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.

HSP-MVS		90.38 2	91.89 1	85.84 68	92.83 55	64.03 168	93.06 74	94.52 30	82.19 19	93.65 1	96.15 10	85.89 1	97.19 57	91.02 8	97.75 1	96.29 16

GG-mvs-BLEND				86.53 49	91.91 77	69.67 34	75.02 300	94.75 24		78.67 81	90.85 112	77.91 2	94.56 143	72.25 119	93.74 29	95.36 34

gg-mvs-nofinetune		77.18 165	74.31 181	85.80 70	91.42 93	68.36 54	71.78 303	94.72 25	49.61 301	77.12 95	45.92 324	77.41 3	93.98 179	67.62 159	93.16 37	95.05 50

TSAR-MVS + GP.		87.96 12	88.37 11	86.70 41	93.51 42	65.32 138	95.15 25	93.84 42	78.17 55	85.93 22	94.80 48	75.80 4	98.21 21	89.38 11	88.78 77	96.59 10

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 103	93.00 52	58.16 252	96.72 3	94.41 35	86.50 5	90.25 3	97.83 1	75.46 5	98.67 12	92.78 2	95.49 7	97.32 1

MVSTER		82.47 79	82.05 74	83.74 120	92.68 60	69.01 41	91.90 121	93.21 67	79.83 32	72.14 140	85.71 172	74.72 6	94.72 139	75.72 96	72.49 188	87.50 182

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 49	73.88 6	97.01 2	94.40 36	88.32 2	85.71 24	94.91 45	74.11 7	98.91 6	87.26 26	95.94 3	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
CSCG		86.87 27	86.26 30	88.72 9	95.05 18	70.79 17	93.83 56	95.33 12	68.48 210	77.63 88	94.35 59	73.04 8	98.45 16	84.92 41	93.71 30	96.92 6

MCST-MVS		91.08 1	91.46 2	89.94 2	97.66 2	73.37 7	97.13 1	95.58 10	89.33 1	85.77 23	96.26 7	72.84 9	99.38 1	92.64 4	95.93 4	97.08 4

TSAR-MVS + MP.		88.11 11	88.64 9	86.54 47	91.73 81	68.04 61	90.36 171	93.55 54	82.89 14	91.29 2	92.89 87	72.27 10	96.03 100	87.99 20	94.77 13	95.54 31

EPP-MVSNet		81.79 90	81.52 81	82.61 143	88.77 138	60.21 229	93.02 76	93.66 50	68.52 209	72.90 127	90.39 119	72.19 11	94.96 130	74.93 105	79.29 140	92.67 122

CostFormer		82.33 81	81.15 84	85.86 67	89.01 133	68.46 51	82.39 263	93.01 77	75.59 84	80.25 63	81.57 217	72.03 12	94.96 130	79.06 78	77.48 157	94.16 79

HPM-MVS++		89.37 7	89.95 7	87.64 20	95.10 17	68.23 59	95.24 22	94.49 32	82.43 17	88.90 8	96.35 5	71.89 13	98.63 13	88.76 18	96.40 2	96.06 19

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 21	84.83 9	89.07 7	96.80 2	70.86 14	99.06 3	92.64 4	95.71 5	96.12 18

DWT-MVSNet_test		83.95 60	82.80 67	87.41 25	92.90 54	70.07 25	89.12 197	94.42 34	82.15 20	77.64 87	91.77 103	70.81 15	96.22 92	65.03 183	81.36 128	95.94 23

IB-MVS	77.80 4	82.18 83	80.46 94	87.35 27	89.14 130	70.28 22	95.59 17	95.17 14	78.85 47	70.19 157	85.82 170	70.66 16	97.67 33	72.19 122	66.52 224	94.09 84
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
agg_prior1		87.02 24	87.26 23	86.28 60	94.16 31	66.97 86	94.08 42	93.31 64	71.85 157	84.49 35	95.39 24	68.91 17	96.75 83	88.84 17	94.32 20	95.13 47

PatchFormer-LS_test		83.14 70	81.81 78	87.12 32	92.34 64	69.92 28	88.64 203	93.32 63	82.07 23	74.87 112	91.62 107	68.91 17	96.08 99	66.07 174	78.45 148	95.37 33

alignmvs		87.28 20	86.97 25	88.24 15	91.30 96	71.14 16	95.61 16	93.56 53	79.30 38	87.07 17	95.25 32	68.43 19	96.93 77	87.87 21	84.33 112	96.65 8

PAPM		85.89 39	85.46 41	87.18 30	88.20 150	72.42 9	92.41 97	92.77 85	82.11 21	80.34 62	93.07 81	68.27 20	95.02 128	78.39 83	93.59 32	94.09 84

train_agg		87.21 22	87.42 21	86.60 44	94.18 27	67.28 77	94.16 36	93.51 55	71.87 155	85.52 26	95.33 26	68.19 21	97.27 54	89.09 13	94.90 10	95.25 43

test_8					94.19 26	67.19 79	94.15 38	93.42 60	71.87 155	85.38 28	95.35 25	68.19 21	96.95 74

TEST9					94.18 27	67.28 77	94.16 36	93.51 55	71.75 163	85.52 26	95.33 26	68.01 23	97.27 54

test_prior3		87.38 19	87.70 16	86.42 53	94.71 21	67.35 75	95.10 27	93.10 75	75.40 89	85.25 30	95.61 21	67.94 24	96.84 79	87.47 23	94.77 13	95.05 50

test_prior2							95.10 27		75.40 89	85.25 30	95.61 21	67.94 24		87.47 23	94.77 13

WTY-MVS		86.32 33	85.81 36	87.85 17	92.82 57	69.37 37	95.20 23	95.25 13	82.71 15	81.91 51	94.73 49	67.93 26	97.63 38	79.55 73	82.25 124	96.54 12

Regformer-1		87.24 21	87.60 18	86.15 62	95.14 15	65.83 131	93.95 49	95.12 15	82.11 21	84.25 37	95.73 17	67.88 27	98.35 19	85.60 36	88.64 78	94.26 73

APDe-MVS		87.54 17	87.84 14	86.65 42	96.07 9	66.30 121	94.84 32	93.78 43	69.35 197	88.39 10	96.34 6	67.74 28	97.66 36	90.62 9	93.44 34	96.01 22

tpm2		79.80 119	77.95 128	85.34 87	88.28 148	68.26 58	81.56 271	91.42 135	70.11 190	77.59 90	80.50 233	67.40 29	94.26 163	67.34 161	77.35 158	93.51 100

tpmp4_e23		78.85 134	76.55 148	85.77 72	89.25 126	68.39 53	81.63 270	91.38 137	70.40 187	75.21 110	79.22 249	67.37 30	94.79 134	58.98 227	75.51 168	94.13 81

