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 41	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 43	94.91 65	74.11 16	98.91 13	87.26 47	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 75	93.30 63	66.05 130	95.26 31	94.84 22	87.09 3	88.06 22	94.53 73	66.79 53	97.34 70	83.89 72	91.68 75	95.29 55
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 40	96.53 12	93.78 60	86.89 4	89.68 15	95.78 31	65.94 60	99.10 7	92.99 2	93.91 41	96.58 15

DeepPCF-MVS	81.17 1	89.72 8	91.38 3	84.72 124	93.00 73	58.16 283	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 79	83.52 73	85.81 87	90.30 141	66.82 112	91.87 150	89.01 253	85.27 6	86.09 38	93.74 97	47.71 252	96.98 94	77.90 121	89.78 96	93.65 124

CLD-MVS		82.73 100	82.35 100	83.86 144	87.90 196	67.65 87	95.45 28	92.18 134	85.06 7	72.58 169	92.27 130	52.46 209	95.78 136	84.18 68	79.06 172	88.16 219
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 13	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 51	96.38 14	94.64 33	84.42 9	86.74 31	96.20 25	66.56 56	98.76 18	89.03 30	94.56 29	95.92 37

PS-MVSNAJ		88.14 14	87.61 26	89.71 7	92.06 95	76.72 1	95.75 21	93.26 87	83.86 10	89.55 16	96.06 28	53.55 199	97.89 44	91.10 13	93.31 52	94.54 87

DeepC-MVS_fast	79.48 2	87.95 18	88.00 19	87.79 24	95.86 24	68.32 68	95.74 22	94.11 55	83.82 11	83.49 65	96.19 26	64.53 78	98.44 26	83.42 75	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 31	89.55 10	91.41 120	76.43 3	95.74 22	93.12 96	83.53 12	89.55 16	95.95 29	53.45 203	97.68 50	91.07 14	92.62 61	94.54 87

TSAR-MVS + MP.		88.11 16	88.64 14	86.54 62	91.73 109	68.04 76	90.36 209	93.55 74	82.89 13	91.29 9	92.89 115	72.27 25	96.03 128	87.99 36	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	89.68 152	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 76	97.64 2	97.94 1

WTY-MVS		86.32 44	85.81 49	87.85 22	92.82 78	69.37 46	95.20 33	95.25 12	82.71 15	81.91 74	94.73 69	67.93 42	97.63 56	79.55 104	82.25 150	96.54 16

lupinMVS		87.74 21	87.77 23	87.63 30	89.24 164	71.18 18	96.57 10	92.90 104	82.70 16	87.13 26	95.27 48	64.99 72	95.80 135	89.34 23	91.80 73	95.93 36

HPM-MVS++		89.37 11	89.95 10	87.64 26	95.10 29	68.23 73	95.24 32	94.49 38	82.43 17	88.90 20	96.35 21	71.89 28	98.63 20	88.76 32	96.40 4	96.06 31

PVSNet_Blended		86.73 41	86.86 38	86.31 73	93.76 51	67.53 90	96.33 16	93.61 71	82.34 18	81.00 85	93.08 107	63.19 97	97.29 73	87.08 48	91.38 80	94.13 104

MSP-MVS		90.38 3	91.87 1	85.88 84	92.83 76	64.03 188	93.06 102	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 82	82.80 91	87.41 34	92.90 75	70.07 31	89.12 240	94.42 42	82.15 20	77.64 121	91.77 137	70.81 30	96.22 118	65.03 225	81.36 158	95.94 35

Regformer-1		87.24 29	87.60 27	86.15 78	95.14 27	65.83 138	93.95 68	95.12 15	82.11 21	84.25 56	95.73 33	67.88 43	98.35 29	85.60 58	88.64 102	94.26 95

PAPM		85.89 51	85.46 55	87.18 39	88.20 189	72.42 12	92.41 129	92.77 107	82.11 21	80.34 91	93.07 109	68.27 35	95.02 166	78.39 116	93.59 49	94.09 106

jason		86.40 43	86.17 44	87.11 41	86.16 224	70.54 25	95.71 25	92.19 133	82.00 23	84.58 53	94.34 84	61.86 107	95.53 156	87.76 38	90.89 86	95.27 59
jason: jason.
baseline1		81.84 116	81.03 116	84.28 138	91.60 112	66.62 118	91.08 188	91.66 153	81.87 24	74.86 147	91.67 141	69.98 32	94.92 171	71.76 163	64.75 270	91.29 182

CHOSEN 1792x2688		84.98 63	83.45 76	89.57 9	89.94 147	75.14 5	92.07 141	92.32 123	81.87 24	75.68 139	88.27 181	60.18 120	98.60 21	80.46 100	90.27 93	94.96 73

Regformer-2		87.00 36	87.43 29	85.71 93	95.14 27	64.73 167	93.95 68	94.95 20	81.69 26	84.03 61	95.73 33	67.35 47	98.19 33	85.40 60	88.64 102	94.20 98

hse-mvs3		83.01 96	82.56 96	84.35 135	89.34 160	62.02 229	92.72 116	93.76 63	81.45 27	82.73 69	92.25 131	60.11 121	97.13 83	87.69 39	62.96 281	93.91 116

hse-mvs2		81.12 126	81.11 115	81.16 211	86.52 217	57.48 292	89.40 233	91.16 172	81.45 27	82.73 69	90.49 154	60.11 121	94.58 179	87.69 39	60.41 308	91.41 176

ET-MVSNet_ETH3D		84.01 80	83.15 86	86.58 60	90.78 133	70.89 21	94.74 47	94.62 34	81.44 29	58.19 299	93.64 98	73.64 19	92.35 260	82.66 80	78.66 177	96.50 19

CS-MVS		87.54 24	87.81 22	86.74 53	90.46 137	70.23 29	96.34 15	92.31 124	81.40 30	86.14 37	95.17 55	65.49 67	95.92 132	89.09 26	93.91 41	94.06 109

CS-MVS-test		87.65 22	88.14 18	86.17 76	90.76 134	68.37 66	96.57 10	92.56 118	81.30 31	86.75 30	95.45 40	65.85 62	95.93 131	90.06 20	93.92 40	94.23 97

casdiffmvs		85.37 56	84.87 62	86.84 47	88.25 187	69.07 50	93.04 104	91.76 147	81.27 32	80.84 87	92.07 133	64.23 81	96.06 126	84.98 63	87.43 112	95.39 48

ETV-MVS		86.01 49	86.11 45	85.70 94	90.21 143	67.02 107	93.43 93	91.92 141	81.21 33	84.13 60	94.07 92	60.93 114	95.63 145	89.28 24	89.81 94	94.46 93

Regformer-3		85.80 52	85.92 47	85.46 100	94.17 42	65.09 160	92.95 109	95.11 16	81.13 34	81.68 76	95.04 56	65.82 63	98.32 30	83.02 77	84.36 136	92.97 144

DeepC-MVS	77.85 3	85.52 54	85.24 57	86.37 70	88.80 174	66.64 117	92.15 135	93.68 68	81.07 35	76.91 132	93.64 98	62.59 102	98.44 26	85.50 59	92.84 59	94.03 111
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 62	84.44 66	86.71 54	88.33 184	68.73 58	90.24 213	91.82 146	81.05 36	81.18 81	92.50 122	63.69 89	96.08 125	84.45 67	86.71 120	95.32 53

IU-MVS					96.46 10	69.91 36		95.18 13	80.75 37	95.28 1				92.34 6	95.36 12	96.47 20

Regformer-4		85.45 55	85.69 52	84.73 122	94.17 42	63.23 205	92.95 109	94.83 23	80.66 38	81.29 79	95.04 56	65.12 70	98.08 36	82.74 79	84.36 136	92.88 148

diffmvs		84.28 73	83.83 71	85.61 96	87.40 204	68.02 77	90.88 194	89.24 240	80.54 39	81.64 77	92.52 121	59.83 127	94.52 186	87.32 46	85.11 130	94.29 94

VPNet		78.82 166	77.53 170	82.70 169	84.52 249	66.44 122	93.93 70	92.23 127	80.46 40	72.60 168	88.38 179	49.18 238	93.13 227	72.47 155	63.97 278	88.55 211

canonicalmvs		86.85 38	86.25 43	88.66 15	91.80 108	71.92 13	93.54 88	91.71 150	80.26 41	87.55 24	95.25 50	63.59 92	96.93 100	88.18 35	84.34 138	97.11 6

MVSTER		82.47 104	82.05 101	83.74 147	92.68 83	69.01 52	91.90 149	93.21 88	79.83 42	72.14 177	85.71 213	74.72 13	94.72 176	75.72 131	72.49 218	87.50 224

HQP-NCC					87.54 200		94.06 62		79.80 43	74.18 151

ACMP_Plane					87.54 200		94.06 62		79.80 43	74.18 151

HQP-MVS		81.14 124	80.64 121	82.64 171	87.54 200	63.66 198	94.06 62	91.70 151	79.80 43	74.18 151	90.30 158	51.63 216	95.61 147	77.63 122	78.90 173	88.63 209

baseline2		83.68 89	83.42 79	84.48 132	87.37 205	66.00 132	90.06 217	95.93 7	79.71 46	69.08 212	90.39 156	77.92 4	96.28 116	78.91 111	81.38 157	91.16 183

EI-MVSNet-Vis-set		83.77 86	83.67 72	84.06 141	92.79 81	63.56 201	91.76 157	94.81 25	79.65 47	77.87 118	94.09 90	63.35 95	97.90 43	79.35 105	79.36 169	90.74 187

EIA-MVS		84.84 65	84.88 61	84.69 125	91.30 121	62.36 224	93.85 75	92.04 136	79.45 48	79.33 103	94.28 87	62.42 103	96.35 115	80.05 101	91.25 83	95.38 49

plane_prior						62.42 222	93.85 75		79.38 49						78.80 175

alignmvs		87.28 28	86.97 36	88.24 20	91.30 121	71.14 20	95.61 26	93.56 73	79.30 50	87.07 29	95.25 50	68.43 34	96.93 100	87.87 37	84.33 139	96.65 11

TESTMET0.1,1		82.41 105	81.98 103	83.72 150	88.08 190	63.74 193	92.70 118	93.77 62	79.30 50	77.61 123	87.57 193	58.19 143	94.08 200	73.91 144	86.68 121	93.33 132

EI-MVSNet-UG-set		83.14 94	82.96 87	83.67 152	92.28 90	63.19 208	91.38 174	94.68 31	79.22 52	76.60 133	93.75 96	62.64 101	97.76 49	78.07 119	78.01 180	90.05 195

PVSNet	73.49 8	80.05 143	78.63 151	84.31 136	90.92 128	64.97 163	92.47 128	91.05 180	79.18 53	72.43 174	90.51 153	37.05 308	94.06 202	68.06 192	86.00 125	93.90 118

