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.

MM		89.16 6	89.23 7	88.97 4	90.79 98	73.65 10	92.66 24	91.17 134	86.57 1	87.39 52	94.97 21	71.70 58	97.68 1	92.19 1	95.63 28	95.57 1

UA-Net		85.08 79	84.96 79	85.45 84	92.07 75	68.07 141	89.78 85	90.86 144	82.48 2	84.60 86	93.20 81	69.35 87	95.22 84	71.39 215	90.88 108	93.07 121

MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 188	82.14 3	86.65 60	94.28 41	68.28 105	97.46 6	90.81 6	95.31 34	95.15 8

CANet		86.45 45	86.10 56	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 140	91.43 130	70.34 75	97.23 14	84.26 69	93.36 70	94.37 48

NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 57	92.83 61	81.50 5	85.79 66	93.47 74	73.02 42	97.00 18	84.90 58	94.94 40	94.10 59

EPNet		83.72 97	82.92 111	86.14 68	84.22 314	69.48 97	91.05 59	85.27 302	81.30 6	76.83 230	91.65 120	66.09 132	95.56 64	76.00 163	93.85 64	93.38 102
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 65	93.00 47	80.90 7	88.06 38	94.06 53	76.43 16	96.84 21	88.48 34	95.99 18	94.34 50

3Dnovator+	77.84 4	85.48 68	84.47 87	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 235	93.37 77	60.40 218	96.75 26	77.20 146	93.73 66	95.29 6

TranMVSNet+NR-MVSNet		80.84 159	80.31 156	82.42 221	87.85 208	62.33 287	87.74 173	91.33 129	80.55 9	77.99 204	89.86 171	65.23 141	92.62 209	67.05 262	75.24 356	92.30 157

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 17	94.11 7	80.27 10	91.35 14	94.16 48	78.35 13	96.77 24	89.59 16	94.22 62	94.67 30
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
HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 28	92.85 60	80.26 11	87.78 43	94.27 42	75.89 19	96.81 23	87.45 42	96.44 9	93.05 124

fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 149	88.59 139	89.05 216	80.19 12	90.70 17	95.40 15	74.56 25	93.92 145	91.54 2	92.07 86	95.31 5

UniMVSNet_NR-MVSNet		81.88 135	81.54 134	82.92 200	88.46 180	63.46 264	87.13 190	92.37 82	80.19 12	78.38 193	89.14 196	71.66 60	93.05 195	70.05 230	76.46 329	92.25 159

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 74	72.96 25	93.73 5	93.67 21	80.19 12	88.10 37	94.80 23	73.76 34	97.11 15	87.51 41	95.82 21	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 87	83.81 93	85.31 88	88.18 190	67.85 150	87.66 174	89.73 182	80.05 15	82.95 116	89.59 185	70.74 72	94.82 104	80.66 112	84.72 213	93.28 108

ETV-MVS		84.90 83	84.67 83	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 97	85.71 296	69.32 88	95.38 78	80.82 107	91.37 99	92.72 136

fmvsm_s_conf0.5_n_8		86.56 44	87.17 35	84.73 113	87.76 216	65.62 204	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 100	94.20 130	90.83 5	91.39 98	94.38 47

EI-MVSNet-UG-set		83.81 92	83.38 102	85.09 97	87.87 207	67.53 161	87.44 182	89.66 183	79.74 18	82.23 126	89.41 194	70.24 78	94.74 109	79.95 117	83.92 228	92.99 129

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 190	87.08 244	65.21 213	89.09 116	90.21 165	79.67 19	89.98 19	95.02 20	73.17 39	91.71 251	91.30 3	91.60 93	92.34 154

CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 159	92.83 18	93.30 33	79.67 19	84.57 87	92.27 101	71.47 61	95.02 96	84.24 71	93.46 69	95.13 9

casdiffmvs_mvg		85.99 54	86.09 57	85.70 77	87.65 220	67.22 173	88.69 135	93.04 42	79.64 21	85.33 70	92.54 98	73.30 36	94.50 119	83.49 77	91.14 102	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 215	92.02 98	79.45 22	85.88 64	94.80 23	68.07 107	96.21 46	86.69 47	95.34 32	93.23 109

EC-MVSNet		86.01 53	86.38 47	84.91 106	89.31 143	66.27 187	92.32 31	93.63 22	79.37 23	84.17 96	91.88 112	69.04 95	95.43 73	83.93 75	93.77 65	93.01 127

NormalMVS		86.29 50	85.88 60	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 90	92.18 103	64.64 147	95.53 67	80.70 110	94.65 48	94.56 39

SymmetryMVS		85.38 73	84.81 81	87.07 46	91.47 83	72.47 38	91.65 43	88.06 247	79.31 24	84.39 90	92.18 103	64.64 147	95.53 67	80.70 110	90.91 107	93.21 112

XVS		87.18 34	86.91 41	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 107	94.17 47	67.45 114	96.60 33	83.06 81	94.50 53	94.07 61

X-MVStestdata		80.37 182	77.83 221	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 107	12.47 462	67.45 114	96.60 33	83.06 81	94.50 53	94.07 61

HQP_MVS		83.64 100	83.14 105	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 180	91.00 147	60.42 216	95.38 78	78.71 129	86.32 185	91.33 192

plane_prior2							91.25 55		79.12 28

IS-MVSNet		83.15 114	82.81 112	84.18 137	89.94 119	63.30 268	91.59 46	88.46 240	79.04 30	79.49 169	92.16 105	65.10 142	94.28 125	67.71 253	91.86 91	94.95 12

DU-MVS		81.12 155	80.52 151	82.90 201	87.80 211	63.46 264	87.02 195	91.87 108	79.01 31	78.38 193	89.07 198	65.02 143	93.05 195	70.05 230	76.46 329	92.20 162

NR-MVSNet		80.23 185	79.38 182	82.78 211	87.80 211	63.34 267	86.31 224	91.09 138	79.01 31	72.17 324	89.07 198	67.20 117	92.81 207	66.08 269	75.65 342	92.20 162

SPE-MVS-test		86.29 50	86.48 46	85.71 76	91.02 91	67.21 174	92.36 30	93.78 19	78.97 33	83.51 110	91.20 137	70.65 74	95.15 87	81.96 96	94.89 42	94.77 25

DELS-MVS		85.41 71	85.30 75	85.77 75	88.49 178	67.93 148	85.52 250	93.44 28	78.70 34	83.63 109	89.03 200	74.57 24	95.71 62	80.26 115	94.04 63	93.66 85
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
WR-MVS		79.49 198	79.22 189	80.27 273	88.79 168	58.35 333	85.06 259	88.61 238	78.56 35	77.65 211	88.34 223	63.81 155	90.66 292	64.98 278	77.22 317	91.80 176

plane_prior3						68.60 124			78.44 36	78.92 180

UniMVSNet (Re)		81.60 143	81.11 139	83.09 190	88.38 184	64.41 238	87.60 175	93.02 46	78.42 37	78.56 188	88.16 229	69.78 82	93.26 177	69.58 237	76.49 328	91.60 182

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 61	95.06 1	94.23 3	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 18	96.68 2	94.95 12

test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 18	96.57 7	94.67 30

testing3-2		75.12 298	75.19 280	74.91 359	90.40 105	45.09 441	80.29 349	78.42 393	78.37 40	76.54 240	87.75 239	44.36 375	87.28 346	57.04 352	83.49 240	92.37 153

test_one_0601					95.07 7	71.46 59		94.14 6	78.27 41	92.05 11	95.74 6	80.83 11

SD-MVS		88.06 15	88.50 15	86.71 56	92.60 71	72.71 29	91.81 42	93.19 36	77.87 42	90.32 18	94.00 57	74.83 23	93.78 152	87.63 40	94.27 61	93.65 89
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
casdiffmvs		85.11 78	85.14 77	85.01 99	87.20 235	65.77 201	87.75 172	92.83 61	77.84 43	84.36 93	92.38 100	72.15 51	93.93 144	81.27 103	90.48 113	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
KinetiMVS		83.31 112	82.61 116	85.39 86	87.08 244	67.56 160	88.06 160	91.65 118	77.80 44	82.21 127	91.79 115	57.27 243	94.07 136	77.77 140	89.89 126	94.56 39

BP-MVS1		84.32 86	83.71 95	86.17 64	87.84 209	67.85 150	89.38 102	89.64 185	77.73 45	83.98 100	92.12 108	56.89 248	95.43 73	84.03 74	91.75 92	95.24 7

CP-MVSNet		78.22 233	78.34 207	77.84 324	87.83 210	54.54 390	87.94 165	91.17 134	77.65 46	73.48 306	88.49 219	62.24 179	88.43 331	62.19 301	74.07 365	90.55 223

plane_prior						68.71 119	90.38 73		77.62 47						86.16 189

baseline		84.93 81	84.98 78	84.80 111	87.30 233	65.39 210	87.30 187	92.88 58	77.62 47	84.04 99	92.26 102	71.81 55	93.96 138	81.31 101	90.30 116	95.03 11

VDD-MVS		83.01 119	82.36 120	84.96 101	91.02 91	66.40 184	88.91 121	88.11 243	77.57 49	84.39 90	93.29 79	52.19 289	93.91 146	77.05 149	88.70 148	94.57 38

MP-MVS		87.71 20	87.64 23	87.93 21	94.36 26	73.88 6	92.71 23	92.65 71	77.57 49	83.84 103	94.40 36	72.24 50	96.28 43	85.65 53	95.30 35	93.62 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 248	77.69 229	77.84 324	87.07 246	53.91 395	87.91 167	91.18 133	77.56 51	73.14 310	88.82 209	61.23 200	89.17 317	59.95 321	72.37 380	90.43 228

OPM-MVS		83.50 105	82.95 110	85.14 92	88.79 168	70.95 71	89.13 114	91.52 123	77.55 52	80.96 148	91.75 116	60.71 208	94.50 119	79.67 121	86.51 183	89.97 255
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepPCF-MVS	80.84 1	88.10 13	88.56 14	86.73 55	92.24 73	69.03 106	89.57 93	93.39 31	77.53 53	89.79 20	94.12 50	78.98 12	96.58 35	85.66 52	95.72 24	94.58 36

PS-CasMVS		78.01 242	78.09 213	77.77 326	87.71 217	54.39 392	88.02 161	91.22 131	77.50 54	73.26 308	88.64 214	60.73 207	88.41 332	61.88 305	73.88 369	90.53 224

MSLP-MVS++		85.43 70	85.76 64	84.45 121	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 94	92.81 93	67.16 118	92.94 199	80.36 113	94.35 59	90.16 239

RRT-MVS		82.60 125	82.10 125	84.10 139	87.98 203	62.94 279	87.45 181	91.27 130	77.42 56	79.85 164	90.28 163	56.62 251	94.70 112	79.87 119	88.15 157	94.67 30

DVP-MVS		89.60 3	90.35 3	87.33 41	95.27 5	71.25 61	93.49 10	92.73 65	77.33 57	92.12 9	95.78 4	80.98 9	97.40 9	89.08 21	96.41 12	93.33 106
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					95.27 5	71.25 61	93.60 7	94.11 7	77.33 57	92.81 3	95.79 3	80.98 9

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 157	90.51 65	92.90 57	77.26 59	87.44 51	91.63 122	71.27 65	96.06 50	85.62 54	95.01 37	94.78 24

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 68	93.57 8	94.06 11	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 23	96.63 4	94.88 16

test_241102_TWO								94.06 11	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 23	96.58 6	94.26 54

3Dnovator	76.31 5	83.38 109	82.31 121	86.59 57	87.94 204	72.94 28	90.64 63	92.14 97	77.21 62	75.47 261	92.83 91	58.56 230	94.72 110	73.24 194	92.71 77	92.13 169

test_241102_ONE					95.30 2	70.98 68		94.06 11	77.17 63	93.10 1	95.39 16	82.99 1	97.27 12

WR-MVS_H		78.51 228	78.49 202	78.56 308	88.02 200	56.38 367	88.43 144	92.67 68	77.14 64	73.89 300	87.55 247	66.25 128	89.24 315	58.92 332	73.55 372	90.06 249

lecture		88.09 14	88.59 13	86.58 58	93.26 52	69.77 92	93.70 6	94.16 5	77.13 65	89.76 21	95.52 14	72.26 49	96.27 44	86.87 45	94.65 48	93.70 84

DeepC-MVS	79.81 2	87.08 37	86.88 42	87.69 33	91.16 87	72.32 45	90.31 74	93.94 15	77.12 66	82.82 120	94.23 45	72.13 52	97.09 16	84.83 61	95.37 31	93.65 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		81.52 147	82.02 128	80.03 278	88.42 183	55.97 373	87.95 164	93.42 30	77.10 67	77.38 216	90.98 149	69.96 80	91.79 246	68.46 249	84.50 216	92.33 155

DTE-MVSNet		76.99 264	76.80 249	77.54 332	86.24 263	53.06 404	87.52 177	90.66 147	77.08 68	72.50 318	88.67 213	60.48 215	89.52 309	57.33 349	70.74 392	90.05 250

LFMVS		81.82 137	81.23 137	83.57 171	91.89 78	63.43 266	89.84 81	81.85 355	77.04 69	83.21 112	93.10 82	52.26 288	93.43 171	71.98 210	89.95 124	93.85 73

