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 2	97.66 2	73.37 7	97.13 1	95.58 13	89.33 1	85.77 28	96.26 10	72.84 11	99.38 1	92.64 4	95.93 5	97.08 4

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 56	73.88 6	97.01 2	94.40 43	88.32 2	85.71 29	94.91 48	74.11 8	98.91 6	87.26 28	95.94 4	97.03 5
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 17	88.38 11	86.23 67	93.30 50	66.05 133	95.26 23	94.84 25	87.09 3	88.06 15	94.53 55	66.79 39	97.34 55	83.89 52	91.68 57	95.29 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		89.61 6	89.99 6	88.46 13	94.39 29	69.71 34	96.53 5	93.78 54	86.89 4	89.68 8	95.78 18	65.94 47	99.10 2	92.99 1	93.91 27	96.58 11

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 114	93.00 59	58.16 274	96.72 3	94.41 42	86.50 5	90.25 7	97.83 1	75.46 6	98.67 12	92.78 2	95.49 8	97.32 1

MVS_0304		88.39 10	88.35 13	88.50 12	93.01 58	70.11 25	95.90 10	92.20 129	86.27 6	88.70 13	95.92 16	56.76 141	99.02 4	92.68 3	93.76 30	96.37 15

CANet_DTU		84.09 65	83.52 58	85.81 77	90.30 125	66.82 100	91.87 131	89.01 243	85.27 7	86.09 25	93.74 74	47.71 241	96.98 78	77.90 95	89.78 76	93.65 110

CLD-MVS		82.73 83	82.35 81	83.86 130	87.90 182	67.65 76	95.45 20	92.18 132	85.06 8	72.58 145	92.27 104	52.46 202	95.78 118	84.18 48	79.06 150	88.16 198
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 3	90.89 4	88.61 11	96.76 4	70.65 20	96.47 6	94.83 26	84.83 9	89.07 11	96.80 4	70.86 18	99.06 3	92.64 4	95.71 6	96.12 22

NCCC		89.07 8	89.46 8	87.91 17	96.60 5	69.05 44	96.38 7	94.64 35	84.42 10	86.74 23	96.20 11	66.56 42	98.76 11	89.03 18	94.56 20	95.92 29

PS-MVSNAJ		88.14 11	87.61 19	89.71 4	92.06 80	76.72 1	95.75 13	93.26 84	83.86 11	89.55 9	96.06 14	53.55 189	97.89 32	91.10 6	93.31 37	94.54 76

DeepC-MVS_fast	79.48 2	87.95 15	88.00 15	87.79 20	95.86 15	68.32 60	95.74 14	94.11 49	83.82 12	83.49 50	96.19 12	64.53 67	98.44 18	83.42 55	94.88 14	96.61 9
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 16	87.38 24	89.55 7	91.41 106	76.43 2	95.74 14	93.12 94	83.53 13	89.55 9	95.95 15	53.45 194	97.68 35	91.07 7	92.62 44	94.54 76

TSAR-MVS + MP.		88.11 13	88.64 10	86.54 53	91.73 92	68.04 68	90.36 187	93.55 65	82.89 14	91.29 3	92.89 91	72.27 14	96.03 111	87.99 22	94.77 15	95.54 38

WTY-MVS		86.32 35	85.81 38	87.85 18	92.82 64	69.37 39	95.20 25	95.25 16	82.71 15	81.91 57	94.73 52	67.93 30	97.63 41	79.55 81	82.25 130	96.54 12

lupinMVS		87.74 18	87.77 17	87.63 27	89.24 152	71.18 16	96.57 4	92.90 103	82.70 16	87.13 20	95.27 33	64.99 63	95.80 117	89.34 13	91.80 55	95.93 28

HPM-MVS++		89.37 7	89.95 7	87.64 23	95.10 20	68.23 65	95.24 24	94.49 39	82.43 17	88.90 12	96.35 8	71.89 17	98.63 13	88.76 20	96.40 2	96.06 23

PVSNet_Blended		86.73 33	86.86 29	86.31 65	93.76 40	67.53 80	96.33 8	93.61 62	82.34 18	81.00 65	93.08 83	63.19 82	97.29 58	87.08 30	91.38 62	94.13 91

HSP-MVS		90.38 2	91.89 1	85.84 76	92.83 62	64.03 183	93.06 82	94.52 37	82.19 19	93.65 1	96.15 13	85.89 1	97.19 64	91.02 8	97.75 1	96.29 17

DWT-MVSNet_test		83.95 67	82.80 74	87.41 31	92.90 61	70.07 27	89.12 215	94.42 41	82.15 20	77.64 97	91.77 109	70.81 19	96.22 102	65.03 195	81.36 137	95.94 27

Regformer-1		87.24 23	87.60 20	86.15 69	95.14 18	65.83 140	93.95 53	95.12 18	82.11 21	84.25 43	95.73 20	67.88 31	98.35 21	85.60 39	88.64 82	94.26 83

PAPM		85.89 42	85.46 44	87.18 36	88.20 174	72.42 9	92.41 106	92.77 106	82.11 21	80.34 71	93.07 85	68.27 24	95.02 142	78.39 91	93.59 34	94.09 94

PatchFormer-LS_test		83.14 78	81.81 86	87.12 38	92.34 72	69.92 30	88.64 223	93.32 81	82.07 23	74.87 124	91.62 113	68.91 21	96.08 109	66.07 185	78.45 157	95.37 40

jason		86.40 34	86.17 34	87.11 39	86.16 205	70.54 22	95.71 17	92.19 131	82.00 24	84.58 39	94.34 63	61.86 91	95.53 134	87.76 24	90.89 67	95.27 47
jason: jason.
CHOSEN 1792x2688		84.98 51	83.45 61	89.57 6	89.94 131	75.14 4	92.07 116	92.32 121	81.87 25	75.68 115	88.27 157	60.18 107	98.60 14	80.46 77	90.27 74	94.96 62

Regformer-2		87.00 27	87.43 22	85.71 84	95.14 18	64.73 164	93.95 53	94.95 23	81.69 26	84.03 47	95.73 20	67.35 36	98.19 25	85.40 41	88.64 82	94.20 85

Regformer-3		85.80 43	85.92 36	85.46 89	94.17 34	65.09 158	92.95 87	95.11 19	81.13 27	81.68 59	95.04 40	65.82 49	98.32 22	83.02 56	84.36 115	92.97 128

DeepC-MVS	77.85 3	85.52 45	85.24 46	86.37 62	88.80 161	66.64 115	92.15 110	93.68 60	81.07 28	76.91 108	93.64 75	62.59 87	98.44 18	85.50 40	92.84 42	94.03 98
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-4		85.45 46	85.69 42	84.73 112	94.17 34	63.23 199	92.95 87	94.83 26	80.66 29	81.29 61	95.04 40	65.12 55	98.08 28	82.74 57	84.36 115	92.88 132

VPNet		78.82 146	77.53 146	82.70 151	84.52 223	66.44 124	93.93 55	92.23 124	80.46 30	72.60 144	88.38 155	49.18 227	93.13 215	72.47 127	63.97 272	88.55 188

canonicalmvs		86.85 30	86.25 33	88.66 10	91.80 91	71.92 10	93.54 70	91.71 147	80.26 31	87.55 18	95.25 35	63.59 78	96.93 84	88.18 21	84.34 117	97.11 3

MVSTER		82.47 88	82.05 82	83.74 132	92.68 68	69.01 45	91.90 130	93.21 86	79.83 32	72.14 153	85.71 198	74.72 7	94.72 154	75.72 105	72.49 205	87.50 208

HQP-NCC					87.54 185		94.06 47		79.80 33	74.18 127

ACMP_Plane					87.54 185		94.06 47		79.80 33	74.18 127

HQP-MVS		81.14 107	80.64 100	82.64 154	87.54 185	63.66 193	94.06 47	91.70 148	79.80 33	74.18 127	90.30 128	51.63 209	95.61 128	77.63 96	78.90 151	88.63 186

EI-MVSNet-Vis-set		83.77 71	83.67 57	84.06 127	92.79 67	63.56 195	91.76 138	94.81 28	79.65 36	77.87 94	94.09 68	63.35 80	97.90 31	79.35 82	79.36 147	90.74 164

plane_prior						62.42 215	93.85 59		79.38 37						78.80 153

alignmvs		87.28 22	86.97 27	88.24 15	91.30 107	71.14 18	95.61 18	93.56 64	79.30 38	87.07 22	95.25 35	68.43 23	96.93 84	87.87 23	84.33 118	96.65 8

TESTMET0.1,1		82.41 89	81.98 84	83.72 135	88.08 175	63.74 188	92.70 95	93.77 56	79.30 38	77.61 99	87.57 172	58.19 124	94.08 188	73.91 118	86.68 97	93.33 117

EI-MVSNet-UG-set		83.14 78	82.96 70	83.67 137	92.28 75	63.19 203	91.38 154	94.68 33	79.22 40	76.60 109	93.75 73	62.64 86	97.76 34	78.07 93	78.01 158	90.05 172

PVSNet	73.49 8	80.05 123	78.63 129	84.31 123	90.92 114	64.97 161	92.47 105	91.05 172	79.18 41	72.43 150	90.51 126	37.05 302	94.06 190	68.06 165	86.00 105	93.90 105

HY-MVS	76.49 5	84.28 60	83.36 66	87.02 42	92.22 77	67.74 73	84.65 274	94.50 38	79.15 42	82.23 55	87.93 164	66.88 38	96.94 82	80.53 76	82.20 131	96.39 14

PVSNet_BlendedMVS		83.38 75	83.43 62	83.22 143	93.76 40	67.53 80	94.06 47	93.61 62	79.13 43	81.00 65	85.14 201	63.19 82	97.29 58	87.08 30	73.91 194	84.83 264

plane_prior3						61.95 225			79.09 44	72.53 146

MVS_111021_HR		86.19 39	85.80 39	87.37 32	93.17 55	69.79 32	93.99 51	93.76 57	79.08 45	78.88 86	93.99 70	62.25 88	98.15 26	85.93 38	91.15 65	94.15 90

MSLP-MVS++		86.27 36	85.91 37	87.35 33	92.01 81	68.97 47	95.04 31	92.70 108	79.04 46	81.50 60	96.50 7	58.98 120	96.78 88	83.49 54	93.93 26	96.29 17

IB-MVS	77.80 4	82.18 92	80.46 103	87.35 33	89.14 154	70.28 24	95.59 19	95.17 17	78.85 47	70.19 174	85.82 195	70.66 20	97.67 36	72.19 131	66.52 248	94.09 94
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 86	80.82 97	88.31 14	89.57 143	71.26 14	92.60 100	94.39 44	78.84 48	67.89 213	92.48 99	48.42 233	98.52 15	68.80 163	94.40 21	95.15 53

