51.29 169	87.32 73	86.40 88	59.01 208	58.49 247	88.32 137	32.40 286	91.27 74	57.04 223	82.15 67	90.38 93

UnsupCasMVSNet_eth		57.56 316	55.15 315	64.79 330	64.57 376	33.12 377	73.17 323	83.87 156	58.98 209	41.75 367	70.03 355	22.54 354	79.92 323	46.12 300	35.31 387	81.32 287

BH-RMVSNet		70.08 169	68.01 181	76.27 121	84.21 91	51.22 171	87.29 76	79.33 247	58.96 210	63.63 180	86.77 165	33.29 279	90.30 105	44.63 306	73.96 157	87.30 177

PatchmatchNet		67.07 237	63.63 257	77.40 95	83.10 115	58.03 11	72.11 335	77.77 275	58.85 211	59.37 227	70.83 350	37.84 207	84.93 273	42.96 314	69.83 195	89.26 124
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_vis1_n_1920		68.59 202	68.31 176	69.44 286	69.16 349	41.51 342	84.63 154	68.58 362	58.80 212	73.26 67	88.37 133	25.30 335	80.60 314	79.10 46	67.55 210	86.23 199

SF-MVS		77.64 41	77.42 39	78.32 76	83.75 100	52.47 140	86.63 92	87.80 59	58.78 213	74.63 52	92.38 42	47.75 83	91.35 72	78.18 58	86.85 27	91.15 75

Vis-MVSNet		70.61 162	69.34 163	74.42 177	80.95 181	48.49 238	86.03 103	77.51 280	58.74 214	65.55 149	87.78 149	34.37 268	85.95 256	52.53 258	80.61 78	88.80 138
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS		76.91 49	75.48 64	81.23 19	84.56 82	55.21 65	80.23 270	91.64 4	58.65 215	65.37 150	91.48 66	45.72 108	95.05 16	72.11 102	89.52 10	93.44 9

CDPH-MVS		76.05 64	75.19 70	78.62 66	86.51 50	54.98 75	87.32 73	84.59 138	58.62 216	70.75 101	90.85 78	43.10 151	90.63 95	70.50 108	84.51 53	90.24 97

GBi-Net		67.09 235	65.47 237	71.96 244	82.71 133	46.36 284	83.52 183	83.31 165	58.55 217	57.58 262	76.23 302	36.72 239	86.20 240	47.25 290	63.40 246	83.32 250

test1		67.09 235	65.47 237	71.96 244	82.71 133	46.36 284	83.52 183	83.31 165	58.55 217	57.58 262	76.23 302	36.72 239	86.20 240	47.25 290	63.40 246	83.32 250

FMVSNet2		67.57 221	65.79 229	72.90 218	82.71 133	47.97 260	85.15 130	84.93 127	58.55 217	56.71 276	78.26 272	36.72 239	86.67 227	46.15 299	62.94 257	84.07 234

HyFIR lowres test		69.94 175	67.58 191	77.04 104	77.11 255	57.29 22	81.49 249	79.11 250	58.27 220	58.86 239	80.41 252	42.33 157	86.96 219	61.91 171	68.68 203	86.87 182

MSLP-MVS++		74.21 93	72.25 110	80.11 36	81.45 169	56.47 38	86.32 96	79.65 236	58.19 221	66.36 138	92.29 44	36.11 247	90.66 93	67.39 128	82.49 63	93.18 17

PHI-MVS		77.49 42	77.00 44	78.95 53	85.33 69	50.69 175	88.57 49	88.59 48	58.14 222	73.60 61	93.31 24	43.14 149	93.79 27	73.81 91	88.53 13	92.37 34

XVS		72.92 115	71.62 122	76.81 113	83.41 105	52.48 138	84.88 144	83.20 170	58.03 223	63.91 174	89.63 109	35.50 254	89.78 117	65.50 143	80.50 80	88.16 154

X-MVStestdata		65.85 255	62.20 263	76.81 113	83.41 105	52.48 138	84.88 144	83.20 170	58.03 223	63.91 174	4.82 426	35.50 254	89.78 117	65.50 143	80.50 80	88.16 154

DVP-MVS++		82.44 3	82.38 6	82.62 4	91.77 4	57.49 17	84.98 139	88.88 34	58.00 225	83.60 6	93.39 21	67.21 2	96.39 4	81.64 33	91.98 4	93.98 5

test_0728_THIRD									58.00 225	81.91 15	93.64 14	56.54 21	96.44 2	81.64 33	86.86 26	92.23 37

test_yl		75.85 68	74.83 77	78.91 54	88.08 37	51.94 151	91.30 17	89.28 26	57.91 227	71.19 95	89.20 117	42.03 164	92.77 38	69.41 114	75.07 148	92.01 46

DCV-MVSNet		75.85 68	74.83 77	78.91 54	88.08 37	51.94 151	91.30 17	89.28 26	57.91 227	71.19 95	89.20 117	42.03 164	92.77 38	69.41 114	75.07 148	92.01 46

MP-MVS		74.99 85	74.33 83	76.95 110	82.89 127	53.05 128	85.63 114	83.50 163	57.86 229	67.25 128	90.24 93	43.38 146	88.85 153	76.03 68	82.23 65	88.96 133
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
train_agg		76.91 49	76.40 51	78.45 72	85.68 59	55.42 56	87.59 67	84.00 152	57.84 230	72.99 69	90.98 71	44.99 119	88.58 160	78.19 56	85.32 44	91.34 70

test_8					85.72 58	55.31 61	87.60 66	83.88 155	57.84 230	72.84 73	90.99 70	44.99 119	88.34 171

TEST9					85.68 59	55.42 56	87.59 67	84.00 152	57.72 232	72.99 69	90.98 71	44.87 123	88.58 160

DVP-MVS		81.30 10	81.00 13	82.20 8	89.40 20	57.45 19	92.34 5	89.99 20	57.71 233	81.91 15	93.64 14	55.17 29	96.44 2	81.68 31	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 45	57.71 233	83.14 9	93.96 6	55.17 29

BH-untuned		68.28 207	66.40 213	73.91 193	81.62 160	50.01 195	85.56 117	77.39 282	57.63 235	57.47 267	83.69 202	36.36 244	87.08 215	44.81 304	73.08 166	84.65 226

thisisatest0530		70.47 165	68.56 171	76.20 125	79.78 202	51.52 163	83.49 189	88.58 49	57.62 236	58.60 243	82.79 215	51.03 56	91.48 69	52.84 252	62.36 263	85.59 214

test_241102_ONE					89.48 17	56.89 29		88.94 32	57.53 237	84.61 4	93.29 25	58.81 12	96.45 1

API-MVS		74.17 94	72.07 116	80.49 25	90.02 11	58.55 9	87.30 75	84.27 145	57.51 238	65.77 147	87.77 150	41.61 170	95.97 11	51.71 260	82.63 61	86.94 180