UGNet		80.83 160	79.59 178	84.54 117	88.04 199	68.09 140	89.42 99	88.16 242	76.95 70	76.22 247	89.46 190	49.30 332	93.94 141	68.48 248	90.31 115	91.60 182
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
FIs		82.07 131	82.42 117	81.04 255	88.80 167	58.34 334	88.26 153	93.49 27	76.93 71	78.47 192	91.04 143	69.92 81	92.34 227	69.87 234	84.97 209	92.44 152

GST-MVS		87.42 28	87.26 31	87.89 24	94.12 36	72.97 24	92.39 27	93.43 29	76.89 72	84.68 80	93.99 59	70.67 73	96.82 22	84.18 73	95.01 37	93.90 71

mPP-MVS		86.67 43	86.32 48	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 121	94.25 44	66.44 125	96.24 45	82.88 86	94.28 60	93.38 102

ZNCC-MVS		87.94 19	87.85 21	88.20 12	94.39 24	73.33 19	93.03 15	93.81 18	76.81 74	85.24 71	94.32 39	71.76 56	96.93 19	85.53 55	95.79 22	94.32 51

VPNet		78.69 223	78.66 199	78.76 303	88.31 186	55.72 377	84.45 277	86.63 283	76.79 75	78.26 196	90.55 157	59.30 224	89.70 307	66.63 264	77.05 319	90.88 208

HFP-MVS		87.58 23	87.47 28	87.94 19	94.58 16	73.54 15	93.04 13	93.24 34	76.78 76	84.91 76	94.44 34	70.78 71	96.61 32	84.53 66	94.89 42	93.66 85

ACMMPR		87.44 26	87.23 33	88.08 15	94.64 13	73.59 12	93.04 13	93.20 35	76.78 76	84.66 83	94.52 27	68.81 97	96.65 30	84.53 66	94.90 41	94.00 65

ACMMP		85.89 60	85.39 71	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 153	93.82 66	64.33 149	96.29 42	82.67 93	90.69 110	93.23 109
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
region2R		87.42 28	87.20 34	88.09 14	94.63 14	73.55 13	93.03 15	93.12 41	76.73 79	84.45 88	94.52 27	69.09 91	96.70 27	84.37 68	94.83 45	94.03 63

sasdasda		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 134	73.28 37	93.91 146	81.50 99	88.80 144	94.77 25

canonicalmvs		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 134	73.28 37	93.91 146	81.50 99	88.80 144	94.77 25

CP-MVS		87.11 35	86.92 40	87.68 34	94.20 34	73.86 7	93.98 3	92.82 64	76.62 82	83.68 106	94.46 31	67.93 109	95.95 58	84.20 72	94.39 57	93.23 109

DeepC-MVS_fast	79.65 3	86.91 38	86.62 45	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 94	93.36 78	71.44 62	96.76 25	80.82 107	95.33 33	94.16 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCFI-Net		85.06 80	85.51 69	83.70 166	89.42 135	63.01 274	89.43 97	92.62 74	76.43 84	87.53 48	91.34 132	72.82 46	93.42 172	81.28 102	88.74 147	94.66 33

TSAR-MVS + GP.		85.71 64	85.33 73	86.84 52	91.34 84	72.50 36	89.07 117	87.28 267	76.41 85	85.80 65	90.22 167	74.15 32	95.37 81	81.82 97	91.88 88	92.65 141

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 221

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 221

HQP-MVS		82.61 123	82.02 128	84.37 123	89.33 140	66.98 177	89.17 109	92.19 92	76.41 85	77.23 221	90.23 166	60.17 219	95.11 90	77.47 143	85.99 193	91.03 202

CANet_DTU		80.61 171	79.87 169	82.83 204	85.60 280	63.17 273	87.36 184	88.65 236	76.37 89	75.88 254	88.44 221	53.51 277	93.07 193	73.30 192	89.74 128	92.25 159

VNet		82.21 128	82.41 118	81.62 236	90.82 96	60.93 305	84.47 274	89.78 177	76.36 90	84.07 98	91.88 112	64.71 146	90.26 295	70.68 222	88.89 142	93.66 85

Vis-MVSNet		83.46 106	82.80 113	85.43 85	90.25 108	68.74 117	90.30 75	90.13 168	76.33 91	80.87 150	92.89 89	61.00 205	94.20 130	72.45 207	90.97 105	93.35 105
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP_NAP		88.05 17	88.08 18	87.94 19	93.70 41	73.05 22	90.86 60	93.59 24	76.27 92	88.14 36	95.09 19	71.06 68	96.67 29	87.67 39	96.37 14	94.09 60

alignmvs		85.48 68	85.32 74	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 47	91.46 129	70.32 76	93.78 152	81.51 98	88.95 141	94.63 34

MVS_111021_HR		85.14 77	84.75 82	86.32 61	91.65 81	72.70 30	85.98 232	90.33 160	76.11 94	82.08 129	91.61 124	71.36 64	94.17 133	81.02 104	92.58 78	92.08 170

HPM-MVS		87.11 35	86.98 38	87.50 39	93.88 39	72.16 47	92.19 34	93.33 32	76.07 95	83.81 104	93.95 62	69.77 83	96.01 54	85.15 56	94.66 47	94.32 51
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
h-mvs33		83.15 114	82.19 123	86.02 72	90.56 101	70.85 75	88.15 158	89.16 211	76.02 96	84.67 81	91.39 131	61.54 191	95.50 69	82.71 90	75.48 346	91.72 181

hse-mvs2		81.72 138	80.94 143	84.07 145	88.72 171	67.68 155	85.87 236	87.26 269	76.02 96	84.67 81	88.22 228	61.54 191	93.48 167	82.71 90	73.44 374	91.06 200

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 46	94.10 9	75.90 98	92.29 7	95.66 10	81.67 6	97.38 11	87.44 43	96.34 15	93.95 68
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CLD-MVS		82.31 127	81.65 133	84.29 129	88.47 179	67.73 154	85.81 240	92.35 83	75.78 99	78.33 195	86.58 278	64.01 152	94.35 123	76.05 162	87.48 166	90.79 211
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewmacassd2359aftdt		83.76 95	83.66 97	84.07 145	86.59 258	64.56 230	86.88 202	91.82 111	75.72 100	83.34 111	92.15 107	68.24 106	92.88 202	79.05 122	89.15 139	94.77 25

SF-MVS		88.46 12	88.74 12	87.64 35	92.78 66	71.95 51	92.40 25	94.74 2	75.71 101	89.16 24	95.10 18	75.65 21	96.19 47	87.07 44	96.01 17	94.79 23

testdata1							84.14 286		75.71 101

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 54	93.83 4	93.96 14	75.70 103	91.06 16	96.03 1	76.84 14	97.03 17	89.09 20	95.65 27	94.47 43
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VPA-MVSNet		80.60 173	80.55 150	80.76 262	88.07 198	60.80 308	86.86 203	91.58 122	75.67 104	80.24 160	89.45 192	63.34 156	90.25 296	70.51 224	79.22 296	91.23 195

fmvsm_l_conf0.5_n_9		85.84 61	86.63 44	83.46 173	87.12 243	66.01 191	88.56 141	89.43 192	75.59 105	89.32 23	94.32 39	72.89 43	91.21 275	90.11 10	92.33 83	93.16 116

PGM-MVS		86.68 42	86.27 50	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 106	83.86 102	94.42 35	67.87 111	96.64 31	82.70 92	94.57 52	93.66 85

Effi-MVS+		83.62 102	83.08 106	85.24 90	88.38 184	67.45 162	88.89 122	89.15 212	75.50 107	82.27 125	88.28 225	69.61 85	94.45 122	77.81 139	87.84 160	93.84 75

fmvsm_s_conf0.5_n_4		85.39 72	85.75 65	84.30 128	86.70 254	65.83 197	88.77 129	89.78 177	75.46 108	88.35 31	93.73 68	69.19 90	93.06 194	91.30 3	88.44 153	94.02 64

fmvsm_s_conf0.5_n_6		85.55 67	86.20 51	83.60 168	87.32 232	65.13 216	88.86 123	91.63 119	75.41 109	88.23 35	93.45 75	68.56 101	92.47 219	89.52 17	92.78 75	93.20 114

test_prior2							88.85 125		75.41 109	84.91 76	93.54 70	74.28 30		83.31 79	95.86 20

LPG-MVS_test		82.08 130	81.27 136	84.50 118	89.23 148	68.76 115	90.22 76	91.94 104	75.37 111	76.64 236	91.51 126	54.29 268	94.91 98	78.44 131	83.78 229	89.83 260

LGP-MVS_train				84.50 118	89.23 148	68.76 115		91.94 104	75.37 111	76.64 236	91.51 126	54.29 268	94.91 98	78.44 131	83.78 229	89.83 260

fmvsm_l_conf0.5_n_3		86.02 52	86.32 48	85.14 92	87.20 235	68.54 126	89.57 93	90.44 154	75.31 113	87.49 49	94.39 37	72.86 44	92.72 208	89.04 25	90.56 112	94.16 56

MG-MVS		83.41 107	83.45 100	83.28 180	92.74 67	62.28 289	88.17 156	89.50 190	75.22 114	81.49 138	92.74 97	66.75 119	95.11 90	72.85 197	91.58 95	92.45 151

SSC-MVS3.2		73.35 320	73.39 304	73.23 376	85.30 289	49.01 426	74.58 409	81.57 357	75.21 115	73.68 303	85.58 302	52.53 282	82.05 392	54.33 370	77.69 313	88.63 303

LCM-MVSNet-Re		77.05 263	76.94 246	77.36 333	87.20 235	51.60 412	80.06 351	80.46 371	75.20 116	67.69 370	86.72 268	62.48 173	88.98 321	63.44 288	89.25 135	91.51 186

SDMVSNet		80.38 180	80.18 159	80.99 256	89.03 157	64.94 223	80.45 346	89.40 193	75.19 117	76.61 238	89.98 169	60.61 213	87.69 341	76.83 154	83.55 238	90.33 233

sd_testset		77.70 251	77.40 236	78.60 306	89.03 157	60.02 319	79.00 366	85.83 297	75.19 117	76.61 238	89.98 169	54.81 260	85.46 366	62.63 297	83.55 238	90.33 233

MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 119	86.34 62	95.29 17	70.86 70	96.00 55	88.78 29	96.04 16	94.58 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test1111		79.43 201	79.18 190	80.15 276	89.99 117	53.31 401	87.33 186	77.05 405	75.04 120	80.23 161	92.77 96	48.97 337	92.33 228	68.87 244	92.40 82	94.81 22

Effi-MVS+-dtu		80.03 189	78.57 201	84.42 122	85.13 295	68.74 117	88.77 129	88.10 244	74.99 121	74.97 285	83.49 352	57.27 243	93.36 173	73.53 188	80.88 273	91.18 196

reproduce-ours		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 122	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 122

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 122	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 122

fmvsm_s_conf0.5_n_7		83.34 110	84.03 91	81.28 247	85.73 276	65.13 216	85.40 251	89.90 175	74.96 124	82.13 128	93.89 63	66.65 120	87.92 337	86.56 48	91.05 103	90.80 210

OMC-MVS		82.69 121	81.97 130	84.85 108	88.75 170	67.42 163	87.98 162	90.87 143	74.92 125	79.72 166	91.65 120	62.19 180	93.96 138	75.26 173	86.42 184	93.16 116

viewmanbaseed2359cas		83.66 98	83.55 98	84.00 156	86.81 250	64.53 231	86.65 212	91.75 116	74.89 126	83.15 115	91.68 118	68.74 99	92.83 206	79.02 123	89.24 136	94.63 34

test2506		77.30 260	76.49 257	79.74 284	90.08 112	52.02 405	87.86 170	63.10 448	74.88 127	80.16 162	92.79 94	38.29 412	92.35 226	68.74 246	92.50 80	94.86 19

ECVR-MVS		79.61 194	79.26 187	80.67 264	90.08 112	54.69 388	87.89 168	77.44 401	74.88 127	80.27 159	92.79 94	48.96 338	92.45 220	68.55 247	92.50 80	94.86 19

MonoMVSNet		76.49 276	75.80 265	78.58 307	81.55 374	58.45 332	86.36 223	86.22 290	74.87 129	74.73 289	83.73 345	51.79 301	88.73 326	70.78 219	72.15 383	88.55 306

nrg030		83.88 91	83.53 99	84.96 101	86.77 252	69.28 105	90.46 70	92.67 68	74.79 130	82.95 116	91.33 133	72.70 47	93.09 192	80.79 109	79.28 295	92.50 147

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 25	93.63 22	74.77 131	92.29 7	95.97 2	74.28 30	97.24 13	88.58 31	96.91 1	94.87 18
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
reproduce_model		87.28 32	87.39 30	86.95 50	93.10 58	71.24 65	91.60 45	93.19 36	74.69 132	88.80 28	95.61 11	70.29 77	96.44 39	86.20 51	93.08 71	93.16 116

MVS_111021_LR		82.61 123	82.11 124	84.11 138	88.82 162	71.58 57	85.15 256	86.16 292	74.69 132	80.47 158	91.04 143	62.29 177	90.55 293	80.33 114	90.08 121	90.20 238

EIA-MVS		83.31 112	82.80 113	84.82 109	89.59 126	65.59 205	88.21 154	92.68 67	74.66 134	78.96 178	86.42 283	69.06 93	95.26 83	75.54 169	90.09 120	93.62 92

TSAR-MVS + MP.		88.02 18	88.11 17	87.72 30	93.68 43	72.13 48	91.41 53	92.35 83	74.62 135	88.90 27	93.85 65	75.75 20	96.00 55	87.80 38	94.63 50	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 40	86.67 43	86.91 51	94.11 37	72.11 49	92.37 29	92.56 76	74.50 136	86.84 59	94.65 26	67.31 116	95.77 60	84.80 62	92.85 74	92.84 135