Regformer-2		87.00 25	87.43 20	85.71 76	95.14 15	64.73 150	93.95 49	94.95 18	81.69 26	84.03 41	95.73 17	67.35 31	98.19 23	85.40 38	88.64 78	94.20 75

HY-MVS	76.49 5	84.28 55	83.36 61	87.02 36	92.22 69	67.74 66	84.65 245	94.50 31	79.15 42	82.23 49	87.93 146	66.88 32	96.94 75	80.53 69	82.20 125	96.39 14

EPNet		87.84 15	88.38 10	86.23 61	93.30 44	66.05 125	95.26 21	94.84 20	87.09 3	88.06 11	94.53 52	66.79 33	97.34 50	83.89 48	91.68 54	95.29 38
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FIs		79.47 125	79.41 108	79.67 212	85.95 181	59.40 241	91.68 133	93.94 40	78.06 56	68.96 178	88.28 139	66.61 34	91.77 234	66.20 173	74.99 173	87.82 179

NCCC		89.07 8	89.46 8	87.91 16	96.60 5	69.05 40	96.38 7	94.64 29	84.42 10	86.74 18	96.20 8	66.56 35	98.76 11	89.03 16	94.56 18	95.92 25

SD-MVS		87.49 18	87.49 19	87.50 24	93.60 39	68.82 46	93.90 53	92.63 91	76.86 71	87.90 12	95.76 16	66.17 36	97.63 38	89.06 15	91.48 58	96.05 20

agg_prior3		86.93 26	87.08 24	86.48 50	94.21 25	66.95 88	94.14 39	93.40 61	71.80 160	84.86 32	95.13 36	66.16 37	97.25 56	89.09 13	94.90 10	95.25 43

UniMVSNet_NR-MVSNet		78.15 148	77.55 134	79.98 205	84.46 198	60.26 227	92.25 99	93.20 69	77.50 64	68.88 179	86.61 161	66.10 38	92.13 227	66.38 170	62.55 250	87.54 181

CHOSEN 280x420		77.35 160	76.95 145	78.55 233	87.07 166	62.68 195	69.71 309	82.95 291	68.80 203	71.48 148	87.27 158	66.03 39	84.00 298	76.47 94	82.81 122	88.95 165

CANet		89.61 6	89.99 6	88.46 13	94.39 24	69.71 32	96.53 5	93.78 43	86.89 4	89.68 4	95.78 15	65.94 40	99.10 2	92.99 1	93.91 25	96.58 11

segment_acmp												65.94 40

Regformer-3		85.80 40	85.92 34	85.46 80	94.17 29	65.09 146	92.95 78	95.11 16	81.13 27	81.68 53	95.04 37	65.82 42	98.32 20	83.02 52	84.36 109	92.97 116

Vis-MVSNet (Re-imp)		79.24 128	79.57 103	78.24 238	88.46 144	52.29 283	90.41 170	89.12 213	74.24 106	69.13 174	91.91 101	65.77 43	90.09 259	59.00 226	88.09 82	92.33 129

FC-MVSNet-test		77.99 150	78.08 126	77.70 243	84.89 192	55.51 270	90.27 173	93.75 47	76.87 70	66.80 208	87.59 149	65.71 44	90.23 253	62.89 203	73.94 178	87.37 190

test12				87.09 34	94.60 23	68.86 44		92.91 81		82.67 48		65.44 45	97.55 41		93.69 31	94.84 58

旧先验1					91.94 74	60.74 219		91.50 132			94.36 55	65.23 46			91.84 51	94.55 64

Regformer-4		85.45 43	85.69 39	84.73 101	94.17 29	63.23 183	92.95 78	94.83 21	80.66 29	81.29 55	95.04 37	65.12 47	98.08 26	82.74 53	84.36 109	92.88 120

1112_ss		80.56 103	79.83 100	82.77 137	88.65 139	60.78 216	92.29 98	88.36 231	72.58 139	72.46 136	94.95 40	65.09 48	93.42 194	66.38 170	77.71 151	94.10 83

MVSFormer		83.75 65	82.88 65	86.37 56	89.24 128	71.18 14	89.07 198	90.69 156	65.80 230	87.13 15	94.34 60	64.99 49	92.67 213	72.83 113	91.80 52	95.27 40

lupinMVS		87.74 16	87.77 15	87.63 22	89.24 128	71.18 14	96.57 4	92.90 82	82.70 16	87.13 15	95.27 30	64.99 49	95.80 106	89.34 12	91.80 52	95.93 24

tpmrst		80.57 102	79.14 115	84.84 99	90.10 111	68.28 57	81.70 267	89.72 195	77.63 62	75.96 102	79.54 247	64.94 51	92.71 211	75.43 98	77.28 160	93.55 99

Test_1112_low_res		79.56 124	78.60 119	82.43 148	88.24 149	60.39 225	92.09 105	87.99 238	72.10 151	71.84 143	87.42 152	64.62 52	93.04 198	65.80 178	77.30 159	93.85 95

DeepC-MVS_fast	79.48 2	87.95 13	88.00 13	87.79 19	95.86 12	68.32 55	95.74 12	94.11 39	83.82 12	83.49 44	96.19 9	64.53 53	98.44 17	83.42 51	94.88 12	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
MG-MVS		87.11 23	86.27 29	89.62 5	97.79 1	76.27 3	94.96 31	94.49 32	78.74 51	83.87 43	92.94 84	64.34 54	96.94 75	75.19 100	94.09 22	95.66 27

tpm		78.58 143	77.03 143	83.22 131	85.94 183	64.56 151	83.21 258	91.14 145	78.31 54	73.67 122	79.68 244	64.01 55	92.09 229	66.07 174	71.26 197	93.03 114

CDS-MVSNet		81.43 93	80.74 89	83.52 127	86.26 176	64.45 155	92.09 105	90.65 159	75.83 83	73.95 121	89.81 127	63.97 56	92.91 205	71.27 129	82.82 121	93.20 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_Test		84.16 57	83.20 62	87.05 35	91.56 85	69.82 29	89.99 179	92.05 111	77.77 59	82.84 47	86.57 162	63.93 57	96.09 97	74.91 106	89.18 75	95.25 43