SED-MVS		81.92 8	81.75 9	82.44 7	89.48 17	56.89 29	92.48 3	88.94 32	57.50 239	84.61 4	94.09 3	58.81 12	96.37 6	82.28 27	87.60 18	94.06 3

test_241102_TWO								88.76 41	57.50 239	83.60 6	94.09 3	56.14 25	96.37 6	82.28 27	87.43 20	92.55 30

Patchmatch-RL test		58.72 307	54.32 320	71.92 249	63.91 378	44.25 313	61.73 376	55.19 388	57.38 241	49.31 332	54.24 398	37.60 215	80.89 307	62.19 169	47.28 359	90.63 87

Test_1112_low_res		67.18 232	66.23 218	70.02 281	78.75 223	41.02 347	83.43 190	73.69 323	57.29 242	58.45 249	82.39 228	45.30 114	80.88 308	50.50 267	66.26 226	88.16 154

FA-MVS(test-final)		69.00 192	66.60 211	76.19 126	83.48 104	47.96 262	74.73 310	82.07 186	57.27 243	62.18 196	78.47 271	36.09 248	92.89 34	53.76 246	71.32 182	87.73 166

OpenMVS	61.00 11	69.99 173	67.55 193	77.30 97	78.37 234	54.07 101	84.36 160	85.76 101	57.22 244	56.71 276	87.67 152	30.79 301	92.83 36	43.04 313	84.06 56	85.01 220

test_one_0601					89.39 22	57.29 22		88.09 55	57.21 245	82.06 14	93.39 21	54.94 34

TR-MVS		69.71 178	67.85 187	75.27 161	82.94 124	48.48 239	87.40 72	80.86 211	57.15 246	64.61 162	87.08 161	32.67 284	89.64 123	46.38 297	71.55 180	87.68 168

ZD-MVS					89.55 14	53.46 110		84.38 142	57.02 247	73.97 59	91.03 69	44.57 129	91.17 79	75.41 77	81.78 71

TransMVSNet (Re)		62.82 275	60.76 277	69.02 289	73.98 302	41.61 341	86.36 95	79.30 248	56.90 248	52.53 312	76.44 298	41.85 167	87.60 202	38.83 325	40.61 378	77.86 326

USDC		54.36 332	51.23 336	63.76 333	64.29 377	37.71 362	62.84 373	73.48 328	56.85 249	35.47 389	71.94 346	9.23 399	78.43 332	38.43 326	48.57 350	75.13 352

region2R		73.75 103	72.55 103	77.33 96	83.90 97	52.98 130	85.54 119	84.09 150	56.83 250	65.10 153	90.45 86	37.34 222	90.24 106	68.89 120	80.83 77	88.77 140

HFP-MVS		74.37 91	73.13 99	78.10 80	84.30 87	53.68 106	85.58 115	84.36 143	56.82 251	65.78 146	90.56 82	40.70 181	90.90 88	69.18 118	80.88 75	89.71 113

ACMMPR		73.76 102	72.61 101	77.24 101	83.92 96	52.96 131	85.58 115	84.29 144	56.82 251	65.12 152	90.45 86	37.24 225	90.18 108	69.18 118	80.84 76	88.58 144

SD-MVS		76.18 60	74.85 76	80.18 32	85.39 67	56.90 28	85.75 109	82.45 182	56.79 253	74.48 55	91.81 56	43.72 139	90.75 91	74.61 82	78.65 101	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		63.84 264	60.01 285	75.32 154	78.58 229	57.92 12	61.61 377	77.53 279	56.71 254	57.75 259	70.77 351	31.97 291	79.91 325	48.80 279	56.36 307	88.13 157

cascas		69.01 191	66.13 220	77.66 89	79.36 207	55.41 58	86.99 83	83.75 157	56.69 255	58.92 237	81.35 245	24.31 344	92.10 58	53.23 247	70.61 188	85.46 215

ACMMP		70.81 159	69.29 165	75.39 152	81.52 167	51.92 153	83.43 190	83.03 173	56.67 256	58.80 241	88.91 122	31.92 293	88.58 160	65.89 142	73.39 161	85.67 210
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		71.88 137	69.33 164	79.52 40	82.20 143	54.30 93	86.30 97	88.77 40	56.61 257	59.72 219	87.48 154	33.90 273	95.36 13	47.48 288	81.49 72	88.90 134

TSAR-MVS + GP.		77.82 38	77.59 36	78.49 69	85.25 71	50.27 192	90.02 26	90.57 16	56.58 258	74.26 57	91.60 63	54.26 36	92.16 55	75.87 70	79.91 90	93.05 20

PGM-MVS		72.60 121	71.20 131	76.80 115	82.95 123	52.82 134	83.07 204	82.14 184	56.51 259	63.18 184	89.81 106	35.68 253	89.76 119	67.30 129	80.19 85	87.83 163

PCF-MVS	61.03 10	70.10 168	68.40 175	75.22 163	77.15 254	51.99 150	79.30 282	82.12 185	56.47 260	61.88 200	86.48 172	43.98 132	87.24 211	55.37 235	72.79 168	86.43 196
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		71.99 134	70.31 147	77.01 106	90.65 8	53.44 113	89.37 37	82.97 175	56.33 261	63.56 182	89.47 111	34.02 271	92.15 57	54.05 243	72.41 171	85.43 216

EPP-MVSNet		71.14 149	70.07 153	74.33 180	79.18 213	46.52 282	83.81 179	86.49 85	56.32 262	57.95 253	84.90 189	54.23 37	89.14 136	58.14 208	69.65 197	87.33 175

HPM-MVS		72.60 121	71.50 124	75.89 134	82.02 144	51.42 165	80.70 262	83.05 172	56.12 263	64.03 172	89.53 110	37.55 216	88.37 168	70.48 109	80.04 88	87.88 162
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 89	89.76 33	87.77 62	55.91 264	78.56 31	92.49 41	48.20 77	92.65 42	79.49 43	83.04 59	90.39 92
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
xiu_mvs_v1_base_debu		71.60 143	70.29 148	75.55 145	77.26 250	53.15 123	85.34 120	79.37 241	55.83 265	72.54 75	90.19 96	22.38 355	86.66 228	73.28 95	76.39 123	86.85 184

xiu_mvs_v1_base		71.60 143	70.29 148	75.55 145	77.26 250	53.15 123	85.34 120	79.37 241	55.83 265	72.54 75	90.19 96	22.38 355	86.66 228	73.28 95	76.39 123	86.85 184

xiu_mvs_v1_base_debi		71.60 143	70.29 148	75.55 145	77.26 250	53.15 123	85.34 120	79.37 241	55.83 265	72.54 75	90.19 96	22.38 355	86.66 228	73.28 95	76.39 123	86.85 184

mPP-MVS		71.79 140	70.38 145	76.04 131	82.65 136	52.06 148	84.45 158	81.78 194	55.59 268	62.05 199	89.68 108	33.48 277	88.28 176	65.45 148	78.24 106	87.77 165