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 137	91.30 15

ACMP	74.13 6	81.51 149	80.57 149	84.36 124	89.42 135	68.69 122	89.97 80	91.50 127	74.46 138	75.04 283	90.41 159	53.82 274	94.54 116	77.56 142	82.91 249	89.86 259
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 108	83.02 108	84.57 116	90.13 110	64.47 236	92.32 31	90.73 146	74.45 139	79.35 174	91.10 140	69.05 94	95.12 88	72.78 198	87.22 170	94.13 58

fmvsm_s_conf0.5_n_2		84.04 89	84.11 90	83.81 164	86.17 266	65.00 221	86.96 197	87.28 267	74.35 140	88.25 34	94.23 45	61.82 186	92.60 211	89.85 11	88.09 158	93.84 75

fmvsm_s_conf0.1_n_2		83.80 93	83.79 94	83.83 162	85.62 279	64.94 223	87.03 194	86.62 284	74.32 141	87.97 42	94.33 38	60.67 210	92.60 211	89.72 13	87.79 161	93.96 66

save fliter					93.80 40	72.35 44	90.47 69	91.17 134	74.31 142

MVS_Test		83.15 114	83.06 107	83.41 177	86.86 247	63.21 270	86.11 230	92.00 100	74.31 142	82.87 118	89.44 193	70.03 79	93.21 181	77.39 145	88.50 152	93.81 77

myMVS_eth3d28		73.62 313	73.53 303	73.90 372	88.20 189	47.41 431	78.06 381	79.37 385	74.29 144	73.98 299	84.29 331	44.67 371	83.54 381	51.47 384	87.39 167	90.74 215

UniMVSNet_ETH3D		79.10 212	78.24 210	81.70 235	86.85 248	60.24 317	87.28 188	88.79 227	74.25 145	76.84 229	90.53 158	49.48 328	91.56 257	67.98 251	82.15 258	93.29 107

IterMVS-LS		80.06 188	79.38 182	82.11 227	85.89 272	63.20 271	86.79 206	89.34 195	74.19 146	75.45 264	86.72 268	66.62 121	92.39 223	72.58 200	76.86 322	90.75 214
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 177	79.98 165	82.12 225	84.28 312	63.19 272	86.41 220	88.95 223	74.18 147	78.69 183	87.54 248	66.62 121	92.43 221	72.57 201	80.57 279	90.74 215

Vis-MVSNet (Re-imp)		78.36 231	78.45 203	78.07 320	88.64 174	51.78 411	86.70 210	79.63 383	74.14 148	75.11 280	90.83 151	61.29 199	89.75 305	58.10 342	91.60 93	92.69 139

v8		79.97 191	79.02 193	82.80 207	84.09 317	64.50 235	87.96 163	90.29 163	74.13 149	75.24 276	86.81 265	62.88 169	93.89 149	74.39 181	75.40 351	90.00 251

guyue		81.13 154	80.64 148	82.60 218	86.52 259	63.92 248	86.69 211	87.73 258	73.97 150	80.83 152	89.69 179	56.70 249	91.33 271	78.26 138	85.40 206	92.54 144

CSCG		86.41 48	86.19 53	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 151	83.16 114	91.07 142	75.94 18	95.19 85	79.94 118	94.38 58	93.55 97

thres100view900		76.50 273	75.55 272	79.33 293	89.52 129	56.99 356	85.83 239	83.23 333	73.94 152	76.32 245	87.12 260	51.89 298	91.95 240	48.33 404	83.75 232	89.07 278

9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 153	88.57 30	94.67 25	75.57 22	95.79 59	86.77 46	95.76 23

HPM-MVS_fast		85.35 74	84.95 80	86.57 59	93.69 42	70.58 80	92.15 36	91.62 120	73.89 154	82.67 123	94.09 51	62.60 170	95.54 66	80.93 105	92.93 73	93.57 95

PAPM_NR		83.02 118	82.41 118	84.82 109	92.47 72	66.37 185	87.93 166	91.80 112	73.82 155	77.32 218	90.66 153	67.90 110	94.90 100	70.37 225	89.48 133	93.19 115

thres600view7		76.50 273	75.44 273	79.68 286	89.40 137	57.16 353	85.53 248	83.23 333	73.79 156	76.26 246	87.09 261	51.89 298	91.89 243	48.05 409	83.72 235	90.00 251

testing91		76.54 271	75.66 270	79.18 297	88.43 182	55.89 374	81.08 333	83.00 340	73.76 157	75.34 269	84.29 331	46.20 359	90.07 299	64.33 282	84.50 216	91.58 184

AstraMVS		80.81 161	80.14 162	82.80 207	86.05 271	63.96 245	86.46 219	85.90 296	73.71 158	80.85 151	90.56 156	54.06 272	91.57 256	79.72 120	83.97 227	92.86 133

v7n		78.97 216	77.58 232	83.14 188	83.45 333	65.51 206	88.32 151	91.21 132	73.69 159	72.41 320	86.32 286	57.93 234	93.81 151	69.18 240	75.65 342	90.11 243

dcpmvs_2		85.63 65	86.15 55	84.06 148	91.71 80	64.94 223	86.47 218	91.87 108	73.63 160	86.60 61	93.02 87	76.57 15	91.87 245	83.36 78	92.15 84	95.35 3

v2v482		80.23 185	79.29 186	83.05 194	83.62 329	64.14 242	87.04 193	89.97 172	73.61 161	78.18 199	87.22 256	61.10 203	93.82 150	76.11 160	76.78 325	91.18 196

Baseline_NR-MVSNet		78.15 237	78.33 208	77.61 329	85.79 274	56.21 371	86.78 207	85.76 298	73.60 162	77.93 205	87.57 245	65.02 143	88.99 320	67.14 261	75.33 353	87.63 323

BH-RMVSNet		79.61 194	78.44 204	83.14 188	89.38 139	65.93 194	84.95 262	87.15 272	73.56 163	78.19 198	89.79 177	56.67 250	93.36 173	59.53 326	86.74 179	90.13 241

APD-MVS_3200maxsize		85.97 56	85.88 60	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 164	85.94 63	94.51 30	65.80 137	95.61 63	83.04 83	92.51 79	93.53 99

SR-MVS-dyc-post		85.77 62	85.61 67	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 165	85.69 67	94.45 32	65.00 145	95.56 64	82.75 88	91.87 89	92.50 147

RE-MVS-def			85.48 70		93.06 60	70.63 78	91.88 39	92.27 85	73.53 165	85.69 67	94.45 32	63.87 153		82.75 88	91.87 89	92.50 147

reproduce_monomvs		75.40 294	74.38 292	78.46 313	83.92 322	57.80 345	83.78 291	86.94 276	73.47 167	72.25 323	84.47 325	38.74 408	89.27 314	75.32 172	70.53 393	88.31 310

test_fmvsmconf_n		85.92 57	86.04 58	85.57 82	85.03 298	69.51 96	89.62 92	90.58 149	73.42 168	87.75 45	94.02 55	72.85 45	93.24 178	90.37 7	90.75 109	93.96 66

tfpn200view9		76.42 277	75.37 277	79.55 291	89.13 152	57.65 347	85.17 254	83.60 325	73.41 169	76.45 241	86.39 284	52.12 290	91.95 240	48.33 404	83.75 232	89.07 278

thres400		76.50 273	75.37 277	79.86 281	89.13 152	57.65 347	85.17 254	83.60 325	73.41 169	76.45 241	86.39 284	52.12 290	91.95 240	48.33 404	83.75 232	90.00 251

diffmvs_AUTHOR		82.38 126	82.27 122	82.73 215	83.26 337	63.80 250	83.89 289	89.76 179	73.35 171	82.37 124	90.84 150	66.25 128	90.79 287	82.77 87	87.93 159	93.59 94

test_fmvsmconf0.1_n		85.61 66	85.65 66	85.50 83	82.99 349	69.39 103	89.65 89	90.29 163	73.31 172	87.77 44	94.15 49	71.72 57	93.23 179	90.31 8	90.67 111	93.89 72

testing99		76.09 283	75.12 282	79.00 298	88.16 191	55.50 380	80.79 337	81.40 360	73.30 173	75.17 277	84.27 334	44.48 374	90.02 300	64.28 283	84.22 225	91.48 189

v148		78.72 222	77.80 223	81.47 240	82.73 355	61.96 293	86.30 225	88.08 245	73.26 174	76.18 249	85.47 305	62.46 174	92.36 225	71.92 211	73.82 370	90.09 245

FA-MVS(test-final)		80.96 157	79.91 167	84.10 139	88.30 187	65.01 220	84.55 273	90.01 171	73.25 175	79.61 167	87.57 245	58.35 232	94.72 110	71.29 216	86.25 187	92.56 143

test_fmvsmconf0.01_n		84.73 84	84.52 86	85.34 87	80.25 391	69.03 106	89.47 95	89.65 184	73.24 176	86.98 57	94.27 42	66.62 121	93.23 179	90.26 9	89.95 124	93.78 81

viewmsd2359difaftdt		80.37 182	79.73 173	82.30 224	83.70 328	62.39 285	84.20 284	86.67 281	73.22 177	80.90 149	90.62 154	63.00 168	91.56 257	76.81 155	78.44 302	92.95 131

v10		79.74 193	78.67 198	82.97 199	84.06 318	64.95 222	87.88 169	90.62 148	73.11 178	75.11 280	86.56 279	61.46 194	94.05 137	73.68 186	75.55 344	89.90 257

MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 179	84.86 79	92.89 89	76.22 17	96.33 41	84.89 60	95.13 36	94.40 46

baseline1		76.98 265	76.75 253	77.66 327	88.13 194	55.66 378	85.12 257	81.89 353	73.04 180	76.79 231	88.90 206	62.43 175	87.78 340	63.30 290	71.18 390	89.55 269

APD-MVS		87.44 26	87.52 27	87.19 43	94.24 32	72.39 41	91.86 41	92.83 61	73.01 181	88.58 29	94.52 27	73.36 35	96.49 38	84.26 69	95.01 37	92.70 137
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 129	81.88 131	82.76 213	83.00 347	63.78 252	83.68 294	89.76 179	72.94 182	82.02 130	89.85 172	65.96 136	90.79 287	82.38 94	87.30 169	93.71 83
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
K. test v3		71.19 340	68.51 352	79.21 296	83.04 346	57.78 346	84.35 281	76.91 406	72.90 183	62.99 413	82.86 364	39.27 404	91.09 281	61.65 308	52.66 439	88.75 298

MVSMamba_PlusPlus		85.99 54	85.96 59	86.05 69	91.09 88	67.64 156	89.63 91	92.65 71	72.89 184	84.64 84	91.71 117	71.85 54	96.03 51	84.77 63	94.45 56	94.49 42

GDP-MVS		83.52 104	82.64 115	86.16 65	88.14 193	68.45 128	89.13 114	92.69 66	72.82 185	83.71 105	91.86 114	55.69 255	95.35 82	80.03 116	89.74 128	94.69 29

fmvsm_s_conf0.5_n_5		85.22 76	85.55 68	84.25 135	86.26 262	67.40 165	89.18 108	89.31 201	72.50 186	88.31 32	93.86 64	69.66 84	91.96 239	89.81 12	91.05 103	93.38 102

Fast-Effi-MVS+-dtu		78.02 241	76.49 257	82.62 217	83.16 343	66.96 179	86.94 199	87.45 265	72.45 187	71.49 332	84.17 336	54.79 264	91.58 254	67.61 254	80.31 282	89.30 276

PHI-MVS		86.43 46	86.17 54	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 187	85.22 72	91.90 111	69.47 86	96.42 40	83.28 80	95.94 19	94.35 49

thres200		75.55 289	74.47 290	78.82 302	87.78 214	57.85 343	83.07 312	83.51 328	72.44 189	75.84 255	84.42 326	52.08 293	91.75 248	47.41 411	83.64 237	86.86 346

test_yl		81.17 152	80.47 153	83.24 183	89.13 152	63.62 253	86.21 227	89.95 173	72.43 190	81.78 135	89.61 183	57.50 240	93.58 160	70.75 220	86.90 175	92.52 145

DCV-MVSNet		81.17 152	80.47 153	83.24 183	89.13 152	63.62 253	86.21 227	89.95 173	72.43 190	81.78 135	89.61 183	57.50 240	93.58 160	70.75 220	86.90 175	92.52 145

BH-untuned		79.47 199	78.60 200	82.05 228	89.19 150	65.91 195	86.07 231	88.52 239	72.18 192	75.42 265	87.69 242	61.15 202	93.54 164	60.38 318	86.83 178	86.70 350

TransMVSNet (Re)		75.39 295	74.56 288	77.86 323	85.50 284	57.10 355	86.78 207	86.09 294	72.17 193	71.53 331	87.34 251	63.01 167	89.31 313	56.84 355	61.83 421	87.17 336

GA-MVS		76.87 267	75.17 281	81.97 231	82.75 354	62.58 282	81.44 330	86.35 289	72.16 194	74.74 288	82.89 363	46.20 359	92.02 237	68.85 245	81.09 270	91.30 194