HY-MVS	76.49 5	84.28 73	83.36 82	87.02 44	92.22 92	67.74 84	84.65 281	94.50 37	79.15 54	82.23 72	87.93 188	66.88 51	96.94 98	80.53 99	82.20 151	96.39 24

PVSNet_BlendedMVS		83.38 91	83.43 77	83.22 161	93.76 51	67.53 90	94.06 62	93.61 71	79.13 55	81.00 85	85.14 217	63.19 97	97.29 73	87.08 48	73.91 208	84.83 277

plane_prior3						61.95 231			79.09 56	72.53 170

MVS_111021_HR		86.19 47	85.80 50	87.37 35	93.17 69	69.79 38	93.99 66	93.76 63	79.08 57	78.88 111	93.99 93	62.25 104	98.15 34	85.93 57	91.15 84	94.15 103

MSLP-MVS++		86.27 45	85.91 48	87.35 36	92.01 98	68.97 54	95.04 41	92.70 109	79.04 58	81.50 78	96.50 17	58.98 139	96.78 104	83.49 74	93.93 39	96.29 26

IB-MVS	77.80 4	82.18 108	80.46 125	87.35 36	89.14 166	70.28 28	95.59 27	95.17 14	78.85 59	70.19 199	85.82 211	70.66 31	97.67 51	72.19 159	66.52 257	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	82.69 103	80.82 117	88.31 19	89.57 155	71.26 17	92.60 123	94.39 46	78.84 60	67.89 231	92.48 125	48.42 244	98.52 22	68.80 189	94.40 33	95.15 64

HQP_MVS		80.34 138	79.75 133	82.12 189	86.94 211	62.42 222	93.13 100	91.31 165	78.81 61	72.53 170	89.14 172	50.66 223	95.55 153	76.74 125	78.53 178	88.39 215

plane_prior2							93.13 100		78.81 61

MG-MVS		87.11 32	86.27 41	89.62 8	97.79 1	76.27 4	94.96 43	94.49 38	78.74 63	83.87 64	92.94 112	64.34 80	96.94 98	75.19 134	94.09 36	95.66 42

gm-plane-assit					88.42 182	67.04 106			78.62 64		91.83 136		97.37 67	76.57 127

DROMVSNet		85.15 59	85.60 54	83.80 145	89.58 154	63.21 206	94.76 46	91.26 169	78.47 65	83.91 63	94.36 78	60.06 125	95.55 153	86.16 54	91.82 72	92.64 152

VNet		86.20 46	85.65 53	87.84 23	93.92 47	69.99 32	95.73 24	95.94 6	78.43 66	86.00 39	93.07 109	58.22 142	97.00 90	85.22 61	84.33 139	96.52 17

tpm		78.58 174	77.03 178	83.22 161	85.94 229	64.56 168	83.21 294	91.14 175	78.31 67	73.67 158	79.68 287	64.01 83	92.09 266	66.07 214	71.26 228	93.03 142

xxxxxxxxxxxxxcwj		87.14 31	87.19 33	86.99 45	93.84 49	67.89 80	95.05 39	84.72 308	78.19 68	86.25 32	96.44 18	66.98 49	97.79 47	88.68 33	94.56 29	95.28 57

save fliter					93.84 49	67.89 80	95.05 39	92.66 112	78.19 68

TSAR-MVS + GP.		87.96 17	88.37 16	86.70 56	93.51 60	65.32 149	95.15 35	93.84 59	78.17 70	85.93 40	94.80 68	75.80 11	98.21 31	89.38 22	88.78 100	96.59 13

FIs		79.47 155	79.41 141	79.67 246	85.95 227	59.40 270	91.68 161	93.94 57	78.06 71	68.96 215	88.28 180	66.61 55	91.77 272	66.20 213	74.99 202	87.82 221

sss		82.71 102	82.38 99	83.73 149	89.25 163	59.58 268	92.24 133	94.89 21	77.96 72	79.86 97	92.38 127	56.70 162	97.05 85	77.26 124	80.86 162	94.55 85

PMMVS		81.98 114	82.04 102	81.78 196	89.76 151	56.17 301	91.13 187	90.69 188	77.96 72	80.09 94	93.57 100	46.33 260	94.99 167	81.41 92	87.46 111	94.17 101

MVS_Test		84.16 78	83.20 83	87.05 43	91.56 114	69.82 37	89.99 222	92.05 135	77.77 74	82.84 68	86.57 203	63.93 85	96.09 123	74.91 140	89.18 98	95.25 62

SteuartSystems-ACMMP		86.82 40	86.90 37	86.58 60	90.42 138	66.38 123	96.09 17	93.87 58	77.73 75	84.01 62	95.66 35	63.39 94	97.94 39	87.40 44	93.55 50	95.42 47
Skip Steuart: Steuart Systems R&D Blog.
EPNet_dtu		78.80 167	79.26 145	77.43 274	88.06 191	49.71 333	91.96 148	91.95 140	77.67 76	76.56 134	91.28 143	58.51 141	90.20 294	56.37 269	80.95 161	92.39 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpmrst		80.57 133	79.14 148	84.84 120	90.10 144	68.28 70	81.70 302	89.72 228	77.63 77	75.96 137	79.54 289	64.94 74	92.71 243	75.43 132	77.28 192	93.55 126

testdata1							89.21 237		77.55 78

UniMVSNet_NR-MVSNet		78.15 181	77.55 169	79.98 237	84.46 251	60.26 258	92.25 132	93.20 90	77.50 79	68.88 216	86.61 202	66.10 58	92.13 264	66.38 210	62.55 285	87.54 223

UA-Net		80.02 144	79.65 134	81.11 214	89.33 161	57.72 287	86.33 275	89.00 254	77.44 80	81.01 84	89.15 171	59.33 134	95.90 133	61.01 250	84.28 141	89.73 199

PVSNet_Blended_VisFu		83.97 81	83.50 74	85.39 104	90.02 145	66.59 120	93.77 80	91.73 148	77.43 81	77.08 131	89.81 166	63.77 88	96.97 95	79.67 103	88.21 105	92.60 153

ETH3 D test6400		90.27 5	90.44 6	89.75 6	96.82 6	74.33 7	95.89 18	94.80 26	77.13 82	89.13 18	97.38 2	74.49 15	98.48 24	92.32 9	95.98 6	96.46 21

NR-MVSNet		76.05 211	74.59 207	80.44 226	82.96 271	62.18 228	90.83 196	91.73 148	77.12 83	60.96 285	86.35 204	59.28 135	91.80 271	60.74 251	61.34 300	87.35 229

RRT_test8_iter05		80.61 132	79.62 135	83.60 154	91.87 107	66.90 110	93.42 95	93.68 68	77.09 84	68.83 218	85.63 214	66.82 52	95.42 157	76.46 129	62.74 284	88.48 212

FC-MVSNet-test		77.99 183	78.08 159	77.70 269	84.89 244	55.51 306	90.27 211	93.75 66	76.87 85	66.80 246	87.59 192	65.71 65	90.23 293	62.89 240	73.94 207	87.37 228

SD-MVS		87.49 26	87.49 28	87.50 32	93.60 56	68.82 57	93.90 72	92.63 115	76.86 86	87.90 23	95.76 32	66.17 57	97.63 56	89.06 28	91.48 79	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		79.87 147	78.68 150	83.45 159	89.96 146	61.51 237	92.13 136	90.79 186	76.83 87	78.85 113	86.33 206	38.16 294	96.17 120	67.93 194	87.17 113	92.67 150
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		82.02 113	81.52 108	83.51 156	88.42 182	62.88 217	89.77 225	88.93 255	76.78 88	75.55 143	93.10 105	50.31 226	95.38 159	83.82 73	87.02 114	92.26 165

TranMVSNet+NR-MVSNet		75.86 216	74.52 210	79.89 240	82.44 274	60.64 254	91.37 175	91.37 164	76.63 89	67.65 234	86.21 207	52.37 210	91.55 276	61.84 246	60.81 303	87.48 225

PAPR		85.15 59	84.47 64	87.18 39	96.02 22	68.29 69	91.85 152	93.00 101	76.59 90	79.03 107	95.00 58	61.59 108	97.61 58	78.16 118	89.00 99	95.63 43

UniMVSNet (Re)		77.58 190	76.78 182	79.98 237	84.11 257	60.80 247	91.76 157	93.17 93	76.56 91	69.93 205	84.78 222	63.32 96	92.36 259	64.89 226	62.51 287	86.78 238

DU-MVS		76.86 198	75.84 195	79.91 239	82.96 271	60.26 258	91.26 180	91.54 156	76.46 92	68.88 216	86.35 204	56.16 168	92.13 264	66.38 210	62.55 285	87.35 229

OPM-MVS		79.00 161	78.09 158	81.73 197	83.52 265	63.83 190	91.64 163	90.30 203	76.36 93	71.97 180	89.93 165	46.30 261	95.17 164	75.10 135	77.70 183	86.19 251

WR-MVS		76.76 202	75.74 197	79.82 242	84.60 247	62.27 227	92.60 123	92.51 120	76.06 94	67.87 232	85.34 215	56.76 160	90.24 292	62.20 244	63.69 280	86.94 236

GA-MVS		78.33 179	76.23 190	84.65 126	83.65 263	66.30 126	91.44 167	90.14 210	76.01 95	70.32 197	84.02 229	42.50 277	94.72 176	70.98 167	77.00 194	92.94 145

PVSNet_0	68.08 15	71.81 254	68.32 266	82.27 181	84.68 245	62.31 226	88.68 247	90.31 202	75.84 96	57.93 304	80.65 275	37.85 299	94.19 196	69.94 176	29.05 357	90.31 192

CDS-MVSNet		81.43 120	80.74 118	83.52 155	86.26 222	64.45 173	92.09 139	90.65 191	75.83 97	73.95 157	89.81 166	63.97 84	92.91 236	71.27 166	82.82 147	93.20 137
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CostFormer		82.33 106	81.15 111	85.86 86	89.01 169	68.46 64	82.39 299	93.01 99	75.59 98	80.25 92	81.57 258	72.03 27	94.96 168	79.06 109	77.48 189	94.16 102

nrg030		80.93 129	79.86 131	84.13 140	83.69 262	68.83 56	93.23 98	91.20 170	75.55 99	75.06 146	88.22 185	63.04 100	94.74 175	81.88 86	66.88 254	88.82 207

VDD-MVS		83.06 95	81.81 106	86.81 49	90.86 131	67.70 85	95.40 29	91.50 159	75.46 100	81.78 75	92.34 129	40.09 285	97.13 83	86.85 51	82.04 152	95.60 44