APD-MVS		85.93 38	85.99 33	85.76 73	95.98 11	65.21 140	93.59 61	92.58 93	66.54 225	86.17 19	95.88 14	63.83 58	97.00 67	86.39 32	92.94 38	95.06 49
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs_anonymous		81.36 94	79.99 97	85.46 80	90.39 108	68.40 52	86.88 233	90.61 160	74.41 100	70.31 156	84.67 181	63.79 59	92.32 224	73.13 110	85.70 100	95.67 26

PVSNet_Blended_VisFu		83.97 59	83.50 54	85.39 85	90.02 112	66.59 111	93.77 57	91.73 122	77.43 66	77.08 97	89.81 127	63.77 60	96.97 72	79.67 72	88.21 81	92.60 124

CDPH-MVS		85.71 41	85.46 41	86.46 51	94.75 20	67.19 79	93.89 54	92.83 84	70.90 176	83.09 46	95.28 29	63.62 61	97.36 48	80.63 68	94.18 21	94.84 58

HyFIR lowres test		81.03 98	79.56 104	85.43 83	87.81 157	68.11 60	90.18 175	90.01 184	70.65 184	72.95 126	86.06 168	63.61 62	94.50 147	75.01 104	79.75 136	93.67 96

canonicalmvs		86.85 28	86.25 31	88.66 10	91.80 80	71.92 10	93.54 63	91.71 124	80.26 31	87.55 13	95.25 32	63.59 63	96.93 77	88.18 19	84.34 111	97.11 3

SteuartSystems-ACMMP		86.82 30	86.90 26	86.58 46	90.42 106	66.38 118	96.09 9	93.87 41	77.73 60	84.01 42	95.66 19	63.39 64	97.94 27	87.40 25	93.55 33	95.42 32
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		83.77 64	83.67 52	84.06 115	92.79 59	63.56 180	91.76 129	94.81 23	79.65 36	77.87 84	94.09 64	63.35 65	97.90 29	79.35 74	79.36 138	90.74 150

UniMVSNet (Re)		77.58 154	76.78 146	79.98 205	84.11 204	60.80 215	91.76 129	93.17 72	76.56 77	69.93 163	84.78 180	63.32 66	92.36 223	64.89 184	62.51 252	86.78 202

PVSNet_BlendedMVS		83.38 67	83.43 57	83.22 131	93.76 35	67.53 72	94.06 43	93.61 51	79.13 43	81.00 57	85.14 176	63.19 67	97.29 52	87.08 27	73.91 179	84.83 237

PVSNet_Blended		86.73 31	86.86 27	86.31 59	93.76 35	67.53 72	96.33 8	93.61 51	82.34 18	81.00 57	93.08 79	63.19 67	97.29 52	87.08 27	91.38 59	94.13 81

PAPM_NR		82.97 73	81.84 77	86.37 56	94.10 33	66.76 100	87.66 220	92.84 83	69.96 192	74.07 119	93.57 72	63.10 69	97.50 43	70.66 137	90.58 68	94.85 57

nrg030		80.93 99	79.86 99	84.13 114	83.69 208	68.83 45	93.23 70	91.20 142	75.55 85	75.06 111	88.22 144	63.04 70	94.74 138	81.88 59	66.88 221	88.82 167

EI-MVSNet-UG-set		83.14 70	82.96 63	83.67 125	92.28 67	63.19 184	91.38 143	94.68 26	79.22 40	76.60 99	93.75 69	62.64 71	97.76 31	78.07 85	78.01 149	90.05 157

DeepC-MVS	77.85 3	85.52 42	85.24 43	86.37 56	88.80 137	66.64 108	92.15 101	93.68 49	81.07 28	76.91 98	93.64 71	62.59 72	98.44 17	85.50 37	92.84 40	94.03 88
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		86.19 36	85.80 37	87.37 26	93.17 48	69.79 30	93.99 47	93.76 46	79.08 45	78.88 76	93.99 66	62.25 73	98.15 24	85.93 35	91.15 62	94.15 80

PHI-MVS		86.83 29	86.85 28	86.78 40	93.47 43	65.55 135	95.39 20	95.10 17	71.77 162	85.69 25	96.52 3	62.07 74	98.77 10	86.06 34	95.60 6	96.03 21

MP-MVS		85.02 46	84.97 45	85.17 92	92.60 61	64.27 165	93.24 69	92.27 100	73.13 130	79.63 69	94.43 53	61.90 75	97.17 58	85.00 40	92.56 43	94.06 87

jason		86.40 32	86.17 32	87.11 33	86.16 178	70.54 20	95.71 15	92.19 108	82.00 24	84.58 34	94.34 60	61.86 76	95.53 122	87.76 22	90.89 64	95.27 40
jason: jason.
PAPR		85.15 45	84.47 47	87.18 30	96.02 10	68.29 56	91.85 124	93.00 79	76.59 76	79.03 75	95.00 39	61.59 77	97.61 40	78.16 84	89.00 76	95.63 28

IS-MVSNet		80.14 111	79.41 108	82.33 155	87.91 155	60.08 233	91.97 113	88.27 234	72.90 135	71.44 149	91.73 106	61.44 78	93.66 190	62.47 208	86.53 95	93.24 107

EI-MVSNet		78.97 132	78.22 124	81.25 180	85.33 186	62.73 194	89.53 190	93.21 67	72.39 143	72.14 140	90.13 123	60.99 79	94.72 139	67.73 158	72.49 188	86.29 208

IterMVS-LS		76.49 176	75.18 172	80.43 197	84.49 197	62.74 193	90.64 165	88.80 222	72.40 142	65.16 212	81.72 216	60.98 80	92.27 225	67.74 157	64.65 242	86.29 208
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view600		76.93 167	75.50 167	81.23 181	91.44 89	62.00 202	89.94 180	96.56 1	70.68 180	68.54 185	87.31 153	60.79 81	94.19 164	38.90 296	75.31 169	87.48 183

view800		76.93 167	75.50 167	81.23 181	91.44 89	62.00 202	89.94 180	96.56 1	70.68 180	68.54 185	87.31 153	60.79 81	94.19 164	38.90 296	75.31 169	87.48 183

conf0.05thres1000		76.93 167	75.50 167	81.23 181	91.44 89	62.00 202	89.94 180	96.56 1	70.68 180	68.54 185	87.31 153	60.79 81	94.19 164	38.90 296	75.31 169	87.48 183

tfpn		76.93 167	75.50 167	81.23 181	91.44 89	62.00 202	89.94 180	96.56 1	70.68 180	68.54 185	87.31 153	60.79 81	94.19 164	38.90 296	75.31 169	87.48 183

tpm cat1		75.30 192	72.21 206	84.58 106	88.52 141	67.77 65	78.16 295	88.02 237	61.88 258	68.45 190	76.37 266	60.65 85	94.03 177	53.77 244	74.11 176	91.93 136