VortexMVS		78.57 227	77.89 219	80.59 265	85.89 272	62.76 281	85.61 241	89.62 186	72.06 195	74.99 284	85.38 307	55.94 254	90.77 290	74.99 174	76.58 326	88.23 311

mmtdpeth		74.16 306	73.01 310	77.60 331	83.72 327	61.13 301	85.10 258	85.10 305	72.06 195	77.21 225	80.33 392	43.84 379	85.75 360	77.14 148	52.61 440	85.91 365

v1144		80.03 189	79.03 192	83.01 196	83.78 325	64.51 233	87.11 192	90.57 151	71.96 197	78.08 202	86.20 288	61.41 195	93.94 141	74.93 175	77.23 316	90.60 221

PS-MVSNAJss		82.07 131	81.31 135	84.34 126	86.51 260	67.27 170	89.27 105	91.51 124	71.75 198	79.37 173	90.22 167	63.15 163	94.27 126	77.69 141	82.36 257	91.49 188

EPNet_dtu		75.46 291	74.86 283	77.23 336	82.57 359	54.60 389	86.89 201	83.09 337	71.64 199	66.25 392	85.86 294	55.99 253	88.04 336	54.92 366	86.55 182	89.05 283
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 229	77.40 236	81.40 243	87.60 221	63.01 274	88.39 146	89.28 202	71.63 200	75.34 269	87.28 252	54.80 261	91.11 276	62.72 293	79.57 289	90.09 245

test1		78.40 229	77.40 236	81.40 243	87.60 221	63.01 274	88.39 146	89.28 202	71.63 200	75.34 269	87.28 252	54.80 261	91.11 276	62.72 293	79.57 289	90.09 245

FMVSNet2		78.20 235	77.21 240	81.20 250	87.60 221	62.89 280	87.47 179	89.02 218	71.63 200	75.29 275	87.28 252	54.80 261	91.10 279	62.38 298	79.38 293	89.61 267

patch_mono-2		83.65 99	84.54 84	80.99 256	90.06 116	65.83 197	84.21 283	88.74 232	71.60 203	85.01 73	92.44 99	74.51 26	83.50 382	82.15 95	92.15 84	93.64 91

V42		79.38 205	78.24 210	82.83 204	81.10 383	65.50 207	85.55 246	89.82 176	71.57 204	78.21 197	86.12 290	60.66 211	93.18 187	75.64 166	75.46 348	89.81 262

API-MVS		81.99 133	81.23 137	84.26 134	90.94 93	70.18 87	91.10 58	89.32 200	71.51 205	78.66 185	88.28 225	65.26 140	95.10 93	64.74 280	91.23 101	87.51 327

tttt0517		79.40 203	77.91 217	83.90 161	88.10 196	63.84 249	88.37 149	84.05 320	71.45 206	76.78 232	89.12 197	49.93 325	94.89 101	70.18 229	83.18 247	92.96 130

pm-mvs1		77.25 261	76.68 255	78.93 300	84.22 314	58.62 331	86.41 220	88.36 241	71.37 207	73.31 307	88.01 235	61.22 201	89.15 318	64.24 284	73.01 377	89.03 284

Elysia		81.53 145	80.16 160	85.62 79	85.51 282	68.25 135	88.84 126	92.19 92	71.31 208	80.50 156	89.83 173	46.89 349	94.82 104	76.85 151	89.57 130	93.80 79

StellarMVS		81.53 145	80.16 160	85.62 79	85.51 282	68.25 135	88.84 126	92.19 92	71.31 208	80.50 156	89.83 173	46.89 349	94.82 104	76.85 151	89.57 130	93.80 79

testing222		74.04 308	72.66 314	78.19 316	87.89 206	55.36 381	81.06 334	79.20 388	71.30 210	74.65 291	83.57 351	39.11 407	88.67 328	51.43 386	85.75 200	90.53 224

GeoE		81.71 139	81.01 142	83.80 165	89.51 130	64.45 237	88.97 119	88.73 233	71.27 211	78.63 186	89.76 178	66.32 127	93.20 184	69.89 233	86.02 192	93.74 82

tt0805		78.73 221	77.83 221	81.43 241	85.17 291	60.30 316	89.41 100	90.90 141	71.21 212	77.17 226	88.73 210	46.38 354	93.21 181	72.57 201	78.96 297	90.79 211

FMVSNet3		77.88 245	76.85 248	80.97 258	86.84 249	62.36 286	86.52 217	88.77 228	71.13 213	75.34 269	86.66 274	54.07 271	91.10 279	62.72 293	79.57 289	89.45 271

VDDNet		81.52 147	80.67 147	84.05 151	90.44 104	64.13 243	89.73 87	85.91 295	71.11 214	83.18 113	93.48 72	50.54 315	93.49 166	73.40 191	88.25 155	94.54 41

fmvsm_s_conf0.5_n		83.80 93	83.71 95	84.07 145	86.69 255	67.31 168	89.46 96	83.07 338	71.09 215	86.96 58	93.70 69	69.02 96	91.47 265	88.79 28	84.62 215	93.44 101

XVG-OURS		80.41 178	79.23 188	83.97 158	85.64 278	69.02 108	83.03 314	90.39 155	71.09 215	77.63 212	91.49 128	54.62 267	91.35 269	75.71 165	83.47 241	91.54 185

SSM_0407		81.58 144	80.48 152	84.87 107	88.81 163	67.96 145	87.37 183	89.25 206	71.06 217	79.48 170	90.39 160	59.57 221	94.48 121	72.45 207	85.93 195	92.18 164

SSM_0404		81.91 134	80.84 145	85.13 95	89.24 147	68.26 133	87.84 171	89.25 206	71.06 217	80.62 154	90.39 160	59.57 221	94.65 114	72.45 207	87.19 171	92.47 150

SixPastTwentyTwo		73.37 317	71.26 331	79.70 285	85.08 296	57.89 342	85.57 242	83.56 327	71.03 219	65.66 395	85.88 293	42.10 391	92.57 213	59.11 330	63.34 417	88.65 302

ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 220	87.75 45	94.07 52	74.01 33	96.70 27	84.66 64	94.84 44

mamba_0408		79.37 206	77.52 233	84.93 104	88.81 163	67.96 145	65.03 445	88.66 234	70.96 221	79.48 170	89.80 175	58.69 227	94.65 114	70.35 226	85.93 195	92.18 164

SSM_04072		77.67 253	77.52 233	78.12 318	88.81 163	67.96 145	65.03 445	88.66 234	70.96 221	79.48 170	89.80 175	58.69 227	74.23 438	70.35 226	85.93 195	92.18 164

v1192		79.59 196	78.43 205	83.07 193	83.55 331	64.52 232	86.93 200	90.58 149	70.83 223	77.78 209	85.90 292	59.15 225	93.94 141	73.96 185	77.19 318	90.76 213

Fast-Effi-MVS+		80.81 161	79.92 166	83.47 172	88.85 159	64.51 233	85.53 248	89.39 194	70.79 224	78.49 190	85.06 316	67.54 113	93.58 160	67.03 263	86.58 181	92.32 156

PS-MVSNAJ		81.69 140	81.02 141	83.70 166	89.51 130	68.21 138	84.28 282	90.09 169	70.79 224	81.26 144	85.62 301	63.15 163	94.29 124	75.62 167	88.87 143	88.59 304

LTVRE_ROB	69.57 13	76.25 280	74.54 289	81.41 242	88.60 175	64.38 239	79.24 361	89.12 215	70.76 226	69.79 353	87.86 238	49.09 335	93.20 184	56.21 361	80.16 283	86.65 351
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
testing11		75.14 297	74.01 295	78.53 310	88.16 191	56.38 367	80.74 340	80.42 373	70.67 227	72.69 317	83.72 346	43.61 381	89.86 302	62.29 300	83.76 231	89.36 274

fmvsm_s_conf0.1_n		83.56 103	83.38 102	84.10 139	84.86 300	67.28 169	89.40 101	83.01 339	70.67 227	87.08 55	93.96 61	68.38 103	91.45 266	88.56 32	84.50 216	93.56 96

xiu_mvs_v2_base		81.69 140	81.05 140	83.60 168	89.15 151	68.03 143	84.46 276	90.02 170	70.67 227	81.30 143	86.53 281	63.17 162	94.19 132	75.60 168	88.54 150	88.57 305

XVG-OURS-SEG-HR		80.81 161	79.76 172	83.96 159	85.60 280	68.78 114	83.54 301	90.50 152	70.66 230	76.71 234	91.66 119	60.69 209	91.26 272	76.94 150	81.58 265	91.83 174

Anonymous202405211		78.25 232	77.01 243	81.99 230	91.03 90	60.67 310	84.77 265	83.90 322	70.65 231	80.00 163	91.20 137	41.08 397	91.43 267	65.21 275	85.26 207	93.85 73

DP-MVS Recon		83.11 117	82.09 126	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 232	79.17 176	91.03 145	64.12 151	96.03 51	68.39 250	90.14 119	91.50 187

icg_test_0407_2		78.92 218	78.93 195	78.90 301	87.13 238	63.59 257	76.58 392	89.33 196	70.51 233	77.82 206	89.03 200	61.84 184	81.38 397	72.56 203	85.56 202	91.74 177

IMVS_0407		80.61 171	79.90 168	82.75 214	87.13 238	63.59 257	85.33 252	89.33 196	70.51 233	77.82 206	89.03 200	61.84 184	92.91 200	72.56 203	85.56 202	91.74 177

IMVS_0404		77.16 262	76.42 260	79.37 292	87.13 238	63.59 257	77.12 390	89.33 196	70.51 233	66.22 393	89.03 200	50.36 317	82.78 387	72.56 203	85.56 202	91.74 177

IMVS_0403		80.80 164	80.12 163	82.87 203	87.13 238	63.59 257	85.19 253	89.33 196	70.51 233	78.49 190	89.03 200	63.26 159	93.27 176	72.56 203	85.56 202	91.74 177

FMVSNet1		77.44 256	76.12 264	81.40 243	86.81 250	63.01 274	88.39 146	89.28 202	70.49 237	74.39 295	87.28 252	49.06 336	91.11 276	60.91 314	78.52 300	90.09 245

LuminaMVS		80.68 169	79.62 177	83.83 162	85.07 297	68.01 144	86.99 196	88.83 225	70.36 238	81.38 139	87.99 236	50.11 320	92.51 218	79.02 123	86.89 177	90.97 205

testing3		68.56 369	67.67 368	71.22 396	87.33 231	42.87 446	83.06 313	71.54 426	70.36 238	69.08 359	84.38 328	30.33 434	85.69 362	37.50 439	75.45 349	85.09 380

ab-mvs		79.51 197	78.97 194	81.14 252	88.46 180	60.91 306	83.84 290	89.24 208	70.36 238	79.03 177	88.87 208	63.23 161	90.21 297	65.12 276	82.57 255	92.28 158

tfpnnormal		74.39 302	73.16 308	78.08 319	86.10 270	58.05 337	84.65 270	87.53 262	70.32 241	71.22 335	85.63 300	54.97 259	89.86 302	43.03 427	75.02 358	86.32 354

ACMM	73.20 8	80.78 168	79.84 170	83.58 170	89.31 143	68.37 130	89.99 79	91.60 121	70.28 242	77.25 219	89.66 181	53.37 279	93.53 165	74.24 183	82.85 250	88.85 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_a		83.63 101	83.41 101	84.28 130	86.14 267	68.12 139	89.43 97	82.87 343	70.27 243	87.27 54	93.80 67	69.09 91	91.58 254	88.21 36	83.65 236	93.14 119

ACMH+	68.96 14	76.01 284	74.01 295	82.03 229	88.60 175	65.31 212	88.86 123	87.55 261	70.25 244	67.75 369	87.47 250	41.27 395	93.19 186	58.37 339	75.94 339	87.60 324

IB-MVS	68.01 15	75.85 286	73.36 306	83.31 179	84.76 303	66.03 189	83.38 303	85.06 306	70.21 245	69.40 355	81.05 382	45.76 364	94.66 113	65.10 277	75.49 345	89.25 277
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
thisisatest0530		79.40 203	77.76 226	84.31 127	87.69 219	65.10 219	87.36 184	84.26 318	70.04 246	77.42 215	88.26 227	49.94 323	94.79 108	70.20 228	84.70 214	93.03 125

mvsmamba		80.60 173	79.38 182	84.27 132	89.74 124	67.24 172	87.47 179	86.95 275	70.02 247	75.38 267	88.93 205	51.24 306	92.56 214	75.47 171	89.22 137	93.00 128

test_fmvsmvis_n_1920		84.02 90	83.87 92	84.49 120	84.12 316	69.37 104	88.15 158	87.96 250	70.01 248	83.95 101	93.23 80	68.80 98	91.51 263	88.61 30	89.96 123	92.57 142

v144192		79.47 199	78.37 206	82.78 211	83.35 334	63.96 245	86.96 197	90.36 159	69.99 249	77.50 213	85.67 299	60.66 211	93.77 154	74.27 182	76.58 326	90.62 219

test_fmvsm_n_1920		85.29 75	85.34 72	85.13 95	86.12 268	69.93 88	88.65 137	90.78 145	69.97 250	88.27 33	93.98 60	71.39 63	91.54 260	88.49 33	90.45 114	93.91 69

c3_l		78.75 220	77.91 217	81.26 248	82.89 352	61.56 298	84.09 287	89.13 214	69.97 250	75.56 259	84.29 331	66.36 126	92.09 235	73.47 190	75.48 346	90.12 242

v1921920		79.22 208	78.03 214	82.80 207	83.30 336	63.94 247	86.80 205	90.33 160	69.91 252	77.48 214	85.53 303	58.44 231	93.75 156	73.60 187	76.85 323	90.71 217