DPE-MVS		79.82 19	79.66 17	80.29 30	89.27 24	55.08 72	88.70 47	87.92 58	55.55 269	81.21 20	93.69 13	56.51 22	94.27 22	78.36 55	85.70 40	91.51 63
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
pm-mvs1		64.12 262	62.56 260	68.78 294	71.68 327	38.87 355	82.89 208	81.57 196	55.54 270	53.89 304	77.82 276	37.73 211	86.74 225	48.46 283	53.49 336	80.72 294

ACMP	61.11 9	66.24 251	64.33 252	72.00 243	74.89 290	49.12 216	83.18 200	79.83 231	55.41 271	52.29 314	82.68 220	25.83 331	86.10 246	60.89 179	63.94 242	80.78 293
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_cas_vis1_n_1920		67.10 234	66.60 211	68.59 299	65.17 371	43.23 326	83.23 198	69.84 355	55.34 272	70.67 103	87.71 151	24.70 342	76.66 353	78.57 53	64.20 238	85.89 207

CP-MVS		72.59 123	71.46 125	76.00 133	82.93 125	52.32 144	86.93 86	82.48 181	55.15 273	63.65 179	90.44 89	35.03 261	88.53 164	68.69 121	77.83 109	87.15 178

pmmvs4		63.34 270	61.07 275	70.16 276	70.14 342	50.53 179	79.97 274	71.41 344	55.08 274	54.12 301	78.58 269	32.79 283	82.09 300	50.33 268	57.22 304	77.86 326

KD-MVS_2432*1600		59.04 303	56.44 307	66.86 313	79.07 214	45.87 294	72.13 333	80.42 219	55.03 275	48.15 337	71.01 348	36.73 237	78.05 338	35.21 343	30.18 400	76.67 336

miper_refine_blended		59.04 303	56.44 307	66.86 313	79.07 214	45.87 294	72.13 333	80.42 219	55.03 275	48.15 337	71.01 348	36.73 237	78.05 338	35.21 343	30.18 400	76.67 336

MDTV_nov1_ep13_2view						43.62 319	71.13 340		54.95 277	59.29 231		36.76 236		46.33 298		87.32 176

Anonymous202405211		70.11 167	67.88 184	76.79 116	87.20 44	47.24 274	89.49 35	77.38 283	54.88 278	66.14 139	86.84 164	20.93 364	91.54 68	56.45 230	71.62 178	91.59 58

OMC-MVS		65.97 254	65.06 245	68.71 296	72.97 313	42.58 335	78.61 286	75.35 309	54.72 279	59.31 229	86.25 173	33.30 278	77.88 342	57.99 209	67.05 213	85.66 211

LPG-MVS_test		66.44 248	64.58 249	72.02 241	74.42 296	48.60 233	83.07 204	80.64 214	54.69 280	53.75 305	83.83 198	25.73 333	86.98 217	60.33 190	64.71 233	80.48 297

LGP-MVS_train				72.02 241	74.42 296	48.60 233		80.64 214	54.69 280	53.75 305	83.83 198	25.73 333	86.98 217	60.33 190	64.71 233	80.48 297

tfpnnormal		61.47 287	59.09 291	68.62 298	76.29 267	41.69 339	81.14 253	85.16 121	54.48 282	51.32 320	73.63 327	32.32 287	86.89 223	21.78 395	55.71 319	77.29 332

mmtdpeth		57.93 314	54.78 318	67.39 308	72.32 322	43.38 323	72.72 325	68.93 360	54.45 283	56.85 273	62.43 380	17.02 380	83.46 291	57.95 212	30.31 399	75.31 349

tttt0517		68.33 206	66.29 216	74.46 175	78.08 236	49.06 217	80.88 259	89.08 30	54.40 284	54.75 294	80.77 250	51.31 53	90.33 102	49.35 275	58.01 295	83.99 237

pmmvs5		62.80 276	61.18 273	67.66 305	69.53 346	42.37 338	82.65 212	75.19 310	54.30 285	52.03 317	78.51 270	31.64 296	80.67 312	48.60 281	58.15 291	79.95 304

APD-MVS		76.15 61	75.68 59	77.54 92	88.52 27	53.44 113	87.26 78	85.03 125	53.79 286	74.91 50	91.68 60	43.80 135	90.31 103	74.36 84	81.82 69	88.87 136
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
114514_t		69.87 176	67.88 184	75.85 135	88.38 29	52.35 143	86.94 85	83.68 158	53.70 287	55.68 286	85.60 179	30.07 306	91.20 78	55.84 233	71.02 184	83.99 237

testing3		59.97 293	60.19 283	59.32 356	77.60 243	30.01 391	81.75 237	81.79 193	53.54 288	50.34 327	79.94 255	48.99 75	76.91 349	17.19 406	50.59 346	71.03 379

PAPM_NR		71.80 139	69.98 154	77.26 100	81.54 165	53.34 118	78.60 287	85.25 117	53.46 289	60.53 212	88.66 127	45.69 109	89.24 132	56.49 227	79.62 96	89.19 128

test-mter		68.36 204	67.29 198	71.60 253	78.67 225	48.17 251	85.13 131	79.72 233	53.38 290	63.13 185	82.58 223	27.23 322	80.24 319	60.56 184	75.17 144	86.39 197

jajsoiax		63.21 271	60.84 276	70.32 274	68.33 356	44.45 309	81.23 251	81.05 205	53.37 291	50.96 324	77.81 277	17.49 378	85.49 262	59.31 193	58.05 294	81.02 291

testgi		54.25 333	52.57 332	59.29 357	62.76 383	21.65 412	72.21 332	70.47 350	53.25 292	41.94 365	77.33 284	14.28 388	77.95 341	29.18 368	51.72 344	78.28 322

tpm cat1		66.28 249	62.78 259	76.77 117	81.40 170	57.14 24	70.03 344	77.19 285	53.00 293	58.76 242	70.73 353	46.17 100	86.73 226	43.27 312	64.46 237	86.44 195

mvs_tets		62.96 274	60.55 278	70.19 275	68.22 359	44.24 314	80.90 258	80.74 213	52.99 294	50.82 326	77.56 278	16.74 382	85.44 263	59.04 196	57.94 296	80.89 292

test20.03		55.22 329	54.07 322	58.68 359	63.14 382	25.00 403	77.69 293	74.78 313	52.64 295	43.43 359	72.39 340	26.21 328	74.76 360	29.31 367	47.05 362	76.28 343

VDDNet		74.37 91	72.13 114	81.09 20	79.58 204	56.52 37	90.02 26	86.70 81	52.61 296	71.23 94	87.20 159	31.75 295	93.96 25	74.30 86	75.77 136	92.79 27

v7n		62.50 279	59.27 290	72.20 237	67.25 362	49.83 200	77.87 292	80.12 223	52.50 297	48.80 335	73.07 331	32.10 289	87.90 186	46.83 293	54.92 323	78.86 311

FMVSNet1		64.57 258	62.11 264	71.96 244	77.32 248	46.36 284	83.52 183	83.31 165	52.43 298	54.42 297	76.23 302	27.80 318	86.20 240	42.59 317	61.34 267	83.32 250