TAMVS		80.37 106	79.45 107	83.13 133	85.14 189	63.37 181	91.23 150	90.76 155	74.81 98	72.65 130	88.49 135	60.63 86	92.95 201	69.41 146	81.95 127	93.08 113

thres600view7		78.00 149	76.66 147	82.03 169	91.93 75	63.69 176	91.30 148	96.33 5	72.43 141	70.46 153	87.89 147	60.31 87	94.92 133	42.64 283	76.64 162	87.48 183

CHOSEN 1792x2688		84.98 47	83.45 56	89.57 6	89.94 114	75.14 4	92.07 107	92.32 98	81.87 25	75.68 103	88.27 140	60.18 88	98.60 14	80.46 70	90.27 71	94.96 55

tfpn200view9		78.79 137	77.43 137	82.88 135	92.21 70	64.49 152	92.05 108	96.28 7	73.48 125	71.75 145	88.26 141	60.07 89	95.32 125	45.16 273	77.58 154	88.83 166

thres400		78.68 140	77.43 137	82.43 148	92.21 70	64.49 152	92.05 108	96.28 7	73.48 125	71.75 145	88.26 141	60.07 89	95.32 125	45.16 273	77.58 154	87.48 183

MVS		84.66 51	82.86 66	90.06 1	90.93 100	74.56 5	87.91 215	95.54 11	68.55 208	72.35 139	94.71 50	59.78 91	98.90 7	81.29 67	94.69 17	96.74 7

thres200		79.66 121	78.33 121	83.66 126	92.54 62	65.82 132	93.06 74	96.31 6	74.90 97	73.30 124	88.66 133	59.67 92	95.61 116	47.84 264	78.67 145	89.56 163

Effi-MVS+		83.82 63	82.76 68	86.99 37	89.56 121	69.40 36	91.35 145	86.12 265	72.59 138	83.22 45	92.81 89	59.60 93	96.01 102	81.76 60	87.80 84	95.56 30

ACMMP_Plus		86.05 37	85.80 37	86.80 39	91.58 84	67.53 72	91.79 126	93.49 57	74.93 96	84.61 33	95.30 28	59.42 94	97.92 28	86.13 33	94.92 9	94.94 56

UA-Net		80.02 114	79.65 102	81.11 187	89.33 124	57.72 256	86.33 238	89.00 219	77.44 65	81.01 56	89.15 130	59.33 95	95.90 103	61.01 215	84.28 114	89.73 161

NR-MVSNet		76.05 182	74.59 176	80.44 196	82.96 216	62.18 201	90.83 159	91.73 122	77.12 67	60.96 238	86.35 163	59.28 96	91.80 233	60.74 216	61.34 262	87.35 192

MP-MVS-pluss		85.24 44	85.13 44	85.56 77	91.42 93	65.59 134	91.54 138	92.51 95	74.56 99	80.62 59	95.64 20	59.15 97	97.00 67	86.94 29	93.80 26	94.07 86
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		84.73 49	84.40 49	85.72 74	93.75 37	65.01 147	93.50 64	93.19 70	72.19 147	79.22 72	94.93 42	59.04 98	97.67 33	81.55 62	92.21 46	94.49 70

#test#		84.98 47	84.74 46	85.72 74	93.75 37	65.01 147	94.09 41	93.19 70	73.55 124	79.22 72	94.93 42	59.04 98	97.67 33	82.66 54	92.21 46	94.49 70

MSLP-MVS++		86.27 34	85.91 35	87.35 27	92.01 73	68.97 43	95.04 29	92.70 86	79.04 46	81.50 54	96.50 4	58.98 100	96.78 81	83.49 50	93.93 24	96.29 16

Patchmatch-test		65.86 264	60.94 275	80.62 195	83.75 207	58.83 247	58.91 326	75.26 313	44.50 316	50.95 290	77.09 264	58.81 101	87.90 280	35.13 308	64.03 245	95.12 48

EPNet_dtu		78.80 136	79.26 112	77.43 248	88.06 152	49.71 296	91.96 114	91.95 116	77.67 61	76.56 100	91.28 109	58.51 102	90.20 254	56.37 234	80.95 131	92.39 128
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VNet		86.20 35	85.65 40	87.84 18	93.92 34	69.99 26	95.73 14	95.94 9	78.43 53	86.00 21	93.07 81	58.22 103	97.00 67	85.22 39	84.33 112	96.52 13

TESTMET0.1,1		82.41 80	81.98 76	83.72 123	88.08 151	63.74 173	92.70 86	93.77 45	79.30 38	77.61 89	87.57 150	58.19 104	94.08 172	73.91 109	86.68 92	93.33 105

原ACMM1				84.42 110	93.21 47	64.27 165		93.40 61	65.39 233	79.51 70	92.50 91	58.11 105	96.69 85	65.27 182	93.96 23	92.32 130

sam_mvs1												57.85 106

CR-MVSNet		73.79 207	70.82 216	82.70 139	83.15 214	67.96 62	70.25 306	84.00 281	73.67 122	69.97 161	72.41 285	57.82 107	89.48 268	52.99 248	73.13 182	90.64 152

Patchmtry		67.53 256	63.93 260	78.34 234	82.12 221	64.38 159	68.72 310	84.00 281	48.23 306	59.24 244	72.41 285	57.82 107	89.27 270	46.10 270	56.68 281	81.36 274

patchmatchnet-post											67.62 303	57.62 109	90.25 250

PCF-MVS	73.15 9	79.29 127	77.63 133	84.29 112	86.06 179	65.96 127	87.03 228	91.10 146	69.86 193	69.79 164	90.64 114	57.54 110	96.59 86	64.37 190	82.29 123	90.32 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.14 96	80.01 96	84.51 109	90.24 110	65.86 129	94.12 40	89.15 212	73.81 118	75.37 109	88.26 141	57.26 111	94.53 146	66.97 165	84.92 104	93.15 110

1121		81.25 95	80.05 95	84.87 98	92.30 66	64.31 162	87.91 215	91.39 136	59.44 271	79.94 65	92.91 85	57.09 112	97.01 65	66.63 166	92.81 41	93.29 106