ACMH	67.68 16	75.89 285	73.93 297	81.77 234	88.71 172	66.61 182	88.62 138	89.01 219	69.81 253	66.78 383	86.70 272	41.95 393	91.51 263	55.64 362	78.14 307	87.17 336
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.1_n_a		83.32 111	82.99 109	84.28 130	83.79 324	68.07 141	89.34 104	82.85 344	69.80 254	87.36 53	94.06 53	68.34 104	91.56 257	87.95 37	83.46 242	93.21 112

DPM-MVS		84.93 81	84.29 88	86.84 52	90.20 109	73.04 23	87.12 191	93.04 42	69.80 254	82.85 119	91.22 136	73.06 41	96.02 53	76.72 157	94.63 50	91.46 191

MAR-MVS		81.84 136	80.70 146	85.27 89	91.32 85	71.53 58	89.82 82	90.92 140	69.77 256	78.50 189	86.21 287	62.36 176	94.52 118	65.36 274	92.05 87	89.77 263
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
XVG-ACMP-BASELINE		76.11 282	74.27 294	81.62 236	83.20 340	64.67 229	83.60 298	89.75 181	69.75 257	71.85 327	87.09 261	32.78 427	92.11 234	69.99 232	80.43 281	88.09 315

BH-w/o		78.21 234	77.33 239	80.84 260	88.81 163	65.13 216	84.87 263	87.85 255	69.75 257	74.52 293	84.74 323	61.34 197	93.11 191	58.24 341	85.84 198	84.27 388

v1240		78.99 215	77.78 224	82.64 216	83.21 339	63.54 261	86.62 214	90.30 162	69.74 259	77.33 217	85.68 298	57.04 246	93.76 155	73.13 195	76.92 320	90.62 219

ET-MVSNet_ETH3D		78.63 224	76.63 256	84.64 115	86.73 253	69.47 98	85.01 260	84.61 311	69.54 260	66.51 390	86.59 276	50.16 319	91.75 248	76.26 159	84.24 224	92.69 139

eth_miper_zixun_eth		77.92 244	76.69 254	81.61 238	83.00 347	61.98 292	83.15 308	89.20 210	69.52 261	74.86 287	84.35 330	61.76 187	92.56 214	71.50 214	72.89 378	90.28 236

PVSNet_Blended_VisFu		82.62 122	81.83 132	84.96 101	90.80 97	69.76 93	88.74 133	91.70 117	69.39 262	78.96 178	88.46 220	65.47 139	94.87 103	74.42 180	88.57 149	90.24 237

mvs_tets		79.13 211	77.77 225	83.22 185	84.70 304	66.37 185	89.17 109	90.19 166	69.38 263	75.40 266	89.46 190	44.17 377	93.15 188	76.78 156	80.70 277	90.14 240

PVSNet_BlendedMVS		80.60 173	80.02 164	82.36 223	88.85 159	65.40 208	86.16 229	92.00 100	69.34 264	78.11 200	86.09 291	66.02 134	94.27 126	71.52 212	82.06 260	87.39 329

SD_0403		74.65 301	74.77 285	74.29 367	86.20 265	47.42 430	83.71 293	85.12 304	69.30 265	68.50 365	87.95 237	59.40 223	86.05 357	49.38 398	83.35 243	89.40 272

AdaColmap		80.58 176	79.42 181	84.06 148	93.09 59	68.91 111	89.36 103	88.97 222	69.27 266	75.70 257	89.69 179	57.20 245	95.77 60	63.06 291	88.41 154	87.50 328

ETVMVS		72.25 333	71.05 332	75.84 345	87.77 215	51.91 408	79.39 359	74.98 414	69.26 267	73.71 302	82.95 361	40.82 399	86.14 356	46.17 417	84.43 221	89.47 270

ITE_SJBPF				78.22 315	81.77 370	60.57 311		83.30 331	69.25 268	67.54 371	87.20 257	36.33 420	87.28 346	54.34 369	74.62 362	86.80 347

cl____		77.72 249	76.76 251	80.58 266	82.49 361	60.48 313	83.09 310	87.87 253	69.22 269	74.38 296	85.22 312	62.10 181	91.53 261	71.09 217	75.41 350	89.73 265

DIV-MVS_self_test		77.72 249	76.76 251	80.58 266	82.48 362	60.48 313	83.09 310	87.86 254	69.22 269	74.38 296	85.24 310	62.10 181	91.53 261	71.09 217	75.40 351	89.74 264

jajsoiax		79.29 207	77.96 215	83.27 181	84.68 305	66.57 183	89.25 106	90.16 167	69.20 271	75.46 263	89.49 187	45.75 365	93.13 190	76.84 153	80.80 275	90.11 243

IterMVS-SCA-FT		75.43 292	73.87 299	80.11 277	82.69 356	64.85 226	81.57 327	83.47 329	69.16 272	70.49 339	84.15 337	51.95 296	88.15 334	69.23 239	72.14 384	87.34 331

CL-MVSNet_self_test		72.37 331	71.46 326	75.09 357	79.49 404	53.53 397	80.76 339	85.01 308	69.12 273	70.51 338	82.05 376	57.92 235	84.13 376	52.27 380	66.00 411	87.60 324

AUN-MVS		79.21 209	77.60 231	84.05 151	88.71 172	67.61 157	85.84 238	87.26 269	69.08 274	77.23 221	88.14 233	53.20 281	93.47 168	75.50 170	73.45 373	91.06 200

xiu_mvs_v1_base_debu		80.80 164	79.72 174	84.03 153	87.35 226	70.19 84	85.56 243	88.77 228	69.06 275	81.83 131	88.16 229	50.91 309	92.85 203	78.29 135	87.56 163	89.06 280

xiu_mvs_v1_base		80.80 164	79.72 174	84.03 153	87.35 226	70.19 84	85.56 243	88.77 228	69.06 275	81.83 131	88.16 229	50.91 309	92.85 203	78.29 135	87.56 163	89.06 280

xiu_mvs_v1_base_debi		80.80 164	79.72 174	84.03 153	87.35 226	70.19 84	85.56 243	88.77 228	69.06 275	81.83 131	88.16 229	50.91 309	92.85 203	78.29 135	87.56 163	89.06 280

MVSTER		79.01 214	77.88 220	82.38 222	83.07 344	64.80 227	84.08 288	88.95 223	69.01 278	78.69 183	87.17 259	54.70 265	92.43 221	74.69 176	80.57 279	89.89 258

cl22		78.07 239	77.01 243	81.23 249	82.37 364	61.83 295	83.55 299	87.98 249	68.96 279	75.06 282	83.87 339	61.40 196	91.88 244	73.53 188	76.39 331	89.98 254

miper_ehance_all_eth		78.59 226	77.76 226	81.08 254	82.66 357	61.56 298	83.65 295	89.15 212	68.87 280	75.55 260	83.79 343	66.49 124	92.03 236	73.25 193	76.39 331	89.64 266

PAPR		81.66 142	80.89 144	83.99 157	90.27 107	64.00 244	86.76 209	91.77 115	68.84 281	77.13 228	89.50 186	67.63 112	94.88 102	67.55 255	88.52 151	93.09 120

CPTT-MVS		83.73 96	83.33 104	84.92 105	93.28 49	70.86 74	92.09 37	90.38 156	68.75 282	79.57 168	92.83 91	60.60 214	93.04 197	80.92 106	91.56 96	90.86 209

train_agg		86.43 46	86.20 51	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 283	85.00 74	93.10 82	74.43 27	95.41 76	84.97 57	95.71 25	93.02 126

test_8					93.13 56	72.57 35	88.68 136	91.84 110	68.69 283	84.87 78	93.10 82	74.43 27	95.16 86

dmvs_re		71.14 341	70.58 336	72.80 382	81.96 367	59.68 322	75.60 400	79.34 386	68.55 285	69.27 358	80.72 388	49.42 329	76.54 419	52.56 379	77.79 310	82.19 413

MVSFormer		82.85 120	82.05 127	85.24 90	87.35 226	70.21 82	90.50 67	90.38 156	68.55 285	81.32 140	89.47 188	61.68 188	93.46 169	78.98 126	90.26 117	92.05 171

test_djsdf		80.30 184	79.32 185	83.27 181	83.98 320	65.37 211	90.50 67	90.38 156	68.55 285	76.19 248	88.70 211	56.44 252	93.46 169	78.98 126	80.14 285	90.97 205

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 288	85.00 74	93.10 82	74.36 29	95.41 76

FE-MVS		77.78 247	75.68 268	84.08 144	88.09 197	66.00 192	83.13 309	87.79 256	68.42 289	78.01 203	85.23 311	45.50 368	95.12 88	59.11 330	85.83 199	91.11 198

CDPH-MVS		85.76 63	85.29 76	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 290	84.61 85	93.48 72	72.32 48	96.15 49	79.00 125	95.43 30	94.28 53

PC_three_1452									68.21 291	92.02 12	94.00 57	82.09 5	95.98 57	84.58 65	96.68 2	94.95 12

fmvsm_l_conf0.5_n		84.47 85	84.54 84	84.27 132	85.42 285	68.81 112	88.49 143	87.26 269	68.08 292	88.03 39	93.49 71	72.04 53	91.77 247	88.90 27	89.14 140	92.24 161

IterMVS		74.29 303	72.94 311	78.35 314	81.53 375	63.49 263	81.58 326	82.49 347	68.06 293	69.99 348	83.69 347	51.66 303	85.54 364	65.85 271	71.64 387	86.01 362
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_testset		62.63 397	64.11 388	58.19 427	78.55 410	24.76 465	75.28 401	65.94 442	67.91 294	60.34 421	76.01 424	53.56 276	73.94 440	31.79 445	67.65 404	75.88 434

TAMVS		78.89 219	77.51 235	83.03 195	87.80 211	67.79 153	84.72 266	85.05 307	67.63 295	76.75 233	87.70 241	62.25 178	90.82 286	58.53 337	87.13 172	90.49 226

PVSNet_Blended		80.98 156	80.34 155	82.90 201	88.85 159	65.40 208	84.43 278	92.00 100	67.62 296	78.11 200	85.05 317	66.02 134	94.27 126	71.52 212	89.50 132	89.01 285

TR-MVS		77.44 256	76.18 263	81.20 250	88.24 188	63.24 269	84.61 271	86.40 287	67.55 297	77.81 208	86.48 282	54.10 270	93.15 188	57.75 345	82.72 253	87.20 335

CDS-MVSNet		79.07 213	77.70 228	83.17 187	87.60 221	68.23 137	84.40 280	86.20 291	67.49 298	76.36 244	86.54 280	61.54 191	90.79 287	61.86 306	87.33 168	90.49 226
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_l_conf0.5_n_a		84.13 88	84.16 89	84.06 148	85.38 286	68.40 129	88.34 150	86.85 279	67.48 299	87.48 50	93.40 76	70.89 69	91.61 252	88.38 35	89.22 137	92.16 168

mvs_anonymous		79.42 202	79.11 191	80.34 271	84.45 311	57.97 340	82.59 316	87.62 260	67.40 300	76.17 251	88.56 218	68.47 102	89.59 308	70.65 223	86.05 191	93.47 100

viewmambaseed2359dif		80.41 178	79.84 170	82.12 225	82.95 351	62.50 284	83.39 302	88.06 247	67.11 301	80.98 147	90.31 162	66.20 130	91.01 283	74.62 177	84.90 210	92.86 133

mvs5depth		69.45 361	67.45 372	75.46 353	73.93 428	55.83 375	79.19 363	83.23 333	66.89 302	71.63 330	83.32 354	33.69 426	85.09 369	59.81 323	55.34 436	85.46 371

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 303	92.39 6				88.94 26	96.63 4	94.85 21

baseline2		75.70 287	73.83 300	81.30 246	83.26 337	61.79 296	82.57 317	80.65 367	66.81 303	66.88 381	83.42 353	57.86 236	92.19 232	63.47 287	79.57 289	89.91 256

miper_lstm_enhance		74.11 307	73.11 309	77.13 337	80.11 393	59.62 323	72.23 416	86.92 278	66.76 305	70.40 340	82.92 362	56.93 247	82.92 386	69.06 242	72.63 379	88.87 292

OpenMVS	72.83 10	79.77 192	78.33 208	84.09 143	85.17 291	69.91 89	90.57 64	90.97 139	66.70 306	72.17 324	91.91 110	54.70 265	93.96 138	61.81 307	90.95 106	88.41 309

test-LLR		72.94 327	72.43 316	74.48 364	81.35 379	58.04 338	78.38 375	77.46 399	66.66 307	69.95 349	79.00 406	48.06 341	79.24 405	66.13 266	84.83 211	86.15 358

test20.03		67.45 376	66.95 377	68.94 405	75.48 423	44.84 442	77.50 386	77.67 397	66.66 307	63.01 412	83.80 342	47.02 347	78.40 409	42.53 430	68.86 402	83.58 398

test0.0.03 1		68.00 374	67.69 367	68.90 406	77.55 413	47.43 429	75.70 399	72.95 425	66.66 307	66.56 386	82.29 373	48.06 341	75.87 428	44.97 424	74.51 363	83.41 399

Syy-MVS		68.05 373	67.85 362	68.67 409	84.68 305	40.97 452	78.62 372	73.08 423	66.65 310	66.74 384	79.46 401	52.11 292	82.30 390	32.89 444	76.38 334	82.75 408

myMVS_eth3d		67.02 379	66.29 380	69.21 404	84.68 305	42.58 447	78.62 372	73.08 423	66.65 310	66.74 384	79.46 401	31.53 431	82.30 390	39.43 436	76.38 334	82.75 408