K. test v3		54.04 334	49.42 346	67.92 304	68.55 353	42.57 336	75.51 305	63.07 379	52.07 299	39.21 378	64.59 375	19.34 369	82.21 297	37.11 331	25.31 405	78.97 310

原ACMM1				76.13 128	84.89 77	54.59 88		85.26 116	51.98 300	66.70 131	87.07 162	40.15 187	89.70 121	51.23 264	85.06 48	84.10 233

tpmvs		62.45 281	59.42 288	71.53 256	83.93 95	54.32 92	70.03 344	77.61 278	51.91 301	53.48 308	68.29 363	37.91 206	86.66 228	33.36 352	58.27 289	73.62 364

PEN-MVS		58.35 312	57.15 302	61.94 345	67.55 361	34.39 369	77.01 295	78.35 267	51.87 302	47.72 341	76.73 295	33.91 272	73.75 365	34.03 350	47.17 360	77.68 328

EG-PatchMatch MVS		62.40 282	59.59 286	70.81 267	73.29 307	49.05 218	85.81 105	84.78 132	51.85 303	44.19 355	73.48 329	15.52 387	89.85 115	40.16 322	67.24 212	73.54 365

UniMVSNet_ETH3D		62.51 278	60.49 279	68.57 300	68.30 357	40.88 349	73.89 316	79.93 229	51.81 304	54.77 293	79.61 259	24.80 340	81.10 305	49.93 270	61.35 266	83.73 245

CP-MVSNet		58.54 311	57.57 300	61.46 349	68.50 354	33.96 374	76.90 297	78.60 262	51.67 305	47.83 340	76.60 297	34.99 262	72.79 370	35.45 340	47.58 356	77.64 330

WR-MVS_H		58.91 305	58.04 297	61.54 348	69.07 350	33.83 375	76.91 296	81.99 187	51.40 306	48.17 336	74.67 314	40.23 185	74.15 361	31.78 359	48.10 352	76.64 339

PS-CasMVS		58.12 313	57.03 304	61.37 350	68.24 358	33.80 376	76.73 298	78.01 271	51.20 307	47.54 344	76.20 305	32.85 281	72.76 371	35.17 345	47.37 358	77.55 331

DTE-MVSNet		57.03 318	55.73 313	60.95 353	65.94 365	32.57 381	75.71 301	77.09 288	51.16 308	46.65 350	76.34 300	32.84 282	73.22 369	30.94 363	44.87 369	77.06 333

HPM-MVS_fast		67.86 213	66.28 217	72.61 225	80.67 189	48.34 244	81.18 252	75.95 304	50.81 309	59.55 224	88.05 144	27.86 317	85.98 253	58.83 197	73.58 160	83.51 248

MVSMamba_PlusPlus		75.28 78	73.39 91	80.96 21	80.85 183	58.25 10	74.47 313	87.61 67	50.53 310	65.24 151	83.41 207	57.38 18	92.83 36	73.92 90	87.13 21	91.80 54

MVSFormer		73.53 108	72.19 112	77.57 91	83.02 120	55.24 63	81.63 241	81.44 199	50.28 311	76.67 41	90.91 76	44.82 125	86.11 244	60.83 180	80.09 86	91.36 68

test_djsdf		63.84 264	61.56 268	70.70 268	68.78 351	44.69 307	81.63 241	81.44 199	50.28 311	52.27 315	76.26 301	26.72 325	86.11 244	60.83 180	55.84 318	81.29 288

FMVSNet5		58.61 308	56.45 306	65.10 328	77.20 253	39.74 351	74.77 309	77.12 287	50.27 313	43.28 361	67.71 364	26.15 330	76.90 351	36.78 335	54.78 325	78.65 315

FE-MVS		64.15 261	60.43 281	75.30 157	80.85 183	49.86 199	68.28 353	78.37 266	50.26 314	59.31 229	73.79 322	26.19 329	91.92 61	40.19 321	66.67 216	84.12 232

Anonymous20231206		59.08 302	57.59 299	63.55 334	68.77 352	32.14 383	80.26 269	79.78 232	50.00 315	49.39 331	72.39 340	26.64 326	78.36 333	33.12 355	57.94 296	80.14 302

ACMH	53.70 16	59.78 294	55.94 312	71.28 258	76.59 261	48.35 243	80.15 272	76.11 302	49.74 316	41.91 366	73.45 330	16.50 384	90.31 103	31.42 360	57.63 302	75.17 351
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs-eth3d		55.97 326	52.78 330	65.54 323	61.02 387	46.44 283	75.36 307	67.72 365	49.61 317	43.65 358	67.58 365	21.63 361	77.04 347	44.11 309	44.33 370	73.15 369

AdaColmap		67.86 213	65.48 236	75.00 167	88.15 36	54.99 74	86.10 101	76.63 298	49.30 318	57.80 256	86.65 169	29.39 309	88.94 148	45.10 303	70.21 192	81.06 290

无先验							85.19 129	78.00 272	49.08 319				85.13 270	52.78 254		87.45 173

ppachtmachnet_test		58.56 309	54.34 319	71.24 259	71.42 331	54.74 80	81.84 234	72.27 334	49.02 320	45.86 354	68.99 361	26.27 327	83.30 293	30.12 364	43.23 373	75.69 345

SR-MVS		70.92 157	69.73 157	74.50 174	83.38 109	50.48 181	84.27 163	79.35 245	48.96 321	66.57 136	90.45 86	33.65 276	87.11 214	66.42 134	74.56 154	85.91 206

tt0805		63.39 269	61.31 272	69.64 283	69.36 347	38.87 355	78.00 290	85.48 103	48.82 322	55.66 288	81.66 242	24.38 343	86.37 238	49.04 278	59.36 279	83.68 246

reproduce-ours		71.77 141	70.43 142	75.78 136	81.96 146	49.54 208	82.54 217	81.01 208	48.77 323	69.21 112	90.96 73	37.13 228	89.40 127	66.28 137	76.01 131	88.39 151

our_new_method		71.77 141	70.43 142	75.78 136	81.96 146	49.54 208	82.54 217	81.01 208	48.77 323	69.21 112	90.96 73	37.13 228	89.40 127	66.28 137	76.01 131	88.39 151

our_test_3		59.11 301	55.08 317	71.18 262	71.42 331	53.29 121	81.96 229	74.52 314	48.32 325	42.08 364	69.28 360	28.14 313	82.15 298	34.35 349	45.68 368	78.11 325

kuosan		50.20 351	50.09 341	50.52 375	73.09 311	29.09 397	65.25 360	74.89 312	48.27 326	41.34 369	60.85 386	43.45 145	67.48 383	18.59 404	25.07 406	55.01 400

APD-MVS_3200maxsize		69.62 183	68.23 179	73.80 198	81.58 163	48.22 249	81.91 231	79.50 239	48.21 327	64.24 170	89.75 107	31.91 294	87.55 203	63.08 162	73.85 159	85.64 212