PatchT		69.11 246	65.37 251	80.32 198	82.07 222	63.68 177	67.96 315	87.62 242	50.86 299	69.37 171	65.18 309	57.09 112	88.53 276	41.59 286	66.60 223	88.74 168

testdata				81.34 179	89.02 132	57.72 256		89.84 188	58.65 275	85.32 29	94.09 64	57.03 114	93.28 195	69.34 147	90.56 69	93.03 114

PatchmatchNet		77.46 155	74.63 175	85.96 65	89.55 122	70.35 21	79.97 284	89.55 198	72.23 145	70.94 150	76.91 265	57.03 114	92.79 209	54.27 241	81.17 129	94.74 60
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
region2R		84.36 53	84.03 51	85.36 86	93.54 41	64.31 162	93.43 67	92.95 80	72.16 150	78.86 77	94.84 47	56.97 116	97.53 42	81.38 65	92.11 50	94.24 74

新几何1				84.73 101	92.32 65	64.28 164		91.46 134	59.56 270	79.77 67	92.90 86	56.95 117	96.57 88	63.40 195	92.91 39	93.34 103

diffmvs		80.18 109	78.55 120	85.07 93	88.56 140	66.93 89	86.70 236	88.62 226	70.42 186	78.69 80	85.26 174	56.93 118	94.77 135	68.68 153	83.09 118	93.51 100

MVS_0304		88.39 9	88.35 12	88.50 12	93.01 51	70.11 23	95.90 10	92.20 106	86.27 6	88.70 9	95.92 13	56.76 119	99.02 4	92.68 3	93.76 28	96.37 15

WR-MVS		76.76 174	75.74 159	79.82 210	84.60 194	62.27 200	92.60 91	92.51 95	76.06 80	67.87 196	85.34 173	56.76 119	90.24 252	62.20 209	63.69 249	86.94 200

HPM-MVS		83.25 68	82.95 64	84.17 113	92.25 68	62.88 191	90.91 157	91.86 118	70.30 189	77.12 95	93.96 67	56.75 121	96.28 91	82.04 58	91.34 61	93.34 103

sss		82.71 77	82.38 72	83.73 122	89.25 126	59.58 239	92.24 100	94.89 19	77.96 57	79.86 66	92.38 95	56.70 122	97.05 62	77.26 90	80.86 132	94.55 64

ACMMPR		84.37 52	84.06 50	85.28 88	93.56 40	64.37 160	93.50 64	93.15 73	72.19 147	78.85 78	94.86 46	56.69 123	97.45 44	81.55 62	92.20 48	94.02 89

FMVSNet3		77.73 153	76.04 154	82.80 136	91.20 98	68.99 42	91.87 122	91.99 113	73.35 128	67.04 204	83.19 194	56.62 124	92.14 226	59.80 222	69.34 204	87.28 194

Patchmatch-RL test		68.17 251	64.49 257	79.19 227	71.22 304	53.93 277	70.07 308	71.54 322	69.22 198	56.79 260	62.89 313	56.58 125	88.61 273	69.53 145	52.61 290	95.03 53

test_post											23.01 333	56.49 126	92.67 213

RPMNet		69.58 242	65.21 252	82.70 139	83.15 214	67.96 62	70.25 306	86.15 264	46.83 309	69.97 161	65.10 310	56.48 127	89.48 268	35.79 307	73.13 182	90.64 152

DU-MVS		76.86 171	75.84 157	79.91 207	82.96 216	60.26 227	91.26 149	91.54 129	76.46 78	68.88 179	86.35 163	56.16 128	92.13 227	66.38 170	62.55 250	87.35 192

Baseline_NR-MVSNet		73.99 205	72.83 198	77.48 247	80.78 232	59.29 244	91.79 126	84.55 276	68.85 202	68.99 177	80.70 230	56.16 128	92.04 230	62.67 206	60.98 264	81.11 280

API-MVS		82.28 82	80.53 93	87.54 23	96.13 8	70.59 19	93.63 59	91.04 149	65.72 232	75.45 108	92.83 88	56.11 130	98.89 8	64.10 191	89.75 74	93.15 110

MPTG		84.73 49	84.47 47	85.50 78	91.89 78	65.16 141	91.55 137	92.23 101	75.32 91	80.53 60	95.21 34	56.06 131	97.16 59	84.86 42	92.55 44	94.18 76

MTAPA		83.91 61	83.38 60	85.50 78	91.89 78	65.16 141	81.75 266	92.23 101	75.32 91	80.53 60	95.21 34	56.06 131	97.16 59	84.86 42	92.55 44	94.18 76

JIA-IIPM		66.06 263	62.45 269	76.88 254	81.42 228	54.45 276	57.49 327	88.67 224	49.36 302	63.86 222	46.86 323	56.06 131	90.25 250	49.53 258	68.83 208	85.95 219

v148		76.19 179	74.47 180	81.36 178	80.05 260	64.44 156	91.75 131	90.23 173	73.68 121	67.13 203	80.84 229	55.92 134	93.86 186	68.95 150	61.73 259	85.76 225

WR-MVS_H		70.59 234	69.94 218	72.53 280	81.03 229	51.43 287	87.35 225	92.03 112	67.38 221	60.23 240	80.70 230	55.84 135	83.45 302	46.33 269	58.58 275	82.72 261

XVS		83.87 62	83.47 55	85.05 94	93.22 45	63.78 171	92.92 80	92.66 89	73.99 111	78.18 82	94.31 62	55.25 136	97.41 45	79.16 76	91.58 56	93.95 91

X-MVStestdata		76.86 171	74.13 184	85.05 94	93.22 45	63.78 171	92.92 80	92.66 89	73.99 111	78.18 82	10.19 339	55.25 136	97.41 45	79.16 76	91.58 56	93.95 91

BH-w/o		80.49 105	79.30 111	84.05 116	90.83 104	64.36 161	93.60 60	89.42 202	74.35 105	69.09 175	90.15 122	55.23 138	95.61 116	64.61 186	86.43 97	92.17 134

CP-MVS		83.71 66	83.40 59	84.65 104	93.14 49	63.84 169	94.59 33	92.28 99	71.03 174	77.41 91	94.92 44	55.21 139	96.19 93	81.32 66	90.70 66	93.91 93