QAPM		80.88 158	79.50 180	85.03 98	88.01 202	68.97 110	91.59 46	92.00 100	66.63 312	75.15 279	92.16 105	57.70 237	95.45 71	63.52 286	88.76 146	90.66 218

XXY-MVS		75.41 293	75.56 271	74.96 358	83.59 330	57.82 344	80.59 343	83.87 323	66.54 313	74.93 286	88.31 224	63.24 160	80.09 403	62.16 302	76.85 323	86.97 344

OurMVSNet-221017-0		74.26 304	72.42 317	79.80 283	83.76 326	59.59 324	85.92 235	86.64 282	66.39 314	66.96 380	87.58 244	39.46 403	91.60 253	65.76 272	69.27 398	88.22 312

SCA		74.22 305	72.33 318	79.91 280	84.05 319	62.17 290	79.96 354	79.29 387	66.30 315	72.38 321	80.13 395	51.95 296	88.60 329	59.25 328	77.67 314	88.96 289

testgi		66.67 382	66.53 379	67.08 416	75.62 422	41.69 451	75.93 395	76.50 408	66.11 316	65.20 401	86.59 276	35.72 422	74.71 435	43.71 425	73.38 375	84.84 383

HY-MVS	69.67 12	77.95 243	77.15 241	80.36 270	87.57 225	60.21 318	83.37 304	87.78 257	66.11 316	75.37 268	87.06 263	63.27 158	90.48 294	61.38 311	82.43 256	90.40 230

EG-PatchMatch MVS		74.04 308	71.82 322	80.71 263	84.92 299	67.42 163	85.86 237	88.08 245	66.04 318	64.22 405	83.85 340	35.10 423	92.56 214	57.44 347	80.83 274	82.16 414

CNLPA		78.08 238	76.79 250	81.97 231	90.40 105	71.07 67	87.59 176	84.55 312	66.03 319	72.38 321	89.64 182	57.56 239	86.04 358	59.61 325	83.35 243	88.79 296

Anonymous20240529		80.19 187	78.89 196	84.10 139	90.60 100	64.75 228	88.95 120	90.90 141	65.97 320	80.59 155	91.17 139	49.97 322	93.73 158	69.16 241	82.70 254	93.81 77

TAPA-MVS	73.13 9	79.15 210	77.94 216	82.79 210	89.59 126	62.99 278	88.16 157	91.51 124	65.77 321	77.14 227	91.09 141	60.91 206	93.21 181	50.26 394	87.05 173	92.17 167
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 319	70.99 333	80.49 268	84.51 310	65.80 199	80.71 341	86.13 293	65.70 322	65.46 396	83.74 344	44.60 372	90.91 285	51.13 387	76.89 321	84.74 384

anonymousdsp		78.60 225	77.15 241	82.98 198	80.51 389	67.08 175	87.24 189	89.53 189	65.66 323	75.16 278	87.19 258	52.52 283	92.25 230	77.17 147	79.34 294	89.61 267

test_0402		72.79 328	70.44 339	79.84 282	88.13 194	65.99 193	85.93 234	84.29 316	65.57 324	67.40 376	85.49 304	46.92 348	92.61 210	35.88 441	74.38 364	80.94 420

UBG		73.08 324	72.27 319	75.51 351	88.02 200	51.29 416	78.35 378	77.38 402	65.52 325	73.87 301	82.36 370	45.55 366	86.48 353	55.02 365	84.39 222	88.75 298

miper_enhance_ethall		77.87 246	76.86 247	80.92 259	81.65 371	61.38 300	82.68 315	88.98 220	65.52 325	75.47 261	82.30 372	65.76 138	92.00 238	72.95 196	76.39 331	89.39 273

WBMVS		73.43 316	72.81 312	75.28 355	87.91 205	50.99 418	78.59 374	81.31 362	65.51 327	74.47 294	84.83 320	46.39 353	86.68 350	58.41 338	77.86 309	88.17 314

UnsupCasMVSNet_eth		67.33 377	65.99 381	71.37 392	73.48 433	51.47 414	75.16 403	85.19 303	65.20 328	60.78 420	80.93 387	42.35 387	77.20 415	57.12 350	53.69 438	85.44 372

WTY-MVS		75.65 288	75.68 268	75.57 349	86.40 261	56.82 358	77.92 384	82.40 348	65.10 329	76.18 249	87.72 240	63.13 166	80.90 400	60.31 319	81.96 261	89.00 287

thisisatest0515		77.33 259	75.38 276	83.18 186	85.27 290	63.80 250	82.11 321	83.27 332	65.06 330	75.91 253	83.84 341	49.54 327	94.27 126	67.24 259	86.19 188	91.48 189

MVP-Stereo		76.12 281	74.46 291	81.13 253	85.37 287	69.79 91	84.42 279	87.95 251	65.03 331	67.46 373	85.33 308	53.28 280	91.73 250	58.01 343	83.27 245	81.85 415
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 216	77.69 229	82.81 206	90.54 102	64.29 240	90.11 78	91.51 124	65.01 332	76.16 252	88.13 234	50.56 314	93.03 198	69.68 236	77.56 315	91.11 198

pmmvs6		74.69 300	73.39 304	78.61 305	81.38 378	57.48 350	86.64 213	87.95 251	64.99 333	70.18 343	86.61 275	50.43 316	89.52 309	62.12 303	70.18 395	88.83 294

PAPM		77.68 252	76.40 261	81.51 239	87.29 234	61.85 294	83.78 291	89.59 187	64.74 334	71.23 334	88.70 211	62.59 171	93.66 159	52.66 378	87.03 174	89.01 285

MIMVSNet		70.69 347	69.30 346	74.88 360	84.52 309	56.35 369	75.87 398	79.42 384	64.59 335	67.76 368	82.41 369	41.10 396	81.54 395	46.64 415	81.34 266	86.75 349

tpm		72.37 331	71.71 323	74.35 366	82.19 365	52.00 406	79.22 362	77.29 403	64.56 336	72.95 313	83.68 348	51.35 304	83.26 385	58.33 340	75.80 340	87.81 320

MDA-MVSNet-bldmvs		66.68 381	63.66 391	75.75 346	79.28 406	60.56 312	73.92 412	78.35 394	64.43 337	50.13 444	79.87 399	44.02 378	83.67 379	46.10 418	56.86 430	83.03 405

MIMVSNet1		68.58 368	66.78 378	73.98 371	80.07 394	51.82 410	80.77 338	84.37 313	64.40 338	59.75 425	82.16 375	36.47 419	83.63 380	42.73 428	70.33 394	86.48 353

D2MVS		74.82 299	73.21 307	79.64 288	79.81 398	62.56 283	80.34 348	87.35 266	64.37 339	68.86 360	82.66 367	46.37 355	90.10 298	67.91 252	81.24 268	86.25 355

PLC	70.83 11	78.05 240	76.37 262	83.08 192	91.88 79	67.80 152	88.19 155	89.46 191	64.33 340	69.87 351	88.38 222	53.66 275	93.58 160	58.86 333	82.73 252	87.86 319
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 323	71.33 329	78.49 312	83.18 341	60.85 307	79.63 356	78.57 392	64.13 341	71.73 328	79.81 400	51.20 307	85.97 359	57.40 348	76.36 336	88.66 301
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
mamv4		76.81 268	78.23 212	72.54 385	86.12 268	65.75 202	78.76 370	82.07 352	64.12 342	72.97 312	91.02 146	67.97 108	68.08 450	83.04 83	78.02 308	83.80 396

KD-MVS_2432*1600		66.22 386	63.89 389	73.21 377	75.47 424	53.42 399	70.76 423	84.35 314	64.10 343	66.52 388	78.52 410	34.55 424	84.98 370	50.40 390	50.33 443	81.23 418

miper_refine_blended		66.22 386	63.89 389	73.21 377	75.47 424	53.42 399	70.76 423	84.35 314	64.10 343	66.52 388	78.52 410	34.55 424	84.98 370	50.40 390	50.33 443	81.23 418

tpmvs		71.09 342	69.29 347	76.49 341	82.04 366	56.04 372	78.92 368	81.37 361	64.05 345	67.18 378	78.28 412	49.74 326	89.77 304	49.67 397	72.37 380	83.67 397

F-COLMAP		76.38 279	74.33 293	82.50 220	89.28 145	66.95 180	88.41 145	89.03 217	64.05 345	66.83 382	88.61 215	46.78 351	92.89 201	57.48 346	78.55 299	87.67 322

DP-MVS		76.78 269	74.57 287	83.42 175	93.29 48	69.46 100	88.55 142	83.70 324	63.98 347	70.20 342	88.89 207	54.01 273	94.80 107	46.66 413	81.88 263	86.01 362

原ACMM1				84.35 125	93.01 62	68.79 113		92.44 78	63.96 348	81.09 145	91.57 125	66.06 133	95.45 71	67.19 260	94.82 46	88.81 295

PM-MVS		66.41 384	64.14 387	73.20 379	73.92 429	56.45 364	78.97 367	64.96 445	63.88 349	64.72 402	80.24 394	19.84 450	83.44 383	66.24 265	64.52 415	79.71 426

UWE-MVS		72.13 335	71.49 325	74.03 370	86.66 256	47.70 428	81.40 331	76.89 407	63.60 350	75.59 258	84.22 335	39.94 402	85.62 363	48.98 401	86.13 190	88.77 297

jason		81.39 150	80.29 157	84.70 114	86.63 257	69.90 90	85.95 233	86.77 280	63.24 351	81.07 146	89.47 188	61.08 204	92.15 233	78.33 134	90.07 122	92.05 171
jason: jason.
KD-MVS_self_test		68.81 365	67.59 370	72.46 386	74.29 427	45.45 436	77.93 383	87.00 274	63.12 352	63.99 408	78.99 408	42.32 388	84.77 373	56.55 359	64.09 416	87.16 338

gg-mvs-nofinetune		69.95 357	67.96 360	75.94 344	83.07 344	54.51 391	77.23 389	70.29 429	63.11 353	70.32 341	62.33 443	43.62 380	88.69 327	53.88 372	87.76 162	84.62 386

tpmrst		72.39 329	72.13 320	73.18 380	80.54 388	49.91 423	79.91 355	79.08 389	63.11 353	71.69 329	79.95 397	55.32 257	82.77 388	65.66 273	73.89 368	86.87 345

PCF-MVS	73.52 7	80.38 180	78.84 197	85.01 99	87.71 217	68.99 109	83.65 295	91.46 128	63.00 355	77.77 210	90.28 163	66.10 131	95.09 94	61.40 310	88.22 156	90.94 207
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 325	70.41 340	80.81 261	87.13 238	65.63 203	88.30 152	84.19 319	62.96 356	63.80 410	87.69 242	38.04 413	92.56 214	46.66 413	74.91 359	84.24 389
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 353	67.78 366	77.61 329	77.43 414	59.57 325	71.16 420	70.33 428	62.94 357	68.65 362	72.77 434	50.62 313	85.49 365	69.58 237	66.58 408	87.77 321

lupinMVS		81.39 150	80.27 158	84.76 112	87.35 226	70.21 82	85.55 246	86.41 286	62.85 358	81.32 140	88.61 215	61.68 188	92.24 231	78.41 133	90.26 117	91.83 174

test_vis1_n_1920		75.52 290	75.78 266	74.75 363	79.84 397	57.44 351	83.26 306	85.52 300	62.83 359	79.34 175	86.17 289	45.10 370	79.71 404	78.75 128	81.21 269	87.10 342

EPMVS		69.02 364	68.16 356	71.59 390	79.61 402	49.80 425	77.40 387	66.93 439	62.82 360	70.01 346	79.05 404	45.79 363	77.86 413	56.58 358	75.26 355	87.13 339

PatchMatch-RL		72.38 330	70.90 334	76.80 340	88.60 175	67.38 166	79.53 357	76.17 411	62.75 361	69.36 356	82.00 378	45.51 367	84.89 372	53.62 373	80.58 278	78.12 429

gm-plane-assit					81.40 377	53.83 396			62.72 362		80.94 385		92.39 223	63.40 289

FMVSNet5		69.50 360	67.96 360	74.15 369	82.97 350	55.35 382	80.01 353	82.12 351	62.56 363	63.02 411	81.53 379	36.92 416	81.92 393	48.42 403	74.06 366	85.17 378

sss		73.60 314	73.64 302	73.51 375	82.80 353	55.01 386	76.12 394	81.69 356	62.47 364	74.68 290	85.85 295	57.32 242	78.11 411	60.86 315	80.93 271	87.39 329

WB-MVSnew		71.96 337	71.65 324	72.89 381	84.67 308	51.88 409	82.29 319	77.57 398	62.31 365	73.67 304	83.00 360	53.49 278	81.10 399	45.75 420	82.13 259	85.70 368

AllTest		70.96 343	68.09 358	79.58 289	85.15 293	63.62 253	84.58 272	79.83 380	62.31 365	60.32 422	86.73 266	32.02 428	88.96 323	50.28 392	71.57 388	86.15 358

TestCases				79.58 289	85.15 293	63.62 253		79.83 380	62.31 365	60.32 422	86.73 266	32.02 428	88.96 323	50.28 392	71.57 388	86.15 358