CHOSEN 280x420		57.53 317	56.38 309	60.97 352	74.01 301	48.10 255	46.30 400	54.31 390	48.18 328	50.88 325	77.43 283	38.37 204	59.16 396	54.83 237	63.14 254	75.66 346

reproduce_model		71.07 152	69.67 158	75.28 160	81.51 168	48.82 228	81.73 238	80.57 217	47.81 329	68.26 121	90.78 80	36.49 243	88.60 159	65.12 153	74.76 152	88.42 150

FOURS1					83.24 112	49.90 198	84.98 139	78.76 256	47.71 330	73.42 64

ACMM	58.35 12	64.35 260	62.01 265	71.38 257	74.21 299	48.51 237	82.25 224	79.66 235	47.61 331	54.54 296	80.11 254	25.26 336	86.00 251	51.26 263	63.16 253	79.64 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		54.37 331	50.10 340	67.21 309	70.70 339	41.46 344	74.73 310	64.69 372	47.56 332	39.12 379	69.49 356	18.49 375	84.69 276	31.87 358	34.20 393	75.48 347

Anonymous20240529		69.71 178	67.28 199	77.00 107	83.78 99	50.36 187	88.87 46	85.10 124	47.22 333	64.03 172	83.37 208	27.93 316	92.10 58	57.78 217	67.44 211	88.53 147

ACMH+	54.58 15	58.55 310	55.24 314	68.50 301	74.68 292	45.80 297	80.27 268	70.21 352	47.15 334	42.77 363	75.48 310	16.73 383	85.98 253	35.10 347	54.78 325	73.72 363

XVG-OURS		61.88 284	59.34 289	69.49 284	65.37 368	46.27 288	64.80 363	73.49 326	47.04 335	57.41 269	82.85 214	25.15 337	78.18 334	53.00 251	64.98 231	84.01 236

TAPA-MVS	56.12 14	61.82 285	60.18 284	66.71 315	78.48 232	37.97 361	75.19 308	76.41 301	46.82 336	57.04 271	86.52 171	27.67 320	77.03 348	26.50 382	67.02 214	85.14 218
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UnsupCasMVSNet_bld		53.86 335	50.53 339	63.84 332	63.52 381	34.75 368	71.38 338	81.92 190	46.53 337	38.95 380	57.93 393	20.55 365	80.20 321	39.91 323	34.09 394	76.57 340

anonymousdsp		60.46 292	57.65 298	68.88 290	63.63 380	45.09 302	72.93 324	78.63 260	46.52 338	51.12 321	72.80 335	21.46 362	83.07 295	57.79 216	53.97 330	78.47 317

XVG-OURS-SEG-HR		62.02 283	59.54 287	69.46 285	65.30 369	45.88 293	65.06 362	73.57 325	46.45 339	57.42 268	83.35 209	26.95 324	78.09 336	53.77 245	64.03 240	84.42 229

SR-MVS-dyc-post		68.27 208	66.87 203	72.48 230	80.96 178	48.14 253	81.54 245	76.98 289	46.42 340	62.75 190	89.42 112	31.17 299	86.09 248	60.52 186	72.06 175	83.19 255

RE-MVS-def			66.66 209		80.96 178	48.14 253	81.54 245	76.98 289	46.42 340	62.75 190	89.42 112	29.28 310		60.52 186	72.06 175	83.19 255

OpenMVS_ROB	53.19 17	59.20 299	56.00 311	68.83 292	71.13 335	44.30 311	83.64 182	75.02 311	46.42 340	46.48 351	73.03 332	18.69 372	88.14 178	27.74 377	61.80 264	74.05 361

CPTT-MVS		67.15 233	65.84 228	71.07 263	80.96 178	50.32 189	81.94 230	74.10 317	46.18 343	57.91 254	87.64 153	29.57 307	81.31 304	64.10 157	70.18 193	81.56 276

new-patchmatchnet		48.21 354	46.55 356	53.18 371	57.73 393	18.19 420	70.24 342	71.02 348	45.70 344	33.70 393	60.23 387	18.00 376	69.86 380	27.97 376	34.35 391	71.49 377

新几何1				73.30 211	83.10 115	53.48 109		71.43 343	45.55 345	66.14 139	87.17 160	33.88 274	80.54 315	48.50 282	80.33 84	85.88 208

旧先验2							81.73 238		45.53 346	74.66 51			70.48 379	58.31 205

Anonymous20231211		66.08 253	63.67 256	73.31 210	83.07 118	48.75 230	86.01 104	84.67 137	45.27 347	56.54 278	76.67 296	28.06 315	88.95 146	52.78 254	59.95 271	82.23 267

XVG-ACMP-BASELINE		56.03 325	52.85 329	65.58 322	61.91 385	40.95 348	63.36 368	72.43 333	45.20 348	46.02 352	74.09 318	9.20 400	78.12 335	45.13 302	58.27 289	77.66 329

pmmvs6		59.64 295	57.15 302	67.09 310	66.01 364	36.86 365	80.50 263	78.64 259	45.05 349	49.05 333	73.94 321	27.28 321	86.10 246	43.96 310	49.94 348	78.31 321

mvs5depth		50.97 348	46.98 354	62.95 339	56.63 395	34.23 372	62.73 374	67.35 367	45.03 350	48.00 339	65.41 373	10.40 396	79.88 327	36.00 337	31.27 398	74.73 356

ADS-MVSNet2		55.21 330	51.44 335	66.51 318	80.60 190	49.56 205	55.03 392	65.44 370	44.72 351	51.00 322	61.19 384	22.83 351	75.41 358	28.54 372	53.63 333	74.57 358

ADS-MVSNet		56.17 324	51.95 334	68.84 291	80.60 190	53.07 127	55.03 392	70.02 354	44.72 351	51.00 322	61.19 384	22.83 351	78.88 331	28.54 372	53.63 333	74.57 358

testdata				67.08 311	77.59 244	45.46 300		69.20 359	44.47 353	71.50 91	88.34 136	31.21 298	70.76 378	52.20 259	75.88 134	85.03 219

MSDG		59.44 296	55.14 316	72.32 235	74.69 291	50.71 174	74.39 314	73.58 324	44.44 354	43.40 360	77.52 279	19.45 368	90.87 89	31.31 361	57.49 303	75.38 348

KD-MVS_self_test		49.24 352	46.85 355	56.44 365	54.32 397	22.87 406	57.39 387	73.36 330	44.36 355	37.98 383	59.30 391	18.97 371	71.17 376	33.48 351	42.44 374	75.26 350

YYNet1		53.82 336	49.96 342	65.41 325	70.09 344	48.95 222	72.30 330	71.66 341	44.25 356	31.89 399	63.07 379	23.73 347	73.95 363	33.26 353	39.40 380	73.34 366

MDA-MVSNet_test_wron		53.82 336	49.95 343	65.43 324	70.13 343	49.05 218	72.30 330	71.65 342	44.23 357	31.85 400	63.13 378	23.68 348	74.01 362	33.25 354	39.35 381	73.23 368

MDA-MVSNet-bldmvs		51.56 346	47.75 353	63.00 338	71.60 329	47.32 272	69.70 347	72.12 335	43.81 358	27.65 407	63.38 377	21.97 360	75.96 355	27.30 379	32.19 395	65.70 390

