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.

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 39	96.26 24	72.84 22	99.38 1	92.64 4	95.93 8	97.08 7

DELS-MVS		90.05 6	90.09 8	89.94 4	93.14 70	73.88 8	97.01 3	94.40 45	88.32 2	85.71 41	94.91 63	74.11 16	98.91 13	87.26 45	95.94 7	97.03 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
EPNet		87.84 20	88.38 15	86.23 74	93.30 63	66.05 129	95.26 30	94.84 22	87.09 3	88.06 22	94.53 72	66.79 53	97.34 70	83.89 69	91.68 73	95.29 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		89.61 9	89.99 9	88.46 17	94.39 38	69.71 39	96.53 11	93.78 60	86.89 4	89.68 15	95.78 31	65.94 60	99.10 7	92.99 2	93.91 40	96.58 15

DeepPCF-MVS	81.17 1	89.72 8	91.38 3	84.72 123	93.00 73	58.16 280	96.72 7	94.41 43	86.50 5	90.25 14	97.83 1	75.46 12	98.67 19	92.78 3	95.49 11	97.32 4

CANet_DTU		84.09 77	83.52 71	85.81 86	90.30 140	66.82 110	91.87 147	89.01 250	85.27 6	86.09 36	93.74 95	47.71 248	96.98 94	77.90 118	89.78 94	93.65 122

CLD-MVS		82.73 98	82.35 98	83.86 143	87.90 193	67.65 85	95.45 26	92.18 133	85.06 7	72.58 166	92.27 128	52.46 205	95.78 135	84.18 65	79.06 169	88.16 215
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CNVR-MVS		90.32 4	90.89 4	88.61 16	96.76 7	70.65 23	96.47 12	94.83 23	84.83 8	89.07 19	96.80 12	70.86 29	99.06 11	92.64 4	95.71 9	96.12 30

NCCC		89.07 12	89.46 12	87.91 21	96.60 9	69.05 50	96.38 13	94.64 33	84.42 9	86.74 30	96.20 25	66.56 56	98.76 18	89.03 28	94.56 29	95.92 37

PS-MVSNAJ		88.14 14	87.61 24	89.71 7	92.06 95	76.72 1	95.75 19	93.26 87	83.86 10	89.55 16	96.06 28	53.55 195	97.89 44	91.10 13	93.31 50	94.54 88

DeepC-MVS_fast	79.48 2	87.95 18	88.00 18	87.79 24	95.86 24	68.32 66	95.74 20	94.11 55	83.82 11	83.49 63	96.19 26	64.53 76	98.44 26	83.42 72	94.88 22	96.61 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 19	87.38 29	89.55 10	91.41 121	76.43 3	95.74 20	93.12 96	83.53 12	89.55 16	95.95 29	53.45 199	97.68 50	91.07 14	92.62 59	94.54 88

TSAR-MVS + MP.		88.11 16	88.64 14	86.54 61	91.73 109	68.04 74	90.36 206	93.55 74	82.89 13	91.29 9	92.89 113	72.27 25	96.03 129	87.99 35	94.77 23	95.54 46
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		90.70 2	90.52 5	91.24 1	89.68 151	76.68 2	97.29 1	95.35 10	82.87 14	91.58 8	97.22 4	79.93 3	99.10 7	83.12 73	97.64 2	97.94 1

WTY-MVS		86.32 43	85.81 48	87.85 22	92.82 78	69.37 45	95.20 32	95.25 12	82.71 15	81.91 71	94.73 68	67.93 42	97.63 56	79.55 101	82.25 148	96.54 16

lupinMVS		87.74 21	87.77 21	87.63 30	89.24 162	71.18 18	96.57 10	92.90 104	82.70 16	87.13 26	95.27 47	64.99 70	95.80 134	89.34 22	91.80 71	95.93 36

HPM-MVS++		89.37 11	89.95 10	87.64 26	95.10 29	68.23 71	95.24 31	94.49 38	82.43 17	88.90 20	96.35 21	71.89 28	98.63 20	88.76 30	96.40 4	96.06 31

PVSNet_Blended		86.73 39	86.86 36	86.31 72	93.76 51	67.53 88	96.33 14	93.61 71	82.34 18	81.00 82	93.08 105	63.19 95	97.29 73	87.08 46	91.38 78	94.13 104

MSP-MVS		90.38 3	91.87 1	85.88 82	92.83 76	64.03 187	93.06 100	94.33 48	82.19 19	93.65 2	96.15 27	85.89 1	97.19 79	91.02 15	97.75 1	96.43 22
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
DWT-MVSNet_test		83.95 80	82.80 89	87.41 34	92.90 75	70.07 30	89.12 236	94.42 42	82.15 20	77.64 118	91.77 135	70.81 30	96.22 118	65.03 222	81.36 156	95.94 35

Regformer-1		87.24 27	87.60 25	86.15 76	95.14 27	65.83 137	93.95 66	95.12 15	82.11 21	84.25 54	95.73 33	67.88 43	98.35 29	85.60 55	88.64 100	94.26 96

PAPM		85.89 50	85.46 53	87.18 39	88.20 186	72.42 12	92.41 127	92.77 107	82.11 21	80.34 88	93.07 107	68.27 35	95.02 164	78.39 113	93.59 47	94.09 106

jason		86.40 42	86.17 43	87.11 41	86.16 220	70.54 25	95.71 23	92.19 132	82.00 23	84.58 51	94.34 82	61.86 106	95.53 154	87.76 37	90.89 84	95.27 60
jason: jason.
baseline1		81.84 114	81.03 113	84.28 137	91.60 112	66.62 116	91.08 185	91.66 152	81.87 24	74.86 144	91.67 139	69.98 32	94.92 169	71.76 160	64.75 267	91.29 178

CHOSEN 1792x2688		84.98 61	83.45 74	89.57 9	89.94 146	75.14 5	92.07 138	92.32 122	81.87 24	75.68 136	88.27 177	60.18 119	98.60 21	80.46 97	90.27 91	94.96 74

Regformer-2		87.00 34	87.43 27	85.71 92	95.14 27	64.73 166	93.95 66	94.95 20	81.69 26	84.03 60	95.73 33	67.35 47	98.19 33	85.40 57	88.64 100	94.20 98

hse-mvs3		83.01 94	82.56 94	84.35 134	89.34 158	62.02 227	92.72 114	93.76 63	81.45 27	82.73 67	92.25 129	60.11 120	97.13 83	87.69 38	62.96 278	93.91 115

ET-MVSNet_ETH3D		84.01 78	83.15 84	86.58 59	90.78 134	70.89 21	94.74 45	94.62 34	81.44 28	58.19 295	93.64 96	73.64 19	92.35 256	82.66 77	78.66 174	96.50 19

casdiffmvs		85.37 55	84.87 60	86.84 47	88.25 184	69.07 49	93.04 102	91.76 146	81.27 29	80.84 84	92.07 131	64.23 79	96.06 127	84.98 60	87.43 110	95.39 48

ETV-MVS		86.01 48	86.11 44	85.70 93	90.21 142	67.02 105	93.43 91	91.92 140	81.21 30	84.13 58	94.07 90	60.93 113	95.63 144	89.28 23	89.81 92	94.46 94

Regformer-3		85.80 51	85.92 46	85.46 99	94.17 42	65.09 159	92.95 107	95.11 16	81.13 31	81.68 73	95.04 54	65.82 62	98.32 30	83.02 74	84.36 134	92.97 142

DeepC-MVS	77.85 3	85.52 53	85.24 55	86.37 69	88.80 171	66.64 115	92.15 132	93.68 68	81.07 32	76.91 129	93.64 96	62.59 101	98.44 26	85.50 56	92.84 57	94.03 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		85.01 60	84.44 64	86.71 53	88.33 181	68.73 57	90.24 210	91.82 145	81.05 33	81.18 78	92.50 120	63.69 87	96.08 125	84.45 64	86.71 118	95.32 53

IU-MVS					96.46 10	69.91 35		95.18 13	80.75 34	95.28 1				92.34 6	95.36 12	96.47 20

Regformer-4		85.45 54	85.69 51	84.73 121	94.17 42	63.23 204	92.95 107	94.83 23	80.66 35	81.29 76	95.04 54	65.12 68	98.08 36	82.74 76	84.36 134	92.88 146

diffmvs		84.28 71	83.83 69	85.61 95	87.40 201	68.02 75	90.88 191	89.24 237	80.54 36	81.64 74	92.52 119	59.83 124	94.52 183	87.32 44	85.11 128	94.29 95

VPNet		78.82 162	77.53 167	82.70 166	84.52 245	66.44 120	93.93 68	92.23 126	80.46 37	72.60 165	88.38 175	49.18 234	93.13 223	72.47 152	63.97 275	88.55 207

CS-MVS		86.61 41	86.85 37	85.88 82	91.52 117	66.25 126	95.42 27	92.25 125	80.36 38	84.10 59	94.82 66	62.88 99	96.08 125	88.25 33	92.07 69	95.30 55

canonicalmvs		86.85 36	86.25 42	88.66 15	91.80 108	71.92 13	93.54 86	91.71 149	80.26 39	87.55 24	95.25 49	63.59 90	96.93 100	88.18 34	84.34 136	97.11 6

MVSTER		82.47 102	82.05 99	83.74 145	92.68 83	69.01 51	91.90 146	93.21 88	79.83 40	72.14 174	85.71 209	74.72 13	94.72 174	75.72 128	72.49 215	87.50 220

HQP-NCC					87.54 197		94.06 60		79.80 41	74.18 148

ACMP_Plane					87.54 197		94.06 60		79.80 41	74.18 148

HQP-MVS		81.14 122	80.64 118	82.64 168	87.54 197	63.66 197	94.06 60	91.70 150	79.80 41	74.18 148	90.30 155	51.63 212	95.61 146	77.63 119	78.90 170	88.63 205

baseline2		83.68 87	83.42 77	84.48 131	87.37 202	66.00 131	90.06 214	95.93 7	79.71 44	69.08 208	90.39 153	77.92 4	96.28 116	78.91 108	81.38 155	91.16 179

EI-MVSNet-Vis-set		83.77 84	83.67 70	84.06 140	92.79 81	63.56 200	91.76 154	94.81 25	79.65 45	77.87 115	94.09 88	63.35 93	97.90 43	79.35 102	79.36 166	90.74 183

EIA-MVS		84.84 63	84.88 59	84.69 124	91.30 122	62.36 222	93.85 73	92.04 135	79.45 46	79.33 100	94.28 85	62.42 102	96.35 115	80.05 98	91.25 81	95.38 49

plane_prior						62.42 220	93.85 73		79.38 47						78.80 172

alignmvs		87.28 26	86.97 34	88.24 20	91.30 122	71.14 20	95.61 24	93.56 73	79.30 48	87.07 29	95.25 49	68.43 34	96.93 100	87.87 36	84.33 137	96.65 11