HQP_MVS		80.34 118	79.75 112	82.12 180	86.94 194	62.42 215	93.13 80	91.31 162	78.81 49	72.53 146	89.14 148	50.66 213	95.55 132	76.74 100	78.53 155	88.39 191

plane_prior2							93.13 80		78.81 49

MG-MVS		87.11 25	86.27 31	89.62 5	97.79 1	76.27 3	94.96 33	94.49 39	78.74 51	83.87 49	92.94 88	64.34 69	96.94 82	75.19 109	94.09 24	95.66 33

gm-plane-assit					88.42 169	67.04 93			78.62 52		91.83 108		97.37 52	76.57 102

VNet		86.20 38	85.65 43	87.84 19	93.92 39	69.99 28	95.73 16	95.94 12	78.43 53	86.00 26	93.07 85	58.22 123	97.00 74	85.22 42	84.33 118	96.52 13

tpm		78.58 154	77.03 154	83.22 143	85.94 210	64.56 165	83.21 287	91.14 168	78.31 54	73.67 134	79.68 270	64.01 70	92.09 248	66.07 185	71.26 215	93.03 126

TSAR-MVS + GP.		87.96 14	88.37 12	86.70 47	93.51 48	65.32 149	95.15 27	93.84 53	78.17 55	85.93 27	94.80 51	75.80 5	98.21 23	89.38 12	88.78 81	96.59 10

FIs		79.47 136	79.41 119	79.67 231	85.95 208	59.40 262	91.68 142	93.94 51	78.06 56	68.96 195	88.28 156	66.61 41	91.77 253	66.20 184	74.99 187	87.82 205

sss		82.71 85	82.38 80	83.73 134	89.25 150	59.58 260	92.24 109	94.89 24	77.96 57	79.86 75	92.38 101	56.70 144	97.05 69	77.26 99	80.86 141	94.55 74

PMMVS		81.98 98	82.04 83	81.78 187	89.76 135	56.17 295	91.13 168	90.69 180	77.96 57	80.09 73	93.57 76	46.33 251	94.99 143	81.41 69	87.46 91	94.17 88

MVS_Test		84.16 64	83.20 67	87.05 41	91.56 96	69.82 31	89.99 196	92.05 134	77.77 59	82.84 53	86.57 187	63.93 72	96.09 107	74.91 115	89.18 79	95.25 50

SteuartSystems-ACMMP		86.82 32	86.90 28	86.58 52	90.42 122	66.38 125	96.09 9	93.87 52	77.73 60	84.01 48	95.66 22	63.39 79	97.94 29	87.40 27	93.55 35	95.42 39
Skip Steuart: Steuart Systems R&D Blog.
EPNet_dtu		78.80 147	79.26 123	77.43 276	88.06 177	49.71 325	91.96 123	91.95 139	77.67 61	76.56 110	91.28 115	58.51 122	90.20 281	56.37 248	80.95 140	92.39 140
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpmrst		80.57 113	79.14 126	84.84 109	90.10 128	68.28 62	81.70 296	89.72 220	77.63 62	75.96 113	79.54 273	64.94 65	92.71 229	75.43 107	77.28 171	93.55 112

testdata1							89.21 212		77.55 63

UniMVSNet_NR-MVSNet		78.15 161	77.55 145	79.98 223	84.46 225	60.26 248	92.25 108	93.20 88	77.50 64	68.88 196	86.61 186	66.10 45	92.13 246	66.38 181	62.55 275	87.54 207

UA-Net		80.02 124	79.65 113	81.11 204	89.33 148	57.72 278	86.33 267	89.00 244	77.44 65	81.01 64	89.15 147	59.33 115	95.90 114	61.01 229	84.28 120	89.73 176

PVSNet_Blended_VisFu		83.97 66	83.50 59	85.39 94	90.02 129	66.59 118	93.77 63	91.73 145	77.43 66	77.08 107	89.81 142	63.77 75	96.97 79	79.67 80	88.21 85	92.60 136

NR-MVSNet		76.05 199	74.59 192	80.44 213	82.96 244	62.18 220	90.83 173	91.73 145	77.12 67	60.96 265	86.35 188	59.28 116	91.80 252	60.74 230	61.34 288	87.35 218

casdiffmvs		85.23 48	84.38 53	87.79 20	90.73 119	71.38 13	90.71 178	92.52 117	77.08 68	84.58 39	87.18 182	64.43 68	96.34 100	84.32 47	87.86 88	95.65 34

DI_MVS_plusplus_test		79.78 130	77.50 147	86.62 49	80.90 258	69.46 37	90.69 179	91.97 138	77.00 69	59.07 275	82.34 227	46.82 245	95.88 115	82.14 61	86.59 99	94.53 78

test_normal		79.66 131	77.36 152	86.54 53	80.72 262	69.21 42	90.68 180	92.16 133	76.99 70	58.63 279	82.03 236	46.70 247	95.86 116	81.74 65	86.63 98	94.56 73

FC-MVSNet-test		77.99 164	78.08 137	77.70 271	84.89 219	55.51 299	90.27 189	93.75 58	76.87 71	66.80 227	87.59 171	65.71 51	90.23 280	62.89 217	73.94 193	87.37 216

SD-MVS		87.49 20	87.49 21	87.50 29	93.60 45	68.82 50	93.90 57	92.63 113	76.86 72	87.90 17	95.76 19	66.17 43	97.63 41	89.06 17	91.48 61	96.05 24

UGNet		79.87 127	78.68 128	83.45 142	89.96 130	61.51 229	92.13 111	90.79 177	76.83 73	78.85 88	86.33 190	38.16 288	96.17 104	67.93 167	87.17 92	92.67 134
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 97	81.52 90	83.51 140	88.42 169	62.88 210	89.77 203	88.93 245	76.78 74	75.55 119	93.10 81	50.31 216	95.38 136	83.82 53	87.02 93	92.26 146

Test4		76.45 194	73.45 217	85.45 91	76.07 322	67.61 77	88.38 229	90.83 176	76.71 75	53.06 309	79.65 272	31.61 321	94.35 173	78.47 89	86.22 104	94.40 82

TranMVSNet+NR-MVSNet		75.86 202	74.52 195	79.89 226	82.44 247	60.64 243	91.37 155	91.37 161	76.63 76	67.65 216	86.21 191	52.37 203	91.55 263	61.84 225	60.81 291	87.48 209

PAPR		85.15 49	84.47 50	87.18 36	96.02 13	68.29 61	91.85 133	93.00 100	76.59 77	79.03 84	95.00 42	61.59 92	97.61 43	78.16 92	89.00 80	95.63 35

diffmvs		81.52 101	80.44 104	84.76 110	87.98 180	65.79 142	86.97 261	88.84 248	76.57 78	78.24 91	85.79 197	58.10 126	94.55 159	77.40 98	84.11 123	93.95 101

casdiffmvs1		86.26 37	85.70 41	87.97 16	90.76 118	71.19 15	90.74 177	93.07 97	76.57 78	88.02 16	87.85 169	67.81 32	96.48 98	87.22 29	89.22 78	96.23 19

UniMVSNet (Re)		77.58 169	76.78 159	79.98 223	84.11 231	60.80 236	91.76 138	93.17 91	76.56 80	69.93 180	84.78 205	63.32 81	92.36 241	64.89 196	62.51 277	86.78 228

DU-MVS		76.86 186	75.84 171	79.91 225	82.96 244	60.26 248	91.26 160	91.54 152	76.46 81	68.88 196	86.35 188	56.16 150	92.13 246	66.38 181	62.55 275	87.35 218

diffmvs1		82.57 87	81.71 88	85.15 102	88.07 176	66.09 132	87.52 251	88.92 246	76.45 82	80.41 70	87.04 183	60.29 106	94.77 150	80.30 78	86.36 103	94.59 72

OPM-MVS		79.00 142	78.09 136	81.73 188	83.52 238	63.83 185	91.64 145	90.30 195	76.36 83	71.97 155	89.93 141	46.30 252	95.17 141	75.10 110	77.70 161	86.19 236

WR-MVS		76.76 190	75.74 173	79.82 228	84.60 221	62.27 219	92.60 100	92.51 118	76.06 84	67.87 214	85.34 199	56.76 141	90.24 279	62.20 223	63.69 274	86.94 226

GA-MVS		78.33 158	76.23 166	84.65 115	83.65 236	66.30 128	91.44 148	90.14 203	76.01 85	70.32 172	84.02 211	42.50 268	94.72 154	70.98 142	77.00 173	92.94 129

PVSNet_0	68.08 15	71.81 245	68.32 259	82.27 172	84.68 220	62.31 218	88.68 221	90.31 194	75.84 86	57.93 282	80.65 258	37.85 293	94.19 180	69.94 150	29.05 354	90.31 169

CDS-MVSNet		81.43 103	80.74 98	83.52 139	86.26 203	64.45 170	92.09 114	90.65 183	75.83 87	73.95 133	89.81 142	63.97 71	92.91 223	71.27 138	82.82 127	93.20 121
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CostFormer		82.33 90	81.15 93	85.86 75	89.01 157	68.46 56	82.39 292	93.01 98	75.59 88	80.25 72	81.57 242	72.03 16	94.96 144	79.06 86	77.48 168	94.16 89

nrg030		80.93 110	79.86 110	84.13 126	83.69 235	68.83 49	93.23 78	91.20 165	75.55 89	75.06 123	88.22 161	63.04 85	94.74 153	81.88 63	66.88 245	88.82 184

VDD-MVS		83.06 80	81.81 86	86.81 44	90.86 116	67.70 74	95.40 21	91.50 155	75.46 90	81.78 58	92.34 103	40.09 279	97.13 68	86.85 33	82.04 132	95.60 36

Effi-MVS+-dtu		76.14 197	75.28 186	78.72 259	83.22 240	55.17 301	89.87 201	87.78 266	75.42 91	67.98 210	81.43 243	45.08 258	92.52 235	75.08 111	71.63 210	88.48 189

mvs-test1		78.74 150	77.95 139	81.14 202	83.22 240	57.13 285	93.96 52	87.78 266	75.42 91	72.68 142	90.80 120	45.08 258	94.54 161	75.08 111	77.49 167	91.74 151

test_prior3		87.38 21	87.70 18	86.42 59	94.71 24	67.35 83	95.10 29	93.10 95	75.40 93	85.25 35	95.61 24	67.94 28	96.84 86	87.47 25	94.77 15	95.05 57

test_prior2							95.10 29		75.40 93	85.25 35	95.61 24	67.94 28		87.47 25	94.77 15

zzz-MVS		84.73 53	84.47 50	85.50 87	91.89 86	65.16 153	91.55 146	92.23 124	75.32 95	80.53 68	95.21 37	56.06 153	97.16 66	84.86 45	92.55 46	94.18 86

MTAPA		83.91 68	83.38 65	85.50 87	91.89 86	65.16 153	81.75 295	92.23 124	75.32 95	80.53 68	95.21 37	56.06 153	97.16 66	84.86 45	92.55 46	94.18 86

EPMVS		78.49 156	75.98 169	86.02 70	91.21 108	69.68 35	80.23 308	91.20 165	75.25 97	72.48 148	78.11 280	54.65 175	93.69 206	57.66 246	83.04 125	94.69 68

v2v482		77.42 171	75.65 177	82.73 150	80.38 276	67.13 90	91.85 133	90.23 197	75.09 98	69.37 188	83.39 217	53.79 187	94.44 164	71.77 132	65.00 262	86.63 232

VPA-MVSNet		79.03 141	78.00 138	82.11 183	85.95 208	64.48 169	93.22 79	94.66 34	75.05 99	74.04 132	84.95 203	52.17 204	93.52 209	74.90 116	67.04 244	88.32 193