PLC	52.38 18	60.89 289	58.97 293	66.68 317	81.77 152	45.70 298	78.96 284	74.04 320	43.66 359	47.63 342	83.19 212	23.52 349	77.78 345	37.47 327	60.46 270	76.55 341
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
IterMVS-SCA-FT		59.12 300	58.81 294	60.08 354	70.68 341	45.07 303	80.42 266	74.25 316	43.54 360	50.02 328	73.73 323	31.97 291	56.74 400	51.06 266	53.60 335	78.42 319

MIMVSNet1		50.35 350	47.81 351	57.96 361	61.53 386	27.80 401	67.40 355	74.06 319	43.25 361	33.31 398	65.38 374	16.03 385	71.34 375	21.80 394	47.55 357	74.75 355

LTVRE_ROB	45.45 19	52.73 340	49.74 344	61.69 347	69.78 345	34.99 367	44.52 401	67.60 366	43.11 362	43.79 357	74.03 319	18.54 374	81.45 303	28.39 374	57.94 296	68.62 382
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		56.45 322	53.03 326	66.69 316	76.78 260	50.31 190	81.76 236	69.61 357	42.79 363	43.88 356	72.13 343	22.82 353	86.46 235	16.57 407	50.94 345	63.31 394

test222					79.36 207	50.97 172	77.99 291	67.84 364	42.54 364	62.84 189	86.53 170	30.26 304			76.91 117	85.23 217

CNLPA		60.59 291	58.44 295	67.05 312	79.21 212	47.26 273	79.75 276	64.34 376	42.46 365	51.90 318	83.94 196	27.79 319	75.41 358	37.12 330	59.49 277	78.47 317

PatchMatch-RL		56.66 319	53.75 324	65.37 326	77.91 241	45.28 301	69.78 346	60.38 382	41.35 366	47.57 343	73.73 323	16.83 381	76.91 349	36.99 333	59.21 280	73.92 362

DP-MVS		59.24 298	56.12 310	68.63 297	88.24 34	50.35 188	82.51 219	64.43 375	41.10 367	46.70 349	78.77 268	24.75 341	88.57 163	22.26 393	56.29 311	66.96 385

F-COLMAP		55.96 327	53.65 325	62.87 340	72.76 316	42.77 332	74.70 312	70.37 351	40.03 368	41.11 372	79.36 261	17.77 377	73.70 366	32.80 356	53.96 331	72.15 371

dongtai		43.51 361	44.07 362	41.82 386	63.75 379	21.90 410	63.80 366	72.05 336	39.59 369	33.35 397	54.54 397	41.04 175	57.30 398	10.75 415	17.77 415	46.26 409

gg-mvs-nofinetune		67.43 225	64.53 250	76.13 128	85.95 55	47.79 266	64.38 365	88.28 53	39.34 370	66.62 133	41.27 407	58.69 14	89.00 142	49.64 273	86.62 31	91.59 58

TinyColmap		48.15 355	44.49 359	59.13 358	65.73 367	38.04 359	63.34 369	62.86 380	38.78 371	29.48 402	67.23 367	6.46 410	73.30 368	24.59 386	41.90 376	66.04 388

PatchT		56.60 320	52.97 327	67.48 306	72.94 314	46.16 291	57.30 388	73.78 322	38.77 372	54.37 298	57.26 395	37.52 217	78.06 337	32.02 357	52.79 340	78.23 324

OurMVSNet-221017-0		52.39 343	48.73 347	63.35 337	65.21 370	38.42 358	68.54 352	64.95 371	38.19 373	39.57 377	71.43 347	13.23 390	79.92 323	37.16 329	40.32 379	71.72 374

ANet_high		34.39 374	29.59 380	48.78 378	30.34 423	22.28 408	55.53 391	63.79 377	38.11 374	15.47 415	36.56 412	6.94 406	59.98 392	13.93 411	5.64 426	64.08 392

PM-MVS		46.92 357	43.76 364	56.41 366	52.18 401	32.26 382	63.21 371	38.18 409	37.99 375	40.78 373	66.20 368	5.09 414	65.42 385	48.19 284	41.99 375	71.54 376

Patchmtry		56.56 321	52.95 328	67.42 307	72.53 319	50.59 178	59.05 384	71.72 339	37.86 376	46.92 347	65.86 369	38.94 198	80.06 322	36.94 334	46.72 364	71.60 375

JIA-IIPM		52.33 344	47.77 352	66.03 320	71.20 334	46.92 276	40.00 409	76.48 300	37.10 377	46.73 348	37.02 409	32.96 280	77.88 342	35.97 338	52.45 342	73.29 367

CVMVSNet		60.85 290	60.44 280	62.07 342	75.00 288	32.73 380	79.54 277	73.49 326	36.98 378	56.28 282	83.74 200	29.28 310	69.53 381	46.48 296	63.23 251	83.94 242

ITE_SJBPF				51.84 372	58.03 392	31.94 384		53.57 393	36.67 379	41.32 370	75.23 312	11.17 394	51.57 405	25.81 383	48.04 353	72.02 373

Anonymous20240521		51.65 345	48.42 348	61.34 351	56.43 396	39.65 353	73.57 319	73.47 329	36.64 380	36.59 385	63.98 376	10.75 395	72.25 374	35.35 341	49.01 349	72.11 372

COLMAP_ROB	43.60 20	50.90 349	48.05 350	59.47 355	67.81 360	40.57 350	71.25 339	62.72 381	36.49 381	36.19 387	73.51 328	13.48 389	73.92 364	20.71 397	50.26 347	63.92 393
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPMNet		59.29 297	54.25 321	74.42 177	73.97 303	56.57 34	60.52 380	76.98 289	35.72 382	57.49 265	58.87 392	37.73 211	85.26 266	27.01 380	59.93 272	81.42 280

N_pmnet		41.25 364	39.77 367	45.66 382	68.50 354	0.82 434	72.51 328	0.38 433	35.61 383	35.26 390	61.51 383	20.07 367	67.74 382	23.51 389	40.63 377	68.42 383

AllTest		47.32 356	44.66 358	55.32 369	65.08 372	37.50 363	62.96 372	54.25 391	35.45 384	33.42 395	72.82 333	9.98 397	59.33 393	24.13 387	43.84 371	69.13 380

TestCases				55.32 369	65.08 372	37.50 363		54.25 391	35.45 384	33.42 395	72.82 333	9.98 397	59.33 393	24.13 387	43.84 371	69.13 380

LS3D		56.40 323	53.82 323	64.12 331	81.12 174	45.69 299	73.42 321	66.14 368	35.30 386	43.24 362	79.88 256	22.18 358	79.62 328	19.10 402	64.00 241	67.05 384

WB-MVS		37.41 371	36.37 371	40.54 389	54.23 398	10.43 427	65.29 359	43.75 400	34.86 387	27.81 406	54.63 396	24.94 339	63.21 386	6.81 422	15.00 417	47.98 408