1112_ss		77.40 258	76.43 259	80.32 272	89.11 156	60.41 315	83.65 295	87.72 259	62.13 368	73.05 311	86.72 268	62.58 172	89.97 301	62.11 304	80.80 275	90.59 222

PVSNet	64.34 18	72.08 336	70.87 335	75.69 347	86.21 264	56.44 365	74.37 410	80.73 366	62.06 369	70.17 344	82.23 374	42.86 385	83.31 384	54.77 367	84.45 220	87.32 332

UWE-MVS-28		65.32 389	64.93 383	66.49 417	78.70 409	38.55 454	77.86 385	64.39 446	62.00 370	64.13 406	83.60 349	41.44 394	76.00 426	31.39 446	80.89 272	84.92 381

LS3D		76.95 266	74.82 284	83.37 178	90.45 103	67.36 167	89.15 113	86.94 276	61.87 371	69.52 354	90.61 155	51.71 302	94.53 117	46.38 416	86.71 180	88.21 313

CostFormer		75.24 296	73.90 298	79.27 294	82.65 358	58.27 335	80.80 336	82.73 346	61.57 372	75.33 273	83.13 358	55.52 256	91.07 282	64.98 278	78.34 306	88.45 307

new-patchmatchnet		61.73 399	61.73 400	61.70 423	72.74 439	24.50 466	69.16 430	78.03 395	61.40 373	56.72 434	75.53 428	38.42 410	76.48 421	45.95 419	57.67 429	84.13 391

ANet_high		50.57 417	46.10 421	63.99 420	48.67 465	39.13 453	70.99 422	80.85 364	61.39 374	31.18 454	57.70 450	17.02 453	73.65 441	31.22 447	15.89 462	79.18 427

MS-PatchMatch		73.83 311	72.67 313	77.30 335	83.87 323	66.02 190	81.82 322	84.66 310	61.37 375	68.61 363	82.82 365	47.29 344	88.21 333	59.27 327	84.32 223	77.68 430

USDC		70.33 352	68.37 353	76.21 343	80.60 387	56.23 370	79.19 363	86.49 285	60.89 376	61.29 418	85.47 305	31.78 430	89.47 311	53.37 375	76.21 337	82.94 407

cascas		76.72 270	74.64 286	82.99 197	85.78 275	65.88 196	82.33 318	89.21 209	60.85 377	72.74 314	81.02 383	47.28 345	93.75 156	67.48 256	85.02 208	89.34 275

sc_t1		72.19 334	69.51 345	80.23 274	84.81 301	61.09 303	84.68 267	80.22 377	60.70 378	71.27 333	83.58 350	36.59 418	89.24 315	60.41 317	63.31 418	90.37 231

MDTV_nov1_ep13			69.97 344		83.18 341	53.48 398	77.10 391	80.18 379	60.45 379	69.33 357	80.44 389	48.89 339	86.90 348	51.60 383	78.51 301

TinyColmap		67.30 378	64.81 384	74.76 362	81.92 369	56.68 362	80.29 349	81.49 359	60.33 380	56.27 436	83.22 355	24.77 442	87.66 342	45.52 421	69.47 397	79.95 425

test-mter		71.41 339	70.39 341	74.48 364	81.35 379	58.04 338	78.38 375	77.46 399	60.32 381	69.95 349	79.00 406	36.08 421	79.24 405	66.13 266	84.83 211	86.15 358

1314		76.53 272	75.30 279	80.21 275	83.93 321	62.32 288	84.66 268	88.81 226	60.23 382	70.16 345	84.07 338	55.30 258	90.73 291	67.37 257	83.21 246	87.59 326

PatchT		68.46 371	67.85 362	70.29 400	80.70 386	43.93 444	72.47 415	74.88 415	60.15 383	70.55 337	76.57 421	49.94 323	81.59 394	50.58 388	74.83 360	85.34 373

无先验							87.48 178	88.98 220	60.00 384				94.12 134	67.28 258		88.97 288

CR-MVSNet		73.37 317	71.27 330	79.67 287	81.32 381	65.19 214	75.92 396	80.30 375	59.92 385	72.73 315	81.19 380	52.50 284	86.69 349	59.84 322	77.71 311	87.11 340

TDRefinement		67.49 375	64.34 386	76.92 338	73.47 434	61.07 304	84.86 264	82.98 341	59.77 386	58.30 429	85.13 314	26.06 438	87.89 338	47.92 410	60.59 426	81.81 416

dp		66.80 380	65.43 382	70.90 399	79.74 401	48.82 427	75.12 405	74.77 416	59.61 387	64.08 407	77.23 418	42.89 384	80.72 401	48.86 402	66.58 408	83.16 402

our_test_3		69.14 363	67.00 376	75.57 349	79.80 399	58.80 329	77.96 382	77.81 396	59.55 388	62.90 414	78.25 413	47.43 343	83.97 377	51.71 382	67.58 405	83.93 394

Test_1112_low_res		76.40 278	75.44 273	79.27 294	89.28 145	58.09 336	81.69 325	87.07 273	59.53 389	72.48 319	86.67 273	61.30 198	89.33 312	60.81 316	80.15 284	90.41 229

pmmvs4		74.03 310	71.91 321	80.39 269	81.96 367	68.32 131	81.45 329	82.14 350	59.32 390	69.87 351	85.13 314	52.40 286	88.13 335	60.21 320	74.74 361	84.73 385

testdata				79.97 279	90.90 94	64.21 241		84.71 309	59.27 391	85.40 69	92.91 88	62.02 183	89.08 319	68.95 243	91.37 99	86.63 352

WB-MVS		54.94 407	54.72 408	55.60 433	73.50 432	20.90 467	74.27 411	61.19 450	59.16 392	50.61 442	74.15 430	47.19 346	75.78 429	17.31 458	35.07 452	70.12 440

ppachtmachnet_test		70.04 356	67.34 374	78.14 317	79.80 399	61.13 301	79.19 363	80.59 368	59.16 392	65.27 398	79.29 403	46.75 352	87.29 345	49.33 399	66.72 406	86.00 364

RPSCF		73.23 322	71.46 326	78.54 309	82.50 360	59.85 320	82.18 320	82.84 345	58.96 394	71.15 336	89.41 194	45.48 369	84.77 373	58.82 334	71.83 386	91.02 204

pmmvs-eth3d		70.50 350	67.83 364	78.52 311	77.37 415	66.18 188	81.82 322	81.51 358	58.90 395	63.90 409	80.42 390	42.69 386	86.28 355	58.56 336	65.30 413	83.11 403

tt0320-xc		70.11 355	67.45 372	78.07 320	85.33 288	59.51 326	83.28 305	78.96 390	58.77 396	67.10 379	80.28 393	36.73 417	87.42 344	56.83 356	59.77 428	87.29 333

OpenMVS_ROB	64.09 19	70.56 349	68.19 355	77.65 328	80.26 390	59.41 327	85.01 260	82.96 342	58.76 397	65.43 397	82.33 371	37.63 415	91.23 274	45.34 423	76.03 338	82.32 411

114514_t		80.68 169	79.51 179	84.20 136	94.09 38	67.27 170	89.64 90	91.11 137	58.75 398	74.08 298	90.72 152	58.10 233	95.04 95	69.70 235	89.42 134	90.30 235

Patchmtry		70.74 346	69.16 349	75.49 352	80.72 385	54.07 394	74.94 407	80.30 375	58.34 399	70.01 346	81.19 380	52.50 284	86.54 351	53.37 375	71.09 391	85.87 367

test_cas_vis1_n_1920		73.76 312	73.74 301	73.81 373	75.90 419	59.77 321	80.51 344	82.40 348	58.30 400	81.62 137	85.69 297	44.35 376	76.41 422	76.29 158	78.61 298	85.23 375

Anonymous20240521		68.80 366	67.22 375	73.55 374	74.33 426	54.11 393	83.18 307	85.61 299	58.15 401	61.68 417	80.94 385	30.71 433	81.27 398	57.00 353	73.34 376	85.28 374

tt0320		70.49 351	68.03 359	77.89 322	84.78 302	59.12 328	83.55 299	80.44 372	58.13 402	67.43 375	80.41 391	39.26 405	87.54 343	55.12 364	63.18 419	86.99 343

旧先验2							86.56 216		58.10 403	87.04 56			88.98 321	74.07 184

JIA-IIPM		66.32 385	62.82 397	76.82 339	77.09 416	61.72 297	65.34 443	75.38 412	58.04 404	64.51 403	62.32 444	42.05 392	86.51 352	51.45 385	69.22 399	82.21 412

pmmvs5		71.55 338	70.20 343	75.61 348	77.83 412	56.39 366	81.74 324	80.89 363	57.76 405	67.46 373	84.49 324	49.26 333	85.32 368	57.08 351	75.29 354	85.11 379

TESTMET0.1,1		69.89 358	69.00 350	72.55 384	79.27 407	56.85 357	78.38 375	74.71 418	57.64 406	68.09 367	77.19 419	37.75 414	76.70 418	63.92 285	84.09 226	84.10 392

RPMNet		73.51 315	70.49 338	82.58 219	81.32 381	65.19 214	75.92 396	92.27 85	57.60 407	72.73 315	76.45 422	52.30 287	95.43 73	48.14 408	77.71 311	87.11 340

SSC-MVS		53.88 410	53.59 410	54.75 435	72.87 438	19.59 468	73.84 413	60.53 452	57.58 408	49.18 446	73.45 433	46.34 357	75.47 432	16.20 461	32.28 454	69.20 441

新几何1				83.42 175	93.13 56	70.71 76		85.48 301	57.43 409	81.80 134	91.98 109	63.28 157	92.27 229	64.60 281	92.99 72	87.27 334

YYNet1		65.03 390	62.91 395	71.38 391	75.85 420	56.60 363	69.12 431	74.66 419	57.28 410	54.12 438	77.87 415	45.85 362	74.48 436	49.95 395	61.52 423	83.05 404

MDA-MVSNet_test_wron		65.03 390	62.92 394	71.37 392	75.93 418	56.73 359	69.09 432	74.73 417	57.28 410	54.03 439	77.89 414	45.88 361	74.39 437	49.89 396	61.55 422	82.99 406

Anonymous20231206		68.60 367	67.80 365	71.02 397	80.23 392	50.75 420	78.30 379	80.47 370	56.79 412	66.11 394	82.63 368	46.35 356	78.95 407	43.62 426	75.70 341	83.36 400

tpm2		73.26 321	71.46 326	78.63 304	83.34 335	56.71 361	80.65 342	80.40 374	56.63 413	73.55 305	82.02 377	51.80 300	91.24 273	56.35 360	78.42 304	87.95 316

CHOSEN 1792x2688		77.63 254	75.69 267	83.44 174	89.98 118	68.58 125	78.70 371	87.50 263	56.38 414	75.80 256	86.84 264	58.67 229	91.40 268	61.58 309	85.75 200	90.34 232

HyFIR lowres test		77.53 255	75.40 275	83.94 160	89.59 126	66.62 181	80.36 347	88.64 237	56.29 415	76.45 241	85.17 313	57.64 238	93.28 175	61.34 312	83.10 248	91.91 173

PVSNet_0	57.27 20	61.67 400	59.27 403	68.85 407	79.61 402	57.44 351	68.01 433	73.44 422	55.93 416	58.54 428	70.41 439	44.58 373	77.55 414	47.01 412	35.91 451	71.55 439

UnsupCasMVSNet_bld		63.70 395	61.53 401	70.21 401	73.69 431	51.39 415	72.82 414	81.89 353	55.63 417	57.81 431	71.80 436	38.67 409	78.61 408	49.26 400	52.21 441	80.63 422

MDTV_nov1_ep13_2view						37.79 455	75.16 403		55.10 418	66.53 387		49.34 331		53.98 371		87.94 317

MVS		78.19 236	76.99 245	81.78 233	85.66 277	66.99 176	84.66 268	90.47 153	55.08 419	72.02 326	85.27 309	63.83 154	94.11 135	66.10 268	89.80 127	84.24 389

test222					91.50 82	68.26 133	84.16 285	83.20 336	54.63 420	79.74 165	91.63 122	58.97 226			91.42 97	86.77 348

dongtai		45.42 421	45.38 422	45.55 439	73.36 435	26.85 463	67.72 434	34.19 465	54.15 421	49.65 445	56.41 452	25.43 439	62.94 455	19.45 456	28.09 456	46.86 455

CHOSEN 280x420		66.51 383	64.71 385	71.90 388	81.45 376	63.52 262	57.98 452	68.95 435	53.57 422	62.59 415	76.70 420	46.22 358	75.29 434	55.25 363	79.68 288	76.88 432

ADS-MVSNet2		66.20 388	63.33 392	74.82 361	79.92 395	58.75 330	67.55 435	75.19 413	53.37 423	65.25 399	75.86 425	42.32 388	80.53 402	41.57 431	68.91 400	85.18 376

ADS-MVSNet		64.36 393	62.88 396	68.78 408	79.92 395	47.17 432	67.55 435	71.18 427	53.37 423	65.25 399	75.86 425	42.32 388	73.99 439	41.57 431	68.91 400	85.18 376

LF4IMVS		64.02 394	62.19 398	69.50 403	70.90 442	53.29 402	76.13 393	77.18 404	52.65 425	58.59 427	80.98 384	23.55 445	76.52 420	53.06 377	66.66 407	78.68 428

tpm cat1		70.57 348	68.31 354	77.35 334	82.41 363	57.95 341	78.08 380	80.22 377	52.04 426	68.54 364	77.66 417	52.00 295	87.84 339	51.77 381	72.07 385	86.25 355

test_vis1_n		69.85 359	69.21 348	71.77 389	72.66 440	55.27 384	81.48 328	76.21 410	52.03 427	75.30 274	83.20 357	28.97 435	76.22 424	74.60 178	78.41 305	83.81 395