ACMMP_Plus		86.05 40	85.80 39	86.80 45	91.58 95	67.53 80	91.79 135	93.49 68	74.93 100	84.61 38	95.30 31	59.42 113	97.92 30	86.13 36	94.92 10	94.94 63

thres200		79.66 131	78.33 132	83.66 138	92.54 70	65.82 141	93.06 82	96.31 9	74.90 101	73.30 136	88.66 150	59.67 111	95.61 128	47.84 278	78.67 154	89.56 178

TAMVS		80.37 117	79.45 118	83.13 145	85.14 216	63.37 196	91.23 161	90.76 179	74.81 102	72.65 143	88.49 152	60.63 101	92.95 219	69.41 156	81.95 133	93.08 125

MP-MVS-pluss		85.24 47	85.13 47	85.56 86	91.42 104	65.59 145	91.54 147	92.51 118	74.56 103	80.62 67	95.64 23	59.15 117	97.00 74	86.94 32	93.80 28	94.07 96
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
v1141		77.28 177	75.57 178	82.42 164	80.63 270	66.73 108	91.96 123	90.42 188	74.41 104	69.46 185	82.12 233	55.09 165	94.40 169	70.99 141	65.05 258	86.12 238

divwei89l23v2f112		77.28 177	75.57 178	82.42 164	80.62 271	66.72 110	91.96 123	90.42 188	74.41 104	69.46 185	82.12 233	55.11 164	94.40 169	71.00 139	65.04 259	86.12 238

v1		77.29 176	75.57 178	82.42 164	80.61 274	66.73 108	91.96 123	90.42 188	74.41 104	69.46 185	82.12 233	55.14 163	94.40 169	71.00 139	65.04 259	86.13 237

mvs_anonymous		81.36 104	79.99 108	85.46 89	90.39 124	68.40 57	86.88 263	90.61 184	74.41 104	70.31 173	84.67 206	63.79 74	92.32 242	73.13 119	85.70 106	95.67 32

MAR-MVS		84.18 63	83.43 62	86.44 58	96.25 10	65.93 137	94.28 38	94.27 45	74.41 104	79.16 83	95.61 24	53.99 184	98.88 9	69.62 154	93.26 38	94.50 79
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
BH-w/o		80.49 116	79.30 122	84.05 128	90.83 117	64.36 176	93.60 67	89.42 227	74.35 109	69.09 192	90.15 131	55.23 160	95.61 128	64.61 198	86.43 102	92.17 147

thisisatest0515		83.41 74	82.49 78	86.16 68	89.46 147	68.26 63	93.54 70	94.70 32	74.31 110	75.75 114	90.92 117	72.62 12	96.52 97	69.64 152	81.50 136	93.71 108

Vis-MVSNet (Re-imp)		79.24 139	79.57 114	78.24 266	88.46 167	52.29 312	90.41 186	89.12 238	74.24 111	69.13 191	91.91 107	65.77 50	90.09 286	59.00 240	88.09 86	92.33 142

v1neww		77.39 172	75.71 174	82.44 158	80.69 264	66.83 98	91.94 127	90.18 200	74.19 112	69.60 182	82.51 223	54.99 169	94.44 164	71.68 134	65.60 251	86.05 241

v7new		77.39 172	75.71 174	82.44 158	80.69 264	66.83 98	91.94 127	90.18 200	74.19 112	69.60 182	82.51 223	54.99 169	94.44 164	71.68 134	65.60 251	86.05 241

v6		77.39 172	75.71 174	82.44 158	80.67 266	66.82 100	91.94 127	90.18 200	74.19 112	69.60 182	82.50 226	55.00 168	94.44 164	71.68 134	65.60 251	86.05 241

SMA-MVS		88.14 11	88.29 14	87.67 22	93.21 53	68.72 51	93.85 59	94.03 50	74.18 115	91.74 2	96.67 5	65.61 52	98.42 20	89.24 14	96.08 3	95.88 30

3Dnovator+	73.60 7	82.10 96	80.60 101	86.60 50	90.89 115	66.80 106	95.20 25	93.44 76	74.05 116	67.42 218	92.49 98	49.46 224	97.65 40	70.80 144	91.68 57	95.33 42

XVS		83.87 69	83.47 60	85.05 103	93.22 51	63.78 186	92.92 89	92.66 111	73.99 117	78.18 92	94.31 65	55.25 158	97.41 50	79.16 84	91.58 59	93.95 101

X-MVStestdata		76.86 186	74.13 201	85.05 103	93.22 51	63.78 186	92.92 89	92.66 111	73.99 117	78.18 92	10.19 367	55.25 158	97.41 50	79.16 84	91.58 59	93.95 101

MS-PatchMatch		77.90 167	76.50 163	82.12 180	85.99 207	69.95 29	91.75 140	92.70 108	73.97 119	62.58 261	84.44 209	41.11 275	95.78 118	63.76 205	92.17 52	80.62 315

LCM-MVSNet-Re		72.93 236	71.84 232	76.18 287	88.49 165	48.02 329	80.07 311	70.17 351	73.96 120	52.25 313	80.09 267	49.98 219	88.24 306	67.35 171	84.23 121	92.28 145

Vis-MVSNet		80.92 111	79.98 109	83.74 132	88.48 166	61.80 226	93.44 74	88.26 261	73.96 120	77.73 95	91.76 110	49.94 220	94.76 151	65.84 188	90.37 73	94.65 71
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-mter		79.96 125	79.38 121	81.72 189	86.93 196	61.17 232	92.70 95	91.54 152	73.85 122	75.62 116	86.94 184	49.84 222	92.38 239	72.21 129	84.76 113	91.60 152

OMC-MVS		78.67 153	77.91 141	80.95 210	85.76 212	57.40 283	88.49 225	88.67 251	73.85 122	72.43 150	92.10 105	49.29 226	94.55 159	72.73 124	77.89 159	90.91 163

Fast-Effi-MVS+		81.14 107	80.01 107	84.51 120	90.24 127	65.86 138	94.12 44	89.15 237	73.81 124	75.37 121	88.26 158	57.26 132	94.53 162	66.97 176	84.92 110	93.15 122

V42		76.46 193	74.55 194	82.19 177	79.14 298	67.82 71	90.26 190	89.42 227	73.75 125	68.63 200	81.89 238	51.31 211	94.09 187	71.69 133	64.84 263	84.66 265

v1144		76.73 191	74.88 190	82.27 172	80.23 284	66.60 116	91.68 142	90.21 199	73.69 126	69.06 193	81.89 238	52.73 199	94.40 169	69.21 158	65.23 255	85.80 249

v148		76.19 196	74.47 196	81.36 194	80.05 288	64.44 171	91.75 140	90.23 197	73.68 127	67.13 222	80.84 254	55.92 156	93.86 203	68.95 161	61.73 284	85.76 252

CR-MVSNet		73.79 231	70.82 240	82.70 151	83.15 242	67.96 69	70.25 334	84.00 309	73.67 128	69.97 178	72.41 314	57.82 128	89.48 296	52.99 262	73.13 198	90.64 166

XXY-MVS		77.94 166	76.44 164	82.43 161	82.60 246	64.44 171	92.01 119	91.83 143	73.59 129	70.00 177	85.82 195	54.43 179	94.76 151	69.63 153	68.02 239	88.10 199

#test#		84.98 51	84.74 49	85.72 82	93.75 42	65.01 159	94.09 45	93.19 89	73.55 130	79.22 81	94.93 45	59.04 118	97.67 36	82.66 58	92.21 49	94.49 80

tfpn200view9		78.79 148	77.43 148	82.88 147	92.21 78	64.49 167	92.05 117	96.28 10	73.48 131	71.75 158	88.26 158	60.07 108	95.32 137	45.16 287	77.58 163	88.83 182

thres400		78.68 151	77.43 148	82.43 161	92.21 78	64.49 167	92.05 117	96.28 10	73.48 131	71.75 158	88.26 158	60.07 108	95.32 137	45.16 287	77.58 163	87.48 209

v7		76.83 189	75.01 189	82.29 171	80.35 277	66.70 112	91.68 142	89.97 210	73.47 133	69.22 190	82.22 230	52.52 200	94.43 168	69.73 151	65.96 250	85.74 253

FMVSNet3		77.73 168	76.04 168	82.80 148	91.20 109	68.99 46	91.87 131	91.99 136	73.35 134	67.04 223	83.19 219	56.62 146	92.14 245	59.80 236	69.34 228	87.28 220

GST-MVS		84.63 56	84.29 54	85.66 85	92.82 64	65.27 150	93.04 84	93.13 93	73.20 135	78.89 85	94.18 67	59.41 114	97.85 33	81.45 68	92.48 48	93.86 106

USDC		67.43 286	64.51 283	76.19 286	77.94 311	55.29 300	78.38 320	85.00 301	73.17 136	48.36 326	80.37 261	21.23 345	92.48 237	52.15 263	64.02 271	80.81 313

MP-MVS		85.02 50	84.97 48	85.17 101	92.60 69	64.27 180	93.24 77	92.27 123	73.13 137	79.63 78	94.43 56	61.90 90	97.17 65	85.00 43	92.56 45	94.06 97
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 93	81.12 94	85.26 98	86.42 199	68.72 51	92.59 102	90.44 185	73.12 138	84.20 44	94.36 58	38.04 290	95.73 121	84.12 49	86.81 94	91.33 156

xiu_mvs_v1_base		82.16 93	81.12 94	85.26 98	86.42 199	68.72 51	92.59 102	90.44 185	73.12 138	84.20 44	94.36 58	38.04 290	95.73 121	84.12 49	86.81 94	91.33 156

xiu_mvs_v1_base_debi		82.16 93	81.12 94	85.26 98	86.42 199	68.72 51	92.59 102	90.44 185	73.12 138	84.20 44	94.36 58	38.04 290	95.73 121	84.12 49	86.81 94	91.33 156

BH-RMVSNet		79.46 137	77.65 143	84.89 106	91.68 94	65.66 143	93.55 69	88.09 262	72.93 141	73.37 135	91.12 116	46.20 253	96.12 106	56.28 249	85.61 108	92.91 130

IS-MVSNet		80.14 121	79.41 119	82.33 169	87.91 181	60.08 254	91.97 122	88.27 260	72.90 142	71.44 162	91.73 112	61.44 93	93.66 207	62.47 222	86.53 100	93.24 119

PS-MVSNAJss		77.26 179	76.31 165	80.13 220	80.64 269	59.16 266	90.63 183	91.06 171	72.80 143	68.58 201	84.57 208	53.55 189	93.96 197	72.97 120	71.96 209	87.27 221

v1192		75.98 201	73.92 205	82.15 178	79.73 289	66.24 130	91.22 162	89.75 215	72.67 144	68.49 206	81.42 244	49.86 221	94.27 177	67.08 174	65.02 261	85.95 246

Effi-MVS+		83.82 70	82.76 75	86.99 43	89.56 144	69.40 38	91.35 156	86.12 291	72.59 145	83.22 51	92.81 95	59.60 112	96.01 113	81.76 64	87.80 89	95.56 37