Patchmatch-test		53.33 339	48.17 349	68.81 293	73.31 306	42.38 337	42.98 404	58.23 384	32.53 388	38.79 381	70.77 351	39.66 193	73.51 367	25.18 384	52.06 343	90.55 88

test_fmvs1		53.60 338	52.54 333	56.78 363	58.07 391	30.26 387	68.95 350	42.19 403	32.46 389	63.59 181	82.56 225	11.55 392	60.81 390	58.25 206	55.27 321	79.28 307

test_fmvs1_n		52.55 342	51.19 337	56.65 364	51.90 402	30.14 388	67.66 354	42.84 402	32.27 390	62.30 195	82.02 239	9.12 401	60.84 389	57.82 215	54.75 327	78.99 309

test_vis1_n		51.19 347	49.66 345	55.76 368	51.26 404	29.85 392	67.20 357	38.86 408	32.12 391	59.50 225	79.86 257	8.78 402	58.23 397	56.95 224	52.46 341	79.19 308

SSC-MVS		35.20 373	34.30 375	37.90 392	52.58 400	8.65 430	61.86 375	41.64 404	31.81 392	25.54 409	52.94 402	23.39 350	59.28 395	6.10 423	12.86 418	45.78 411

EU-MVSNet		52.63 341	50.72 338	58.37 360	62.69 384	28.13 400	72.60 326	75.97 303	30.94 393	40.76 374	72.11 344	20.16 366	70.80 377	35.11 346	46.11 366	76.19 344

CMPMVS	40.41 21	55.34 328	52.64 331	63.46 335	60.88 388	43.84 317	61.58 378	71.06 347	30.43 394	36.33 386	74.63 315	24.14 345	75.44 357	48.05 285	66.62 217	71.12 378
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TDRefinement		40.91 365	38.37 369	48.55 379	50.45 406	33.03 379	58.98 385	50.97 394	28.50 395	29.89 401	67.39 366	6.21 412	54.51 402	17.67 405	35.25 388	58.11 397

ttmdpeth		40.58 366	37.50 370	49.85 376	49.40 407	22.71 407	56.65 389	46.78 395	28.35 396	40.29 376	69.42 358	5.35 413	61.86 388	20.16 399	21.06 412	64.96 391

pmmvs3		45.53 360	41.55 366	57.44 362	48.97 409	39.68 352	70.06 343	57.66 385	28.32 397	34.06 392	57.29 394	8.50 403	66.85 384	34.86 348	34.26 392	65.80 389

mvsany_test1		43.38 362	42.57 365	45.82 381	50.96 405	26.10 402	55.80 390	27.74 421	27.15 398	47.41 346	74.39 317	18.67 373	44.95 412	44.66 305	36.31 385	66.40 387

RPSCF		45.77 359	44.13 361	50.68 373	57.67 394	29.66 393	54.92 394	45.25 399	26.69 399	45.92 353	75.92 308	17.43 379	45.70 411	27.44 378	45.95 367	76.67 336

test_fmvs2		45.89 358	44.32 360	50.62 374	45.85 413	24.70 404	58.87 386	37.84 411	25.22 400	52.46 313	74.56 316	7.07 405	54.69 401	49.28 276	47.70 355	72.48 370

mamv4		42.60 363	44.05 363	38.26 391	59.21 390	38.00 360	44.14 403	39.03 407	25.03 401	40.61 375	68.39 362	37.01 231	24.28 425	46.62 295	36.43 384	52.50 403

MVS-HIRNet		49.01 353	44.71 357	61.92 346	76.06 271	46.61 281	63.23 370	54.90 389	24.77 402	33.56 394	36.60 411	21.28 363	75.88 356	29.49 366	62.54 260	63.26 395

test_vis1_rt		40.29 367	38.64 368	45.25 383	48.91 410	30.09 389	59.44 383	27.07 422	24.52 403	38.48 382	51.67 403	6.71 408	49.44 406	44.33 307	46.59 365	56.23 398

new_pmnet		33.56 376	31.89 378	38.59 390	49.01 408	20.42 413	51.01 395	37.92 410	20.58 404	23.45 410	46.79 405	6.66 409	49.28 408	20.00 401	31.57 397	46.09 410

LF4IMVS		33.04 377	32.55 377	34.52 395	40.96 414	22.03 409	44.45 402	35.62 413	20.42 405	28.12 405	62.35 381	5.03 415	31.88 424	21.61 396	34.42 390	49.63 406

FPMVS		35.40 372	33.67 376	40.57 388	46.34 412	28.74 399	41.05 406	57.05 386	20.37 406	22.27 411	53.38 400	6.87 407	44.94 413	8.62 416	47.11 361	48.01 407

DSMNet-mixed		38.35 368	35.36 373	47.33 380	48.11 411	14.91 424	37.87 410	36.60 412	19.18 407	34.37 391	59.56 390	15.53 386	53.01 404	20.14 400	46.89 363	74.07 360

PMMVS2		26.71 382	22.98 387	37.87 393	36.89 417	8.51 431	42.51 405	29.32 420	19.09 408	13.01 417	37.54 408	2.23 422	53.11 403	14.54 410	11.71 419	51.99 405

test_fmvs3		37.95 370	35.75 372	44.55 384	35.50 419	18.92 416	48.32 397	34.00 416	18.36 409	41.31 371	61.58 382	2.29 421	48.06 410	42.72 316	37.71 383	66.66 386

MVStest1		38.35 368	34.53 374	49.82 377	51.43 403	30.41 386	50.39 396	55.25 387	17.56 410	26.45 408	65.85 371	11.72 391	57.00 399	14.79 409	17.31 416	62.05 396

mvsany_test3		28.00 379	25.98 381	34.05 396	28.97 424	15.31 422	34.54 413	18.17 427	16.24 411	29.30 403	53.37 401	2.79 419	33.38 423	30.01 365	20.41 413	53.45 402

PMVS	19.57 22	25.07 384	22.43 389	32.99 399	23.12 430	22.98 405	40.98 407	35.19 414	15.99 412	11.95 421	35.87 413	1.47 427	49.29 407	5.41 425	31.90 396	26.70 418
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		27.47 380	24.26 385	37.12 394	60.55 389	29.17 396	11.68 421	60.00 383	14.18 413	10.52 422	15.12 423	2.20 423	63.01 387	8.39 417	35.65 386	19.18 419
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis3_rt		24.79 385	22.95 388	30.31 401	28.59 425	18.92 416	37.43 411	17.27 429	12.90 414	21.28 412	29.92 418	1.02 428	36.35 417	28.28 375	29.82 402	35.65 412

LCM-MVSNet		28.07 378	23.85 386	40.71 387	27.46 428	18.93 415	30.82 416	46.19 396	12.76 415	16.40 413	34.70 414	1.90 424	48.69 409	20.25 398	24.22 407	54.51 401

test_f		27.12 381	24.85 382	33.93 397	26.17 429	15.25 423	30.24 417	22.38 426	12.53 416	28.23 404	49.43 404	2.59 420	34.34 422	25.12 385	26.99 403	52.20 404