Patchmatch-test		64.82 392	63.24 393	69.57 402	79.42 405	49.82 424	63.49 449	69.05 434	51.98 428	59.95 424	80.13 395	50.91 309	70.98 443	40.66 433	73.57 371	87.90 318

N_pmnet		52.79 413	53.26 411	51.40 437	78.99 408	7.68 471	69.52 427	3.89 470	51.63 429	57.01 433	74.98 429	40.83 398	65.96 452	37.78 438	64.67 414	80.56 424

test_fmvs1_n		70.86 345	70.24 342	72.73 383	72.51 441	55.28 383	81.27 332	79.71 382	51.49 430	78.73 182	84.87 319	27.54 437	77.02 416	76.06 161	79.97 287	85.88 366

test_fmvs1		70.93 344	70.52 337	72.16 387	73.71 430	55.05 385	80.82 335	78.77 391	51.21 431	78.58 187	84.41 327	31.20 432	76.94 417	75.88 164	80.12 286	84.47 387

PMMVS		69.34 362	68.67 351	71.35 394	75.67 421	62.03 291	75.17 402	73.46 421	50.00 432	68.68 361	79.05 404	52.07 294	78.13 410	61.16 313	82.77 251	73.90 436

test_fmvs2		68.35 372	67.48 371	70.98 398	69.50 444	51.95 407	80.05 352	76.38 409	49.33 433	74.65 291	84.38 328	23.30 446	75.40 433	74.51 179	75.17 357	85.60 369

ttmdpeth		59.91 402	57.10 406	68.34 411	67.13 448	46.65 435	74.64 408	67.41 438	48.30 434	62.52 416	85.04 318	20.40 448	75.93 427	42.55 429	45.90 449	82.44 410

CMPMVS	51.72 21	70.19 354	68.16 356	76.28 342	73.15 437	57.55 349	79.47 358	83.92 321	48.02 435	56.48 435	84.81 321	43.13 383	86.42 354	62.67 296	81.81 264	84.89 382
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvsany_test1		62.30 398	61.26 402	65.41 419	69.52 443	54.86 387	66.86 437	49.78 459	46.65 436	68.50 365	83.21 356	49.15 334	66.28 451	56.93 354	60.77 424	75.11 435

kuosan		39.70 425	40.40 426	37.58 442	64.52 451	26.98 461	65.62 442	33.02 466	46.12 437	42.79 449	48.99 455	24.10 444	46.56 463	12.16 464	26.30 457	39.20 456

test_fmvs3		63.36 396	61.82 399	67.98 413	62.51 453	46.96 434	77.37 388	74.03 420	45.24 438	67.50 372	78.79 409	12.16 458	72.98 442	72.77 199	66.02 410	83.99 393

CVMVSNet		72.99 326	72.58 315	74.25 368	84.28 312	50.85 419	86.41 220	83.45 330	44.56 439	73.23 309	87.54 248	49.38 330	85.70 361	65.90 270	78.44 302	86.19 357

test_vis1_rt		60.28 401	58.42 404	65.84 418	67.25 447	55.60 379	70.44 425	60.94 451	44.33 440	59.00 426	66.64 441	24.91 441	68.67 448	62.80 292	69.48 396	73.25 437

mvsany_test3		53.99 409	51.45 414	61.61 424	55.51 458	44.74 443	63.52 448	45.41 463	43.69 441	58.11 430	76.45 422	17.99 451	63.76 454	54.77 367	47.59 445	76.34 433

EU-MVSNet		68.53 370	67.61 369	71.31 395	78.51 411	47.01 433	84.47 274	84.27 317	42.27 442	66.44 391	84.79 322	40.44 400	83.76 378	58.76 335	68.54 403	83.17 401

FPMVS		53.68 411	51.64 413	59.81 426	65.08 450	51.03 417	69.48 428	69.58 432	41.46 443	40.67 450	72.32 435	16.46 454	70.00 447	24.24 454	65.42 412	58.40 450

pmmvs3		57.79 404	54.26 409	68.37 410	64.02 452	56.72 360	75.12 405	65.17 443	40.20 444	52.93 440	69.86 440	20.36 449	75.48 431	45.45 422	55.25 437	72.90 438

new_pmnet		50.91 416	50.29 416	52.78 436	68.58 445	34.94 458	63.71 447	56.63 456	39.73 445	44.95 447	65.47 442	21.93 447	58.48 456	34.98 442	56.62 431	64.92 444

MVS-HIRNet		59.14 403	57.67 405	63.57 421	81.65 371	43.50 445	71.73 417	65.06 444	39.59 446	51.43 441	57.73 449	38.34 411	82.58 389	39.53 434	73.95 367	64.62 445

MVStest1		56.63 406	52.76 412	68.25 412	61.67 454	53.25 403	71.67 418	68.90 436	38.59 447	50.59 443	83.05 359	25.08 440	70.66 444	36.76 440	38.56 450	80.83 421

PMMVS2		40.82 424	38.86 428	46.69 438	53.84 460	16.45 469	48.61 455	49.92 458	37.49 448	31.67 453	60.97 446	8.14 464	56.42 458	28.42 449	30.72 455	67.19 443

test_vis3_rt		49.26 418	47.02 420	56.00 430	54.30 459	45.27 440	66.76 439	48.08 460	36.83 449	44.38 448	53.20 453	7.17 465	64.07 453	56.77 357	55.66 433	58.65 449

test_f		52.09 414	50.82 415	55.90 431	53.82 461	42.31 450	59.42 451	58.31 455	36.45 450	56.12 437	70.96 438	12.18 457	57.79 457	53.51 374	56.57 432	67.60 442

LCM-MVSNet		54.25 408	49.68 418	67.97 414	53.73 462	45.28 439	66.85 438	80.78 365	35.96 451	39.45 452	62.23 445	8.70 462	78.06 412	48.24 407	51.20 442	80.57 423

APD_test1		53.31 412	49.93 417	63.42 422	65.68 449	50.13 422	71.59 419	66.90 440	34.43 452	40.58 451	71.56 437	8.65 463	76.27 423	34.64 443	55.36 435	63.86 446

PMVS	37.38 22	44.16 423	40.28 427	55.82 432	40.82 467	42.54 449	65.12 444	63.99 447	34.43 452	24.48 458	57.12 451	3.92 468	76.17 425	17.10 459	55.52 434	48.75 453
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 422	41.86 425	55.16 434	77.03 417	51.52 413	32.50 458	80.52 369	32.46 454	27.12 457	35.02 458	9.52 461	75.50 430	22.31 455	60.21 427	38.45 457
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 405	56.90 407	60.38 425	67.70 446	35.61 456	69.18 429	53.97 457	32.30 455	57.49 432	79.88 398	40.39 401	68.57 449	38.78 437	72.37 380	76.97 431

testf1		45.72 419	41.96 423	57.00 428	56.90 456	45.32 437	66.14 440	59.26 453	26.19 456	30.89 455	60.96 447	4.14 466	70.64 445	26.39 452	46.73 447	55.04 451

APD_test2		45.72 419	41.96 423	57.00 428	56.90 456	45.32 437	66.14 440	59.26 453	26.19 456	30.89 455	60.96 447	4.14 466	70.64 445	26.39 452	46.73 447	55.04 451

E-PMN		31.77 426	30.64 429	35.15 443	52.87 463	27.67 460	57.09 453	47.86 461	24.64 458	16.40 463	33.05 459	11.23 459	54.90 459	14.46 462	18.15 460	22.87 459

EMVS		30.81 428	29.65 430	34.27 444	50.96 464	25.95 464	56.58 454	46.80 462	24.01 459	15.53 464	30.68 460	12.47 456	54.43 460	12.81 463	17.05 461	22.43 460

MVE	26.22 23	30.37 429	25.89 433	43.81 440	44.55 466	35.46 457	28.87 459	39.07 464	18.20 460	18.58 462	40.18 457	2.68 469	47.37 462	17.07 460	23.78 459	48.60 454
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 445	40.17 468	26.90 462		24.59 469	17.44 461	23.95 459	48.61 456	9.77 460	26.48 464	18.06 457	24.47 458	28.83 458

wuyk23d		16.82 432	15.94 435	19.46 446	58.74 455	31.45 459	39.22 456	3.74 471	6.84 462	6.04 465	2.70 465	1.27 470	24.29 465	10.54 465	14.40 464	2.63 462

test_method		31.52 427	29.28 431	38.23 441	27.03 469	6.50 472	20.94 460	62.21 449	4.05 463	22.35 461	52.50 454	13.33 455	47.58 461	27.04 451	34.04 453	60.62 447

tmp_tt		18.61 431	21.40 434	10.23 447	4.82 470	10.11 470	34.70 457	30.74 468	1.48 464	23.91 460	26.07 461	28.42 436	13.41 466	27.12 450	15.35 463	7.17 461

EGC-MVSNET		52.07 415	47.05 419	67.14 415	83.51 332	60.71 309	80.50 345	67.75 437	0.07 465	0.43 466	75.85 427	24.26 443	81.54 395	28.82 448	62.25 420	59.16 448

testmvs		6.04 435	8.02 438	0.10 449	0.08 471	0.03 474	69.74 426	0.04 472	0.05 466	0.31 467	1.68 466	0.02 472	0.04 467	0.24 466	0.02 465	0.25 464

test123		6.12 434	8.11 437	0.14 448	0.06 472	0.09 473	71.05 421	0.03 473	0.04 467	0.25 468	1.30 467	0.05 471	0.03 468	0.21 467	0.01 466	0.29 463

mmdepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

monomultidepth		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

test_blank		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet_test		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

DCPMVS		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

cdsmvs_eth3d_5k		19.96 430	26.61 432	0.00 450	0.00 473	0.00 475	0.00 461	89.26 205	0.00 468	0.00 469	88.61 215	61.62 190	0.00 469	0.00 468	0.00 467	0.00 465

pcd_1.5k_mvsjas		5.26 436	7.02 439	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	63.15 163	0.00 469	0.00 468	0.00 467	0.00 465

sosnet-low-res		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

sosnet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uncertanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

Regformer		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

ab-mvs-re		7.23 433	9.64 436	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	86.72 268	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

uanet		0.00 437	0.00 440	0.00 450	0.00 473	0.00 475	0.00 461	0.00 474	0.00 468	0.00 469	0.00 468	0.00 473	0.00 469	0.00 468	0.00 467	0.00 465

WAC-MVS						42.58 447								39.46 435

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 14	96.44 9	94.41 44

No_MVS				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 14	96.44 9	94.41 44

eth-test2					0.00 473

eth-test					0.00 473

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 55	82.45 3	96.87 20	83.77 76	96.48 8	94.88 16

test_0728_SECOND				87.71 32	95.34 1	71.43 60	93.49 10	94.23 3					97.49 4	89.08 21	96.41 12	94.21 55

GSMVS																88.96 289

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 305				88.96 289

sam_mvs												50.01 321

ambc				75.24 356	73.16 436	50.51 421	63.05 450	87.47 264		64.28 404	77.81 416	17.80 452	89.73 306	57.88 344	60.64 425	85.49 370

MTGPA								92.02 98

test_post1							78.90 369				5.43 464	48.81 340	85.44 367	59.25 328

test_post											5.46 463	50.36 317	84.24 375

patchmatchnet-post											74.00 431	51.12 308	88.60 329

GG-mvs-BLEND				75.38 354	81.59 373	55.80 376	79.32 360	69.63 431		67.19 377	73.67 432	43.24 382	88.90 325	50.41 389	84.50 216	81.45 417

MTMP							92.18 35	32.83 467

test9_res														84.90 58	95.70 26	92.87 132

agg_prior2														82.91 85	95.45 29	92.70 137

agg_prior					92.85 64	71.94 52		91.78 114		84.41 89			94.93 97

test_prior4						72.60 34	89.01 118

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 69

新几何2							86.29 226

旧先验1					91.96 76	65.79 200		86.37 288			93.08 86	69.31 89			92.74 76	88.74 300

原ACMM2							86.86 203

testdata2													91.01 283	62.37 299

segment_acmp												73.08 40

test12				86.80 54	92.63 69	70.70 77		91.79 113		82.71 122		71.67 59	96.16 48		94.50 53	93.54 98

plane_prior7					90.08 112	68.51 127

plane_prior6					89.84 121	68.70 121						60.42 216

plane_prior5								92.44 78					95.38 78	78.71 129	86.32 185	91.33 192

plane_prior4											91.00 147

plane_prior1					89.90 120

n2								0.00 474

nn								0.00 474

door-mid								69.98 430

lessismore_v0				78.97 299	81.01 384	57.15 354		65.99 441		61.16 419	82.82 365	39.12 406	91.34 270	59.67 324	46.92 446	88.43 308

test11								92.23 88

door								69.44 433

HQP5-MVS						66.98 177

BP-MVS														77.47 143

HQP4-MVS										77.24 220			95.11 90			91.03 202

HQP3-MVS								92.19 92							85.99 193

HQP2-MVS												60.17 219

NP-MVS					89.62 125	68.32 131					90.24 165

ACMMP++_ref															81.95 262

ACMMP++															81.25 267

Test By Simon												64.33 149

High-resolution multi-view benchmark