TESTMET0.1,1		82.41 103	81.98 101	83.72 148	88.08 187	63.74 192	92.70 116	93.77 62	79.30 48	77.61 120	87.57 189	58.19 140	94.08 197	73.91 141	86.68 119	93.33 130

EI-MVSNet-UG-set		83.14 92	82.96 85	83.67 150	92.28 90	63.19 206	91.38 171	94.68 31	79.22 50	76.60 130	93.75 94	62.64 100	97.76 49	78.07 116	78.01 177	90.05 191

PVSNet	73.49 8	80.05 140	78.63 148	84.31 135	90.92 129	64.97 162	92.47 126	91.05 177	79.18 51	72.43 171	90.51 151	37.05 304	94.06 199	68.06 189	86.00 123	93.90 117

HY-MVS	76.49 5	84.28 71	83.36 80	87.02 44	92.22 92	67.74 82	84.65 277	94.50 37	79.15 52	82.23 69	87.93 184	66.88 51	96.94 98	80.53 96	82.20 149	96.39 24

PVSNet_BlendedMVS		83.38 89	83.43 75	83.22 158	93.76 51	67.53 88	94.06 60	93.61 71	79.13 53	81.00 82	85.14 213	63.19 95	97.29 73	87.08 46	73.91 205	84.83 273

plane_prior3						61.95 228			79.09 54	72.53 167

MVS_111021_HR		86.19 46	85.80 49	87.37 35	93.17 69	69.79 37	93.99 64	93.76 63	79.08 55	78.88 108	93.99 91	62.25 103	98.15 34	85.93 54	91.15 82	94.15 103

MSLP-MVS++		86.27 44	85.91 47	87.35 36	92.01 98	68.97 53	95.04 40	92.70 109	79.04 56	81.50 75	96.50 17	58.98 136	96.78 104	83.49 71	93.93 39	96.29 26

IB-MVS	77.80 4	82.18 106	80.46 122	87.35 36	89.14 164	70.28 28	95.59 25	95.17 14	78.85 57	70.19 195	85.82 207	70.66 31	97.67 51	72.19 156	66.52 254	94.09 106
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
3Dnovator	73.91 6	82.69 101	80.82 114	88.31 19	89.57 153	71.26 17	92.60 121	94.39 46	78.84 58	67.89 227	92.48 123	48.42 240	98.52 22	68.80 186	94.40 33	95.15 65

HQP_MVS		80.34 135	79.75 130	82.12 186	86.94 208	62.42 220	93.13 98	91.31 164	78.81 59	72.53 167	89.14 168	50.66 219	95.55 152	76.74 122	78.53 175	88.39 211

plane_prior2							93.13 98		78.81 59

MG-MVS		87.11 30	86.27 40	89.62 8	97.79 1	76.27 4	94.96 42	94.49 38	78.74 61	83.87 62	92.94 110	64.34 78	96.94 98	75.19 131	94.09 36	95.66 42

gm-plane-assit					88.42 179	67.04 104			78.62 62		91.83 134		97.37 67	76.57 124

VNet		86.20 45	85.65 52	87.84 23	93.92 47	69.99 31	95.73 22	95.94 6	78.43 63	86.00 37	93.07 107	58.22 139	97.00 90	85.22 58	84.33 137	96.52 17

tpm		78.58 170	77.03 174	83.22 158	85.94 225	64.56 167	83.21 290	91.14 172	78.31 64	73.67 155	79.68 283	64.01 81	92.09 262	66.07 211	71.26 225	93.03 140

xxxxxxxxxxxxxcwj		87.14 29	87.19 31	86.99 45	93.84 49	67.89 78	95.05 38	84.72 304	78.19 65	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

save fliter					93.84 49	67.89 78	95.05 38	92.66 112	78.19 65

TSAR-MVS + GP.		87.96 17	88.37 16	86.70 55	93.51 60	65.32 148	95.15 34	93.84 59	78.17 67	85.93 38	94.80 67	75.80 11	98.21 31	89.38 21	88.78 98	96.59 13

FIs		79.47 152	79.41 138	79.67 242	85.95 223	59.40 267	91.68 158	93.94 57	78.06 68	68.96 211	88.28 176	66.61 55	91.77 268	66.20 210	74.99 199	87.82 217

sss		82.71 100	82.38 97	83.73 147	89.25 161	59.58 265	92.24 130	94.89 21	77.96 69	79.86 94	92.38 125	56.70 159	97.05 85	77.26 121	80.86 160	94.55 86

PMMVS		81.98 112	82.04 100	81.78 193	89.76 150	56.17 297	91.13 184	90.69 185	77.96 69	80.09 91	93.57 98	46.33 256	94.99 165	81.41 89	87.46 109	94.17 101

MVS_Test		84.16 76	83.20 81	87.05 43	91.56 114	69.82 36	89.99 219	92.05 134	77.77 71	82.84 66	86.57 199	63.93 83	96.09 123	74.91 137	89.18 96	95.25 63

SteuartSystems-ACMMP		86.82 38	86.90 35	86.58 59	90.42 137	66.38 121	96.09 15	93.87 58	77.73 72	84.01 61	95.66 35	63.39 92	97.94 39	87.40 42	93.55 48	95.42 47
Skip Steuart: Steuart Systems R&D Blog.
EPNet_dtu		78.80 163	79.26 142	77.43 270	88.06 188	49.71 329	91.96 145	91.95 139	77.67 73	76.56 131	91.28 141	58.51 138	90.20 290	56.37 265	80.95 159	92.39 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpmrst		80.57 130	79.14 145	84.84 119	90.10 143	68.28 68	81.70 298	89.72 225	77.63 74	75.96 134	79.54 285	64.94 72	92.71 239	75.43 129	77.28 189	93.55 124

testdata1							89.21 233		77.55 75

UniMVSNet_NR-MVSNet		78.15 177	77.55 166	79.98 233	84.46 247	60.26 255	92.25 129	93.20 90	77.50 76	68.88 212	86.61 198	66.10 58	92.13 260	66.38 207	62.55 282	87.54 219

UA-Net		80.02 141	79.65 131	81.11 210	89.33 159	57.72 284	86.33 271	89.00 251	77.44 77	81.01 81	89.15 167	59.33 131	95.90 132	61.01 246	84.28 139	89.73 195

PVSNet_Blended_VisFu		83.97 79	83.50 72	85.39 103	90.02 144	66.59 118	93.77 78	91.73 147	77.43 78	77.08 128	89.81 163	63.77 86	96.97 95	79.67 100	88.21 103	92.60 150

ETH3 D test6400		90.27 5	90.44 6	89.75 6	96.82 6	74.33 7	95.89 16	94.80 26	77.13 79	89.13 18	97.38 2	74.49 15	98.48 24	92.32 9	95.98 6	96.46 21

NR-MVSNet		76.05 207	74.59 203	80.44 222	82.96 267	62.18 226	90.83 193	91.73 147	77.12 80	60.96 281	86.35 200	59.28 132	91.80 267	60.74 247	61.34 297	87.35 225

RRT_test8_iter05		80.61 129	79.62 132	83.60 152	91.87 107	66.90 108	93.42 93	93.68 68	77.09 81	68.83 214	85.63 210	66.82 52	95.42 155	76.46 126	62.74 281	88.48 208

FC-MVSNet-test		77.99 179	78.08 156	77.70 265	84.89 240	55.51 302	90.27 208	93.75 66	76.87 82	66.80 242	87.59 188	65.71 64	90.23 289	62.89 236	73.94 204	87.37 224

SD-MVS		87.49 24	87.49 26	87.50 32	93.60 56	68.82 56	93.90 70	92.63 115	76.86 83	87.90 23	95.76 32	66.17 57	97.63 56	89.06 26	91.48 77	96.05 32
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
UGNet		79.87 144	78.68 147	83.45 157	89.96 145	61.51 234	92.13 133	90.79 183	76.83 84	78.85 110	86.33 202	38.16 290	96.17 120	67.93 191	87.17 111	92.67 148
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
MVS_111021_LR		82.02 111	81.52 106	83.51 154	88.42 179	62.88 215	89.77 222	88.93 252	76.78 85	75.55 140	93.10 103	50.31 222	95.38 157	83.82 70	87.02 112	92.26 162

TranMVSNet+NR-MVSNet		75.86 212	74.52 206	79.89 236	82.44 270	60.64 251	91.37 172	91.37 163	76.63 86	67.65 230	86.21 203	52.37 206	91.55 272	61.84 242	60.81 300	87.48 221

PAPR		85.15 58	84.47 62	87.18 39	96.02 22	68.29 67	91.85 149	93.00 101	76.59 87	79.03 104	95.00 56	61.59 107	97.61 58	78.16 115	89.00 97	95.63 43

UniMVSNet (Re)		77.58 186	76.78 178	79.98 233	84.11 253	60.80 244	91.76 154	93.17 93	76.56 88	69.93 201	84.78 218	63.32 94	92.36 255	64.89 223	62.51 284	86.78 234

DU-MVS		76.86 194	75.84 191	79.91 235	82.96 267	60.26 255	91.26 177	91.54 155	76.46 89	68.88 212	86.35 200	56.16 165	92.13 260	66.38 207	62.55 282	87.35 225

OPM-MVS		79.00 158	78.09 155	81.73 194	83.52 261	63.83 189	91.64 160	90.30 200	76.36 90	71.97 177	89.93 162	46.30 257	95.17 162	75.10 132	77.70 180	86.19 247

WR-MVS		76.76 198	75.74 193	79.82 238	84.60 243	62.27 225	92.60 121	92.51 119	76.06 91	67.87 228	85.34 211	56.76 157	90.24 288	62.20 240	63.69 277	86.94 232

GA-MVS		78.33 175	76.23 186	84.65 125	83.65 259	66.30 124	91.44 164	90.14 207	76.01 92	70.32 193	84.02 225	42.50 273	94.72 174	70.98 164	77.00 191	92.94 143

PVSNet_0	68.08 15	71.81 250	68.32 262	82.27 178	84.68 241	62.31 224	88.68 243	90.31 199	75.84 93	57.93 300	80.65 271	37.85 295	94.19 193	69.94 173	29.05 352	90.31 188

CDS-MVSNet		81.43 118	80.74 115	83.52 153	86.26 218	64.45 172	92.09 136	90.65 188	75.83 94	73.95 154	89.81 163	63.97 82	92.91 232	71.27 163	82.82 145	93.20 135
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CostFormer		82.33 104	81.15 109	85.86 85	89.01 167	68.46 63	82.39 295	93.01 99	75.59 95	80.25 89	81.57 254	72.03 27	94.96 166	79.06 106	77.48 186	94.16 102

nrg030		80.93 126	79.86 128	84.13 139	83.69 258	68.83 55	93.23 96	91.20 168	75.55 96	75.06 143	88.22 181	63.04 98	94.74 173	81.88 83	66.88 251	88.82 203

VDD-MVS		83.06 93	81.81 104	86.81 49	90.86 132	67.70 83	95.40 28	91.50 158	75.46 97	81.78 72	92.34 127	40.09 281	97.13 83	86.85 49	82.04 150	95.60 44