Effi-MVS+-dtu		76.14 207	75.28 201	78.72 260	83.22 267	55.17 308	89.87 223	87.78 281	75.42 101	67.98 228	81.43 260	45.08 267	92.52 252	75.08 136	71.63 223	88.48 212

mvs-test1		78.74 170	77.95 162	81.14 212	83.22 267	57.13 296	93.96 67	87.78 281	75.42 101	72.68 166	90.80 148	45.08 267	94.54 184	75.08 136	77.49 188	91.74 170

test_prior3		87.38 27	87.70 24	86.42 67	94.71 33	67.35 95	95.10 37	93.10 97	75.40 103	85.25 50	95.61 37	67.94 40	96.84 102	87.47 42	94.77 23	95.05 69

test_prior2							95.10 37		75.40 103	85.25 50	95.61 37	67.94 40		87.47 42	94.77 23

zzz-MVS		84.73 66	84.47 64	85.50 98	91.89 104	65.16 154	91.55 165	92.23 127	75.32 105	80.53 89	95.21 52	56.06 171	97.16 81	84.86 65	92.55 63	94.18 99

MTAPA		83.91 83	83.38 81	85.50 98	91.89 104	65.16 154	81.75 301	92.23 127	75.32 105	80.53 89	95.21 52	56.06 171	97.16 81	84.86 65	92.55 63	94.18 99

EPMVS		78.49 176	75.98 193	86.02 80	91.21 123	69.68 41	80.23 314	91.20 170	75.25 107	72.48 172	78.11 297	54.65 185	93.69 218	57.66 266	83.04 145	94.69 79

miper_enhance_ethall		78.86 165	77.97 161	81.54 202	88.00 194	65.17 153	91.41 168	89.15 246	75.19 108	68.79 219	83.98 230	67.17 48	92.82 238	72.73 151	65.30 261	86.62 244

v2v482		77.42 192	75.65 198	82.73 168	80.38 290	67.13 103	91.85 152	90.23 207	75.09 109	69.37 208	83.39 237	53.79 197	94.44 188	71.77 162	65.00 267	86.63 243

VPA-MVSNet		79.03 160	78.00 160	82.11 192	85.95 227	64.48 172	93.22 99	94.66 32	75.05 110	74.04 156	84.95 220	52.17 211	93.52 221	74.90 141	67.04 253	88.32 217

ACMMP_NAP		86.05 48	85.80 50	86.80 50	91.58 113	67.53 90	91.79 154	93.49 78	74.93 111	84.61 52	95.30 45	59.42 132	97.92 41	86.13 55	94.92 17	94.94 74

thres200		79.66 150	78.33 154	83.66 153	92.54 85	65.82 139	93.06 102	96.31 3	74.90 112	73.30 160	88.66 174	59.67 129	95.61 147	47.84 301	78.67 176	89.56 202

TAMVS		80.37 137	79.45 140	83.13 163	85.14 239	63.37 202	91.23 181	90.76 187	74.81 113	72.65 167	88.49 176	60.63 116	92.95 231	69.41 181	81.95 153	93.08 141

MP-MVS-pluss		85.24 58	85.13 58	85.56 97	91.42 118	65.59 144	91.54 166	92.51 120	74.56 114	80.62 88	95.64 36	59.15 136	97.00 90	86.94 50	93.80 43	94.07 108
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvs_anonymous		81.36 121	79.99 129	85.46 100	90.39 140	68.40 65	86.88 272	90.61 192	74.41 115	70.31 198	84.67 223	63.79 87	92.32 261	73.13 146	85.70 126	95.67 41

MAR-MVS		84.18 77	83.43 77	86.44 66	96.25 19	65.93 135	94.28 53	94.27 50	74.41 115	79.16 106	95.61 37	53.99 194	98.88 16	69.62 179	93.26 53	94.50 90
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		80.49 136	79.30 144	84.05 142	90.83 132	64.36 180	93.60 85	89.42 235	74.35 117	69.09 211	90.15 161	55.23 179	95.61 147	64.61 227	86.43 124	92.17 166

thisisatest0515		83.41 90	82.49 97	86.16 77	89.46 159	68.26 71	93.54 88	94.70 30	74.31 118	75.75 138	90.92 145	72.62 23	96.52 113	69.64 177	81.50 156	93.71 122

bset_n11_16_dypcd		75.95 215	74.16 216	81.30 207	76.91 326	65.14 156	88.89 243	87.48 284	74.30 119	69.90 206	83.40 236	42.16 280	92.42 255	78.39 116	66.03 258	86.32 247

Vis-MVSNet (Re-imp)		79.24 158	79.57 136	78.24 266	88.46 180	52.29 320	90.41 207	89.12 248	74.24 120	69.13 210	91.91 135	65.77 64	90.09 296	59.00 262	88.09 107	92.33 159

SMA-MVS		88.14 14	88.29 17	87.67 25	93.21 67	68.72 59	93.85 75	94.03 56	74.18 121	91.74 7	96.67 13	65.61 66	98.42 28	89.24 25	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		78.37 177	77.43 171	81.17 210	86.60 216	57.45 293	89.46 232	91.16 172	74.11 122	74.40 150	90.49 154	55.52 176	94.57 180	74.73 143	60.43 307	91.48 174

3Dnovator+	73.60 7	82.10 112	80.60 122	86.60 58	90.89 130	66.80 114	95.20 33	93.44 81	74.05 123	67.42 237	92.49 124	49.46 234	97.65 55	70.80 169	91.68 75	95.33 51

XVS		83.87 84	83.47 75	85.05 112	93.22 65	63.78 191	92.92 111	92.66 112	73.99 124	78.18 116	94.31 86	55.25 177	97.41 65	79.16 107	91.58 77	93.95 114

X-MVStestdata		76.86 198	74.13 218	85.05 112	93.22 65	63.78 191	92.92 111	92.66 112	73.99 124	78.18 116	10.19 366	55.25 177	97.41 65	79.16 107	91.58 77	93.95 114

MS-PatchMatch		77.90 187	76.50 186	82.12 189	85.99 226	69.95 35	91.75 159	92.70 109	73.97 126	62.58 279	84.44 226	41.11 282	95.78 136	63.76 233	92.17 69	80.62 321

LCM-MVSNet-Re		72.93 245	71.84 243	76.18 288	88.49 178	48.02 338	80.07 317	70.17 350	73.96 127	52.25 321	80.09 284	49.98 229	88.24 309	67.35 198	84.23 142	92.28 162

Vis-MVSNet		80.92 130	79.98 130	83.74 147	88.48 179	61.80 232	93.44 92	88.26 275	73.96 127	77.73 119	91.76 138	49.94 230	94.76 173	65.84 216	90.37 92	94.65 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-mter		79.96 145	79.38 143	81.72 198	86.93 213	61.17 240	92.70 118	91.54 156	73.85 129	75.62 140	86.94 200	49.84 232	92.38 257	72.21 157	84.76 134	91.60 171

OMC-MVS		78.67 173	77.91 164	80.95 221	85.76 231	57.40 294	88.49 250	88.67 263	73.85 129	72.43 174	92.10 132	49.29 237	94.55 183	72.73 151	77.89 181	90.91 186

Fast-Effi-MVS+		81.14 124	80.01 128	84.51 131	90.24 142	65.86 136	94.12 59	89.15 246	73.81 131	75.37 145	88.26 182	57.26 150	94.53 185	66.97 203	84.92 131	93.15 138

ZNCC-MVS		85.33 57	85.08 59	86.06 79	93.09 72	65.65 142	93.89 73	93.41 83	73.75 132	79.94 95	94.68 71	60.61 117	98.03 37	82.63 82	93.72 46	94.52 89

V42		76.46 205	74.55 209	82.19 185	79.14 307	67.82 82	90.26 212	89.42 235	73.75 132	68.63 222	81.89 251	51.31 218	94.09 199	71.69 164	64.84 268	84.66 278

v1144		76.73 203	74.88 203	82.27 181	80.23 295	66.60 119	91.68 161	90.21 209	73.69 134	69.06 213	81.89 251	52.73 207	94.40 189	69.21 184	65.23 264	85.80 262

v148		76.19 206	74.47 211	81.36 205	80.05 296	64.44 174	91.75 159	90.23 207	73.68 135	67.13 241	80.84 271	55.92 174	93.86 216	68.95 187	61.73 296	85.76 265

CR-MVSNet		73.79 239	70.82 251	82.70 169	83.15 269	67.96 78	70.25 337	84.00 316	73.67 136	69.97 203	72.41 327	57.82 146	89.48 300	52.99 282	73.13 212	90.64 189

XXY-MVS		77.94 185	76.44 187	82.43 175	82.60 273	64.44 174	92.01 144	91.83 145	73.59 137	70.00 202	85.82 211	54.43 190	94.76 173	69.63 178	68.02 247	88.10 220

#test#		84.98 63	84.74 63	85.72 91	93.75 53	65.01 161	94.09 60	93.19 91	73.55 138	79.22 104	94.93 62	59.04 137	97.67 51	82.66 80	92.21 66	94.49 91

tfpn200view9		78.79 168	77.43 171	82.88 165	92.21 93	64.49 170	92.05 142	96.28 4	73.48 139	71.75 183	88.26 182	60.07 123	95.32 160	45.16 311	77.58 185	88.83 205

thres400		78.68 171	77.43 171	82.43 175	92.21 93	64.49 170	92.05 142	96.28 4	73.48 139	71.75 183	88.26 182	60.07 123	95.32 160	45.16 311	77.58 185	87.48 225

FMVSNet3		77.73 188	76.04 192	82.80 166	91.20 124	68.99 53	91.87 150	91.99 138	73.35 141	67.04 242	83.19 239	56.62 164	92.14 263	59.80 258	69.34 236	87.28 231

GST-MVS		84.63 69	84.29 68	85.66 95	92.82 78	65.27 150	93.04 104	93.13 95	73.20 142	78.89 108	94.18 89	59.41 133	97.85 46	81.45 91	92.48 65	93.86 119

USDC		67.43 288	64.51 287	76.19 287	77.94 320	55.29 307	78.38 324	85.00 306	73.17 143	48.36 335	80.37 278	21.23 349	92.48 254	52.15 283	64.02 277	80.81 319

MP-MVS		85.02 61	84.97 60	85.17 111	92.60 84	64.27 184	93.24 97	92.27 126	73.13 144	79.63 100	94.43 76	61.90 106	97.17 80	85.00 62	92.56 62	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		82.16 109	81.12 112	85.26 108	86.42 218	68.72 59	92.59 125	90.44 196	73.12 145	84.20 57	94.36 78	38.04 296	95.73 139	84.12 69	86.81 115	91.33 177

xiu_mvs_v1_base		82.16 109	81.12 112	85.26 108	86.42 218	68.72 59	92.59 125	90.44 196	73.12 145	84.20 57	94.36 78	38.04 296	95.73 139	84.12 69	86.81 115	91.33 177

xiu_mvs_v1_base_debi		82.16 109	81.12 112	85.26 108	86.42 218	68.72 59	92.59 125	90.44 196	73.12 145	84.20 57	94.36 78	38.04 296	95.73 139	84.12 69	86.81 115	91.33 177