APD_test1		26.46 383	24.41 384	32.62 400	37.58 416	21.74 411	40.50 408	30.39 418	11.45 417	16.33 414	43.76 406	1.63 426	41.62 414	11.24 413	26.82 404	34.51 414

E-PMN		19.16 389	18.40 393	21.44 405	36.19 418	13.63 425	47.59 398	30.89 417	10.73 418	5.91 425	16.59 421	3.66 417	39.77 415	5.95 424	8.14 421	10.92 421

DeepMVS_CX				13.10 407	21.34 431	8.99 429		10.02 431	10.59 419	7.53 424	30.55 417	1.82 425	14.55 426	6.83 421	7.52 422	15.75 420

EMVS		18.42 390	17.66 394	20.71 406	34.13 420	12.64 426	46.94 399	29.94 419	10.46 420	5.58 426	14.93 424	4.23 416	38.83 416	5.24 426	7.51 423	10.67 422

testf1		21.11 387	19.08 391	27.18 403	30.56 421	18.28 418	33.43 414	24.48 423	8.02 421	12.02 419	33.50 415	0.75 430	35.09 420	7.68 418	21.32 409	28.17 416

APD_test2		21.11 387	19.08 391	27.18 403	30.56 421	18.28 418	33.43 414	24.48 423	8.02 421	12.02 419	33.50 415	0.75 430	35.09 420	7.68 418	21.32 409	28.17 416

MVE	16.60 23	17.34 392	13.39 395	29.16 402	28.43 426	19.72 414	13.73 420	23.63 425	7.23 423	7.96 423	21.41 419	0.80 429	36.08 418	6.97 420	10.39 420	31.69 415
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		24.09 386	21.07 390	33.16 398	27.67 427	8.35 432	26.63 418	35.11 415	3.40 424	14.35 416	36.98 410	3.46 418	35.31 419	19.08 403	22.95 408	55.81 399

wuyk23d		9.11 394	8.77 398	10.15 408	40.18 415	16.76 421	20.28 419	1.01 432	2.58 425	2.66 427	0.98 427	0.23 432	12.49 427	4.08 427	6.90 424	1.19 424

tmp_tt		9.44 393	10.68 396	5.73 409	2.49 432	4.21 433	10.48 422	18.04 428	0.34 426	12.59 418	20.49 420	11.39 393	7.03 428	13.84 412	6.46 425	5.95 423

EGC-MVSNET		33.75 375	30.42 379	43.75 385	64.94 374	36.21 366	60.47 382	40.70 406	0.02 427	0.10 428	53.79 399	7.39 404	60.26 391	11.09 414	35.23 389	34.79 413

mmdepth		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

monomultidepth		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

test_blank		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

uanet_test		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

DCPMVS		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

cdsmvs_eth3d_5k		18.33 391	24.44 383	0.00 412	0.00 434	0.00 436	0.00 423	89.40 25	0.00 428	0.00 431	92.02 50	38.55 202	0.00 429	0.00 430	0.00 427	0.00 427

pcd_1.5k_mvsjas		3.15 398	4.20 401	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	37.77 208	0.00 429	0.00 430	0.00 427	0.00 427

sosnet-low-res		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

sosnet		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

uncertanet		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

Regformer		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

testmvs		6.14 396	8.18 399	0.01 410	0.01 433	0.00 436	73.40 322	0.00 434	0.00 428	0.02 429	0.15 428	0.00 433	0.00 429	0.02 428	0.00 427	0.02 425

test123		6.01 397	8.01 400	0.01 410	0.00 434	0.01 435	71.93 336	0.00 434	0.00 428	0.02 429	0.11 429	0.00 433	0.00 429	0.02 428	0.00 427	0.02 425

ab-mvs-re		7.68 395	10.24 397	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	92.12 46	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

uanet		0.00 399	0.00 402	0.00 412	0.00 434	0.00 436	0.00 423	0.00 434	0.00 428	0.00 431	0.00 430	0.00 433	0.00 429	0.00 430	0.00 427	0.00 427

WAC-MVS						34.28 370								22.56 392

MSC_two_6792asdad				81.53 15	91.77 4	56.03 46		91.10 11					96.22 8	81.46 36	86.80 28	92.34 35

No_MVS				81.53 15	91.77 4	56.03 46		91.10 11					96.22 8	81.46 36	86.80 28	92.34 35

eth-test2					0.00 434

eth-test					0.00 434

OPU-MVS				81.71 13	92.05 3	55.97 48	92.48 3				94.01 5	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 34					96.39 4	81.68 31	87.13 21	92.47 31

GSMVS																88.13 157

test_part2					89.33 23	55.48 54				82.27 12

sam_mvs1												38.86 200				88.13 157

sam_mvs												35.99 252

ambc				62.06 343	53.98 399	29.38 395	35.08 412	79.65 236		41.37 368	59.96 388	6.27 411	82.15 298	35.34 342	38.22 382	74.65 357

MTGPA								81.31 201

test_post1							70.84 341				14.72 425	34.33 269	83.86 283	48.80 279

test_post											16.22 422	37.52 217	84.72 275

patchmatchnet-post											59.74 389	38.41 203	79.91 325

GG-mvs-BLEND				77.77 86	86.68 48	50.61 176	68.67 351	88.45 51		68.73 118	87.45 155	59.15 11	90.67 92	54.83 237	87.67 17	92.03 45

MTMP							87.27 77	15.34 430

test9_res														78.72 52	85.44 43	91.39 66

agg_prior2														75.65 72	85.11 47	91.01 78

agg_prior					85.64 62	54.92 76		83.61 162		72.53 78			88.10 181

test_prior4						56.39 40	87.15 81

test_prior				78.39 74	86.35 53	54.91 77		85.45 106					89.70 121			90.55 88

新几何2							81.61 243

旧先验1					81.57 164	47.48 268		71.83 337			88.66 127	36.94 233			78.34 105	88.67 141

原ACMM2							83.77 180

testdata2													77.81 344	45.64 301

segment_acmp												44.97 121

test12				79.24 44	86.89 46	56.08 45		85.16 121		72.27 82		47.15 89	91.10 82		85.93 37	90.54 90

plane_prior7					77.95 238	48.46 240

plane_prior6					78.42 233	49.39 213						36.04 250

plane_prior5								82.59 179					88.30 174	65.46 146	72.34 172	84.49 227

plane_prior4											83.28 210

plane_prior1					78.31 235

n2								0.00 434

nn								0.00 434

door-mid								41.31 405

lessismore_v0				67.98 303	64.76 375	41.25 345		45.75 398		36.03 388	65.63 372	19.29 370	84.11 281	35.67 339	21.24 411	78.59 316

test11								84.25 146

door								43.27 401

HQP5-MVS						51.56 161

BP-MVS														66.70 132

HQP4-MVS										64.47 168			88.61 158			84.91 223

HQP3-MVS								83.68 158							73.12 163

HQP2-MVS												37.35 220

NP-MVS					78.76 222	50.43 182					85.12 184

ACMMP++_ref															63.20 252

ACMMP++															59.38 278

Test By Simon												39.38 194

High-resolution multi-view benchmark