Effi-MVS+-dtu		76.14 203	75.28 197	78.72 256	83.22 263	55.17 304	89.87 220	87.78 278	75.42 98	67.98 224	81.43 256	45.08 263	92.52 248	75.08 133	71.63 220	88.48 208

mvs-test1		78.74 166	77.95 159	81.14 208	83.22 263	57.13 292	93.96 65	87.78 278	75.42 98	72.68 163	90.80 146	45.08 263	94.54 181	75.08 133	77.49 185	91.74 167

test_prior3		87.38 25	87.70 22	86.42 66	94.71 33	67.35 93	95.10 36	93.10 97	75.40 100	85.25 48	95.61 37	67.94 40	96.84 102	87.47 40	94.77 23	95.05 70

test_prior2							95.10 36		75.40 100	85.25 48	95.61 37	67.94 40		87.47 40	94.77 23

zzz-MVS		84.73 64	84.47 62	85.50 97	91.89 104	65.16 153	91.55 162	92.23 126	75.32 102	80.53 86	95.21 51	56.06 168	97.16 81	84.86 62	92.55 61	94.18 99

MTAPA		83.91 81	83.38 79	85.50 97	91.89 104	65.16 153	81.75 297	92.23 126	75.32 102	80.53 86	95.21 51	56.06 168	97.16 81	84.86 62	92.55 61	94.18 99

EPMVS		78.49 172	75.98 189	86.02 78	91.21 124	69.68 40	80.23 310	91.20 168	75.25 104	72.48 169	78.11 293	54.65 182	93.69 214	57.66 262	83.04 143	94.69 80

miper_enhance_ethall		78.86 161	77.97 158	81.54 199	88.00 191	65.17 152	91.41 165	89.15 243	75.19 105	68.79 215	83.98 226	67.17 48	92.82 234	72.73 148	65.30 258	86.62 240

v2v482		77.42 188	75.65 194	82.73 165	80.38 286	67.13 101	91.85 149	90.23 204	75.09 106	69.37 204	83.39 233	53.79 193	94.44 185	71.77 159	65.00 264	86.63 239

VPA-MVSNet		79.03 157	78.00 157	82.11 189	85.95 223	64.48 171	93.22 97	94.66 32	75.05 107	74.04 153	84.95 216	52.17 207	93.52 217	74.90 138	67.04 250	88.32 213

ACMMP_NAP		86.05 47	85.80 49	86.80 50	91.58 113	67.53 88	91.79 151	93.49 78	74.93 108	84.61 50	95.30 44	59.42 129	97.92 41	86.13 52	94.92 17	94.94 75

thres200		79.66 147	78.33 151	83.66 151	92.54 85	65.82 138	93.06 100	96.31 3	74.90 109	73.30 157	88.66 170	59.67 126	95.61 146	47.84 297	78.67 173	89.56 198

TAMVS		80.37 134	79.45 137	83.13 160	85.14 235	63.37 201	91.23 178	90.76 184	74.81 110	72.65 164	88.49 172	60.63 115	92.95 227	69.41 178	81.95 151	93.08 139

MP-MVS-pluss		85.24 57	85.13 56	85.56 96	91.42 119	65.59 143	91.54 163	92.51 119	74.56 111	80.62 85	95.64 36	59.15 133	97.00 90	86.94 48	93.80 41	94.07 108
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvs_anonymous		81.36 119	79.99 126	85.46 99	90.39 139	68.40 64	86.88 268	90.61 189	74.41 112	70.31 194	84.67 219	63.79 85	92.32 257	73.13 143	85.70 124	95.67 41

MAR-MVS		84.18 75	83.43 75	86.44 65	96.25 19	65.93 134	94.28 51	94.27 50	74.41 112	79.16 103	95.61 37	53.99 190	98.88 16	69.62 176	93.26 51	94.50 91
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
BH-w/o		80.49 133	79.30 141	84.05 141	90.83 133	64.36 179	93.60 83	89.42 232	74.35 114	69.09 207	90.15 158	55.23 176	95.61 146	64.61 224	86.43 122	92.17 163

thisisatest0515		83.41 88	82.49 95	86.16 75	89.46 157	68.26 69	93.54 86	94.70 30	74.31 115	75.75 135	90.92 143	72.62 23	96.52 113	69.64 174	81.50 154	93.71 120

bset_n11_16_dypcd		75.95 211	74.16 212	81.30 204	76.91 322	65.14 155	88.89 239	87.48 281	74.30 116	69.90 202	83.40 232	42.16 276	92.42 251	78.39 113	66.03 255	86.32 243

Vis-MVSNet (Re-imp)		79.24 155	79.57 133	78.24 262	88.46 177	52.29 316	90.41 204	89.12 245	74.24 117	69.13 206	91.91 133	65.77 63	90.09 292	59.00 258	88.09 105	92.33 156

SMA-MVS		88.14 14	88.29 17	87.67 25	93.21 67	68.72 58	93.85 73	94.03 56	74.18 118	91.74 7	96.67 13	65.61 65	98.42 28	89.24 24	96.08 5	95.88 39
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
AUN-MVS		78.37 173	77.43 168	81.17 207	86.60 213	57.45 289	89.46 229	91.16 170	74.11 119	74.40 147	90.49 152	55.52 173	94.57 177	74.73 140	60.43 304	91.48 171

3Dnovator+	73.60 7	82.10 110	80.60 119	86.60 57	90.89 131	66.80 112	95.20 32	93.44 81	74.05 120	67.42 233	92.49 122	49.46 230	97.65 55	70.80 166	91.68 73	95.33 51

XVS		83.87 82	83.47 73	85.05 111	93.22 65	63.78 190	92.92 109	92.66 112	73.99 121	78.18 113	94.31 84	55.25 174	97.41 65	79.16 104	91.58 75	93.95 113

X-MVStestdata		76.86 194	74.13 214	85.05 111	93.22 65	63.78 190	92.92 109	92.66 112	73.99 121	78.18 113	10.19 361	55.25 174	97.41 65	79.16 104	91.58 75	93.95 113

MS-PatchMatch		77.90 183	76.50 182	82.12 186	85.99 222	69.95 34	91.75 156	92.70 109	73.97 123	62.58 275	84.44 222	41.11 278	95.78 135	63.76 230	92.17 67	80.62 317

LCM-MVSNet-Re		72.93 241	71.84 239	76.18 284	88.49 175	48.02 334	80.07 313	70.17 346	73.96 124	52.25 317	80.09 280	49.98 225	88.24 305	67.35 195	84.23 140	92.28 159

Vis-MVSNet		80.92 127	79.98 127	83.74 145	88.48 176	61.80 229	93.44 90	88.26 272	73.96 124	77.73 116	91.76 136	49.94 226	94.76 171	65.84 213	90.37 90	94.65 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-mter		79.96 142	79.38 140	81.72 195	86.93 210	61.17 237	92.70 116	91.54 155	73.85 126	75.62 137	86.94 196	49.84 228	92.38 253	72.21 154	84.76 132	91.60 168

OMC-MVS		78.67 169	77.91 161	80.95 217	85.76 227	57.40 290	88.49 246	88.67 260	73.85 126	72.43 171	92.10 130	49.29 233	94.55 180	72.73 148	77.89 178	90.91 182

Fast-Effi-MVS+		81.14 122	80.01 125	84.51 130	90.24 141	65.86 135	94.12 57	89.15 243	73.81 128	75.37 142	88.26 178	57.26 147	94.53 182	66.97 200	84.92 129	93.15 136

ZNCC-MVS		85.33 56	85.08 57	86.06 77	93.09 72	65.65 141	93.89 71	93.41 83	73.75 129	79.94 92	94.68 70	60.61 116	98.03 37	82.63 79	93.72 44	94.52 90

V42		76.46 201	74.55 205	82.19 182	79.14 303	67.82 80	90.26 209	89.42 232	73.75 129	68.63 218	81.89 247	51.31 214	94.09 196	71.69 161	64.84 265	84.66 274

v1144		76.73 199	74.88 199	82.27 178	80.23 291	66.60 117	91.68 158	90.21 206	73.69 131	69.06 209	81.89 247	52.73 203	94.40 186	69.21 181	65.23 261	85.80 258

v148		76.19 202	74.47 207	81.36 202	80.05 292	64.44 173	91.75 156	90.23 204	73.68 132	67.13 237	80.84 267	55.92 171	93.86 212	68.95 184	61.73 293	85.76 261

CR-MVSNet		73.79 235	70.82 247	82.70 166	83.15 265	67.96 76	70.25 333	84.00 312	73.67 133	69.97 199	72.41 323	57.82 143	89.48 296	52.99 278	73.13 209	90.64 185

XXY-MVS		77.94 181	76.44 183	82.43 172	82.60 269	64.44 173	92.01 141	91.83 144	73.59 134	70.00 198	85.82 207	54.43 187	94.76 171	69.63 175	68.02 244	88.10 216

#test#		84.98 61	84.74 61	85.72 90	93.75 53	65.01 160	94.09 58	93.19 91	73.55 135	79.22 101	94.93 60	59.04 134	97.67 51	82.66 77	92.21 64	94.49 92

tfpn200view9		78.79 164	77.43 168	82.88 162	92.21 93	64.49 169	92.05 139	96.28 4	73.48 136	71.75 180	88.26 178	60.07 121	95.32 158	45.16 307	77.58 182	88.83 201

thres400		78.68 167	77.43 168	82.43 172	92.21 93	64.49 169	92.05 139	96.28 4	73.48 136	71.75 180	88.26 178	60.07 121	95.32 158	45.16 307	77.58 182	87.48 221

FMVSNet3		77.73 184	76.04 188	82.80 163	91.20 125	68.99 52	91.87 147	91.99 137	73.35 138	67.04 238	83.19 235	56.62 161	92.14 259	59.80 254	69.34 233	87.28 227

GST-MVS		84.63 67	84.29 66	85.66 94	92.82 78	65.27 149	93.04 102	93.13 95	73.20 139	78.89 105	94.18 87	59.41 130	97.85 46	81.45 88	92.48 63	93.86 118

USDC		67.43 284	64.51 283	76.19 283	77.94 316	55.29 303	78.38 320	85.00 302	73.17 140	48.36 331	80.37 274	21.23 345	92.48 250	52.15 279	64.02 274	80.81 315

MP-MVS		85.02 59	84.97 58	85.17 110	92.60 84	64.27 183	93.24 95	92.27 124	73.13 141	79.63 97	94.43 75	61.90 105	97.17 80	85.00 59	92.56 60	94.06 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
xiu_mvs_v1_base_debu		82.16 107	81.12 110	85.26 107	86.42 214	68.72 58	92.59 123	90.44 193	73.12 142	84.20 55	94.36 77	38.04 292	95.73 138	84.12 66	86.81 113	91.33 173

xiu_mvs_v1_base		82.16 107	81.12 110	85.26 107	86.42 214	68.72 58	92.59 123	90.44 193	73.12 142	84.20 55	94.36 77	38.04 292	95.73 138	84.12 66	86.81 113	91.33 173

xiu_mvs_v1_base_debi		82.16 107	81.12 110	85.26 107	86.42 214	68.72 58	92.59 123	90.44 193	73.12 142	84.20 55	94.36 77	38.04 292	95.73 138	84.12 66	86.81 113	91.33 173