D2MVS		73.80 238	72.02 242	79.15 257	79.15 306	62.97 211	88.58 249	90.07 212	72.94 148	59.22 293	78.30 294	42.31 279	92.70 245	65.59 220	72.00 221	81.79 312

BH-RMVSNet		79.46 156	77.65 167	84.89 117	91.68 111	65.66 141	93.55 87	88.09 277	72.93 149	73.37 159	91.12 144	46.20 262	96.12 122	56.28 270	85.61 128	92.91 146

ETH3D-3000-0.1		87.61 23	87.89 20	86.75 52	93.58 57	67.21 99	94.31 52	94.14 54	72.92 150	87.13 26	96.62 14	67.81 44	97.94 39	90.13 18	94.42 32	95.09 67

IS-MVSNet		80.14 141	79.41 141	82.33 179	87.91 195	60.08 262	91.97 147	88.27 273	72.90 151	71.44 187	91.73 140	61.44 109	93.66 219	62.47 243	86.53 122	93.24 135

PS-MVSNAJss		77.26 195	76.31 189	80.13 234	80.64 288	59.16 274	90.63 204	91.06 179	72.80 152	68.58 223	84.57 225	53.55 199	93.96 210	72.97 147	71.96 222	87.27 232

9.14			87.63 25		93.86 48		94.41 50	94.18 52	72.76 153	86.21 34	96.51 16	66.64 54	97.88 45	90.08 19	94.04 37

v1192		75.98 213	73.92 221	82.15 186	79.73 297	66.24 128	91.22 182	89.75 223	72.67 154	68.49 224	81.42 261	49.86 231	94.27 193	67.08 201	65.02 266	85.95 259

Effi-MVS+		83.82 85	82.76 92	86.99 45	89.56 156	69.40 43	91.35 176	86.12 297	72.59 155	83.22 66	92.81 119	59.60 130	96.01 130	81.76 87	87.80 109	95.56 45

UnsupCasMVSNet_eth		65.79 295	63.10 295	73.88 301	70.71 343	50.29 331	81.09 307	89.88 220	72.58 156	49.25 333	74.77 321	32.57 322	87.43 319	55.96 271	41.04 347	83.90 284

1112_ss		80.56 134	79.83 132	82.77 167	88.65 176	60.78 248	92.29 131	88.36 270	72.58 156	72.46 173	94.95 60	65.09 71	93.42 224	66.38 210	77.71 182	94.10 105

test_part1		79.63 151	77.86 165	84.93 115	92.50 86	71.43 15	94.15 57	91.08 178	72.51 158	70.66 191	84.98 219	59.84 126	95.07 165	72.07 160	62.94 282	88.30 218

test_0728_THIRD									72.48 159	90.55 12	96.93 10	76.24 9	99.08 9	91.53 12	94.99 15	96.43 22

cl-mvsnet2		77.94 185	76.78 182	81.42 204	87.57 199	64.93 165	90.67 200	88.86 258	72.45 160	67.63 235	82.68 243	64.07 82	92.91 236	71.79 161	65.30 261	86.44 245

thres600view7		78.00 182	76.66 184	82.03 194	91.93 101	63.69 196	91.30 179	96.33 1	72.43 161	70.46 194	87.89 189	60.31 118	94.92 171	42.64 323	76.64 196	87.48 225

IterMVS-LS		76.49 204	75.18 202	80.43 227	84.49 250	62.74 219	90.64 202	88.80 259	72.40 162	65.16 255	81.72 254	60.98 113	92.27 262	67.74 195	64.65 272	86.29 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		78.97 162	78.22 157	81.25 208	85.33 235	62.73 220	89.53 230	93.21 88	72.39 163	72.14 177	90.13 162	60.99 112	94.72 176	67.73 196	72.49 218	86.29 248

miper_ehance_all_eth		77.60 189	76.44 187	81.09 218	85.70 232	64.41 177	90.65 201	88.64 265	72.31 164	67.37 240	82.52 244	64.77 76	92.64 249	70.67 171	65.30 261	86.24 250

v144192		76.05 211	74.03 219	82.12 189	79.50 301	66.55 121	91.39 172	89.71 229	72.30 165	68.17 226	81.33 263	51.75 214	94.03 207	67.94 193	64.19 274	85.77 263

thres100view900		78.37 177	77.01 179	82.46 174	91.89 104	63.21 206	91.19 185	96.33 1	72.28 166	70.45 195	87.89 189	60.31 118	95.32 160	45.16 311	77.58 185	88.83 205

RRT_MVS		77.38 193	76.59 185	79.77 244	90.91 129	63.61 200	91.15 186	90.91 184	72.28 166	72.06 179	87.28 198	43.92 272	89.04 303	73.32 145	67.47 251	86.67 239

PatchmatchNet		77.46 191	74.63 206	85.96 82	89.55 157	70.35 27	79.97 318	89.55 231	72.23 168	70.94 189	76.91 308	57.03 154	92.79 241	54.27 276	81.17 159	94.74 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
HFP-MVS		84.73 66	84.40 67	85.72 91	93.75 53	65.01 161	93.50 90	93.19 91	72.19 169	79.22 104	94.93 62	59.04 137	97.67 51	81.55 89	92.21 66	94.49 91

ACMMPR		84.37 70	84.06 69	85.28 107	93.56 58	64.37 179	93.50 90	93.15 94	72.19 169	78.85 113	94.86 66	56.69 163	97.45 62	81.55 89	92.20 68	94.02 112

1314		80.70 131	78.95 149	85.94 83	87.77 198	67.56 88	87.91 258	92.55 119	72.17 171	67.44 236	93.09 106	50.27 227	97.04 87	71.68 165	87.64 110	93.23 136

region2R		84.36 71	84.03 70	85.36 105	93.54 59	64.31 181	93.43 93	92.95 102	72.16 172	78.86 112	94.84 67	56.97 158	97.53 60	81.38 93	92.11 70	94.24 96

Test_1112_low_res		79.56 153	78.60 152	82.43 175	88.24 188	60.39 257	92.09 139	87.99 279	72.10 173	71.84 181	87.42 195	64.62 77	93.04 228	65.80 217	77.30 191	93.85 120

v1921920		75.63 221	73.49 226	82.06 193	79.38 302	66.35 124	91.07 190	89.48 232	71.98 174	67.99 227	81.22 266	49.16 240	93.90 213	66.56 206	64.56 273	85.92 261

DVP-MVS		89.41 10	89.73 11	88.45 18	96.40 14	69.99 32	96.64 8	94.52 36	71.92 175	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 32	96.76 6	94.33 48	71.92 175	91.89 6	97.11 7	73.77 17

Fast-Effi-MVS+-dtu		75.04 227	73.37 227	80.07 235	80.86 284	59.52 269	91.20 184	85.38 302	71.90 177	65.20 254	84.84 221	41.46 281	92.97 230	66.50 209	72.96 214	87.73 222

LFMVS		84.34 72	82.73 93	89.18 11	94.76 31	73.25 10	94.99 42	91.89 142	71.90 177	82.16 73	93.49 102	47.98 249	97.05 85	82.55 83	84.82 132	97.25 5

eth_miper_zixun_eth		75.96 214	74.40 212	80.66 224	84.66 246	63.02 210	89.28 235	88.27 273	71.88 179	65.73 250	81.65 255	59.45 131	92.81 239	68.13 191	60.53 305	86.14 252

train_agg		87.21 30	87.42 30	86.60 58	94.18 40	67.28 97	94.16 55	93.51 75	71.87 180	85.52 45	95.33 43	68.19 36	97.27 77	89.09 26	94.90 19	95.25 62

test_8					94.19 39	67.19 100	94.15 57	93.42 82	71.87 180	85.38 47	95.35 42	68.19 36	96.95 97

agg_prior1		87.02 35	87.26 32	86.28 74	94.16 44	66.97 108	94.08 61	93.31 85	71.85 182	84.49 54	95.39 41	68.91 33	96.75 106	88.84 31	94.32 34	95.13 65

MDTV_nov1_ep13			72.61 235		89.06 167	68.48 63	80.33 312	90.11 211	71.84 183	71.81 182	75.92 316	53.01 205	93.92 212	48.04 298	73.38 210

ab-mvs		80.18 140	78.31 155	85.80 88	88.44 181	65.49 148	83.00 296	92.67 111	71.82 184	77.36 126	85.01 218	54.50 186	96.59 109	76.35 130	75.63 201	95.32 53

ACMMP		81.49 119	80.67 120	83.93 143	91.71 110	62.90 216	92.13 136	92.22 131	71.79 185	71.68 185	93.49 102	50.32 225	96.96 96	78.47 115	84.22 143	91.93 168
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 39	86.85 39	86.78 51	93.47 61	65.55 145	95.39 30	95.10 17	71.77 186	85.69 44	96.52 15	62.07 105	98.77 17	86.06 56	95.60 10	96.03 33

TEST9					94.18 40	67.28 97	94.16 55	93.51 75	71.75 187	85.52 45	95.33 43	68.01 39	97.27 77

cl_fuxian		76.83 201	75.47 199	80.93 222	85.02 242	64.18 186	90.39 208	88.11 276	71.66 188	66.65 247	81.64 256	63.58 93	92.56 250	69.31 183	62.86 283	86.04 256

SED-MVS		89.94 7	90.36 7	88.70 13	96.45 11	69.38 44	96.89 4	94.44 40	71.65 189	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	71.65 189	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 44		94.44 40	71.65 189	92.11 3	97.05 8	76.79 7	99.11 4

v8		75.35 223	73.26 228	81.61 201	80.67 287	66.82 112	89.54 229	89.27 239	71.65 189	63.30 273	80.30 280	54.99 183	94.06 202	67.33 200	62.33 288	83.94 283

v1240		75.21 226	72.98 230	81.88 195	79.20 304	66.00 132	90.75 199	89.11 249	71.63 193	67.41 238	81.22 266	47.36 254	93.87 214	65.46 222	64.72 271	85.77 263

SCA		75.82 217	72.76 232	85.01 114	86.63 215	70.08 30	81.06 308	89.19 243	71.60 194	70.01 201	77.09 306	45.53 264	90.25 289	60.43 253	73.27 211	94.68 80

BH-untuned		78.68 171	77.08 177	83.48 158	89.84 149	63.74 193	92.70 118	88.59 266	71.57 195	66.83 245	88.65 175	51.75 214	95.39 158	59.03 261	84.77 133	91.32 180

IterMVS		72.65 252	70.83 250	78.09 267	82.17 275	62.96 212	87.64 263	86.28 293	71.56 196	60.44 287	78.85 292	45.42 266	86.66 322	63.30 236	61.83 293	84.65 279
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mPP-MVS		82.96 98	82.44 98	84.52 130	92.83 76	62.92 215	92.76 114	91.85 144	71.52 197	75.61 142	94.24 88	53.48 202	96.99 93	78.97 110	90.73 87	93.64 125

test-LLR		80.10 142	79.56 137	81.72 198	86.93 213	61.17 240	92.70 118	91.54 156	71.51 198	75.62 140	86.94 200	53.83 195	92.38 257	72.21 157	84.76 134	91.60 171

test0.0.03 1		72.76 248	72.71 234	72.88 309	80.25 294	47.99 339	91.22 182	89.45 233	71.51 198	62.51 280	87.66 191	53.83 195	85.06 328	50.16 288	67.84 250	85.58 266