UnsupCasMVSNet_eth		65.79 293	63.10 292	73.88 298	70.71 336	50.29 323	81.09 301	89.88 212	72.58 146	49.25 324	74.77 304	32.57 317	87.43 311	55.96 250	41.04 345	83.90 271

1112_ss		80.56 114	79.83 111	82.77 149	88.65 163	60.78 237	92.29 107	88.36 257	72.58 146	72.46 149	94.95 43	65.09 56	93.42 212	66.38 181	77.71 160	94.10 93

thres600view7		78.00 162	76.66 160	82.03 185	91.93 83	63.69 191	91.30 159	96.33 5	72.43 148	70.46 167	87.89 165	60.31 102	94.92 147	42.64 300	76.64 175	87.48 209

IterMVS-LS		76.49 192	75.18 188	80.43 214	84.49 224	62.74 212	90.64 181	88.80 249	72.40 149	65.16 238	81.72 241	60.98 95	92.27 244	67.74 168	64.65 266	86.29 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		78.97 143	78.22 135	81.25 196	85.33 213	62.73 213	89.53 208	93.21 86	72.39 150	72.14 153	90.13 132	60.99 94	94.72 154	67.73 169	72.49 205	86.29 234

v144192		76.05 199	74.03 202	82.12 180	79.50 293	66.55 120	91.39 152	89.71 221	72.30 151	68.17 208	81.33 246	51.75 207	94.03 194	67.94 166	64.19 268	85.77 250

tfpn111		78.00 162	76.62 161	82.13 179	91.89 86	63.21 200	91.19 165	96.33 5	72.28 152	70.45 168	87.89 165	60.31 102	94.91 148	42.61 301	76.64 175	88.27 194

conf200view11		78.32 159	77.01 155	82.27 172	91.89 86	63.21 200	91.19 165	96.33 5	72.28 152	70.45 168	87.89 165	60.31 102	95.32 137	45.16 287	77.58 163	88.27 194

thres100view900		78.37 157	77.01 155	82.46 157	91.89 86	63.21 200	91.19 165	96.33 5	72.28 152	70.45 168	87.89 165	60.31 102	95.32 137	45.16 287	77.58 163	88.83 182

PatchmatchNet		77.46 170	74.63 191	85.96 72	89.55 145	70.35 23	79.97 312	89.55 223	72.23 155	70.94 163	76.91 292	57.03 135	92.79 227	54.27 255	81.17 138	94.74 67
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing_2		71.09 254	67.32 267	82.40 168	69.82 339	66.52 122	83.64 279	90.77 178	72.21 156	45.12 336	71.07 328	27.60 334	93.74 204	75.71 106	69.96 223	86.95 225

HFP-MVS		84.73 53	84.40 52	85.72 82	93.75 42	65.01 159	93.50 72	93.19 89	72.19 157	79.22 81	94.93 45	59.04 118	97.67 36	81.55 66	92.21 49	94.49 80

ACMMPR		84.37 57	84.06 55	85.28 97	93.56 46	64.37 175	93.50 72	93.15 92	72.19 157	78.85 88	94.86 49	56.69 145	97.45 47	81.55 66	92.20 51	94.02 99

1314		80.70 112	78.95 127	85.94 73	87.77 184	67.56 78	87.91 237	92.55 116	72.17 159	67.44 217	93.09 82	50.27 217	97.04 71	71.68 134	87.64 90	93.23 120

region2R		84.36 58	84.03 56	85.36 95	93.54 47	64.31 177	93.43 75	92.95 101	72.16 160	78.86 87	94.84 50	56.97 139	97.53 45	81.38 70	92.11 53	94.24 84

Test_1112_low_res		79.56 134	78.60 130	82.43 161	88.24 173	60.39 246	92.09 114	87.99 264	72.10 161	71.84 156	87.42 174	64.62 66	93.04 216	65.80 189	77.30 170	93.85 107

v1921920		75.63 206	73.49 216	82.06 184	79.38 294	66.35 126	91.07 170	89.48 224	71.98 162	67.99 209	81.22 249	49.16 229	93.90 200	66.56 179	64.56 267	85.92 248

Fast-Effi-MVS+-dtu		75.04 213	73.37 218	80.07 221	80.86 259	59.52 261	91.20 164	85.38 298	71.90 163	65.20 236	84.84 204	41.46 274	92.97 218	66.50 180	72.96 201	87.73 206

LFMVS		84.34 59	82.73 76	89.18 8	94.76 22	73.25 8	94.99 32	91.89 140	71.90 163	82.16 56	93.49 78	47.98 238	97.05 69	82.55 59	84.82 111	97.25 2

train_agg		87.21 24	87.42 23	86.60 50	94.18 32	67.28 85	94.16 40	93.51 66	71.87 165	85.52 31	95.33 29	68.19 25	97.27 61	89.09 15	94.90 12	95.25 50

test_8					94.19 31	67.19 87	94.15 42	93.42 77	71.87 165	85.38 33	95.35 28	68.19 25	96.95 81

agg_prior1		87.02 26	87.26 25	86.28 66	94.16 36	66.97 94	94.08 46	93.31 82	71.85 167	84.49 41	95.39 27	68.91 21	96.75 90	88.84 19	94.32 22	95.13 54

MDTV_nov1_ep13			72.61 225		89.06 155	68.48 55	80.33 306	90.11 204	71.84 168	71.81 157	75.92 299	53.01 196	93.92 199	48.04 277	73.38 196

ab-mvs		80.18 120	78.31 133	85.80 78	88.44 168	65.49 148	83.00 289	92.67 110	71.82 169	77.36 102	85.01 202	54.50 176	96.59 93	76.35 104	75.63 181	95.32 44

agg_prior3		86.93 28	87.08 26	86.48 56	94.21 30	66.95 96	94.14 43	93.40 78	71.80 170	84.86 37	95.13 39	66.16 44	97.25 63	89.09 15	94.90 12	95.25 50

ACMMP		81.49 102	80.67 99	83.93 129	91.71 93	62.90 209	92.13 111	92.22 128	71.79 171	71.68 160	93.49 78	50.32 215	96.96 80	78.47 89	84.22 122	91.93 149
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 31	86.85 30	86.78 46	93.47 49	65.55 146	95.39 22	95.10 20	71.77 172	85.69 30	96.52 6	62.07 89	98.77 10	86.06 37	95.60 7	96.03 25

TEST9					94.18 32	67.28 85	94.16 40	93.51 66	71.75 173	85.52 31	95.33 29	68.01 27	97.27 61

v8		75.35 208	73.26 219	81.61 192	80.67 266	66.82 100	89.54 207	89.27 231	71.65 174	63.30 255	80.30 263	54.99 169	94.06 190	67.33 173	62.33 278	83.94 270

v1240		75.21 212	72.98 221	81.88 186	79.20 296	66.00 135	90.75 176	89.11 239	71.63 175	67.41 219	81.22 249	47.36 243	93.87 201	65.46 193	64.72 265	85.77 250

Patchmatch-test1		75.00 215	71.80 234	84.58 117	86.63 198	70.08 26	81.06 302	89.19 234	71.60 176	70.01 176	77.16 290	45.53 255	88.63 300	51.79 264	73.27 197	95.02 61

BH-untuned		78.68 151	77.08 153	83.48 141	89.84 133	63.74 188	92.70 95	88.59 253	71.57 177	66.83 226	88.65 151	51.75 207	95.39 135	59.03 239	84.77 112	91.32 159

IterMVS		72.65 242	70.83 239	78.09 269	82.17 248	62.96 205	87.64 249	86.28 287	71.56 178	60.44 267	78.85 277	45.42 257	86.66 314	63.30 209	61.83 281	84.65 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mPP-MVS		82.96 82	82.44 79	84.52 119	92.83 62	62.92 208	92.76 92	91.85 142	71.52 179	75.61 118	94.24 66	53.48 193	96.99 77	78.97 87	90.73 68	93.64 111

test-LLR		80.10 122	79.56 115	81.72 189	86.93 196	61.17 232	92.70 95	91.54 152	71.51 180	75.62 116	86.94 184	53.83 185	92.38 239	72.21 129	84.76 113	91.60 152

test0.0.03 1		72.76 239	72.71 224	72.88 306	80.25 283	47.99 330	91.22 162	89.45 225	71.51 180	62.51 262	87.66 170	53.83 185	85.06 319	50.16 269	67.84 242	85.58 254

PGM-MVS		83.25 76	82.70 77	84.92 105	92.81 66	64.07 182	90.44 184	92.20 129	71.28 182	77.23 104	94.43 56	55.17 162	97.31 57	79.33 83	91.38 62	93.37 114

thisisatest0530		81.15 106	80.07 105	84.39 122	88.26 172	65.63 144	91.40 150	94.62 36	71.27 183	70.93 164	89.18 146	72.47 13	96.04 110	65.62 191	76.89 174	91.49 154

dp		75.01 214	72.09 231	83.76 131	89.28 149	66.22 131	79.96 313	89.75 215	71.16 184	67.80 215	77.19 288	51.81 206	92.54 234	50.39 268	71.44 214	92.51 139

CP-MVS		83.71 73	83.40 64	84.65 115	93.14 56	63.84 184	94.59 36	92.28 122	71.03 185	77.41 101	94.92 47	55.21 161	96.19 103	81.32 71	90.70 69	93.91 104

v10		74.77 223	72.54 227	81.46 193	80.33 281	66.71 111	89.15 214	89.08 240	70.94 186	63.08 256	79.86 268	52.52 200	94.04 193	65.70 190	62.17 279	83.64 272

CDPH-MVS		85.71 44	85.46 44	86.46 57	94.75 23	67.19 87	93.89 58	92.83 105	70.90 187	83.09 52	95.28 32	63.62 76	97.36 53	80.63 75	94.18 23	94.84 65

GBi-Net		75.65 204	73.83 206	81.10 205	88.85 158	65.11 155	90.01 193	90.32 191	70.84 188	67.04 223	80.25 264	48.03 235	91.54 264	59.80 236	69.34 228	86.64 229

test1		75.65 204	73.83 206	81.10 205	88.85 158	65.11 155	90.01 193	90.32 191	70.84 188	67.04 223	80.25 264	48.03 235	91.54 264	59.80 236	69.34 228	86.64 229

FMVSNet2		76.07 198	74.01 203	82.26 175	88.85 158	67.66 75	91.33 157	91.61 150	70.84 188	65.98 228	82.25 229	48.03 235	92.00 250	58.46 242	68.73 234	87.10 222

view600		76.93 182	75.50 181	81.23 197	91.44 100	62.00 221	89.94 197	96.56 1	70.68 191	68.54 202	87.31 176	60.79 96	94.19 180	38.90 315	75.31 183	87.48 209

view800		76.93 182	75.50 181	81.23 197	91.44 100	62.00 221	89.94 197	96.56 1	70.68 191	68.54 202	87.31 176	60.79 96	94.19 180	38.90 315	75.31 183	87.48 209

conf0.05thres1000		76.93 182	75.50 181	81.23 197	91.44 100	62.00 221	89.94 197	96.56 1	70.68 191	68.54 202	87.31 176	60.79 96	94.19 180	38.90 315	75.31 183	87.48 209

tfpn		76.93 182	75.50 181	81.23 197	91.44 100	62.00 221	89.94 197	96.56 1	70.68 191	68.54 202	87.31 176	60.79 96	94.19 180	38.90 315	75.31 183	87.48 209