D2MVS		73.80 234	72.02 238	79.15 253	79.15 302	62.97 209	88.58 245	90.07 209	72.94 145	59.22 289	78.30 290	42.31 275	92.70 241	65.59 217	72.00 218	81.79 308

BH-RMVSNet		79.46 153	77.65 164	84.89 116	91.68 111	65.66 140	93.55 85	88.09 274	72.93 146	73.37 156	91.12 142	46.20 258	96.12 122	56.28 266	85.61 126	92.91 144

ETH3D-3000-0.1		87.61 22	87.89 19	86.75 52	93.58 57	67.21 97	94.31 50	94.14 54	72.92 147	87.13 26	96.62 14	67.81 44	97.94 39	90.13 18	94.42 32	95.09 68

IS-MVSNet		80.14 138	79.41 138	82.33 176	87.91 192	60.08 259	91.97 144	88.27 270	72.90 148	71.44 184	91.73 138	61.44 108	93.66 215	62.47 239	86.53 120	93.24 133

PS-MVSNAJss		77.26 191	76.31 185	80.13 230	80.64 284	59.16 271	90.63 201	91.06 176	72.80 149	68.58 219	84.57 221	53.55 195	93.96 206	72.97 144	71.96 219	87.27 228

9.14			87.63 23		93.86 48		94.41 48	94.18 52	72.76 150	86.21 33	96.51 16	66.64 54	97.88 45	90.08 19	94.04 37

v1192		75.98 209	73.92 217	82.15 183	79.73 293	66.24 127	91.22 179	89.75 220	72.67 151	68.49 220	81.42 257	49.86 227	94.27 190	67.08 198	65.02 263	85.95 255

Effi-MVS+		83.82 83	82.76 90	86.99 45	89.56 154	69.40 42	91.35 173	86.12 293	72.59 152	83.22 64	92.81 117	59.60 127	96.01 131	81.76 84	87.80 107	95.56 45

UnsupCasMVSNet_eth		65.79 291	63.10 291	73.88 297	70.71 339	50.29 327	81.09 303	89.88 217	72.58 153	49.25 329	74.77 317	32.57 318	87.43 315	55.96 267	41.04 343	83.90 280

1112_ss		80.56 131	79.83 129	82.77 164	88.65 173	60.78 245	92.29 128	88.36 267	72.58 153	72.46 170	94.95 58	65.09 69	93.42 220	66.38 207	77.71 179	94.10 105

test_part1		79.63 148	77.86 162	84.93 114	92.50 86	71.43 15	94.15 55	91.08 175	72.51 155	70.66 187	84.98 215	59.84 123	95.07 163	72.07 157	62.94 279	88.30 214

test_0728_THIRD									72.48 156	90.55 12	96.93 10	76.24 9	99.08 9	91.53 12	94.99 15	96.43 22

cl-mvsnet2		77.94 181	76.78 178	81.42 201	87.57 196	64.93 164	90.67 197	88.86 255	72.45 157	67.63 231	82.68 239	64.07 80	92.91 232	71.79 158	65.30 258	86.44 241

thres600view7		78.00 178	76.66 180	82.03 191	91.93 101	63.69 195	91.30 176	96.33 1	72.43 158	70.46 190	87.89 185	60.31 117	94.92 169	42.64 319	76.64 193	87.48 221

IterMVS-LS		76.49 200	75.18 198	80.43 223	84.49 246	62.74 217	90.64 199	88.80 256	72.40 159	65.16 251	81.72 250	60.98 112	92.27 258	67.74 192	64.65 269	86.29 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		78.97 159	78.22 154	81.25 205	85.33 231	62.73 218	89.53 227	93.21 88	72.39 160	72.14 174	90.13 159	60.99 111	94.72 174	67.73 193	72.49 215	86.29 244

miper_ehance_all_eth		77.60 185	76.44 183	81.09 214	85.70 228	64.41 176	90.65 198	88.64 262	72.31 161	67.37 236	82.52 240	64.77 74	92.64 245	70.67 168	65.30 258	86.24 246

v144192		76.05 207	74.03 215	82.12 186	79.50 297	66.55 119	91.39 169	89.71 226	72.30 162	68.17 222	81.33 259	51.75 210	94.03 203	67.94 190	64.19 271	85.77 259

thres100view900		78.37 173	77.01 175	82.46 171	91.89 104	63.21 205	91.19 182	96.33 1	72.28 163	70.45 191	87.89 185	60.31 117	95.32 158	45.16 307	77.58 182	88.83 201

RRT_MVS		77.38 189	76.59 181	79.77 240	90.91 130	63.61 199	91.15 183	90.91 181	72.28 163	72.06 176	87.28 194	43.92 268	89.04 299	73.32 142	67.47 248	86.67 235

PatchmatchNet		77.46 187	74.63 202	85.96 80	89.55 155	70.35 27	79.97 314	89.55 228	72.23 165	70.94 185	76.91 304	57.03 151	92.79 237	54.27 272	81.17 157	94.74 79
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
HFP-MVS		84.73 64	84.40 65	85.72 90	93.75 53	65.01 160	93.50 88	93.19 91	72.19 166	79.22 101	94.93 60	59.04 134	97.67 51	81.55 86	92.21 64	94.49 92

ACMMPR		84.37 68	84.06 67	85.28 106	93.56 58	64.37 178	93.50 88	93.15 94	72.19 166	78.85 110	94.86 64	56.69 160	97.45 62	81.55 86	92.20 66	94.02 111

1314		80.70 128	78.95 146	85.94 81	87.77 195	67.56 86	87.91 254	92.55 118	72.17 168	67.44 232	93.09 104	50.27 223	97.04 87	71.68 162	87.64 108	93.23 134

region2R		84.36 69	84.03 68	85.36 104	93.54 59	64.31 180	93.43 91	92.95 102	72.16 169	78.86 109	94.84 65	56.97 155	97.53 60	81.38 90	92.11 68	94.24 97

Test_1112_low_res		79.56 150	78.60 149	82.43 172	88.24 185	60.39 254	92.09 136	87.99 276	72.10 170	71.84 178	87.42 191	64.62 75	93.04 224	65.80 214	77.30 188	93.85 119

v1921920		75.63 217	73.49 222	82.06 190	79.38 298	66.35 122	91.07 187	89.48 229	71.98 171	67.99 223	81.22 262	49.16 236	93.90 209	66.56 203	64.56 270	85.92 257

DVP-MVS		89.41 10	89.73 11	88.45 18	96.40 14	69.99 31	96.64 8	94.52 36	71.92 172	90.55 12	96.93 10	73.77 17	99.08 9	91.91 10	94.90 19	96.29 26
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					96.40 14	69.99 31	96.76 6	94.33 48	71.92 172	91.89 6	97.11 7	73.77 17

Fast-Effi-MVS+-dtu		75.04 223	73.37 223	80.07 231	80.86 280	59.52 266	91.20 181	85.38 298	71.90 174	65.20 250	84.84 217	41.46 277	92.97 226	66.50 206	72.96 211	87.73 218

LFMVS		84.34 70	82.73 91	89.18 11	94.76 31	73.25 10	94.99 41	91.89 141	71.90 174	82.16 70	93.49 100	47.98 245	97.05 85	82.55 80	84.82 130	97.25 5

eth_miper_zixun_eth		75.96 210	74.40 208	80.66 220	84.66 242	63.02 208	89.28 231	88.27 270	71.88 176	65.73 246	81.65 251	59.45 128	92.81 235	68.13 188	60.53 302	86.14 248

train_agg		87.21 28	87.42 28	86.60 57	94.18 40	67.28 95	94.16 53	93.51 75	71.87 177	85.52 43	95.33 42	68.19 36	97.27 77	89.09 25	94.90 19	95.25 63

test_8					94.19 39	67.19 98	94.15 55	93.42 82	71.87 177	85.38 45	95.35 41	68.19 36	96.95 97

agg_prior1		87.02 33	87.26 30	86.28 73	94.16 44	66.97 106	94.08 59	93.31 85	71.85 179	84.49 52	95.39 40	68.91 33	96.75 106	88.84 29	94.32 34	95.13 66

MDTV_nov1_ep13			72.61 231		89.06 165	68.48 62	80.33 308	90.11 208	71.84 180	71.81 179	75.92 312	53.01 201	93.92 208	48.04 294	73.38 207

ab-mvs		80.18 137	78.31 152	85.80 87	88.44 178	65.49 147	83.00 292	92.67 111	71.82 181	77.36 123	85.01 214	54.50 183	96.59 109	76.35 127	75.63 198	95.32 53

ACMMP		81.49 117	80.67 117	83.93 142	91.71 110	62.90 214	92.13 133	92.22 130	71.79 182	71.68 182	93.49 100	50.32 221	96.96 96	78.47 112	84.22 141	91.93 165
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
PHI-MVS		86.83 37	86.85 37	86.78 51	93.47 61	65.55 144	95.39 29	95.10 17	71.77 183	85.69 42	96.52 15	62.07 104	98.77 17	86.06 53	95.60 10	96.03 33

TEST9					94.18 40	67.28 95	94.16 53	93.51 75	71.75 184	85.52 43	95.33 42	68.01 39	97.27 77

cl_fuxian		76.83 197	75.47 195	80.93 218	85.02 238	64.18 185	90.39 205	88.11 273	71.66 185	66.65 243	81.64 252	63.58 91	92.56 246	69.31 180	62.86 280	86.04 252

SED-MVS		89.94 7	90.36 7	88.70 13	96.45 11	69.38 43	96.89 4	94.44 40	71.65 186	92.11 3	97.21 5	76.79 7	99.11 4	92.34 6	95.36 12	97.62 2

test_241102_TWO								94.41 43	71.65 186	92.07 5	97.21 5	74.58 14	99.11 4	92.34 6	95.36 12	96.59 13

test_241102_ONE					96.45 11	69.38 43		94.44 40	71.65 186	92.11 3	97.05 8	76.79 7	99.11 4

v8		75.35 219	73.26 224	81.61 198	80.67 283	66.82 110	89.54 226	89.27 236	71.65 186	63.30 269	80.30 276	54.99 180	94.06 199	67.33 197	62.33 285	83.94 279

v1240		75.21 222	72.98 226	81.88 192	79.20 300	66.00 131	90.75 196	89.11 246	71.63 190	67.41 234	81.22 262	47.36 250	93.87 210	65.46 219	64.72 268	85.77 259

SCA		75.82 213	72.76 228	85.01 113	86.63 212	70.08 29	81.06 304	89.19 240	71.60 191	70.01 197	77.09 302	45.53 260	90.25 285	60.43 249	73.27 208	94.68 81