PGM-MVS		83.25 68	82.70 70	84.92 96	92.81 58	64.07 167	90.44 168	92.20 106	71.28 172	77.23 94	94.43 53	55.17 140	97.31 51	79.33 75	91.38 59	93.37 102

v1		77.29 161	75.57 164	82.42 151	80.61 246	66.73 101	91.96 114	90.42 164	74.41 100	69.46 168	82.12 208	55.14 141	94.40 153	71.00 130	65.04 235	86.13 211

divwei89l23v2f112		77.28 162	75.57 164	82.42 151	80.62 243	66.72 103	91.96 114	90.42 164	74.41 100	69.46 168	82.12 208	55.11 142	94.40 153	71.00 130	65.04 235	86.12 212

v1141		77.28 162	75.57 164	82.42 151	80.63 242	66.73 101	91.96 114	90.42 164	74.41 100	69.46 168	82.12 208	55.09 143	94.40 153	70.99 132	65.05 234	86.12 212

tpmvs		72.88 213	69.76 221	82.22 161	90.98 99	67.05 84	78.22 294	88.30 232	63.10 249	64.35 220	74.98 273	55.09 143	94.27 161	43.25 277	69.57 203	85.34 233

v18		71.94 219	69.43 222	79.50 218	80.74 233	66.82 93	88.16 209	86.66 251	68.95 201	55.55 263	72.66 280	55.03 145	90.15 255	64.78 185	52.30 291	81.54 268

v6		77.39 157	75.71 160	82.44 145	80.67 238	66.82 93	91.94 118	90.18 176	74.19 107	69.60 165	82.50 201	55.00 146	94.44 148	71.68 125	65.60 227	86.05 215

v1neww		77.39 157	75.71 160	82.44 145	80.69 236	66.83 91	91.94 118	90.18 176	74.19 107	69.60 165	82.51 198	54.99 147	94.44 148	71.68 125	65.60 227	86.05 215

v7new		77.39 157	75.71 160	82.44 145	80.69 236	66.83 91	91.94 118	90.18 176	74.19 107	69.60 165	82.51 198	54.99 147	94.44 148	71.68 125	65.60 227	86.05 215

v8		75.35 191	73.26 195	81.61 176	80.67 238	66.82 93	89.54 189	89.27 206	71.65 164	63.30 228	80.30 237	54.99 147	94.06 173	67.33 162	62.33 253	83.94 243

sam_mvs												54.91 150

v16		71.81 220	69.26 224	79.47 219	80.66 240	66.81 97	87.93 213	86.63 253	68.70 206	55.35 264	72.51 281	54.75 151	90.12 257	64.51 187	52.28 292	81.47 269

v17		71.77 222	69.20 225	79.46 220	80.62 243	66.81 97	87.93 213	86.63 253	68.71 205	55.25 265	72.49 282	54.72 152	90.11 258	64.50 188	51.97 293	81.47 269

EPMVS		78.49 145	75.98 155	86.02 63	91.21 97	69.68 33	80.23 280	91.20 142	75.25 93	72.48 135	78.11 253	54.65 153	93.69 189	57.66 232	83.04 119	94.69 61

v15		71.40 224	68.75 227	79.35 221	80.39 247	66.70 105	87.57 222	86.64 252	68.66 207	54.68 267	72.00 289	54.50 154	89.98 260	63.69 193	50.66 298	81.38 273

ab-mvs		80.18 109	78.31 122	85.80 70	88.44 145	65.49 137	83.00 260	92.67 88	71.82 159	77.36 92	85.01 177	54.50 154	96.59 86	76.35 95	75.63 167	95.32 37

DP-MVS Recon		82.73 75	81.65 80	85.98 64	97.31 3	67.06 83	95.15 25	91.99 113	69.08 200	76.50 101	93.89 68	54.48 156	98.20 22	70.76 136	85.66 101	92.69 121

XXY-MVS		77.94 151	76.44 150	82.43 148	82.60 218	64.44 156	92.01 110	91.83 120	73.59 123	70.00 160	85.82 170	54.43 157	94.76 136	69.63 143	68.02 215	88.10 178

MDTV_nov1_ep13_2view						59.90 235	80.13 282		67.65 218	72.79 128		54.33 158		59.83 221		92.58 125

V14		71.29 226	68.61 229	79.31 222	80.34 251	66.65 107	87.39 224	86.61 255	68.41 211	54.49 269	71.91 290	54.25 159	89.96 261	63.50 194	50.62 299	81.33 275

Test By Simon												54.21 160

V9		71.16 227	68.46 231	79.27 224	80.26 254	66.60 109	87.21 227	86.56 256	68.17 212	54.26 272	71.81 292	54.00 161	89.93 262	63.28 197	50.57 300	81.27 276

MAR-MVS		84.18 56	83.43 57	86.44 52	96.25 7	65.93 128	94.28 35	94.27 38	74.41 100	79.16 74	95.61 21	53.99 162	98.88 9	69.62 144	93.26 36	94.50 69
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
test-LLR		80.10 112	79.56 104	81.72 173	86.93 169	61.17 212	92.70 86	91.54 129	71.51 170	75.62 104	86.94 159	53.83 163	92.38 221	72.21 120	84.76 107	91.60 139

test0.0.03 1		72.76 214	72.71 200	72.88 278	80.25 255	47.99 301	91.22 151	89.45 200	71.51 170	62.51 235	87.66 148	53.83 163	85.06 291	50.16 255	67.84 218	85.58 227

v2v482		77.42 156	75.65 163	82.73 138	80.38 248	67.13 82	91.85 124	90.23 173	75.09 94	69.37 171	83.39 192	53.79 165	94.44 148	71.77 123	65.00 238	86.63 206

v12		71.02 231	68.29 236	79.22 226	80.18 257	66.53 114	87.01 230	86.54 258	67.90 214	54.00 275	71.70 293	53.66 166	89.91 263	63.09 199	50.51 301	81.21 277

pcd_1.5k_mvsjas		4.46 321	5.95 322	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	53.55 167	0.00 344	0.00 341	0.00 341	0.00 340

PS-MVSNAJss		77.26 164	76.31 151	80.13 202	80.64 241	59.16 245	90.63 167	91.06 147	72.80 136	68.58 184	84.57 183	53.55 167	93.96 180	72.97 111	71.96 191	87.27 195