HyFIR lowres test		81.03 109	79.56 115	85.43 92	87.81 183	68.11 67	90.18 191	90.01 209	70.65 195	72.95 139	86.06 193	63.61 77	94.50 163	75.01 113	79.75 145	93.67 109

MVP-Stereo		77.12 181	76.23 166	79.79 229	81.72 252	66.34 127	89.29 210	90.88 175	70.56 196	62.01 264	82.88 220	49.34 225	94.13 185	65.55 192	93.80 28	78.88 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tpmp4_e23		78.85 145	76.55 162	85.77 80	89.25 150	68.39 58	81.63 299	91.38 160	70.40 197	75.21 122	79.22 275	67.37 35	94.79 149	58.98 241	75.51 182	94.13 91

ACMP	71.68 10	75.58 207	74.23 199	79.62 233	84.97 218	59.64 258	90.80 174	89.07 241	70.39 198	62.95 257	87.30 180	38.28 287	93.87 201	72.89 121	71.45 213	85.36 259
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		83.25 76	82.95 71	84.17 125	92.25 76	62.88 210	90.91 171	91.86 141	70.30 199	77.12 105	93.96 71	56.75 143	96.28 101	82.04 62	91.34 64	93.34 115
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
tpm2		79.80 129	77.95 139	85.34 96	88.28 171	68.26 63	81.56 300	91.42 158	70.11 200	77.59 100	80.50 259	67.40 34	94.26 179	67.34 172	77.35 169	93.51 113

TR-MVS		78.77 149	77.37 151	82.95 146	90.49 121	60.88 235	93.67 65	90.07 205	70.08 201	74.51 126	91.37 114	45.69 254	95.70 126	60.12 234	80.32 142	92.29 144

PAPM_NR		82.97 81	81.84 85	86.37 62	94.10 38	66.76 107	87.66 248	92.84 104	69.96 202	74.07 131	93.57 76	63.10 84	97.50 46	70.66 146	90.58 71	94.85 64

PCF-MVS	73.15 9	79.29 138	77.63 144	84.29 124	86.06 206	65.96 136	87.03 257	91.10 169	69.86 203	69.79 181	90.64 121	57.54 131	96.59 93	64.37 202	82.29 129	90.32 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MIMVSNet		71.64 248	68.44 257	81.23 197	81.97 251	64.44 171	73.05 330	88.80 249	69.67 204	64.59 241	74.79 303	32.79 315	87.82 309	53.99 256	76.35 178	91.42 155

LPG-MVS_test		75.82 203	74.58 193	79.56 235	84.31 228	59.37 263	90.44 184	89.73 218	69.49 205	64.86 239	88.42 153	38.65 284	94.30 175	72.56 125	72.76 202	85.01 262

LGP-MVS_train				79.56 235	84.31 228	59.37 263		89.73 218	69.49 205	64.86 239	88.42 153	38.65 284	94.30 175	72.56 125	72.76 202	85.01 262

APDe-MVS		87.54 19	87.84 16	86.65 48	96.07 12	66.30 128	94.84 35	93.78 54	69.35 207	88.39 14	96.34 9	67.74 33	97.66 39	90.62 9	93.44 36	96.01 26

tttt0517		79.50 135	78.53 131	82.41 167	87.22 190	61.43 231	89.75 204	94.76 29	69.29 208	67.91 212	88.06 163	72.92 10	95.63 127	62.91 216	73.90 195	90.16 170

Patchmatch-RL test		68.17 278	64.49 284	79.19 246	71.22 334	53.93 306	70.07 336	71.54 350	69.22 209	56.79 288	62.89 341	56.58 147	88.61 301	69.53 155	52.61 317	95.03 60

0601test		84.28 60	83.16 68	87.64 23	94.52 27	69.24 40	95.78 11	95.09 21	69.19 210	81.09 62	92.88 92	57.00 137	97.44 48	81.11 73	81.76 134	96.23 19

Anonymous20240521		84.28 60	83.16 68	87.64 23	94.52 27	69.24 40	95.78 11	95.09 21	69.19 210	81.09 62	92.88 92	57.00 137	97.44 48	81.11 73	81.76 134	96.23 19

jajsoiax		73.05 235	71.51 236	77.67 272	77.46 313	54.83 302	88.81 219	90.04 208	69.13 212	62.85 259	83.51 215	31.16 324	92.75 228	70.83 143	69.80 224	85.43 258

DP-MVS Recon		82.73 83	81.65 89	85.98 71	97.31 3	67.06 91	95.15 27	91.99 136	69.08 213	76.50 111	93.89 72	54.48 178	98.20 24	70.76 145	85.66 107	92.69 133

v18		71.94 244	69.43 247	79.50 237	80.74 261	66.82 100	88.16 231	86.66 277	68.95 214	55.55 291	72.66 309	55.03 167	90.15 282	64.78 197	52.30 319	81.54 297

Baseline_NR-MVSNet		73.99 229	72.83 222	77.48 275	80.78 260	59.29 265	91.79 135	84.55 304	68.85 215	68.99 194	80.70 255	56.16 150	92.04 249	62.67 220	60.98 290	81.11 309

CHOSEN 280x420		77.35 175	76.95 158	78.55 260	87.07 193	62.68 214	69.71 337	82.95 319	68.80 216	71.48 161	87.27 181	66.03 46	84.00 325	76.47 103	82.81 128	88.95 181

ESAPD		88.77 9	89.21 9	87.45 30	96.26 9	67.56 78	94.17 39	94.15 48	68.77 217	90.74 6	97.27 2	76.09 4	98.49 16	90.58 10	94.91 11	96.30 16

mvs_tets		72.71 240	71.11 237	77.52 273	77.41 314	54.52 304	88.45 227	89.76 214	68.76 218	62.70 260	83.26 218	29.49 328	92.71 229	70.51 148	69.62 226	85.34 260

v17		71.77 247	69.20 250	79.46 239	80.62 271	66.81 104	87.93 235	86.63 279	68.71 219	55.25 294	72.49 311	54.72 174	90.11 285	64.50 200	51.97 321	81.47 298

v16		71.81 245	69.26 249	79.47 238	80.66 268	66.81 104	87.93 235	86.63 279	68.70 220	55.35 293	72.51 310	54.75 173	90.12 284	64.51 199	52.28 320	81.47 298

v15		71.40 249	68.75 252	79.35 240	80.39 275	66.70 112	87.57 250	86.64 278	68.66 221	54.68 296	72.00 318	54.50 176	89.98 287	63.69 206	50.66 326	81.38 302

MVS		84.66 55	82.86 73	90.06 1	90.93 113	74.56 5	87.91 237	95.54 14	68.55 222	72.35 152	94.71 53	59.78 110	98.90 7	81.29 72	94.69 19	96.74 7

v1.0		37.26 334	49.67 326	0.00 359	96.29 7	0.00 374	0.00 365	94.26 46	68.52 223	90.78 4	97.23 3	0.00 376	0.00 371	0.00 368	0.00 368	0.00 369

EPP-MVSNet		81.79 99	81.52 90	82.61 155	88.77 162	60.21 250	93.02 85	93.66 61	68.52 223	72.90 140	90.39 127	72.19 15	94.96 144	74.93 114	79.29 149	92.67 134

CSCG		86.87 29	86.26 32	88.72 9	95.05 21	70.79 19	93.83 62	95.33 15	68.48 225	77.63 98	94.35 62	73.04 9	98.45 17	84.92 44	93.71 32	96.92 6

V14		71.29 251	68.61 254	79.31 241	80.34 279	66.65 114	87.39 253	86.61 281	68.41 226	54.49 298	71.91 319	54.25 181	89.96 288	63.50 207	50.62 327	81.33 304

V9		71.16 252	68.46 256	79.27 243	80.26 282	66.60 116	87.21 256	86.56 282	68.17 227	54.26 301	71.81 321	54.00 183	89.93 289	63.28 210	50.57 328	81.27 305

v11		71.05 255	68.32 259	79.23 244	80.34 279	66.57 119	87.01 259	86.55 283	68.11 228	54.40 299	71.66 323	52.94 197	89.91 290	62.71 219	51.12 324	81.21 306

v12		71.02 256	68.29 261	79.22 245	80.18 285	66.53 121	87.01 259	86.54 284	67.90 229	54.00 304	71.70 322	53.66 188	89.91 290	63.09 212	50.51 329	81.21 306

CP-MVSNet		70.50 261	69.91 244	72.26 311	80.71 263	51.00 319	87.23 255	90.30 195	67.84 230	59.64 270	82.69 222	50.23 218	82.30 336	51.28 265	59.28 295	83.46 277

pmmvs5		73.35 233	71.52 235	78.86 254	78.64 306	60.61 244	91.08 169	86.90 275	67.69 231	63.32 254	83.64 213	44.33 263	90.53 274	62.04 224	66.02 249	85.46 257

pm-mvs1		72.89 237	71.09 238	78.26 265	79.10 300	57.62 280	90.80 174	89.30 230	67.66 232	62.91 258	81.78 240	49.11 230	92.95 219	60.29 233	58.89 300	84.22 268

MDTV_nov1_ep13_2view						59.90 256	80.13 310		67.65 233	72.79 141		54.33 180		59.83 235		92.58 137

pmmvs4		73.92 230	71.81 233	80.25 217	79.17 297	65.24 151	87.43 252	87.26 274	67.64 234	63.46 253	83.91 212	48.96 231	91.53 267	62.94 215	65.49 254	83.96 269

v13		70.90 257	68.15 262	79.15 249	80.08 286	66.45 123	86.83 264	86.50 285	67.62 235	53.78 306	71.61 324	53.51 192	89.87 292	62.89 217	50.50 330	81.14 308

WR-MVS_H		70.59 259	69.94 243	72.53 308	81.03 257	51.43 316	87.35 254	92.03 135	67.38 236	60.23 268	80.70 255	55.84 157	83.45 329	46.33 283	58.58 301	82.72 288

PS-CasMVS		69.86 265	69.13 251	72.07 314	80.35 277	50.57 321	87.02 258	89.75 215	67.27 237	59.19 273	82.28 228	46.58 249	82.24 337	50.69 267	59.02 298	83.39 279

PEN-MVS		69.46 270	68.56 255	72.17 313	79.27 295	49.71 325	86.90 262	89.24 233	67.24 238	59.08 274	82.51 223	47.23 244	83.54 328	48.42 276	57.12 303	83.25 280

cascas		78.18 160	75.77 172	85.41 93	87.14 192	69.11 43	92.96 86	91.15 167	66.71 239	70.47 166	86.07 192	37.49 296	96.48 98	70.15 149	79.80 144	90.65 165

APD-MVS		85.93 41	85.99 35	85.76 81	95.98 14	65.21 152	93.59 68	92.58 115	66.54 240	86.17 24	95.88 17	63.83 73	97.00 74	86.39 35	92.94 40	95.06 56
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	70.45 11	78.54 155	75.92 170	86.41 61	85.93 211	71.68 11	92.74 93	92.51 118	66.49 241	64.56 242	91.96 106	43.88 264	98.10 27	54.61 253	90.65 70	89.44 179