BH-untuned		78.68 167	77.08 173	83.48 156	89.84 148	63.74 192	92.70 116	88.59 263	71.57 192	66.83 241	88.65 171	51.75 210	95.39 156	59.03 257	84.77 131	91.32 176

IterMVS		72.65 248	70.83 246	78.09 263	82.17 271	62.96 210	87.64 259	86.28 290	71.56 193	60.44 283	78.85 288	45.42 262	86.66 318	63.30 232	61.83 290	84.65 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mPP-MVS		82.96 96	82.44 96	84.52 129	92.83 76	62.92 213	92.76 112	91.85 143	71.52 194	75.61 139	94.24 86	53.48 198	96.99 93	78.97 107	90.73 85	93.64 123

test-LLR		80.10 139	79.56 134	81.72 195	86.93 210	61.17 237	92.70 116	91.54 155	71.51 195	75.62 137	86.94 196	53.83 191	92.38 253	72.21 154	84.76 132	91.60 168

test0.0.03 1		72.76 244	72.71 230	72.88 305	80.25 290	47.99 335	91.22 179	89.45 230	71.51 195	62.51 276	87.66 187	53.83 191	85.06 324	50.16 284	67.84 247	85.58 262

ETH3D cwj APD-0.16		87.06 31	87.18 32	86.71 53	91.99 99	67.48 91	92.97 105	94.21 51	71.48 197	85.72 40	96.32 23	68.13 38	98.00 38	89.06 26	94.70 27	94.65 84

PGM-MVS		83.25 90	82.70 92	84.92 115	92.81 80	64.07 186	90.44 202	92.20 131	71.28 198	77.23 125	94.43 75	55.17 178	97.31 72	79.33 103	91.38 78	93.37 127

thisisatest0530		81.15 121	80.07 123	84.39 133	88.26 183	65.63 142	91.40 167	94.62 34	71.27 199	70.93 186	89.18 166	72.47 24	96.04 128	65.62 216	76.89 192	91.49 170

cl-mvsnet_		76.07 204	74.67 200	80.28 226	85.15 234	61.76 230	90.12 212	88.73 258	71.16 200	65.43 248	81.57 254	61.15 109	92.95 227	66.54 204	62.17 286	86.13 250

cl-mvsnet1		76.07 204	74.67 200	80.28 226	85.14 235	61.75 231	90.12 212	88.73 258	71.16 200	65.42 249	81.60 253	61.15 109	92.94 231	66.54 204	62.16 288	86.14 248

dp		75.01 224	72.09 237	83.76 144	89.28 160	66.22 128	79.96 315	89.75 220	71.16 200	67.80 229	77.19 301	51.81 209	92.54 247	50.39 283	71.44 224	92.51 153

CP-MVS		83.71 86	83.40 78	84.65 125	93.14 70	63.84 188	94.59 46	92.28 123	71.03 203	77.41 122	94.92 62	55.21 177	96.19 119	81.32 91	90.70 86	93.91 115

v10		74.77 226	72.54 233	81.46 200	80.33 289	66.71 114	89.15 235	89.08 247	70.94 204	63.08 270	79.86 281	52.52 204	94.04 202	65.70 215	62.17 286	83.64 281

CDPH-MVS		85.71 52	85.46 53	86.46 64	94.75 32	67.19 98	93.89 71	92.83 106	70.90 205	83.09 65	95.28 45	63.62 88	97.36 68	80.63 95	94.18 35	94.84 77

GBi-Net		75.65 215	73.83 218	81.10 211	88.85 168	65.11 156	90.01 216	90.32 196	70.84 206	67.04 238	80.25 277	48.03 242	91.54 273	59.80 254	69.34 233	86.64 236

test1		75.65 215	73.83 218	81.10 211	88.85 168	65.11 156	90.01 216	90.32 196	70.84 206	67.04 238	80.25 277	48.03 242	91.54 273	59.80 254	69.34 233	86.64 236

FMVSNet2		76.07 204	74.01 216	82.26 180	88.85 168	67.66 84	91.33 174	91.61 153	70.84 206	65.98 245	82.25 243	48.03 242	92.00 264	58.46 259	68.73 239	87.10 229

SF-MVS		87.03 32	87.09 33	86.84 47	92.70 82	67.45 92	93.64 81	93.76 63	70.78 209	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

ZD-MVS					96.63 8	65.50 146		93.50 77	70.74 210	85.26 47	95.19 53	64.92 73	97.29 73	87.51 39	93.01 54

HyFIR lowres test		81.03 125	79.56 134	85.43 101	87.81 194	68.11 73	90.18 211	90.01 214	70.65 211	72.95 160	86.06 205	63.61 89	94.50 184	75.01 135	79.75 164	93.67 121

MVP-Stereo		77.12 193	76.23 186	79.79 239	81.72 275	66.34 123	89.29 230	90.88 182	70.56 212	62.01 278	82.88 236	49.34 231	94.13 194	65.55 218	93.80 41	78.88 330
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMP	71.68 10	75.58 218	74.23 211	79.62 244	84.97 239	59.64 263	90.80 194	89.07 248	70.39 213	62.95 271	87.30 193	38.28 289	93.87 210	72.89 145	71.45 223	85.36 267
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		83.25 90	82.95 86	84.17 138	92.25 91	62.88 215	90.91 188	91.86 142	70.30 214	77.12 126	93.96 92	56.75 158	96.28 116	82.04 82	91.34 80	93.34 128
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
tpm2		79.80 146	77.95 159	85.34 105	88.28 182	68.26 69	81.56 300	91.42 161	70.11 215	77.59 121	80.50 272	67.40 46	94.26 192	67.34 196	77.35 187	93.51 125

TR-MVS		78.77 165	77.37 172	82.95 161	90.49 136	60.88 243	93.67 80	90.07 209	70.08 216	74.51 146	91.37 140	45.69 259	95.70 143	60.12 252	80.32 161	92.29 158

CL-MVSNet_2432*1600		69.92 261	68.09 263	75.41 287	73.25 332	55.90 300	90.05 215	89.90 216	69.96 217	61.96 279	76.54 305	51.05 217	87.64 311	49.51 288	50.59 330	82.70 298

PAPM_NR		82.97 95	81.84 102	86.37 69	94.10 46	66.76 113	87.66 258	92.84 105	69.96 217	74.07 152	93.57 98	63.10 97	97.50 61	70.66 169	90.58 88	94.85 76

PCF-MVS	73.15 9	79.29 154	77.63 165	84.29 136	86.06 221	65.96 133	87.03 264	91.10 173	69.86 219	69.79 203	90.64 147	57.54 146	96.59 109	64.37 226	82.29 147	90.32 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
miper_lstm_enhance		73.05 239	71.73 241	77.03 275	83.80 256	58.32 279	81.76 296	88.88 253	69.80 220	61.01 280	78.23 292	57.19 148	87.51 314	65.34 220	59.53 306	85.27 270

MIMVSNet		71.64 251	68.44 260	81.23 206	81.97 274	64.44 173	73.05 330	88.80 256	69.67 221	64.59 255	74.79 316	32.79 316	87.82 309	53.99 273	76.35 195	91.42 172

LPG-MVS_test		75.82 213	74.58 204	79.56 246	84.31 250	59.37 268	90.44 202	89.73 223	69.49 222	64.86 252	88.42 173	38.65 286	94.30 188	72.56 150	72.76 212	85.01 271

LGP-MVS_train				79.56 246	84.31 250	59.37 268		89.73 223	69.49 222	64.86 252	88.42 173	38.65 286	94.30 188	72.56 150	72.76 212	85.01 271

APDe-MVS		87.54 23	87.84 20	86.65 56	96.07 21	66.30 124	94.84 44	93.78 60	69.35 224	88.39 21	96.34 22	67.74 45	97.66 54	90.62 16	93.44 49	96.01 34

tttt0517		79.50 151	78.53 150	82.41 175	87.22 204	61.43 236	89.75 223	94.76 27	69.29 225	67.91 226	88.06 183	72.92 21	95.63 144	62.91 235	73.90 206	90.16 189

Patchmatch-RL test		68.17 276	64.49 284	79.19 250	71.22 337	53.93 309	70.07 335	71.54 345	69.22 226	56.79 304	62.89 342	56.58 162	88.61 300	69.53 177	52.61 325	95.03 73

test_yl		84.28 71	83.16 82	87.64 26	94.52 36	69.24 46	95.78 17	95.09 18	69.19 227	81.09 79	92.88 114	57.00 153	97.44 63	81.11 93	81.76 152	96.23 28

DCV-MVSNet		84.28 71	83.16 82	87.64 26	94.52 36	69.24 46	95.78 17	95.09 18	69.19 227	81.09 79	92.88 114	57.00 153	97.44 63	81.11 93	81.76 152	96.23 28

jajsoiax		73.05 239	71.51 243	77.67 266	77.46 318	54.83 305	88.81 241	90.04 213	69.13 229	62.85 273	83.51 230	31.16 324	92.75 238	70.83 165	69.80 229	85.43 266

DP-MVS Recon		82.73 98	81.65 105	85.98 79	97.31 4	67.06 102	95.15 34	91.99 137	69.08 230	76.50 132	93.89 93	54.48 186	98.20 32	70.76 167	85.66 125	92.69 147

Baseline_NR-MVSNet		73.99 232	72.83 227	77.48 269	80.78 281	59.29 270	91.79 151	84.55 307	68.85 231	68.99 210	80.70 268	56.16 165	92.04 263	62.67 237	60.98 299	81.11 311

CHOSEN 280x420		77.35 190	76.95 177	78.55 257	87.07 207	62.68 219	69.71 336	82.95 320	68.80 232	71.48 183	87.27 195	66.03 59	84.00 329	76.47 125	82.81 146	88.95 200

DPE-MVS		88.77 13	89.21 13	87.45 33	96.26 18	67.56 86	94.17 52	94.15 53	68.77 233	90.74 11	97.27 3	76.09 10	98.49 23	90.58 17	94.91 18	96.30 25
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mvs_tets		72.71 246	71.11 244	77.52 267	77.41 319	54.52 307	88.45 248	89.76 219	68.76 234	62.70 274	83.26 234	29.49 328	92.71 239	70.51 171	69.62 231	85.34 268

MVS		84.66 66	82.86 88	90.06 2	90.93 128	74.56 6	87.91 254	95.54 9	68.55 235	72.35 173	94.71 69	59.78 125	98.90 14	81.29 92	94.69 28	96.74 10

EPP-MVSNet		81.79 115	81.52 106	82.61 169	88.77 172	60.21 257	93.02 104	93.66 70	68.52 236	72.90 161	90.39 153	72.19 26	94.96 166	74.93 136	79.29 168	92.67 148

CSCG		86.87 35	86.26 41	88.72 12	95.05 30	70.79 22	93.83 77	95.33 11	68.48 237	77.63 119	94.35 81	73.04 20	98.45 25	84.92 61	93.71 45	96.92 9