PS-MVSNAJ		88.14 10	87.61 17	89.71 4	92.06 72	76.72 1	95.75 11	93.26 66	83.86 11	89.55 5	96.06 11	53.55 167	97.89 30	91.10 6	93.31 35	94.54 66

v13		70.90 232	68.15 237	79.15 230	80.08 258	66.45 116	86.83 234	86.50 259	67.62 220	53.78 277	71.61 295	53.51 170	89.87 265	62.89 203	50.50 302	81.14 279

mPP-MVS		82.96 74	82.44 71	84.52 108	92.83 55	62.92 189	92.76 83	91.85 119	71.52 169	75.61 106	94.24 63	53.48 171	96.99 70	78.97 79	90.73 65	93.64 98

xiu_mvs_v2_base		87.92 14	87.38 22	89.55 7	91.41 95	76.43 2	95.74 12	93.12 74	83.53 13	89.55 5	95.95 12	53.45 172	97.68 32	91.07 7	92.62 42	94.54 66

test_post1							78.95 288				20.70 337	53.05 173	91.50 243	60.43 218

MDTV_nov1_ep13			72.61 201		89.06 131	68.48 50	80.33 278	90.11 180	71.84 158	71.81 144	75.92 270	53.01 174	93.92 182	48.04 263	73.38 180

v11		71.05 230	68.32 234	79.23 225	80.34 251	66.57 112	87.01 230	86.55 257	68.11 213	54.40 270	71.66 294	52.94 175	89.91 263	62.71 205	51.12 296	81.21 277

test222					89.77 117	61.60 209	89.55 188	89.42 202	56.83 284	77.28 93	92.43 94	52.76 176			91.14 63	93.09 112

v1144		76.73 175	74.88 174	82.27 158	80.23 256	66.60 109	91.68 133	90.21 175	73.69 120	69.06 176	81.89 213	52.73 177	94.40 153	69.21 148	65.23 231	85.80 222

v7		76.83 173	75.01 173	82.29 157	80.35 249	66.70 105	91.68 133	89.97 185	73.47 127	69.22 173	82.22 205	52.52 178	94.43 152	69.73 142	65.96 226	85.74 226

v10		74.77 199	72.54 203	81.46 177	80.33 253	66.71 104	89.15 196	89.08 215	70.94 175	63.08 229	79.86 242	52.52 178	94.04 176	65.70 179	62.17 254	83.64 245

CLD-MVS		82.73 75	82.35 73	83.86 118	87.90 156	67.65 69	95.45 18	92.18 109	85.06 8	72.58 132	92.27 98	52.46 180	95.78 107	84.18 44	79.06 141	88.16 177
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TranMVSNet+NR-MVSNet		75.86 185	74.52 179	79.89 208	82.44 219	60.64 222	91.37 144	91.37 138	76.63 75	67.65 198	86.21 166	52.37 181	91.55 238	61.84 211	60.81 265	87.48 183

VPA-MVSNet		79.03 130	78.00 127	82.11 167	85.95 181	64.48 154	93.22 71	94.66 27	75.05 95	74.04 120	84.95 178	52.17 182	93.52 192	74.90 107	67.04 220	88.32 176

APD-MVS_3200maxsize		81.64 91	81.32 83	82.59 144	92.36 63	58.74 249	91.39 141	91.01 150	63.35 245	79.72 68	94.62 51	51.82 183	96.14 95	79.71 71	87.93 83	92.89 119

dp		75.01 196	72.09 207	83.76 119	89.28 125	66.22 124	79.96 285	89.75 190	71.16 173	67.80 197	77.19 261	51.81 184	92.54 216	50.39 254	71.44 196	92.51 127

v144192		76.05 182	74.03 185	82.12 164	79.50 265	66.55 113	91.39 141	89.71 196	72.30 144	68.17 191	81.33 221	51.75 185	94.03 177	67.94 155	64.19 244	85.77 223

BH-untuned		78.68 140	77.08 142	83.48 129	89.84 116	63.74 173	92.70 86	88.59 227	71.57 167	66.83 207	88.65 134	51.75 185	95.39 123	59.03 225	84.77 106	91.32 145

HQP2-MVS												51.63 187

HQP-MVS		81.14 96	80.64 91	82.64 142	87.54 159	63.66 178	94.06 43	91.70 125	79.80 33	74.18 115	90.30 120	51.63 187	95.61 116	77.63 88	78.90 142	88.63 169

V42		76.46 177	74.55 178	82.19 162	79.14 270	67.82 64	90.26 174	89.42 202	73.75 119	68.63 183	81.89 213	51.31 189	94.09 171	71.69 124	64.84 239	84.66 238

TransMVSNet (Re)		70.07 237	67.66 239	77.31 251	80.62 243	59.13 246	91.78 128	84.94 274	65.97 229	60.08 241	80.44 234	50.78 190	91.87 232	48.84 260	45.46 311	80.94 282

HQP_MVS		80.34 107	79.75 101	82.12 164	86.94 167	62.42 196	93.13 72	91.31 139	78.81 49	72.53 133	89.14 131	50.66 191	95.55 120	76.74 91	78.53 146	88.39 174

plane_prior6					87.23 163	62.32 198						50.66 191

ACMMP		81.49 92	80.67 90	83.93 117	91.71 82	62.90 190	92.13 102	92.22 105	71.79 161	71.68 147	93.49 74	50.32 193	96.96 73	78.47 81	84.22 116	91.93 136
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
MVS_111021_LR		82.02 88	81.52 81	83.51 128	88.42 146	62.88 191	89.77 186	88.93 220	76.78 73	75.55 107	93.10 77	50.31 194	95.38 124	83.82 49	87.02 88	92.26 133

1314		80.70 101	78.95 116	85.94 66	87.77 158	67.56 71	87.91 215	92.55 94	72.17 149	67.44 199	93.09 78	50.27 195	97.04 64	71.68 125	87.64 85	93.23 108

CP-MVSNet		70.50 236	69.91 219	72.26 283	80.71 235	51.00 290	87.23 226	90.30 171	67.84 215	59.64 242	82.69 197	50.23 196	82.30 309	51.28 251	59.28 269	83.46 250

LCM-MVSNet-Re		72.93 211	71.84 208	76.18 259	88.49 142	48.02 300	80.07 283	70.17 323	73.96 114	52.25 283	80.09 241	49.98 197	88.24 278	67.35 160	84.23 115	92.28 132

Vis-MVSNet		80.92 100	79.98 98	83.74 120	88.48 143	61.80 207	93.44 66	88.26 235	73.96 114	77.73 85	91.76 104	49.94 198	94.76 136	65.84 177	90.37 70	94.65 62
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		75.98 184	73.92 187	82.15 163	79.73 261	66.24 123	91.22 151	89.75 190	72.67 137	68.49 189	81.42 219	49.86 199	94.27 161	67.08 163	65.02 237	85.95 219

test-mter		79.96 115	79.38 110	81.72 173	86.93 169	61.17 212	92.70 86	91.54 129	73.85 116	75.62 104	86.94 159	49.84 200	92.38 221	72.21 120	84.76 107	91.60 139

cdsmvs_eth3d_5k		19.86 316	26.47 312	0.00 332	0.00 345	0.00 346	0.00 337	93.45 58	0.00 341	0.00 342	95.27 30	49.56 201	0.00 344	0.00 341	0.00 341	0.00 340