DTE-MVSNet		68.46 276	67.33 266	71.87 317	77.94 311	49.00 328	86.16 269	88.58 254	66.36 242	58.19 280	82.21 231	46.36 250	83.87 326	44.97 291	55.17 311	82.73 287

semantic-postprocess				76.32 285	81.48 253	60.67 242		85.99 293	66.17 243	59.50 271	78.88 276	45.51 256	83.65 327	62.58 221	61.93 280	84.63 267

TransMVSNet (Re)		70.07 263	67.66 264	77.31 279	80.62 271	59.13 267	91.78 137	84.94 302	65.97 244	60.08 269	80.44 260	50.78 212	91.87 251	48.84 274	45.46 339	80.94 311

tfpn_ndepth		76.45 194	75.22 187	80.14 218	90.97 112	58.92 268	90.11 192	93.24 85	65.96 245	67.37 221	90.52 125	66.67 40	92.29 243	37.71 321	74.44 189	89.21 180

MVSFormer		83.75 72	82.88 72	86.37 62	89.24 152	71.18 16	89.07 216	90.69 180	65.80 246	87.13 20	94.34 63	64.99 63	92.67 231	72.83 122	91.80 55	95.27 47

test_djsdf		73.76 232	72.56 226	77.39 277	77.00 316	53.93 306	89.07 216	90.69 180	65.80 246	63.92 248	82.03 236	43.14 267	92.67 231	72.83 122	68.53 235	85.57 255

API-MVS		82.28 91	80.53 102	87.54 28	96.13 11	70.59 21	93.63 66	91.04 173	65.72 248	75.45 120	92.83 94	56.11 152	98.89 8	64.10 203	89.75 77	93.15 122

原ACMM1				84.42 121	93.21 53	64.27 180		93.40 78	65.39 249	79.51 79	92.50 97	58.11 125	96.69 92	65.27 194	93.96 25	92.32 143

testgi		64.48 298	62.87 295	69.31 321	71.24 333	40.62 345	85.49 270	79.92 329	65.36 250	54.18 302	83.49 216	23.74 341	84.55 320	41.60 303	60.79 292	82.77 286

QAPM		79.95 126	77.39 150	87.64 23	89.63 142	71.41 12	93.30 76	93.70 59	65.34 251	67.39 220	91.75 111	47.83 239	98.96 5	57.71 245	89.81 75	92.54 138

HPM-MVS_fast		80.25 119	79.55 117	82.33 169	91.55 97	59.95 255	91.32 158	89.16 236	65.23 252	74.71 125	93.07 85	47.81 240	95.74 120	74.87 117	88.23 84	91.31 160

conf0.01		74.95 216	73.61 209	78.96 252	89.65 136	56.94 288	87.72 241	93.45 69	65.14 253	65.68 229	89.99 135	65.09 56	91.67 255	35.16 328	70.61 217	88.27 194

conf0.002		74.95 216	73.61 209	78.96 252	89.65 136	56.94 288	87.72 241	93.45 69	65.14 253	65.68 229	89.99 135	65.09 56	91.67 255	35.16 328	70.61 217	88.27 194

thresconf0.02		74.92 219	73.61 209	78.85 255	89.65 136	56.94 288	87.72 241	93.45 69	65.14 253	65.68 229	89.99 135	65.09 56	91.67 255	35.16 328	70.61 217	87.94 201

tfpn_n400		74.92 219	73.61 209	78.85 255	89.65 136	56.94 288	87.72 241	93.45 69	65.14 253	65.68 229	89.99 135	65.09 56	91.67 255	35.16 328	70.61 217	87.94 201

tfpnconf		74.92 219	73.61 209	78.85 255	89.65 136	56.94 288	87.72 241	93.45 69	65.14 253	65.68 229	89.99 135	65.09 56	91.67 255	35.16 328	70.61 217	87.94 201

tfpnview11		74.92 219	73.61 209	78.85 255	89.65 136	56.94 288	87.72 241	93.45 69	65.14 253	65.68 229	89.99 135	65.09 56	91.67 255	35.16 328	70.61 217	87.94 201

v748		70.55 260	67.97 263	78.27 264	75.75 323	58.78 270	86.29 268	89.25 232	65.12 259	56.66 289	77.17 289	45.05 260	92.95 219	58.13 243	58.33 302	83.10 284

tfpnnormal		70.10 262	67.36 265	78.32 262	83.45 239	60.97 234	88.85 218	92.77 106	64.85 260	60.83 266	78.53 278	43.52 266	93.48 210	31.73 344	61.70 285	80.52 316

v52		69.80 266	67.01 269	78.15 267	71.84 332	60.10 252	82.02 293	87.39 269	64.48 261	57.80 283	75.97 297	41.47 273	92.90 224	63.00 213	59.13 297	81.45 300

V4		69.80 266	67.02 268	78.15 267	71.86 331	60.10 252	82.02 293	87.39 269	64.48 261	57.78 284	75.98 296	41.49 272	92.90 224	63.00 213	59.16 296	81.44 301

K. test v3		63.09 305	59.61 307	73.53 301	76.26 319	49.38 327	83.27 285	77.15 334	64.35 263	47.77 327	72.32 316	28.73 330	87.79 310	49.93 271	36.69 349	83.41 278

v7n		71.31 250	68.65 253	79.28 242	76.40 318	60.77 238	86.71 265	89.45 225	64.17 264	58.77 278	78.24 279	44.59 262	93.54 208	57.76 244	61.75 283	83.52 275

tfpn1000		75.25 210	74.00 204	79.03 251	90.30 125	57.56 282	88.55 224	93.36 80	64.14 265	65.17 237	89.76 144	67.06 37	91.46 269	34.54 336	73.09 200	88.06 200

FMVSNet1		72.71 240	69.91 244	81.10 205	83.60 237	65.11 155	90.01 193	90.32 191	63.92 266	63.56 252	80.25 264	36.35 305	91.54 264	54.46 254	66.75 246	86.64 229

XVG-OURS		74.25 227	72.46 228	79.63 232	78.45 307	57.59 281	80.33 306	87.39 269	63.86 267	68.76 198	89.62 145	40.50 278	91.72 254	69.00 160	74.25 190	89.58 177

114514_t		79.17 140	77.67 142	83.68 136	95.32 17	65.53 147	92.85 91	91.60 151	63.49 268	67.92 211	90.63 123	46.65 248	95.72 125	67.01 175	83.54 124	89.79 174

APD-MVS_3200maxsize		81.64 100	81.32 92	82.59 156	92.36 71	58.74 271	91.39 152	91.01 174	63.35 269	79.72 77	94.62 54	51.82 205	96.14 105	79.71 79	87.93 87	92.89 131

test20.03		63.83 302	62.65 296	67.38 326	70.58 338	39.94 346	86.57 266	84.17 306	63.29 270	51.86 314	77.30 285	37.09 301	82.47 334	38.87 319	54.13 315	79.73 323

XVG-OURS-SEG-HR		74.70 224	73.08 220	79.57 234	78.25 308	57.33 284	80.49 304	87.32 272	63.22 271	68.76 198	90.12 134	44.89 261	91.59 262	70.55 147	74.09 192	89.79 174

ACMM	69.62 13	74.34 225	72.73 223	79.17 247	84.25 230	57.87 276	90.36 187	89.93 211	63.17 272	65.64 235	86.04 194	37.79 294	94.10 186	65.89 187	71.52 212	85.55 256
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmvs		72.88 238	69.76 246	82.22 176	90.98 111	67.05 92	78.22 322	88.30 258	63.10 273	64.35 247	74.98 302	55.09 165	94.27 177	43.25 294	69.57 227	85.34 260

SixPastTwentyTwo		64.92 295	61.78 301	74.34 297	78.74 304	49.76 324	83.42 284	79.51 331	62.86 274	50.27 321	77.35 284	30.92 326	90.49 275	45.89 285	47.06 336	82.78 285

TAPA-MVS	70.22 12	74.94 218	73.53 215	79.17 247	90.40 123	52.07 313	89.19 213	89.61 222	62.69 275	70.07 175	92.67 96	48.89 232	94.32 174	38.26 320	79.97 143	91.12 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
abl_6		79.82 128	79.20 124	81.70 191	89.85 132	58.34 273	88.47 226	90.07 205	62.56 276	77.71 96	93.08 83	47.65 242	96.78 88	77.94 94	85.45 109	89.99 173

Anonymous202405211		77.96 165	75.33 185	85.87 74	93.73 44	64.52 166	94.85 34	85.36 299	62.52 277	76.11 112	90.18 130	29.43 329	97.29 58	68.51 164	77.24 172	95.81 31

pmmvs-eth3d		65.53 294	62.32 298	75.19 290	69.39 341	59.59 259	82.80 290	83.43 313	62.52 277	51.30 318	72.49 311	32.86 314	87.16 313	55.32 252	50.73 325	78.83 329

AdaColmap		78.94 144	77.00 157	84.76 110	96.34 6	65.86 138	92.66 99	87.97 265	62.18 279	70.56 165	92.37 102	43.53 265	97.35 54	64.50 200	82.86 126	91.05 162

无先验							92.71 94	92.61 114	62.03 280				97.01 72	66.63 177		93.97 100

XVG-ACMP-BASELINE		68.04 279	65.53 275	75.56 289	74.06 327	52.37 311	78.43 319	85.88 295	62.03 280	58.91 277	81.21 251	20.38 346	91.15 271	60.69 231	68.18 237	83.16 282

anonymousdsp		71.14 253	69.37 248	76.45 284	72.95 328	54.71 303	84.19 276	88.88 247	61.92 282	62.15 263	79.77 269	38.14 289	91.44 270	68.90 162	67.45 243	83.21 281

tpm cat1		75.30 209	72.21 230	84.58 117	88.52 164	67.77 72	78.16 323	88.02 263	61.88 283	68.45 207	76.37 293	60.65 100	94.03 194	53.77 258	74.11 191	91.93 149

FMVSNet5		68.04 279	65.66 274	75.18 291	84.43 226	57.89 275	83.54 280	86.26 288	61.83 284	53.64 307	73.30 306	37.15 300	85.08 318	48.99 273	61.77 282	82.56 292

Anonymous20231206		67.53 284	65.78 272	72.79 307	74.95 324	47.59 332	88.23 230	87.32 272	61.75 285	58.07 281	77.29 286	37.79 294	87.29 312	42.91 296	63.71 273	83.48 276

PatchMatch-RL		72.06 243	69.98 242	78.28 263	89.51 146	55.70 298	83.49 281	83.39 315	61.24 286	63.72 251	82.76 221	34.77 311	93.03 217	53.37 261	77.59 162	86.12 238

PLC	68.80 14	75.23 211	73.68 208	79.86 227	92.93 60	58.68 272	90.64 181	88.30 258	60.90 287	64.43 246	90.53 124	42.38 269	94.57 157	56.52 247	76.54 177	86.33 233
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH	63.93 17	68.62 273	64.81 279	80.03 222	85.22 215	63.25 198	87.72 241	84.66 303	60.83 288	51.57 316	79.43 274	27.29 335	94.96 144	41.76 302	64.84 263	81.88 295
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 274	65.41 276	77.96 270	78.69 305	62.93 206	89.86 202	89.17 235	60.55 289	50.27 321	77.73 283	22.60 343	94.06 190	47.18 281	72.65 204	76.88 336