ETH3D cwj APD-0.16		87.06 33	87.18 34	86.71 54	91.99 99	67.48 93	92.97 107	94.21 51	71.48 200	85.72 42	96.32 23	68.13 38	98.00 38	89.06 28	94.70 27	94.65 83

PGM-MVS		83.25 92	82.70 94	84.92 116	92.81 80	64.07 187	90.44 205	92.20 132	71.28 201	77.23 128	94.43 76	55.17 181	97.31 72	79.33 106	91.38 80	93.37 129

thisisatest0530		81.15 123	80.07 126	84.39 134	88.26 186	65.63 143	91.40 170	94.62 34	71.27 202	70.93 190	89.18 170	72.47 24	96.04 127	65.62 219	76.89 195	91.49 173

cl-mvsnet____		76.07 208	74.67 204	80.28 230	85.15 238	61.76 233	90.12 215	88.73 261	71.16 203	65.43 252	81.57 258	61.15 110	92.95 231	66.54 207	62.17 289	86.13 254

cl-mvsnet1		76.07 208	74.67 204	80.28 230	85.14 239	61.75 234	90.12 215	88.73 261	71.16 203	65.42 253	81.60 257	61.15 110	92.94 235	66.54 207	62.16 291	86.14 252

dp		75.01 228	72.09 241	83.76 146	89.28 162	66.22 129	79.96 319	89.75 223	71.16 203	67.80 233	77.19 305	51.81 213	92.54 251	50.39 287	71.44 227	92.51 156

CP-MVS		83.71 88	83.40 80	84.65 126	93.14 70	63.84 189	94.59 48	92.28 125	71.03 206	77.41 125	94.92 64	55.21 180	96.19 119	81.32 94	90.70 88	93.91 116

v10		74.77 230	72.54 237	81.46 203	80.33 293	66.71 116	89.15 239	89.08 250	70.94 207	63.08 274	79.86 285	52.52 208	94.04 205	65.70 218	62.17 289	83.64 285

CDPH-MVS		85.71 53	85.46 55	86.46 65	94.75 32	67.19 100	93.89 73	92.83 106	70.90 208	83.09 67	95.28 46	63.62 90	97.36 68	80.63 98	94.18 35	94.84 76

GBi-Net		75.65 219	73.83 222	81.10 215	88.85 171	65.11 157	90.01 219	90.32 199	70.84 209	67.04 242	80.25 281	48.03 246	91.54 277	59.80 258	69.34 236	86.64 240

test1		75.65 219	73.83 222	81.10 215	88.85 171	65.11 157	90.01 219	90.32 199	70.84 209	67.04 242	80.25 281	48.03 246	91.54 277	59.80 258	69.34 236	86.64 240

FMVSNet2		76.07 208	74.01 220	82.26 183	88.85 171	67.66 86	91.33 177	91.61 154	70.84 209	65.98 249	82.25 247	48.03 246	92.00 268	58.46 263	68.73 242	87.10 233

SF-MVS		87.03 34	87.09 35	86.84 47	92.70 82	67.45 94	93.64 83	93.76 63	70.78 212	86.25 32	96.44 18	66.98 49	97.79 47	88.68 33	94.56 29	95.28 57

ZD-MVS					96.63 8	65.50 147		93.50 77	70.74 213	85.26 49	95.19 54	64.92 75	97.29 73	87.51 41	93.01 56

HyFIR lowres test		81.03 128	79.56 137	85.43 102	87.81 197	68.11 75	90.18 214	90.01 217	70.65 214	72.95 163	86.06 209	63.61 91	94.50 187	75.01 138	79.75 167	93.67 123

MVP-Stereo		77.12 197	76.23 190	79.79 243	81.72 279	66.34 125	89.29 234	90.88 185	70.56 215	62.01 282	82.88 240	49.34 235	94.13 197	65.55 221	93.80 43	78.88 334
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMP	71.68 10	75.58 222	74.23 215	79.62 248	84.97 243	59.64 266	90.80 197	89.07 251	70.39 216	62.95 275	87.30 197	38.28 293	93.87 214	72.89 148	71.45 226	85.36 271
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		83.25 92	82.95 88	84.17 139	92.25 91	62.88 217	90.91 191	91.86 143	70.30 217	77.12 129	93.96 94	56.75 161	96.28 116	82.04 85	91.34 82	93.34 130
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
GeoE		78.90 164	77.43 171	83.29 160	88.95 170	62.02 229	92.31 130	86.23 295	70.24 218	71.34 188	89.27 169	54.43 190	94.04 205	63.31 235	80.81 163	93.81 121

tpm2		79.80 149	77.95 162	85.34 106	88.28 185	68.26 71	81.56 304	91.42 162	70.11 219	77.59 124	80.50 276	67.40 46	94.26 195	67.34 199	77.35 190	93.51 127

TR-MVS		78.77 169	77.37 176	82.95 164	90.49 136	60.88 246	93.67 82	90.07 212	70.08 220	74.51 149	91.37 142	45.69 263	95.70 144	60.12 256	80.32 164	92.29 161

CL-MVSNet_2432*1600		69.92 265	68.09 267	75.41 291	73.25 336	55.90 304	90.05 218	89.90 219	69.96 221	61.96 283	76.54 309	51.05 221	87.64 315	49.51 292	50.59 334	82.70 302

PAPM_NR		82.97 97	81.84 104	86.37 70	94.10 46	66.76 115	87.66 262	92.84 105	69.96 221	74.07 155	93.57 100	63.10 99	97.50 61	70.66 172	90.58 90	94.85 75

PCF-MVS	73.15 9	79.29 157	77.63 168	84.29 137	86.06 225	65.96 134	87.03 268	91.10 176	69.86 223	69.79 207	90.64 149	57.54 149	96.59 109	64.37 229	82.29 149	90.32 191
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
miper_lstm_enhance		73.05 243	71.73 245	77.03 279	83.80 260	58.32 282	81.76 300	88.88 256	69.80 224	61.01 284	78.23 296	57.19 151	87.51 318	65.34 223	59.53 310	85.27 274

MIMVSNet		71.64 255	68.44 264	81.23 209	81.97 278	64.44 174	73.05 334	88.80 259	69.67 225	64.59 259	74.79 320	32.79 320	87.82 313	53.99 277	76.35 198	91.42 175

LPG-MVS_test		75.82 217	74.58 208	79.56 250	84.31 254	59.37 271	90.44 205	89.73 226	69.49 226	64.86 256	88.42 177	38.65 290	94.30 191	72.56 153	72.76 215	85.01 275

LGP-MVS_train				79.56 250	84.31 254	59.37 271		89.73 226	69.49 226	64.86 256	88.42 177	38.65 290	94.30 191	72.56 153	72.76 215	85.01 275

APDe-MVS		87.54 24	87.84 21	86.65 57	96.07 21	66.30 126	94.84 45	93.78 60	69.35 228	88.39 21	96.34 22	67.74 45	97.66 54	90.62 16	93.44 51	96.01 34

tttt0517		79.50 154	78.53 153	82.41 178	87.22 207	61.43 239	89.75 226	94.76 27	69.29 229	67.91 230	88.06 187	72.92 21	95.63 145	62.91 239	73.90 209	90.16 193

Patchmatch-RL test		68.17 280	64.49 288	79.19 254	71.22 341	53.93 313	70.07 339	71.54 349	69.22 230	56.79 308	62.89 346	56.58 165	88.61 304	69.53 180	52.61 329	95.03 72

test_yl		84.28 73	83.16 84	87.64 26	94.52 36	69.24 47	95.78 19	95.09 18	69.19 231	81.09 82	92.88 116	57.00 156	97.44 63	81.11 96	81.76 154	96.23 28

DCV-MVSNet		84.28 73	83.16 84	87.64 26	94.52 36	69.24 47	95.78 19	95.09 18	69.19 231	81.09 82	92.88 116	57.00 156	97.44 63	81.11 96	81.76 154	96.23 28

jajsoiax		73.05 243	71.51 247	77.67 270	77.46 322	54.83 309	88.81 245	90.04 216	69.13 233	62.85 277	83.51 234	31.16 328	92.75 242	70.83 168	69.80 232	85.43 270

DP-MVS Recon		82.73 100	81.65 107	85.98 81	97.31 4	67.06 104	95.15 35	91.99 138	69.08 234	76.50 135	93.89 95	54.48 189	98.20 32	70.76 170	85.66 127	92.69 149

Baseline_NR-MVSNet		73.99 236	72.83 231	77.48 273	80.78 285	59.29 273	91.79 154	84.55 311	68.85 235	68.99 214	80.70 272	56.16 168	92.04 267	62.67 241	60.98 302	81.11 315

CHOSEN 280x420		77.35 194	76.95 181	78.55 261	87.07 210	62.68 221	69.71 340	82.95 324	68.80 236	71.48 186	87.27 199	66.03 59	84.00 333	76.47 128	82.81 148	88.95 204

DPE-MVS		88.77 13	89.21 13	87.45 33	96.26 18	67.56 88	94.17 54	94.15 53	68.77 237	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		72.71 250	71.11 248	77.52 271	77.41 323	54.52 311	88.45 252	89.76 222	68.76 238	62.70 278	83.26 238	29.49 332	92.71 243	70.51 174	69.62 234	85.34 272

MVS		84.66 68	82.86 90	90.06 2	90.93 127	74.56 6	87.91 258	95.54 9	68.55 239	72.35 176	94.71 70	59.78 128	98.90 14	81.29 95	94.69 28	96.74 10

EPP-MVSNet		81.79 117	81.52 108	82.61 172	88.77 175	60.21 260	93.02 106	93.66 70	68.52 240	72.90 164	90.39 156	72.19 26	94.96 168	74.93 139	79.29 171	92.67 150

CSCG		86.87 37	86.26 42	88.72 12	95.05 30	70.79 22	93.83 79	95.33 11	68.48 241	77.63 122	94.35 83	73.04 20	98.45 25	84.92 64	93.71 47	96.92 9

CP-MVSNet		70.50 261	69.91 257	72.26 314	80.71 286	51.00 327	87.23 267	90.30 203	67.84 242	59.64 290	82.69 242	50.23 228	82.30 345	51.28 284	59.28 311	83.46 290

testtj		86.62 42	86.66 40	86.50 64	96.95 5	65.70 140	94.41 50	93.45 79	67.74 243	86.19 35	96.39 20	64.38 79	97.91 42	87.33 45	93.14 55	95.90 38

pmmvs5		73.35 241	71.52 246	78.86 259	78.64 314	60.61 255	91.08 188	86.90 289	67.69 244	63.32 272	83.64 232	44.33 271	90.53 286	62.04 245	66.02 259	85.46 269

pm-mvs1		72.89 246	71.09 249	78.26 265	79.10 308	57.62 289	90.80 197	89.30 238	67.66 245	62.91 276	81.78 253	49.11 241	92.95 231	60.29 255	58.89 313	84.22 281