CP-MVSNet		70.50 257	69.91 253	72.26 310	80.71 282	51.00 323	87.23 263	90.30 200	67.84 238	59.64 286	82.69 238	50.23 224	82.30 341	51.28 280	59.28 307	83.46 286

testtj		86.62 40	86.66 39	86.50 63	96.95 5	65.70 139	94.41 48	93.45 79	67.74 239	86.19 34	96.39 20	64.38 77	97.91 42	87.33 43	93.14 53	95.90 38

pmmvs5		73.35 237	71.52 242	78.86 255	78.64 310	60.61 252	91.08 185	86.90 286	67.69 240	63.32 268	83.64 228	44.33 267	90.53 282	62.04 241	66.02 256	85.46 265

pm-mvs1		72.89 242	71.09 245	78.26 261	79.10 304	57.62 286	90.80 194	89.30 235	67.66 241	62.91 272	81.78 249	49.11 237	92.95 227	60.29 251	58.89 309	84.22 277

MDTV_nov1_ep13_2view						59.90 261	80.13 312		67.65 242	72.79 162		54.33 188		59.83 253		92.58 151

pmmvs4		73.92 233	71.81 240	80.25 228	79.17 301	65.24 150	87.43 261	87.26 285	67.64 243	63.46 267	83.91 227	48.96 238	91.53 276	62.94 234	65.49 257	83.96 278

WR-MVS_H		70.59 256	69.94 252	72.53 307	81.03 279	51.43 320	87.35 262	92.03 136	67.38 244	60.23 284	80.70 268	55.84 172	83.45 333	46.33 303	58.58 311	82.72 296

KD-MVS_2432*1600		69.03 268	66.37 270	77.01 276	85.56 229	61.06 240	81.44 301	90.25 202	67.27 245	58.00 298	76.53 306	54.49 184	87.63 312	48.04 294	35.77 348	82.34 302

miper_refine_blended		69.03 268	66.37 270	77.01 276	85.56 229	61.06 240	81.44 301	90.25 202	67.27 245	58.00 298	76.53 306	54.49 184	87.63 312	48.04 294	35.77 348	82.34 302

PS-CasMVS		69.86 263	69.13 257	72.07 313	80.35 288	50.57 325	87.02 265	89.75 220	67.27 245	59.19 290	82.28 242	46.58 254	82.24 342	50.69 282	59.02 308	83.39 288

PEN-MVS		69.46 265	68.56 259	72.17 312	79.27 299	49.71 329	86.90 267	89.24 237	67.24 248	59.08 291	82.51 241	47.23 251	83.54 332	48.42 292	57.12 312	83.25 289

cascas		78.18 176	75.77 192	85.41 102	87.14 206	69.11 48	92.96 106	91.15 171	66.71 249	70.47 189	86.07 204	37.49 298	96.48 114	70.15 172	79.80 163	90.65 184

APD-MVS		85.93 49	85.99 45	85.76 89	95.98 23	65.21 151	93.59 84	92.58 117	66.54 250	86.17 35	95.88 30	63.83 84	97.00 90	86.39 51	92.94 55	95.06 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	70.45 11	78.54 171	75.92 190	86.41 68	85.93 226	71.68 14	92.74 113	92.51 119	66.49 251	64.56 256	91.96 132	43.88 269	98.10 35	54.61 270	90.65 87	89.44 199

DTE-MVSNet		68.46 274	67.33 266	71.87 315	77.94 316	49.00 332	86.16 272	88.58 264	66.36 252	58.19 295	82.21 244	46.36 255	83.87 330	44.97 310	55.17 319	82.73 295

IterMVS-SCA-FT		71.55 252	69.97 251	76.32 282	81.48 276	60.67 250	87.64 259	85.99 294	66.17 253	59.50 287	78.88 287	45.53 260	83.65 331	62.58 238	61.93 289	84.63 276

TransMVSNet (Re)		70.07 260	67.66 264	77.31 273	80.62 285	59.13 272	91.78 153	84.94 303	65.97 254	60.08 285	80.44 273	50.78 218	91.87 265	48.84 290	45.46 337	80.94 313

MVSFormer		83.75 85	82.88 87	86.37 69	89.24 162	71.18 18	89.07 237	90.69 185	65.80 255	87.13 26	94.34 82	64.99 70	92.67 242	72.83 146	91.80 71	95.27 60

test_djsdf		73.76 236	72.56 232	77.39 271	77.00 321	53.93 309	89.07 237	90.69 185	65.80 255	63.92 262	82.03 246	43.14 272	92.67 242	72.83 146	68.53 240	85.57 263

API-MVS		82.28 105	80.53 120	87.54 31	96.13 20	70.59 24	93.63 82	91.04 178	65.72 257	75.45 141	92.83 116	56.11 167	98.89 15	64.10 227	89.75 95	93.15 136

原ACMM1				84.42 132	93.21 67	64.27 183		93.40 84	65.39 258	79.51 98	92.50 120	58.11 141	96.69 108	65.27 221	93.96 38	92.32 157

testgi		64.48 296	62.87 294	69.31 320	71.24 336	40.62 348	85.49 273	79.92 328	65.36 259	54.18 310	83.49 231	23.74 341	84.55 325	41.60 321	60.79 301	82.77 294

QAPM		79.95 143	77.39 171	87.64 26	89.63 152	71.41 16	93.30 94	93.70 67	65.34 260	67.39 235	91.75 137	47.83 246	98.96 12	57.71 261	89.81 92	92.54 152

HPM-MVS_fast		80.25 136	79.55 136	82.33 176	91.55 115	59.95 260	91.32 175	89.16 242	65.23 261	74.71 145	93.07 107	47.81 247	95.74 137	74.87 139	88.23 102	91.31 177

tfpnnormal		70.10 259	67.36 265	78.32 259	83.45 262	60.97 242	88.85 240	92.77 107	64.85 262	60.83 282	78.53 289	43.52 271	93.48 218	31.73 348	61.70 294	80.52 318

SR-MVS		82.81 97	82.58 93	83.50 155	93.35 62	61.16 239	92.23 131	91.28 167	64.48 263	81.27 77	95.28 45	53.71 194	95.86 133	82.87 75	88.77 99	93.49 126

K. test v3		63.09 302	59.61 306	73.53 300	76.26 325	49.38 331	83.27 288	77.15 332	64.35 264	47.77 333	72.32 325	28.73 330	87.79 310	49.93 286	36.69 347	83.41 287

test1172		81.90 113	81.83 103	82.13 185	93.23 64	57.52 288	91.61 161	90.98 180	64.32 265	80.20 90	95.00 56	51.26 215	95.61 146	81.73 85	88.13 104	93.26 132

v7n		71.31 253	68.65 258	79.28 249	76.40 324	60.77 246	86.71 269	89.45 230	64.17 266	58.77 294	78.24 291	44.59 266	93.54 216	57.76 260	61.75 292	83.52 284

FMVSNet1		72.71 246	69.91 253	81.10 211	83.60 260	65.11 156	90.01 216	90.32 196	63.92 267	63.56 266	80.25 277	36.35 307	91.54 273	54.46 271	66.75 252	86.64 236

XVG-OURS		74.25 230	72.46 234	79.63 243	78.45 312	57.59 287	80.33 308	87.39 282	63.86 268	68.76 216	89.62 165	40.50 280	91.72 269	69.00 183	74.25 201	89.58 196

UniMVSNet_ETH3D		72.74 245	70.53 248	79.36 248	78.62 311	56.64 295	85.01 275	89.20 239	63.77 269	64.84 254	84.44 222	34.05 313	91.86 266	63.94 228	70.89 227	89.57 197

114514_t		79.17 156	77.67 163	83.68 149	95.32 26	65.53 145	92.85 111	91.60 154	63.49 270	67.92 225	90.63 149	46.65 253	95.72 142	67.01 199	83.54 142	89.79 193

APD-MVS_3200maxsize		81.64 116	81.32 108	82.59 170	92.36 87	58.74 274	91.39 169	91.01 179	63.35 271	79.72 96	94.62 71	51.82 208	96.14 121	79.71 99	87.93 106	92.89 145

test20.03		63.83 299	62.65 295	67.38 326	70.58 341	39.94 349	86.57 270	84.17 309	63.29 272	51.86 318	77.30 298	37.09 303	82.47 339	38.87 332	54.13 323	79.73 324

XVG-OURS-SEG-HR		74.70 227	73.08 225	79.57 245	78.25 313	57.33 291	80.49 306	87.32 283	63.22 273	68.76 216	90.12 161	44.89 265	91.59 271	70.55 170	74.09 203	89.79 193

ACMM	69.62 13	74.34 228	72.73 229	79.17 251	84.25 252	57.87 282	90.36 206	89.93 215	63.17 274	65.64 247	86.04 206	37.79 296	94.10 195	65.89 212	71.52 222	85.55 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmvs		72.88 243	69.76 255	82.22 181	90.98 127	67.05 103	78.22 322	88.30 268	63.10 275	64.35 261	74.98 315	55.09 179	94.27 190	43.25 313	69.57 232	85.34 268

SixPastTwentyTwo		64.92 293	61.78 300	74.34 295	78.74 308	49.76 328	83.42 287	79.51 330	62.86 276	50.27 325	77.35 297	30.92 326	90.49 283	45.89 305	47.06 335	82.78 293

SR-MVS-dyc-post		81.06 124	80.70 116	82.15 183	92.02 96	58.56 276	90.90 189	90.45 190	62.76 277	78.89 105	94.46 73	51.26 215	95.61 146	78.77 110	86.77 116	92.28 159

RE-MVS-def			80.48 121		92.02 96	58.56 276	90.90 189	90.45 190	62.76 277	78.89 105	94.46 73	49.30 232		78.77 110	86.77 116	92.28 159

TAPA-MVS	70.22 12	74.94 225	73.53 221	79.17 251	90.40 138	52.07 317	89.19 234	89.61 227	62.69 279	70.07 196	92.67 118	48.89 239	94.32 187	38.26 333	79.97 162	91.12 180
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
abl_6		79.82 145	79.20 143	81.70 197	89.85 147	58.34 278	88.47 247	90.07 209	62.56 280	77.71 117	93.08 105	47.65 249	96.78 104	77.94 117	85.45 127	89.99 192

Anonymous202405211		77.96 180	75.33 196	85.87 84	93.73 55	64.52 168	94.85 43	85.36 299	62.52 281	76.11 133	90.18 157	29.43 329	97.29 73	68.51 187	77.24 190	95.81 40

pmmvs-eth3d		65.53 292	62.32 297	75.19 288	69.39 343	59.59 264	82.80 293	83.43 316	62.52 281	51.30 322	72.49 321	32.86 315	87.16 317	55.32 269	50.73 329	78.83 331

AdaColmap		78.94 160	77.00 176	84.76 120	96.34 16	65.86 135	92.66 120	87.97 277	62.18 283	70.56 188	92.37 126	43.53 270	97.35 69	64.50 225	82.86 144	91.05 181