VDDNet		80.50 115	78.26 134	87.21 35	86.19 204	69.79 32	94.48 37	91.31 162	60.42 290	79.34 80	90.91 118	38.48 286	96.56 96	82.16 60	81.05 139	95.27 47

CPTT-MVS		79.59 133	79.16 125	80.89 211	91.54 98	59.80 257	92.10 113	88.54 255	60.42 290	72.96 138	93.28 80	48.27 234	92.80 226	78.89 88	86.50 101	90.06 171

our_test_3		68.29 277	64.69 281	79.11 250	78.92 301	64.85 163	88.40 228	85.06 300	60.32 292	52.68 311	76.12 295	40.81 277	89.80 295	44.25 293	55.65 309	82.67 291

ITE_SJBPF				70.43 319	74.44 325	47.06 334		77.32 333	60.16 293	54.04 303	83.53 214	23.30 342	84.01 324	43.07 295	61.58 287	80.21 320

ppachtmachnet_test		67.72 281	63.70 288	79.77 230	78.92 301	66.04 134	88.68 221	82.90 320	60.11 294	55.45 292	75.96 298	39.19 281	90.55 273	39.53 311	52.55 318	82.71 289

new-patchmatchnet		59.30 315	56.48 315	67.79 324	65.86 345	44.19 337	82.47 291	81.77 321	59.94 295	43.65 342	66.20 335	27.67 333	81.68 339	39.34 312	41.40 344	77.50 335

test2356		64.16 300	63.28 291	66.81 328	69.37 342	39.86 348	87.76 240	86.02 292	59.83 296	53.54 308	73.23 307	34.94 310	80.67 341	39.66 310	65.20 256	79.89 321

新几何1				84.73 112	92.32 73	64.28 179		91.46 157	59.56 297	79.77 76	92.90 90	56.95 140	96.57 95	63.40 208	92.91 41	93.34 115

1121		81.25 105	80.05 106	84.87 108	92.30 74	64.31 177	87.91 237	91.39 159	59.44 298	79.94 74	92.91 89	57.09 133	97.01 72	66.63 177	92.81 43	93.29 118

旧先验2							92.00 121		59.37 299	87.54 19			93.47 211	75.39 108

PM-MVS		59.40 313	56.59 314	67.84 323	63.63 346	41.86 342	76.76 325	63.22 359	59.01 300	51.07 319	72.27 317	11.72 355	83.25 331	61.34 227	50.28 331	78.39 332

LTVRE_ROB	59.60 19	66.27 290	63.54 289	74.45 295	84.00 233	51.55 315	67.08 344	83.53 312	58.78 301	54.94 295	80.31 262	34.54 312	93.23 214	40.64 308	68.03 238	78.58 331
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				81.34 195	89.02 156	57.72 278		89.84 213	58.65 302	85.32 34	94.09 68	57.03 135	93.28 213	69.34 157	90.56 72	93.03 126

ACMH+	65.35 16	67.65 282	64.55 282	76.96 281	84.59 222	57.10 286	88.08 232	80.79 326	58.59 303	53.00 310	81.09 253	26.63 337	92.95 219	46.51 282	61.69 286	80.82 312

ADS-MVSNet2		66.90 287	63.44 290	77.26 280	88.06 177	60.70 241	68.01 341	75.56 340	57.57 304	64.48 243	69.87 329	38.68 282	84.10 321	40.87 306	67.89 240	86.97 223

ADS-MVSNet		68.54 275	64.38 286	81.03 208	88.06 177	66.90 97	68.01 341	84.02 308	57.57 304	64.48 243	69.87 329	38.68 282	89.21 299	40.87 306	67.89 240	86.97 223

MDA-MVSNet-bldmvs		61.54 309	57.70 310	73.05 304	79.53 292	57.00 287	83.08 288	81.23 323	57.57 304	34.91 351	72.45 313	32.79 315	86.26 317	35.81 326	41.95 343	75.89 338

testus		59.36 314	57.51 311	64.90 330	66.72 344	37.56 351	84.98 272	81.09 324	57.46 307	47.72 328	72.76 308	11.43 357	78.78 348	36.56 322	58.91 299	78.36 333

UnsupCasMVSNet_bld		61.60 308	57.71 309	73.29 303	68.73 343	51.64 314	78.61 318	89.05 242	57.20 308	46.11 330	61.96 343	28.70 331	88.60 302	50.08 270	38.90 347	79.63 324

MSDG		69.54 269	65.73 273	80.96 209	85.11 217	63.71 190	84.19 276	83.28 316	56.95 309	54.50 297	84.03 210	31.50 322	96.03 111	42.87 298	69.13 231	83.14 283

F-COLMAP		70.66 258	68.44 257	77.32 278	86.37 202	55.91 297	88.00 233	86.32 286	56.94 310	57.28 287	88.07 162	33.58 313	92.49 236	51.02 266	68.37 236	83.55 273

test222					89.77 134	61.60 228	89.55 206	89.42 227	56.83 311	77.28 103	92.43 100	52.76 198			91.14 66	93.09 124

CNLPA		74.31 226	72.30 229	80.32 215	91.49 99	61.66 227	90.85 172	80.72 327	56.67 312	63.85 250	90.64 121	46.75 246	90.84 272	53.79 257	75.99 180	88.47 190

OurMVSNet-221017-0		64.68 296	62.17 299	72.21 312	76.08 321	47.35 333	80.67 303	81.02 325	56.19 313	51.60 315	79.66 271	27.05 336	88.56 303	53.60 259	53.63 316	80.71 314

YYNet1		63.76 304	60.14 305	74.62 294	78.06 310	60.19 251	83.46 283	83.99 311	56.18 314	39.25 347	71.56 327	37.18 299	83.34 330	42.90 297	48.70 334	80.32 318

MDA-MVSNet_test_wron		63.78 303	60.16 304	74.64 293	78.15 309	60.41 245	83.49 281	84.03 307	56.17 315	39.17 348	71.59 326	37.22 298	83.24 332	42.87 298	48.73 333	80.26 319

OpenMVS_ROB	61.12 18	66.39 289	62.92 294	76.80 283	76.51 317	57.77 277	89.22 211	83.41 314	55.48 316	53.86 305	77.84 282	26.28 338	93.95 198	34.90 335	68.76 233	78.68 330

MIMVSNet1		60.16 312	57.33 312	68.67 322	69.71 340	44.13 338	78.92 317	84.21 305	55.05 317	44.63 338	71.85 320	23.91 340	81.54 340	32.63 342	55.03 312	80.35 317

CVMVSNet		74.04 228	74.27 198	73.33 302	85.33 213	43.94 339	89.53 208	88.39 256	54.33 318	70.37 171	90.13 132	49.17 228	84.05 322	61.83 226	79.36 147	91.99 148

Anonymous20240529		76.84 188	74.15 200	84.88 107	91.02 110	64.95 162	93.84 61	91.09 170	53.57 319	73.00 137	87.42 174	35.91 307	97.32 56	69.14 159	72.41 207	92.36 141

pmmvs6		67.57 283	64.76 280	76.00 288	72.82 330	53.37 308	88.71 220	86.78 276	53.19 320	57.58 286	78.03 281	35.33 309	92.41 238	55.56 251	54.88 313	82.21 293

testpf		57.17 316	56.93 313	57.88 337	79.13 299	42.40 340	34.23 361	85.97 294	52.64 321	47.66 329	66.50 333	36.33 306	79.65 344	53.60 259	56.31 308	51.60 355

TinyColmap		60.32 310	56.42 316	72.00 315	78.78 303	53.18 309	78.36 321	75.64 338	52.30 322	41.59 346	75.82 300	14.76 353	88.35 305	35.84 325	54.71 314	74.46 340

test_0402		64.54 297	61.09 302	74.92 292	84.10 232	60.75 239	87.95 234	79.71 330	52.03 323	52.41 312	77.20 287	32.21 319	91.64 261	23.14 353	61.03 289	72.36 343

Anonymous20231211		73.08 234	70.39 241	81.13 203	90.62 120	63.33 197	91.40 150	90.06 207	51.84 324	64.46 245	80.67 257	36.49 304	94.07 189	63.83 204	64.17 269	85.98 245

test1235678		55.73 320	52.74 320	64.68 331	60.16 353	35.56 353	81.65 297	81.46 322	51.27 325	38.93 349	62.82 342	17.44 349	78.58 349	30.87 346	50.09 332	79.89 321

AllTest		61.66 307	58.06 308	72.46 309	79.57 290	51.42 317	80.17 309	68.61 353	51.25 326	45.88 331	81.23 247	19.86 347	86.58 315	38.98 313	57.01 305	79.39 325

TestCases				72.46 309	79.57 290	51.42 317		68.61 353	51.25 326	45.88 331	81.23 247	19.86 347	86.58 315	38.98 313	57.01 305	79.39 325

PatchT		69.11 272	65.37 277	80.32 215	82.07 250	63.68 192	67.96 343	87.62 268	50.86 328	69.37 188	65.18 338	57.09 133	88.53 304	41.59 304	66.60 247	88.74 185

DP-MVS		69.90 264	66.48 270	80.14 218	95.36 16	62.93 206	89.56 205	76.11 335	50.27 329	57.69 285	85.23 200	39.68 280	95.73 121	33.35 338	71.05 216	81.78 296

gg-mvs-nofinetune		77.18 180	74.31 197	85.80 78	91.42 104	68.36 59	71.78 331	94.72 31	49.61 330	77.12 105	45.92 352	77.41 3	93.98 196	67.62 170	93.16 39	95.05 57

JIA-IIPM		66.06 291	62.45 297	76.88 282	81.42 256	54.45 305	57.49 355	88.67 251	49.36 331	63.86 249	46.86 351	56.06 153	90.25 277	49.53 272	68.83 232	85.95 246

N_pmnet		50.55 324	49.11 327	54.88 341	77.17 315	4.02 371	84.36 275	2.00 372	48.59 332	45.86 333	68.82 331	32.22 318	82.80 333	31.58 345	51.38 323	77.81 334

ANet_high		40.27 332	35.20 334	55.47 340	34.74 367	34.47 354	63.84 348	71.56 349	48.42 333	18.80 359	41.08 356	9.52 360	64.45 360	20.18 356	8.66 364	67.49 350

COLMAP_ROB	57.96 20	62.98 306	59.65 306	72.98 305	81.44 255	53.00 310	83.75 278	75.53 341	48.34 334	48.81 325	81.40 245	24.14 339	90.30 276	32.95 340	60.52 293	75.65 339
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmtry		67.53 284	63.93 287	78.34 261	82.12 249	64.38 174	68.72 338	84.00 309	48.23 335	59.24 272	72.41 314	57.82 128	89.27 298	46.10 284	56.68 307	81.36 303

LS3D		69.17 271	66.40 271	77.50 274	91.92 84	56.12 296	85.12 271	80.37 328	46.96 336	56.50 290	87.51 173	37.25 297	93.71 205	32.52 343	79.40 146	82.68 290