MDTV_nov1_ep13_2view						59.90 264	80.13 316		67.65 246	72.79 165		54.33 192		59.83 257		92.58 154

pmmvs4		73.92 237	71.81 244	80.25 232	79.17 305	65.24 151	87.43 265	87.26 288	67.64 247	63.46 271	83.91 231	48.96 242	91.53 280	62.94 238	65.49 260	83.96 282

WR-MVS_H		70.59 260	69.94 256	72.53 311	81.03 283	51.43 324	87.35 266	92.03 137	67.38 248	60.23 288	80.70 272	55.84 175	83.45 337	46.33 307	58.58 315	82.72 300

KD-MVS_2432*1600		69.03 272	66.37 274	77.01 280	85.56 233	61.06 243	81.44 305	90.25 205	67.27 249	58.00 302	76.53 310	54.49 187	87.63 316	48.04 298	35.77 352	82.34 306

miper_refine_blended		69.03 272	66.37 274	77.01 280	85.56 233	61.06 243	81.44 305	90.25 205	67.27 249	58.00 302	76.53 310	54.49 187	87.63 316	48.04 298	35.77 352	82.34 306

PS-CasMVS		69.86 267	69.13 261	72.07 317	80.35 292	50.57 329	87.02 269	89.75 223	67.27 249	59.19 294	82.28 246	46.58 258	82.24 346	50.69 286	59.02 312	83.39 292

PEN-MVS		69.46 269	68.56 263	72.17 316	79.27 303	49.71 333	86.90 271	89.24 240	67.24 252	59.08 295	82.51 245	47.23 255	83.54 336	48.42 296	57.12 316	83.25 293

cascas		78.18 180	75.77 196	85.41 103	87.14 209	69.11 49	92.96 108	91.15 174	66.71 253	70.47 193	86.07 208	37.49 302	96.48 114	70.15 175	79.80 166	90.65 188

APD-MVS		85.93 50	85.99 46	85.76 90	95.98 23	65.21 152	93.59 86	92.58 117	66.54 254	86.17 36	95.88 30	63.83 86	97.00 90	86.39 53	92.94 57	95.06 68
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	70.45 11	78.54 175	75.92 194	86.41 69	85.93 230	71.68 14	92.74 115	92.51 120	66.49 255	64.56 260	91.96 134	43.88 273	98.10 35	54.61 274	90.65 89	89.44 203

DTE-MVSNet		68.46 278	67.33 270	71.87 319	77.94 320	49.00 336	86.16 276	88.58 267	66.36 256	58.19 299	82.21 248	46.36 259	83.87 334	44.97 314	55.17 323	82.73 299

IterMVS-SCA-FT		71.55 256	69.97 255	76.32 286	81.48 280	60.67 253	87.64 263	85.99 298	66.17 257	59.50 291	78.88 291	45.53 264	83.65 335	62.58 242	61.93 292	84.63 280

TransMVSNet (Re)		70.07 264	67.66 268	77.31 277	80.62 289	59.13 275	91.78 156	84.94 307	65.97 258	60.08 289	80.44 277	50.78 222	91.87 269	48.84 294	45.46 341	80.94 317

MVSFormer		83.75 87	82.88 89	86.37 70	89.24 164	71.18 18	89.07 241	90.69 188	65.80 259	87.13 26	94.34 84	64.99 72	92.67 246	72.83 149	91.80 73	95.27 59

test_djsdf		73.76 240	72.56 236	77.39 275	77.00 325	53.93 313	89.07 241	90.69 188	65.80 259	63.92 266	82.03 250	43.14 276	92.67 246	72.83 149	68.53 243	85.57 267

API-MVS		82.28 107	80.53 123	87.54 31	96.13 20	70.59 24	93.63 84	91.04 181	65.72 261	75.45 144	92.83 118	56.11 170	98.89 15	64.10 230	89.75 97	93.15 138

原ACMM1				84.42 133	93.21 67	64.27 184		93.40 84	65.39 262	79.51 101	92.50 122	58.11 144	96.69 108	65.27 224	93.96 38	92.32 160

testgi		64.48 300	62.87 298	69.31 324	71.24 340	40.62 352	85.49 277	79.92 332	65.36 263	54.18 314	83.49 235	23.74 345	84.55 329	41.60 325	60.79 304	82.77 298

QAPM		79.95 146	77.39 175	87.64 26	89.63 153	71.41 16	93.30 96	93.70 67	65.34 264	67.39 239	91.75 139	47.83 250	98.96 12	57.71 265	89.81 94	92.54 155

HPM-MVS_fast		80.25 139	79.55 139	82.33 179	91.55 115	59.95 263	91.32 178	89.16 245	65.23 265	74.71 148	93.07 109	47.81 251	95.74 138	74.87 142	88.23 104	91.31 181

tfpnnormal		70.10 263	67.36 269	78.32 263	83.45 266	60.97 245	88.85 244	92.77 107	64.85 266	60.83 286	78.53 293	43.52 275	93.48 222	31.73 353	61.70 297	80.52 322

SR-MVS		82.81 99	82.58 95	83.50 157	93.35 62	61.16 242	92.23 134	91.28 168	64.48 267	81.27 80	95.28 46	53.71 198	95.86 134	82.87 78	88.77 101	93.49 128

K. test v3		63.09 306	59.61 310	73.53 304	76.26 329	49.38 335	83.27 292	77.15 336	64.35 268	47.77 337	72.32 329	28.73 334	87.79 314	49.93 290	36.69 351	83.41 291

test1172		81.90 115	81.83 105	82.13 188	93.23 64	57.52 291	91.61 164	90.98 183	64.32 269	80.20 93	95.00 58	51.26 219	95.61 147	81.73 88	88.13 106	93.26 134

v7n		71.31 257	68.65 262	79.28 253	76.40 328	60.77 249	86.71 273	89.45 233	64.17 270	58.77 298	78.24 295	44.59 270	93.54 220	57.76 264	61.75 295	83.52 288

FMVSNet1		72.71 250	69.91 257	81.10 215	83.60 264	65.11 157	90.01 219	90.32 199	63.92 271	63.56 270	80.25 281	36.35 311	91.54 277	54.46 275	66.75 255	86.64 240

XVG-OURS		74.25 234	72.46 238	79.63 247	78.45 316	57.59 290	80.33 312	87.39 285	63.86 272	68.76 220	89.62 168	40.50 284	91.72 273	69.00 186	74.25 204	89.58 200

UniMVSNet_ETH3D		72.74 249	70.53 252	79.36 252	78.62 315	56.64 299	85.01 279	89.20 242	63.77 273	64.84 258	84.44 226	34.05 317	91.86 270	63.94 231	70.89 230	89.57 201

114514_t		79.17 159	77.67 166	83.68 151	95.32 26	65.53 146	92.85 113	91.60 155	63.49 274	67.92 229	90.63 151	46.65 257	95.72 143	67.01 202	83.54 144	89.79 197

APD-MVS_3200maxsize		81.64 118	81.32 110	82.59 173	92.36 87	58.74 277	91.39 172	91.01 182	63.35 275	79.72 99	94.62 72	51.82 212	96.14 121	79.71 102	87.93 108	92.89 147

test20.03		63.83 303	62.65 299	67.38 330	70.58 345	39.94 353	86.57 274	84.17 313	63.29 276	51.86 322	77.30 302	37.09 307	82.47 343	38.87 336	54.13 327	79.73 328

XVG-OURS-SEG-HR		74.70 231	73.08 229	79.57 249	78.25 317	57.33 295	80.49 310	87.32 286	63.22 277	68.76 220	90.12 164	44.89 269	91.59 275	70.55 173	74.09 206	89.79 197

ACMM	69.62 13	74.34 232	72.73 233	79.17 255	84.25 256	57.87 285	90.36 209	89.93 218	63.17 278	65.64 251	86.04 210	37.79 300	94.10 198	65.89 215	71.52 225	85.55 268
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmvs		72.88 247	69.76 259	82.22 184	90.98 126	67.05 105	78.22 326	88.30 271	63.10 279	64.35 265	74.98 319	55.09 182	94.27 193	43.25 317	69.57 235	85.34 272

SixPastTwentyTwo		64.92 297	61.78 304	74.34 299	78.74 312	49.76 332	83.42 291	79.51 334	62.86 280	50.27 329	77.35 301	30.92 330	90.49 287	45.89 309	47.06 339	82.78 297

SR-MVS-dyc-post		81.06 127	80.70 119	82.15 186	92.02 96	58.56 279	90.90 192	90.45 193	62.76 281	78.89 108	94.46 74	51.26 219	95.61 147	78.77 113	86.77 118	92.28 162

RE-MVS-def			80.48 124		92.02 96	58.56 279	90.90 192	90.45 193	62.76 281	78.89 108	94.46 74	49.30 236		78.77 113	86.77 118	92.28 162

TAPA-MVS	70.22 12	74.94 229	73.53 225	79.17 255	90.40 139	52.07 321	89.19 238	89.61 230	62.69 283	70.07 200	92.67 120	48.89 243	94.32 190	38.26 337	79.97 165	91.12 184
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
abl_6		79.82 148	79.20 146	81.70 200	89.85 148	58.34 281	88.47 251	90.07 212	62.56 284	77.71 120	93.08 107	47.65 253	96.78 104	77.94 120	85.45 129	89.99 196

Anonymous202405211		77.96 184	75.33 200	85.87 85	93.73 55	64.52 169	94.85 44	85.36 303	62.52 285	76.11 136	90.18 160	29.43 333	97.29 73	68.51 190	77.24 193	95.81 40

pmmvs-eth3d		65.53 296	62.32 301	75.19 292	69.39 347	59.59 267	82.80 297	83.43 320	62.52 285	51.30 326	72.49 325	32.86 319	87.16 321	55.32 273	50.73 333	78.83 335

AdaColmap		78.94 163	77.00 180	84.76 121	96.34 16	65.86 136	92.66 122	87.97 280	62.18 287	70.56 192	92.37 128	43.53 274	97.35 69	64.50 228	82.86 146	91.05 185

无先验							92.71 117	92.61 116	62.03 288				97.01 88	66.63 204		93.97 113

XVG-ACMP-BASELINE		68.04 281	65.53 280	75.56 290	74.06 335	52.37 319	78.43 323	85.88 299	62.03 288	58.91 297	81.21 268	20.38 351	91.15 283	60.69 252	68.18 245	83.16 295

anonymousdsp		71.14 258	69.37 260	76.45 285	72.95 337	54.71 310	84.19 283	88.88 256	61.92 290	62.15 281	79.77 286	38.14 295	91.44 282	68.90 188	67.45 252	83.21 294

tpm cat1		75.30 224	72.21 240	84.58 129	88.52 177	67.77 83	78.16 327	88.02 278	61.88 291	68.45 225	76.37 312	60.65 115	94.03 207	53.77 279	74.11 205	91.93 168