无先验							92.71 115	92.61 116	62.03 284				97.01 88	66.63 201		93.97 112

XVG-ACMP-BASELINE		68.04 277	65.53 276	75.56 286	74.06 331	52.37 315	78.43 319	85.88 295	62.03 284	58.91 293	81.21 264	20.38 347	91.15 279	60.69 248	68.18 242	83.16 291

anonymousdsp		71.14 254	69.37 256	76.45 281	72.95 333	54.71 306	84.19 279	88.88 253	61.92 286	62.15 277	79.77 282	38.14 291	91.44 278	68.90 185	67.45 249	83.21 290

tpm cat1		75.30 220	72.21 236	84.58 128	88.52 174	67.77 81	78.16 323	88.02 275	61.88 287	68.45 221	76.37 308	60.65 114	94.03 203	53.77 275	74.11 202	91.93 165

FMVSNet5		68.04 277	65.66 275	75.18 289	84.43 248	57.89 281	83.54 283	86.26 291	61.83 288	53.64 313	73.30 320	37.15 302	85.08 323	48.99 289	61.77 291	82.56 301

Anonymous20231206		67.53 282	65.78 272	72.79 306	74.95 328	47.59 337	88.23 250	87.32 283	61.75 289	58.07 297	77.29 299	37.79 296	87.29 316	42.91 315	63.71 276	83.48 285

PatchMatch-RL		72.06 249	69.98 250	78.28 260	89.51 156	55.70 301	83.49 284	83.39 318	61.24 290	63.72 265	82.76 237	34.77 311	93.03 225	53.37 277	77.59 181	86.12 251

PLC	68.80 14	75.23 221	73.68 220	79.86 237	92.93 74	58.68 275	90.64 199	88.30 268	60.90 291	64.43 260	90.53 150	42.38 274	94.57 177	56.52 264	76.54 194	86.33 242
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH	63.93 17	68.62 271	64.81 279	80.03 232	85.22 233	63.25 203	87.72 257	84.66 306	60.83 292	51.57 320	79.43 286	27.29 334	94.96 166	41.76 320	64.84 265	81.88 307
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 272	65.41 277	77.96 264	78.69 309	62.93 211	89.86 221	89.17 241	60.55 293	50.27 325	77.73 296	22.60 343	94.06 199	47.18 300	72.65 214	76.88 337

VDDNet		80.50 132	78.26 153	87.21 38	86.19 219	69.79 37	94.48 47	91.31 164	60.42 294	79.34 99	90.91 144	38.48 288	96.56 112	82.16 81	81.05 158	95.27 60

CPTT-MVS		79.59 149	79.16 144	80.89 219	91.54 116	59.80 262	92.10 135	88.54 265	60.42 294	72.96 159	93.28 102	48.27 241	92.80 236	78.89 109	86.50 121	90.06 190

our_test_3		68.29 275	64.69 281	79.11 254	78.92 305	64.85 165	88.40 249	85.06 301	60.32 296	52.68 315	76.12 310	40.81 279	89.80 295	44.25 312	55.65 317	82.67 300

ITE_SJBPF				70.43 318	74.44 329	47.06 339		77.32 331	60.16 297	54.04 311	83.53 229	23.30 342	84.01 328	43.07 314	61.58 296	80.21 323

ppachtmachnet_test		67.72 279	63.70 288	79.77 240	78.92 305	66.04 130	88.68 243	82.90 321	60.11 298	55.45 306	75.96 311	39.19 283	90.55 281	39.53 328	52.55 326	82.71 297

new-patchmatchnet		59.30 313	56.48 314	67.79 323	65.86 347	44.19 342	82.47 294	81.77 322	59.94 299	43.65 344	66.20 338	27.67 333	81.68 344	39.34 329	41.40 342	77.50 336

MVS_0304		68.99 270	67.23 267	74.28 296	80.36 287	52.54 314	87.01 266	86.36 288	59.89 300	66.22 244	73.56 319	24.25 338	88.03 307	57.34 263	70.11 228	82.27 304

新几何1				84.73 121	92.32 88	64.28 182		91.46 160	59.56 301	79.77 95	92.90 112	56.95 156	96.57 111	63.40 231	92.91 56	93.34 128

1121		81.25 120	80.05 124	84.87 118	92.30 89	64.31 180	87.91 254	91.39 162	59.44 302	79.94 92	92.91 111	57.09 149	97.01 88	66.63 201	92.81 58	93.29 131

旧先验2							92.00 143		59.37 303	87.54 25			93.47 219	75.39 130

PM-MVS		59.40 312	56.59 313	67.84 322	63.63 348	41.86 345	76.76 325	63.22 354	59.01 304	51.07 323	72.27 326	11.72 355	83.25 335	61.34 244	50.28 331	78.39 334

LTVRE_ROB	59.60 19	66.27 288	63.54 289	74.45 293	84.00 255	51.55 319	67.08 343	83.53 315	58.78 305	54.94 307	80.31 275	34.54 312	93.23 222	40.64 326	68.03 243	78.58 333
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
testdata				81.34 203	89.02 166	57.72 284		89.84 218	58.65 306	85.32 46	94.09 88	57.03 151	93.28 221	69.34 179	90.56 89	93.03 140

ACMH+	65.35 16	67.65 280	64.55 282	76.96 278	84.59 244	57.10 293	88.08 251	80.79 325	58.59 307	53.00 314	81.09 266	26.63 336	92.95 227	46.51 301	61.69 295	80.82 314

ADS-MVSNet2		66.90 285	63.44 290	77.26 274	88.06 188	60.70 249	68.01 340	75.56 336	57.57 308	64.48 257	69.87 332	38.68 284	84.10 326	40.87 324	67.89 245	86.97 230

ADS-MVSNet		68.54 273	64.38 286	81.03 215	88.06 188	66.90 108	68.01 340	84.02 311	57.57 308	64.48 257	69.87 332	38.68 284	89.21 298	40.87 324	67.89 245	86.97 230

MDA-MVSNet-bldmvs		61.54 307	57.70 311	73.05 303	79.53 296	57.00 294	83.08 291	81.23 323	57.57 308	34.91 350	72.45 322	32.79 316	86.26 321	35.81 337	41.95 341	75.89 339

DIV-MVS_2432*1600		60.87 308	58.60 308	67.68 324	66.13 346	39.93 350	75.63 328	84.70 305	57.32 311	49.57 328	68.45 335	29.55 327	82.87 337	48.09 293	47.94 334	80.25 322

UnsupCasMVSNet_bld		61.60 306	57.71 310	73.29 302	68.73 344	51.64 318	78.61 318	89.05 249	57.20 312	46.11 334	61.96 343	28.70 331	88.60 301	50.08 285	38.90 345	79.63 325

MSDG		69.54 264	65.73 273	80.96 216	85.11 237	63.71 194	84.19 279	83.28 319	56.95 313	54.50 308	84.03 224	31.50 322	96.03 129	42.87 317	69.13 236	83.14 292

F-COLMAP		70.66 255	68.44 260	77.32 272	86.37 217	55.91 299	88.00 252	86.32 289	56.94 314	57.28 303	88.07 182	33.58 314	92.49 249	51.02 281	68.37 241	83.55 282

test222					89.77 149	61.60 233	89.55 225	89.42 232	56.83 315	77.28 124	92.43 124	52.76 202			91.14 83	93.09 138

CNLPA		74.31 229	72.30 235	80.32 224	91.49 118	61.66 232	90.85 192	80.72 326	56.67 316	63.85 264	90.64 147	46.75 252	90.84 280	53.79 274	75.99 197	88.47 210

OurMVSNet-221017-0		64.68 294	62.17 298	72.21 311	76.08 327	47.35 338	80.67 305	81.02 324	56.19 317	51.60 319	79.66 284	27.05 335	88.56 302	53.60 276	53.63 324	80.71 316

YYNet1		63.76 301	60.14 304	74.62 292	78.06 315	60.19 258	83.46 286	83.99 314	56.18 318	39.25 347	71.56 330	37.18 301	83.34 334	42.90 316	48.70 333	80.32 320

MDA-MVSNet_test_wron		63.78 300	60.16 303	74.64 291	78.15 314	60.41 253	83.49 284	84.03 310	56.17 319	39.17 348	71.59 329	37.22 300	83.24 336	42.87 317	48.73 332	80.26 321

OpenMVS_ROB	61.12 18	66.39 287	62.92 293	76.80 280	76.51 323	57.77 283	89.22 232	83.41 317	55.48 320	53.86 312	77.84 295	26.28 337	93.95 207	34.90 340	68.76 238	78.68 332

MIMVSNet1		60.16 311	57.33 312	68.67 321	69.71 342	44.13 343	78.92 317	84.21 308	55.05 321	44.63 341	71.85 327	23.91 340	81.54 345	32.63 346	55.03 320	80.35 319

CVMVSNet		74.04 231	74.27 210	73.33 301	85.33 231	43.94 344	89.53 227	88.39 266	54.33 322	70.37 192	90.13 159	49.17 235	84.05 327	61.83 243	79.36 166	91.99 164

Anonymous20240529		76.84 196	74.15 213	84.88 117	91.02 126	64.95 163	93.84 76	91.09 174	53.57 323	73.00 158	87.42 191	35.91 308	97.32 71	69.14 182	72.41 217	92.36 155

pmmvs6		67.57 281	64.76 280	76.00 285	72.82 335	53.37 311	88.71 242	86.78 287	53.19 324	57.58 302	78.03 294	35.33 310	92.41 252	55.56 268	54.88 321	82.21 305

TinyColmap		60.32 309	56.42 315	72.00 314	78.78 307	53.18 312	78.36 321	75.64 335	52.30 325	41.59 346	75.82 313	14.76 353	88.35 304	35.84 336	54.71 322	74.46 341

test_0402		64.54 295	61.09 301	74.92 290	84.10 254	60.75 247	87.95 253	79.71 329	52.03 326	52.41 316	77.20 300	32.21 320	91.64 270	23.14 351	61.03 298	72.36 343

Anonymous20231211		73.08 238	70.39 249	81.13 209	90.62 135	63.33 202	91.40 167	90.06 212	51.84 327	64.46 259	80.67 270	36.49 306	94.07 198	63.83 229	64.17 272	85.98 254

AllTest		61.66 305	58.06 309	72.46 308	79.57 294	51.42 321	80.17 311	68.61 348	51.25 328	45.88 335	81.23 260	19.86 348	86.58 319	38.98 330	57.01 314	79.39 326

TestCases				72.46 308	79.57 294	51.42 321		68.61 348	51.25 328	45.88 335	81.23 260	19.86 348	86.58 319	38.98 330	57.01 314	79.39 326

PatchT		69.11 267	65.37 278	80.32 224	82.07 273	63.68 196	67.96 342	87.62 280	50.86 330	69.37 204	65.18 339	57.09 149	88.53 303	41.59 322	66.60 253	88.74 204

Anonymous20240521		62.09 304	59.08 307	71.10 316	67.19 345	48.72 333	83.91 281	85.23 300	50.38 331	47.84 332	71.22 331	20.74 346	85.51 322	46.47 302	58.75 310	79.06 329