RPSCF		64.24 299	61.98 300	71.01 318	76.10 320	45.00 336	75.83 327	75.94 337	46.94 337	58.96 276	84.59 207	31.40 323	82.00 338	47.76 279	60.33 294	86.04 244

RPMNet		69.58 268	65.21 278	82.70 151	83.15 242	67.96 69	70.25 334	86.15 290	46.83 338	69.97 178	65.10 339	56.48 149	89.48 296	35.79 327	73.13 198	90.64 166

1111		56.66 319	54.98 318	61.69 333	61.99 350	31.38 355	79.81 314	83.17 317	45.66 339	41.94 344	65.44 336	41.50 270	79.56 345	27.64 348	47.68 335	74.14 341

.test1245		46.52 328	49.68 325	37.02 350	61.99 350	31.38 355	79.81 314	83.17 317	45.66 339	41.94 344	65.44 336	41.50 270	79.56 345	27.64 348	0.01 366	0.13 367

CMPMVS	48.56 21	66.77 288	64.41 285	73.84 299	70.65 337	50.31 322	77.79 324	85.73 297	45.54 341	44.76 337	82.14 232	35.40 308	90.14 283	63.18 211	74.54 188	81.07 310
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		64.01 301	63.01 293	67.02 327	74.40 326	38.86 350	83.27 285	86.19 289	45.11 342	54.27 300	81.15 252	36.91 303	80.01 342	48.79 275	57.02 304	82.19 294

no-one		44.13 330	38.39 332	61.34 334	45.91 363	41.94 341	61.67 350	75.07 343	45.05 343	20.07 357	40.68 358	11.58 356	79.82 343	30.18 347	15.30 357	62.26 352

TDRefinement		55.28 322	51.58 323	66.39 329	59.53 354	46.15 335	76.23 326	72.80 346	44.60 344	42.49 343	76.28 294	15.29 351	82.39 335	33.20 339	43.75 341	70.62 346

Patchmatch-test		65.86 292	60.94 303	80.62 212	83.75 234	58.83 269	58.91 354	75.26 342	44.50 345	50.95 320	77.09 291	58.81 121	87.90 308	35.13 334	64.03 270	95.12 55

test12356		47.51 326	44.82 329	55.56 339	52.53 356	21.09 366	71.45 333	76.03 336	44.14 346	30.69 352	58.18 346	9.01 361	76.14 350	26.95 350	34.43 352	69.46 348

LP		56.71 318	51.64 322	71.91 316	80.08 286	60.33 247	61.72 349	75.61 339	43.87 347	43.76 341	60.30 345	30.46 327	84.05 322	22.94 354	46.06 338	71.34 344

LF4IMVS		54.01 323	52.12 321	59.69 335	62.41 349	39.91 347	68.59 339	68.28 355	42.96 348	44.55 339	75.18 301	14.09 354	68.39 355	41.36 305	51.68 322	70.78 345

testmv		46.98 327	43.53 330	57.35 338	47.75 361	30.41 358	74.99 329	77.69 332	42.84 349	28.03 353	53.36 348	8.18 362	71.18 353	24.36 352	34.55 350	70.46 347

DSMNet-mixed		56.78 317	54.44 319	63.79 332	63.21 347	29.44 359	64.43 347	64.10 358	42.12 350	51.32 317	71.60 325	31.76 320	75.04 351	36.23 324	65.20 256	86.87 227

pmmvs3		55.51 321	51.50 324	67.53 325	57.90 355	50.93 320	80.37 305	73.66 345	40.63 351	44.15 340	64.75 340	16.30 350	78.97 347	44.77 292	40.98 346	72.69 342

new_pmnet		49.31 325	46.44 328	57.93 336	62.84 348	40.74 344	68.47 340	62.96 360	36.48 352	35.09 350	57.81 347	14.97 352	72.18 352	32.86 341	46.44 337	60.88 353

MVS-HIRNet		60.25 311	55.55 317	74.35 296	84.37 227	56.57 294	71.64 332	74.11 344	34.44 353	45.54 335	42.24 355	31.11 325	89.81 293	40.36 309	76.10 179	76.67 337

DeepMVS_CX				34.71 351	51.45 357	24.73 365		28.48 371	31.46 354	17.49 360	52.75 349	5.80 365	42.60 366	18.18 358	19.42 355	36.81 359

FPMVS		45.64 329	43.10 331	53.23 343	51.42 358	36.46 352	64.97 346	71.91 348	29.13 355	27.53 354	61.55 344	9.83 359	65.01 359	16.00 359	55.58 310	58.22 354

PMMVS2		37.93 333	33.61 335	50.92 344	46.31 362	24.76 364	60.55 353	50.05 363	28.94 356	20.93 356	47.59 350	4.41 368	65.13 358	25.14 351	18.55 356	62.87 351

LCM-MVSNet		40.54 331	35.79 333	54.76 342	36.92 366	30.81 357	51.41 356	69.02 352	22.07 357	24.63 355	45.37 353	4.56 367	65.81 357	33.67 337	34.50 351	67.67 349

PNet_i23d		32.77 336	29.98 338	41.11 348	48.05 359	29.17 360	65.82 345	50.02 364	21.42 358	14.74 362	37.19 359	1.11 371	55.11 362	19.75 357	11.77 359	39.06 357

PMVS	26.43 22	31.84 337	28.16 339	42.89 347	25.87 370	27.58 362	50.92 357	49.78 365	21.37 359	14.17 363	40.81 357	2.01 369	66.62 356	9.61 362	38.88 348	34.49 360
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.91 335	31.44 337	45.30 346	70.99 335	39.64 349	19.85 364	72.56 347	20.10 360	16.16 361	21.47 363	5.08 366	71.16 354	13.07 360	43.70 342	25.08 361
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		24.61 340	24.00 341	26.45 353	43.74 364	18.44 368	60.86 351	39.66 366	15.11 361	9.53 365	22.10 362	6.52 364	46.94 364	8.31 363	10.14 360	13.98 363

EMVS		23.76 342	23.20 343	25.46 354	41.52 365	16.90 369	60.56 352	38.79 369	14.62 362	8.99 366	20.24 366	7.35 363	45.82 365	7.25 364	9.46 362	13.64 364

wuykxyi23d		29.03 339	23.09 344	46.84 345	31.67 369	28.82 361	43.46 359	57.72 362	14.39 363	7.52 367	20.84 364	0.64 372	60.29 361	21.57 355	10.04 361	51.40 356

MVE	24.84 23	24.35 341	19.77 345	38.09 349	34.56 368	26.92 363	26.57 362	38.87 368	11.73 364	11.37 364	27.44 360	1.37 370	50.42 363	11.41 361	14.60 358	36.93 358
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 345	10.95 346	12.33 356	48.05 359	19.89 367	25.89 363	1.92 373	3.58 365	3.12 368	1.37 368	0.64 372	15.77 368	6.23 365	7.77 365	1.35 365

tmp_tt		22.26 343	23.75 342	17.80 355	5.23 371	12.06 370	35.26 360	39.48 367	2.82 366	18.94 358	44.20 354	22.23 344	24.64 367	36.30 323	9.31 363	16.69 362

testmvs		7.23 347	9.62 348	0.06 358	0.04 372	0.02 373	84.98 272	0.02 374	0.03 367	0.18 369	1.21 369	0.01 375	0.02 369	0.14 366	0.01 366	0.13 367

test123		6.92 348	9.21 349	0.08 357	0.03 373	0.05 372	81.65 297	0.01 375	0.02 368	0.14 370	0.85 370	0.03 374	0.02 369	0.12 367	0.00 368	0.16 366

cdsmvs_eth3d_5k		19.86 344	26.47 340	0.00 359	0.00 374	0.00 374	0.00 365	93.45 69	0.00 369	0.00 371	95.27 33	49.56 223	0.00 371	0.00 368	0.00 368	0.00 369

pcd_1.5k_mvsjas		4.46 349	5.95 350	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	53.55 189	0.00 371	0.00 368	0.00 368	0.00 369

pcd1.5k->3k		31.17 338	31.85 336	29.12 352	81.48 253	0.00 374	0.00 365	91.79 144	0.00 369	0.00 371	0.00 371	41.05 276	0.00 371	0.00 368	72.34 208	87.36 217

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 368	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 368	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 368	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 368	0.00 369

ab-mvs-re		7.91 346	10.55 347	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	94.95 43	0.00 376	0.00 371	0.00 368	0.00 368	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 374	0.00 374	0.00 365	0.00 376	0.00 369	0.00 371	0.00 371	0.00 376	0.00 371	0.00 368	0.00 368	0.00 369

GSMVS																94.68 69

test_part2					96.29 7	68.16 66				90.78 4

test_part1				0.00 359		0.00 374	0.00 365	94.26 46				0.00 376	0.00 371	0.00 368	0.00 368	0.00 369

sam_mvs1												57.85 127				94.68 69

sam_mvs												54.91 172

ambc				69.61 320	61.38 352	41.35 343	49.07 358	85.86 296		50.18 323	66.40 334	10.16 358	88.14 307	45.73 286	44.20 340	79.32 327

MTGPA								92.23 124

test_post1							78.95 316				20.70 365	53.05 195	91.50 268	60.43 232

test_post											23.01 361	56.49 148	92.67 231

patchmatchnet-post											67.62 332	57.62 130	90.25 277

GG-mvs-BLEND				86.53 55	91.91 85	69.67 36	75.02 328	94.75 30		78.67 90	90.85 119	77.91 2	94.56 158	72.25 128	93.74 31	95.36 41

MTMP							93.77 63	32.52 370

test9_res														89.41 11	94.96 9	95.29 45

agg_prior2														86.41 34	94.75 18	95.33 42

agg_prior					94.16 36	66.97 94		93.31 82		84.49 41			96.75 90

test_prior4						67.18 89	93.92 56

test_prior				86.42 59	94.71 24	67.35 83		93.10 95					96.84 86			95.05 57

新几何2							91.41 149

旧先验1					91.94 82	60.74 240		91.50 155			94.36 58	65.23 54			91.84 54	94.55 74

原ACMM2							92.01 119

testdata2													96.09 107	61.26 228

segment_acmp												65.94 47

test12				87.09 40	94.60 26	68.86 48		92.91 102		82.67 54		65.44 53	97.55 44		93.69 33	94.84 65

plane_prior7					86.94 194	61.51 229

plane_prior6					87.23 189	62.32 217						50.66 213

plane_prior5								91.31 162					95.55 132	76.74 100	78.53 155	88.39 191

plane_prior4											89.14 148

plane_prior1					87.15 191

n2								0.00 376

nn								0.00 376

door-mid								66.01 357

lessismore_v0				73.72 300	72.93 329	47.83 331		61.72 361		45.86 333	73.76 305	28.63 332	89.81 293	47.75 280	31.37 353	83.53 274

test11								93.01 98

door								66.57 356

HQP5-MVS						63.66 193

BP-MVS														77.63 96

HQP4-MVS										74.18 127			95.61 128			88.63 186

HQP3-MVS								91.70 148							78.90 151

HQP2-MVS												51.63 209

NP-MVS					87.41 188	63.04 204					90.30 128

ACMMP++_ref															71.63 210

ACMMP++															69.72 225

Test By Simon												54.21 182

High-resolution multi-view benchmark