FMVSNet5		68.04 281	65.66 279	75.18 293	84.43 252	57.89 284	83.54 287	86.26 294	61.83 292	53.64 317	73.30 324	37.15 306	85.08 327	48.99 293	61.77 294	82.56 305

Anonymous20231206		67.53 286	65.78 276	72.79 310	74.95 332	47.59 341	88.23 254	87.32 286	61.75 293	58.07 301	77.29 303	37.79 300	87.29 320	42.91 319	63.71 279	83.48 289

PatchMatch-RL		72.06 253	69.98 254	78.28 264	89.51 158	55.70 305	83.49 288	83.39 322	61.24 294	63.72 269	82.76 241	34.77 315	93.03 229	53.37 281	77.59 184	86.12 255

PLC	68.80 14	75.23 225	73.68 224	79.86 241	92.93 74	58.68 278	90.64 202	88.30 271	60.90 295	64.43 264	90.53 152	42.38 278	94.57 180	56.52 268	76.54 197	86.33 246
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH	63.93 17	68.62 275	64.81 283	80.03 236	85.22 237	63.25 204	87.72 261	84.66 310	60.83 296	51.57 324	79.43 290	27.29 338	94.96 168	41.76 324	64.84 268	81.88 311
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 276	65.41 281	77.96 268	78.69 313	62.93 213	89.86 224	89.17 244	60.55 297	50.27 329	77.73 300	22.60 347	94.06 202	47.18 304	72.65 217	76.88 341

VDDNet		80.50 135	78.26 156	87.21 38	86.19 223	69.79 38	94.48 49	91.31 165	60.42 298	79.34 102	90.91 146	38.48 292	96.56 112	82.16 84	81.05 160	95.27 59

CPTT-MVS		79.59 152	79.16 147	80.89 223	91.54 116	59.80 265	92.10 138	88.54 268	60.42 298	72.96 162	93.28 104	48.27 245	92.80 240	78.89 112	86.50 123	90.06 194

our_test_3		68.29 279	64.69 285	79.11 258	78.92 309	64.85 166	88.40 253	85.06 305	60.32 300	52.68 319	76.12 314	40.81 283	89.80 299	44.25 316	55.65 321	82.67 304

ITE_SJBPF				70.43 322	74.44 333	47.06 343		77.32 335	60.16 301	54.04 315	83.53 233	23.30 346	84.01 332	43.07 318	61.58 299	80.21 327

ppachtmachnet_test		67.72 283	63.70 292	79.77 244	78.92 309	66.04 131	88.68 247	82.90 325	60.11 302	55.45 310	75.96 315	39.19 287	90.55 285	39.53 332	52.55 330	82.71 301

new-patchmatchnet		59.30 317	56.48 318	67.79 327	65.86 351	44.19 346	82.47 298	81.77 326	59.94 303	43.65 348	66.20 342	27.67 337	81.68 348	39.34 333	41.40 346	77.50 340

MVS_0304		68.99 274	67.23 271	74.28 300	80.36 291	52.54 318	87.01 270	86.36 291	59.89 304	66.22 248	73.56 323	24.25 342	88.03 311	57.34 267	70.11 231	82.27 308

新几何1				84.73 122	92.32 88	64.28 183		91.46 161	59.56 305	79.77 98	92.90 114	56.95 159	96.57 111	63.40 234	92.91 58	93.34 130

1121		81.25 122	80.05 127	84.87 119	92.30 89	64.31 181	87.91 258	91.39 163	59.44 306	79.94 95	92.91 113	57.09 152	97.01 88	66.63 204	92.81 60	93.29 133

旧先验2							92.00 146		59.37 307	87.54 25			93.47 223	75.39 133

PM-MVS		59.40 316	56.59 317	67.84 326	63.63 352	41.86 349	76.76 329	63.22 358	59.01 308	51.07 327	72.27 330	11.72 359	83.25 339	61.34 248	50.28 335	78.39 338

LTVRE_ROB	59.60 19	66.27 292	63.54 293	74.45 297	84.00 259	51.55 323	67.08 347	83.53 319	58.78 309	54.94 311	80.31 279	34.54 316	93.23 226	40.64 330	68.03 246	78.58 337
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				81.34 206	89.02 168	57.72 287		89.84 221	58.65 310	85.32 48	94.09 90	57.03 154	93.28 225	69.34 182	90.56 91	93.03 142

ACMH+	65.35 16	67.65 284	64.55 286	76.96 282	84.59 248	57.10 297	88.08 255	80.79 329	58.59 311	53.00 318	81.09 270	26.63 340	92.95 231	46.51 305	61.69 298	80.82 318

ADS-MVSNet2		66.90 289	63.44 294	77.26 278	88.06 191	60.70 252	68.01 344	75.56 340	57.57 312	64.48 261	69.87 336	38.68 288	84.10 330	40.87 328	67.89 248	86.97 234

ADS-MVSNet		68.54 277	64.38 290	81.03 219	88.06 191	66.90 110	68.01 344	84.02 315	57.57 312	64.48 261	69.87 336	38.68 288	89.21 302	40.87 328	67.89 248	86.97 234

MDA-MVSNet-bldmvs		61.54 311	57.70 315	73.05 307	79.53 300	57.00 298	83.08 295	81.23 327	57.57 312	34.91 354	72.45 326	32.79 320	86.26 325	35.81 341	41.95 345	75.89 343

DIV-MVS_2432*1600		60.87 312	58.60 312	67.68 328	66.13 350	39.93 354	75.63 332	84.70 309	57.32 315	49.57 332	68.45 339	29.55 331	82.87 341	48.09 297	47.94 338	80.25 326

UnsupCasMVSNet_bld		61.60 310	57.71 314	73.29 306	68.73 348	51.64 322	78.61 322	89.05 252	57.20 316	46.11 338	61.96 347	28.70 335	88.60 305	50.08 289	38.90 349	79.63 329

MSDG		69.54 268	65.73 277	80.96 220	85.11 241	63.71 195	84.19 283	83.28 323	56.95 317	54.50 312	84.03 228	31.50 326	96.03 128	42.87 321	69.13 239	83.14 296

F-COLMAP		70.66 259	68.44 264	77.32 276	86.37 221	55.91 303	88.00 256	86.32 292	56.94 318	57.28 307	88.07 186	33.58 318	92.49 253	51.02 285	68.37 244	83.55 286

test222					89.77 150	61.60 236	89.55 228	89.42 235	56.83 319	77.28 127	92.43 126	52.76 206			91.14 85	93.09 140

CNLPA		74.31 233	72.30 239	80.32 228	91.49 117	61.66 235	90.85 195	80.72 330	56.67 320	63.85 268	90.64 149	46.75 256	90.84 284	53.79 278	75.99 200	88.47 214

OurMVSNet-221017-0		64.68 298	62.17 302	72.21 315	76.08 331	47.35 342	80.67 309	81.02 328	56.19 321	51.60 323	79.66 288	27.05 339	88.56 306	53.60 280	53.63 328	80.71 320

YYNet1		63.76 305	60.14 308	74.62 296	78.06 319	60.19 261	83.46 290	83.99 318	56.18 322	39.25 351	71.56 334	37.18 305	83.34 338	42.90 320	48.70 337	80.32 324

MDA-MVSNet_test_wron		63.78 304	60.16 307	74.64 295	78.15 318	60.41 256	83.49 288	84.03 314	56.17 323	39.17 352	71.59 333	37.22 304	83.24 340	42.87 321	48.73 336	80.26 325

OpenMVS_ROB	61.12 18	66.39 291	62.92 297	76.80 284	76.51 327	57.77 286	89.22 236	83.41 321	55.48 324	53.86 316	77.84 299	26.28 341	93.95 211	34.90 344	68.76 241	78.68 336

MIMVSNet1		60.16 315	57.33 316	68.67 325	69.71 346	44.13 347	78.92 321	84.21 312	55.05 325	44.63 345	71.85 331	23.91 344	81.54 349	32.63 351	55.03 324	80.35 323

CVMVSNet		74.04 235	74.27 214	73.33 305	85.33 235	43.94 348	89.53 230	88.39 269	54.33 326	70.37 196	90.13 162	49.17 239	84.05 331	61.83 247	79.36 169	91.99 167

Anonymous20240529		76.84 200	74.15 217	84.88 118	91.02 125	64.95 164	93.84 78	91.09 177	53.57 327	73.00 161	87.42 195	35.91 312	97.32 71	69.14 185	72.41 220	92.36 158

pmmvs6		67.57 285	64.76 284	76.00 289	72.82 339	53.37 315	88.71 246	86.78 290	53.19 328	57.58 306	78.03 298	35.33 314	92.41 256	55.56 272	54.88 325	82.21 309

TinyColmap		60.32 313	56.42 319	72.00 318	78.78 311	53.18 316	78.36 325	75.64 339	52.30 329	41.59 350	75.82 317	14.76 357	88.35 308	35.84 340	54.71 326	74.46 345

test_0402		64.54 299	61.09 305	74.92 294	84.10 258	60.75 250	87.95 257	79.71 333	52.03 330	52.41 320	77.20 304	32.21 324	91.64 274	23.14 356	61.03 301	72.36 348

Anonymous20231211		73.08 242	70.39 253	81.13 213	90.62 135	63.33 203	91.40 170	90.06 215	51.84 331	64.46 263	80.67 274	36.49 310	94.07 201	63.83 232	64.17 275	85.98 258

AllTest		61.66 309	58.06 313	72.46 312	79.57 298	51.42 325	80.17 315	68.61 352	51.25 332	45.88 339	81.23 264	19.86 352	86.58 323	38.98 334	57.01 318	79.39 330

TestCases				72.46 312	79.57 298	51.42 325		68.61 352	51.25 332	45.88 339	81.23 264	19.86 352	86.58 323	38.98 334	57.01 318	79.39 330

PatchT		69.11 271	65.37 282	80.32 228	82.07 277	63.68 197	67.96 346	87.62 283	50.86 334	69.37 208	65.18 343	57.09 152	88.53 307	41.59 326	66.60 256	88.74 208

Anonymous20240521		62.09 308	59.08 311	71.10 320	67.19 349	48.72 337	83.91 285	85.23 304	50.38 335	47.84 336	71.22 335	20.74 350	85.51 326	46.47 306	58.75 314	79.06 333

DP-MVS		69.90 266	66.48 272	80.14 233	95.36 25	62.93 213	89.56 227	76.11 337	50.27 336	57.69 305	85.23 216	39.68 286	95.73 139	33.35 347	71.05 229	81.78 313

gg-mvs-nofinetune		77.18 196	74.31 213	85.80 88	91.42 118	68.36 67	71.78 335	94.72 29	49.61 337	77.12 129	45.92 354	77.41 6	93.98 209	67.62 197	93.16 54	95.05 69