DP-MVS		69.90 262	66.48 268	80.14 229	95.36 25	62.93 211	89.56 224	76.11 333	50.27 332	57.69 301	85.23 212	39.68 282	95.73 138	33.35 342	71.05 226	81.78 309

gg-mvs-nofinetune		77.18 192	74.31 209	85.80 87	91.42 119	68.36 65	71.78 331	94.72 29	49.61 333	77.12 126	45.92 349	77.41 6	93.98 205	67.62 194	93.16 52	95.05 70

JIA-IIPM		66.06 289	62.45 296	76.88 279	81.42 278	54.45 308	57.49 351	88.67 260	49.36 334	63.86 263	46.86 348	56.06 168	90.25 285	49.53 287	68.83 237	85.95 255

N_pmnet		50.55 318	49.11 321	54.88 333	77.17 320	4.02 366	84.36 278	2.00 365	48.59 335	45.86 337	68.82 334	32.22 319	82.80 338	31.58 349	51.38 328	77.81 335

ANet_high		40.27 322	35.20 325	55.47 332	34.74 362	34.47 355	63.84 346	71.56 344	48.42 336	18.80 355	41.08 353	9.52 358	64.45 356	20.18 352	8.66 359	67.49 347

COLMAP_ROB	57.96 20	62.98 303	59.65 305	72.98 304	81.44 277	53.00 313	83.75 282	75.53 337	48.34 337	48.81 330	81.40 258	24.14 339	90.30 284	32.95 344	60.52 303	75.65 340
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmtry		67.53 282	63.93 287	78.34 258	82.12 272	64.38 177	68.72 337	84.00 312	48.23 338	59.24 288	72.41 323	57.82 143	89.27 297	46.10 304	56.68 316	81.36 310

LS3D		69.17 266	66.40 269	77.50 268	91.92 102	56.12 298	85.12 274	80.37 327	46.96 339	56.50 305	87.51 190	37.25 299	93.71 213	32.52 347	79.40 165	82.68 299

RPSCF		64.24 297	61.98 299	71.01 317	76.10 326	45.00 341	75.83 327	75.94 334	46.94 340	58.96 292	84.59 220	31.40 323	82.00 343	47.76 298	60.33 305	86.04 252

RPMNet		70.42 258	65.68 274	84.63 127	83.15 265	67.96 76	70.25 333	90.45 190	46.83 341	69.97 199	65.10 340	56.48 164	95.30 161	35.79 338	73.13 209	90.64 185

CMPMVS	48.56 21	66.77 286	64.41 285	73.84 298	70.65 340	50.31 326	77.79 324	85.73 297	45.54 342	44.76 340	82.14 245	35.40 309	90.14 291	63.18 233	74.54 200	81.07 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		64.01 298	63.01 292	67.02 327	74.40 330	38.86 353	83.27 288	86.19 292	45.11 343	54.27 309	81.15 265	36.91 305	80.01 346	48.79 291	57.02 313	82.19 306

TDRefinement		55.28 316	51.58 319	66.39 328	59.53 353	46.15 340	76.23 326	72.80 341	44.60 344	42.49 345	76.28 309	15.29 351	82.39 340	33.20 343	43.75 339	70.62 345

Patchmatch-test		65.86 290	60.94 302	80.62 221	83.75 257	58.83 273	58.91 350	75.26 338	44.50 345	50.95 324	77.09 302	58.81 137	87.90 308	35.13 339	64.03 273	95.12 67

LF4IMVS		54.01 317	52.12 318	59.69 330	62.41 351	39.91 351	68.59 338	68.28 350	42.96 346	44.55 342	75.18 314	14.09 354	68.39 351	41.36 323	51.68 327	70.78 344

DSMNet-mixed		56.78 314	54.44 317	63.79 329	63.21 349	29.44 357	64.43 345	64.10 353	42.12 347	51.32 321	71.60 328	31.76 321	75.04 348	36.23 335	65.20 262	86.87 233

pmmvs3		55.51 315	51.50 320	67.53 325	57.90 354	50.93 324	80.37 307	73.66 340	40.63 348	44.15 343	64.75 341	16.30 350	78.97 347	44.77 311	40.98 344	72.69 342

new_pmnet		49.31 319	46.44 322	57.93 331	62.84 350	40.74 347	68.47 339	62.96 355	36.48 349	35.09 349	57.81 345	14.97 352	72.18 349	32.86 345	46.44 336	60.88 349

MVS-HIRNet		60.25 310	55.55 316	74.35 294	84.37 249	56.57 296	71.64 332	74.11 339	34.44 350	45.54 339	42.24 352	31.11 325	89.81 293	40.36 327	76.10 196	76.67 338

DeepMVS_CX				34.71 340	51.45 355	24.73 361		28.48 364	31.46 351	17.49 356	52.75 346	5.80 361	42.60 360	18.18 353	19.42 353	36.81 352

FPMVS		45.64 320	43.10 323	53.23 335	51.42 356	36.46 354	64.97 344	71.91 343	29.13 352	27.53 351	61.55 344	9.83 357	65.01 355	16.00 354	55.58 318	58.22 350

PMMVS2		37.93 323	33.61 326	50.92 336	46.31 358	24.76 360	60.55 349	50.05 357	28.94 353	20.93 353	47.59 347	4.41 364	65.13 354	25.14 350	18.55 354	62.87 348

LCM-MVSNet		40.54 321	35.79 324	54.76 334	36.92 361	30.81 356	51.41 352	69.02 347	22.07 354	24.63 352	45.37 350	4.56 363	65.81 353	33.67 341	34.50 350	67.67 346

PMVS	26.43 22	31.84 325	28.16 328	42.89 338	25.87 364	27.58 358	50.92 353	49.78 358	21.37 355	14.17 358	40.81 354	2.01 365	66.62 352	9.61 357	38.88 346	34.49 353
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.91 324	31.44 327	45.30 337	70.99 338	39.64 352	19.85 358	72.56 342	20.10 356	16.16 357	21.47 358	5.08 362	71.16 350	13.07 355	43.70 340	25.08 354
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		24.61 326	24.00 330	26.45 341	43.74 359	18.44 363	60.86 347	39.66 359	15.11 357	9.53 360	22.10 357	6.52 360	46.94 358	8.31 358	10.14 356	13.98 356

EMVS		23.76 328	23.20 332	25.46 342	41.52 360	16.90 364	60.56 348	38.79 362	14.62 358	8.99 361	20.24 360	7.35 359	45.82 359	7.25 359	9.46 357	13.64 357

MVE	24.84 23	24.35 327	19.77 333	38.09 339	34.56 363	26.92 359	26.57 356	38.87 361	11.73 359	11.37 359	27.44 355	1.37 366	50.42 357	11.41 356	14.60 355	36.93 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 331	10.95 334	12.33 344	48.05 357	19.89 362	25.89 357	1.92 366	3.58 360	3.12 362	1.37 362	0.64 367	15.77 362	6.23 360	7.77 360	1.35 358

tmp_tt		22.26 329	23.75 331	17.80 343	5.23 365	12.06 365	35.26 355	39.48 360	2.82 361	18.94 354	44.20 351	22.23 344	24.64 361	36.30 334	9.31 358	16.69 355

testmvs		7.23 333	9.62 336	0.06 346	0.04 366	0.02 368	84.98 276	0.02 367	0.03 362	0.18 363	1.21 363	0.01 369	0.02 363	0.14 361	0.01 361	0.13 360

test123		6.92 334	9.21 337	0.08 345	0.03 367	0.05 367	81.65 299	0.01 368	0.02 363	0.14 364	0.85 364	0.03 368	0.02 363	0.12 362	0.00 362	0.16 359

uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 362	0.00 361

cdsmvs_eth3d_5k		19.86 330	26.47 329	0.00 347	0.00 368	0.00 369	0.00 359	93.45 79	0.00 364	0.00 365	95.27 47	49.56 229	0.00 365	0.00 363	0.00 362	0.00 361

pcd_1.5k_mvsjas		4.46 335	5.95 338	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	53.55 195	0.00 365	0.00 363	0.00 362	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 362	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 362	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 362	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 362	0.00 361

ab-mvs-re		7.91 332	10.55 335	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	94.95 58	0.00 370	0.00 365	0.00 363	0.00 362	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 362	0.00 361

OPU-MVS				89.97 3	97.52 3	73.15 11	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

test_0728_SECOND				88.70 13	96.45 11	70.43 26	96.64 8	94.37 47					99.15 2	91.91 10	94.90 19	96.51 18

GSMVS																94.68 81

test_part2					96.29 17	68.16 72				90.78 10

sam_mvs1												57.85 142				94.68 81

sam_mvs												54.91 181

ambc				69.61 319	61.38 352	41.35 346	49.07 354	85.86 296		50.18 327	66.40 337	10.16 356	88.14 306	45.73 306	44.20 338	79.32 328

MTGPA								92.23 126

test_post1							78.95 316				20.70 359	53.05 200	91.50 277	60.43 249

test_post											23.01 356	56.49 163	92.67 242

patchmatchnet-post											67.62 336	57.62 145	90.25 285

GG-mvs-BLEND				86.53 62	91.91 103	69.67 41	75.02 329	94.75 28		78.67 112	90.85 145	77.91 5	94.56 179	72.25 153	93.74 43	95.36 50

MTMP							93.77 78	32.52 363

test9_res														89.41 20	94.96 16	95.29 56

agg_prior2														86.41 50	94.75 26	95.33 51

agg_prior					94.16 44	66.97 106		93.31 85		84.49 52			96.75 106

test_prior4						67.18 100	93.92 69

test_prior				86.42 66	94.71 33	67.35 93		93.10 97					96.84 102			95.05 70

新几何2							91.41 165

旧先验1					91.94 100	60.74 248		91.50 158			94.36 77	65.23 67			91.84 70	94.55 86

原ACMM2							92.01 141

testdata2													96.09 123	61.26 245

segment_acmp												65.94 60

test12				87.09 42	94.60 35	68.86 54		92.91 103		82.67 68		65.44 66	97.55 59		93.69 46	94.84 77

plane_prior7					86.94 208	61.51 234

plane_prior6					87.23 203	62.32 223						50.66 219

plane_prior5								91.31 164					95.55 152	76.74 122	78.53 175	88.39 211

plane_prior4											89.14 168

plane_prior1					87.15 205

n2								0.00 369

nn								0.00 369

door-mid								66.01 352

lessismore_v0				73.72 299	72.93 334	47.83 336		61.72 356		45.86 337	73.76 318	28.63 332	89.81 293	47.75 299	31.37 351	83.53 283

test11								93.01 99

door								66.57 351

HQP5-MVS						63.66 197

BP-MVS														77.63 119

HQP4-MVS										74.18 148			95.61 146			88.63 205

HQP3-MVS								91.70 150							78.90 170

HQP2-MVS												51.63 212

NP-MVS					87.41 200	63.04 207					90.30 155

ACMMP++_ref															71.63 220

ACMMP++															69.72 230

Test By Simon												54.21 189

High-resolution multi-view benchmark