JIA-IIPM		66.06 293	62.45 300	76.88 283	81.42 282	54.45 312	57.49 355	88.67 263	49.36 338	63.86 267	46.86 353	56.06 171	90.25 289	49.53 291	68.83 240	85.95 259

N_pmnet		50.55 322	49.11 325	54.88 337	77.17 324	4.02 371	84.36 282	2.00 370	48.59 339	45.86 341	68.82 338	32.22 323	82.80 342	31.58 354	51.38 332	77.81 339

ANet_high		40.27 326	35.20 329	55.47 336	34.74 367	34.47 359	63.84 350	71.56 348	48.42 340	18.80 360	41.08 358	9.52 362	64.45 360	20.18 357	8.66 364	67.49 352

COLMAP_ROB	57.96 20	62.98 307	59.65 309	72.98 308	81.44 281	53.00 317	83.75 286	75.53 341	48.34 341	48.81 334	81.40 262	24.14 343	90.30 288	32.95 349	60.52 306	75.65 344
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmtry		67.53 286	63.93 291	78.34 262	82.12 276	64.38 178	68.72 341	84.00 316	48.23 342	59.24 292	72.41 327	57.82 146	89.27 301	46.10 308	56.68 320	81.36 314

LS3D		69.17 270	66.40 273	77.50 272	91.92 102	56.12 302	85.12 278	80.37 331	46.96 343	56.50 309	87.51 194	37.25 303	93.71 217	32.52 352	79.40 168	82.68 303

RPSCF		64.24 301	61.98 303	71.01 321	76.10 330	45.00 345	75.83 331	75.94 338	46.94 344	58.96 296	84.59 224	31.40 327	82.00 347	47.76 302	60.33 309	86.04 256

RPMNet		70.42 262	65.68 278	84.63 128	83.15 269	67.96 78	70.25 337	90.45 193	46.83 345	69.97 203	65.10 344	56.48 167	95.30 163	35.79 342	73.13 212	90.64 189

CMPMVS	48.56 21	66.77 290	64.41 289	73.84 302	70.65 344	50.31 330	77.79 328	85.73 301	45.54 346	44.76 344	82.14 249	35.40 313	90.14 295	63.18 237	74.54 203	81.07 316
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		64.01 302	63.01 296	67.02 331	74.40 334	38.86 357	83.27 292	86.19 296	45.11 347	54.27 313	81.15 269	36.91 309	80.01 350	48.79 295	57.02 317	82.19 310

TDRefinement		55.28 320	51.58 323	66.39 332	59.53 357	46.15 344	76.23 330	72.80 345	44.60 348	42.49 349	76.28 313	15.29 355	82.39 344	33.20 348	43.75 343	70.62 350

Patchmatch-test		65.86 294	60.94 306	80.62 225	83.75 261	58.83 276	58.91 354	75.26 342	44.50 349	50.95 328	77.09 306	58.81 140	87.90 312	35.13 343	64.03 276	95.12 66

LF4IMVS		54.01 321	52.12 322	59.69 334	62.41 355	39.91 355	68.59 342	68.28 354	42.96 350	44.55 346	75.18 318	14.09 358	68.39 355	41.36 327	51.68 331	70.78 349

DSMNet-mixed		56.78 318	54.44 321	63.79 333	63.21 353	29.44 361	64.43 349	64.10 357	42.12 351	51.32 325	71.60 332	31.76 325	75.04 352	36.23 339	65.20 265	86.87 237

pmmvs3		55.51 319	51.50 324	67.53 329	57.90 358	50.93 328	80.37 311	73.66 344	40.63 352	44.15 347	64.75 345	16.30 354	78.97 351	44.77 315	40.98 348	72.69 346

new_pmnet		49.31 323	46.44 326	57.93 335	62.84 354	40.74 351	68.47 343	62.96 359	36.48 353	35.09 353	57.81 349	14.97 356	72.18 353	32.86 350	46.44 340	60.88 354

MVS-HIRNet		60.25 314	55.55 320	74.35 298	84.37 253	56.57 300	71.64 336	74.11 343	34.44 354	45.54 343	42.24 357	31.11 329	89.81 297	40.36 331	76.10 199	76.67 342

DeepMVS_CX				34.71 345	51.45 360	24.73 365		28.48 369	31.46 355	17.49 361	52.75 350	5.80 366	42.60 365	18.18 358	19.42 358	36.81 357

FPMVS		45.64 324	43.10 327	53.23 339	51.42 361	36.46 358	64.97 348	71.91 347	29.13 356	27.53 356	61.55 348	9.83 361	65.01 359	16.00 359	55.58 322	58.22 355

PMMVS2		37.93 328	33.61 331	50.92 340	46.31 363	24.76 364	60.55 353	50.05 362	28.94 357	20.93 358	47.59 352	4.41 369	65.13 358	25.14 355	18.55 359	62.87 353

LCM-MVSNet		40.54 325	35.79 328	54.76 338	36.92 366	30.81 360	51.41 356	69.02 351	22.07 358	24.63 357	45.37 355	4.56 368	65.81 357	33.67 346	34.50 354	67.67 351

PMVS	26.43 22	31.84 330	28.16 333	42.89 343	25.87 369	27.58 362	50.92 357	49.78 363	21.37 359	14.17 363	40.81 359	2.01 370	66.62 356	9.61 362	38.88 350	34.49 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.91 329	31.44 332	45.30 342	70.99 342	39.64 356	19.85 363	72.56 346	20.10 360	16.16 362	21.47 363	5.08 367	71.16 354	13.07 360	43.70 344	25.08 359
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		24.61 331	24.00 335	26.45 346	43.74 364	18.44 368	60.86 351	39.66 364	15.11 361	9.53 365	22.10 362	6.52 365	46.94 363	8.31 363	10.14 361	13.98 361

EMVS		23.76 333	23.20 337	25.46 347	41.52 365	16.90 369	60.56 352	38.79 367	14.62 362	8.99 366	20.24 365	7.35 364	45.82 364	7.25 364	9.46 362	13.64 362

MVE	24.84 23	24.35 332	19.77 338	38.09 344	34.56 368	26.92 363	26.57 361	38.87 366	11.73 363	11.37 364	27.44 360	1.37 371	50.42 362	11.41 361	14.60 360	36.93 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		38.59 327	35.16 330	48.89 341	54.33 359	21.35 366	45.32 359	53.71 361	7.41 364	28.74 355	51.62 351	8.70 363	52.87 361	33.73 345	32.89 355	72.47 347

wuyk23d		11.30 336	10.95 339	12.33 349	48.05 362	19.89 367	25.89 362	1.92 371	3.58 365	3.12 367	1.37 367	0.64 372	15.77 367	6.23 365	7.77 365	1.35 363

tmp_tt		22.26 334	23.75 336	17.80 348	5.23 370	12.06 370	35.26 360	39.48 365	2.82 366	18.94 359	44.20 356	22.23 348	24.64 366	36.30 338	9.31 363	16.69 360

testmvs		7.23 338	9.62 341	0.06 351	0.04 371	0.02 373	84.98 280	0.02 372	0.03 367	0.18 368	1.21 368	0.01 374	0.02 368	0.14 366	0.01 366	0.13 365

test123		6.92 339	9.21 342	0.08 350	0.03 372	0.05 372	81.65 303	0.01 373	0.02 368	0.14 369	0.85 369	0.03 373	0.02 368	0.12 367	0.00 367	0.16 364

uanet_test		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 367	0.00 366

cdsmvs_eth3d_5k		19.86 335	26.47 334	0.00 352	0.00 373	0.00 374	0.00 364	93.45 79	0.00 369	0.00 370	95.27 48	49.56 233	0.00 370	0.00 368	0.00 367	0.00 366

pcd_1.5k_mvsjas		4.46 340	5.95 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	53.55 199	0.00 370	0.00 368	0.00 367	0.00 366

sosnet-low-res		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 367	0.00 366

sosnet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 367	0.00 366

uncertanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 367	0.00 366

Regformer		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 367	0.00 366

ab-mvs-re		7.91 337	10.55 340	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	94.95 60	0.00 375	0.00 370	0.00 368	0.00 367	0.00 366

uanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 367	0.00 366

eth-test2					0.00 373

eth-test					0.00 373

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 80

test_part2					96.29 17	68.16 74				90.78 10

sam_mvs1												57.85 145				94.68 80

sam_mvs												54.91 184

ambc				69.61 323	61.38 356	41.35 350	49.07 358	85.86 300		50.18 331	66.40 341	10.16 360	88.14 310	45.73 310	44.20 342	79.32 332

MTGPA								92.23 127

test_post1							78.95 320				20.70 364	53.05 204	91.50 281	60.43 253

test_post											23.01 361	56.49 166	92.67 246

patchmatchnet-post											67.62 340	57.62 148	90.25 289

GG-mvs-BLEND				86.53 63	91.91 103	69.67 42	75.02 333	94.75 28		78.67 115	90.85 147	77.91 5	94.56 182	72.25 156	93.74 45	95.36 50

MTMP							93.77 80	32.52 368

test9_res														89.41 21	94.96 16	95.29 55

agg_prior2														86.41 52	94.75 26	95.33 51

agg_prior					94.16 44	66.97 108		93.31 85		84.49 54			96.75 106

test_prior4						67.18 102	93.92 71

test_prior				86.42 67	94.71 33	67.35 95		93.10 97					96.84 102			95.05 69

新几何2							91.41 168

旧先验1					91.94 100	60.74 251		91.50 159			94.36 78	65.23 69			91.84 71	94.55 85

原ACMM2							92.01 144

testdata2													96.09 123	61.26 249

segment_acmp												65.94 60

test12				87.09 42	94.60 35	68.86 55		92.91 103		82.67 71		65.44 68	97.55 59		93.69 48	94.84 76

plane_prior7					86.94 211	61.51 237

plane_prior6					87.23 206	62.32 225						50.66 223

plane_prior5								91.31 165					95.55 153	76.74 125	78.53 178	88.39 215

plane_prior4											89.14 172

plane_prior1					87.15 208

n2								0.00 374

nn								0.00 374

door-mid								66.01 356

lessismore_v0				73.72 303	72.93 338	47.83 340		61.72 360		45.86 341	73.76 322	28.63 336	89.81 297	47.75 303	31.37 356	83.53 287

test11								93.01 99

door								66.57 355

HQP5-MVS						63.66 198

BP-MVS														77.63 122

HQP4-MVS										74.18 151			95.61 147			88.63 209

HQP3-MVS								91.70 151							78.90 173

HQP2-MVS												51.63 216

NP-MVS					87.41 203	63.04 209					90.30 158

ACMMP++_ref															71.63 223

ACMMP++															69.72 233

Test By Simon												54.21 193

High-resolution multi-view benchmark