3Dnovator+	73.60 7	82.10 87	80.60 92	86.60 44	90.89 102	66.80 99	95.20 23	93.44 59	74.05 110	67.42 200	92.49 92	49.46 202	97.65 37	70.80 135	91.68 54	95.33 35

MVP-Stereo		77.12 166	76.23 152	79.79 211	81.72 224	66.34 120	89.29 192	90.88 151	70.56 185	62.01 237	82.88 195	49.34 203	94.13 169	65.55 180	93.80 26	78.88 298
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OMC-MVS		78.67 142	77.91 130	80.95 193	85.76 185	57.40 260	88.49 204	88.67 224	73.85 116	72.43 137	92.10 99	49.29 204	94.55 144	72.73 115	77.89 150	90.91 149

VPNet		78.82 135	77.53 135	82.70 139	84.52 196	66.44 117	93.93 51	92.23 101	80.46 30	72.60 131	88.38 138	49.18 205	93.13 197	72.47 118	63.97 247	88.55 171

CVMVSNet		74.04 204	74.27 182	73.33 274	85.33 186	43.94 310	89.53 190	88.39 230	54.33 291	70.37 154	90.13 123	49.17 206	84.05 295	61.83 212	79.36 138	91.99 135

v1921920		75.63 189	73.49 192	82.06 168	79.38 266	66.35 119	91.07 156	89.48 199	71.98 152	67.99 192	81.22 224	49.16 207	93.90 183	66.56 168	64.56 243	85.92 221

pm-mvs1		72.89 212	71.09 214	78.26 237	79.10 272	57.62 258	90.80 160	89.30 205	67.66 217	62.91 231	81.78 215	49.11 208	92.95 201	60.29 219	58.89 274	84.22 241

pmmvs4		73.92 206	71.81 209	80.25 200	79.17 269	65.24 139	87.43 223	87.26 248	67.64 219	63.46 226	83.91 187	48.96 209	91.53 242	62.94 202	65.49 230	83.96 242

TAPA-MVS	70.22 12	74.94 198	73.53 191	79.17 228	90.40 107	52.07 284	89.19 195	89.61 197	62.69 251	70.07 158	92.67 90	48.89 210	94.32 158	38.26 301	79.97 134	91.12 147
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator	73.91 6	82.69 78	80.82 88	88.31 14	89.57 120	71.26 13	92.60 91	94.39 37	78.84 48	67.89 195	92.48 93	48.42 211	98.52 15	68.80 152	94.40 19	95.15 46

CPTT-MVS		79.59 123	79.16 114	80.89 194	91.54 87	59.80 236	92.10 104	88.54 229	60.42 265	72.96 125	93.28 76	48.27 212	92.80 208	78.89 80	86.50 96	90.06 156

GBi-Net		75.65 187	73.83 188	81.10 188	88.85 134	65.11 143	90.01 176	90.32 167	70.84 177	67.04 204	80.25 238	48.03 213	91.54 239	59.80 222	69.34 204	86.64 203

test1		75.65 187	73.83 188	81.10 188	88.85 134	65.11 143	90.01 176	90.32 167	70.84 177	67.04 204	80.25 238	48.03 213	91.54 239	59.80 222	69.34 204	86.64 203

FMVSNet2		76.07 181	74.01 186	82.26 160	88.85 134	67.66 68	91.33 146	91.61 127	70.84 177	65.98 209	82.25 204	48.03 213	92.00 231	58.46 228	68.73 210	87.10 196

LFMVS		84.34 54	82.73 69	89.18 8	94.76 19	73.25 8	94.99 30	91.89 117	71.90 153	82.16 50	93.49 74	47.98 216	97.05 62	82.55 55	84.82 105	97.25 2

QAPM		79.95 116	77.39 139	87.64 20	89.63 119	71.41 12	93.30 68	93.70 48	65.34 235	67.39 202	91.75 105	47.83 217	98.96 5	57.71 231	89.81 72	92.54 126

HPM-MVS_fast		80.25 108	79.55 106	82.33 155	91.55 86	59.95 234	91.32 147	89.16 211	65.23 236	74.71 113	93.07 81	47.81 218	95.74 109	74.87 108	88.23 80	91.31 146

CANet_DTU		84.09 58	83.52 53	85.81 69	90.30 109	66.82 93	91.87 122	89.01 218	85.27 7	86.09 20	93.74 70	47.71 219	96.98 71	77.90 87	89.78 73	93.65 97

abl_6		79.82 118	79.20 113	81.70 175	89.85 115	58.34 251	88.47 205	90.07 181	62.56 252	77.71 86	93.08 79	47.65 220	96.78 81	77.94 86	85.45 103	89.99 158

v1240		75.21 194	72.98 197	81.88 170	79.20 268	66.00 126	90.75 162	89.11 214	71.63 165	67.41 201	81.22 224	47.36 221	93.87 184	65.46 181	64.72 241	85.77 223

PEN-MVS		69.46 244	68.56 230	72.17 285	79.27 267	49.71 296	86.90 232	89.24 208	67.24 223	59.08 246	82.51 198	47.23 222	83.54 301	48.42 262	57.12 277	83.25 253

DI_MVS_plusplus_test		79.78 120	77.50 136	86.62 43	80.90 230	69.46 35	90.69 163	91.97 115	77.00 68	59.07 247	82.34 202	46.82 223	95.88 104	82.14 57	86.59 94	94.53 68

CNLPA		74.31 202	72.30 205	80.32 198	91.49 88	61.66 208	90.85 158	80.72 298	56.67 285	63.85 223	90.64 114	46.75 224	90.84 246	53.79 243	75.99 166	88.47 173

test_normal		79.66 121	77.36 141	86.54 47	80.72 234	69.21 38	90.68 164	92.16 110	76.99 69	58.63 251	82.03 211	46.70 225	95.86 105	81.74 61	86.63 93	94.56 63

114514_t		79.17 129	77.67 131	83.68 124	95.32 14	65.53 136	92.85 82	91.60 128	63.49 244	67.92 194	90.63 116	46.65 226	95.72 114	67.01 164	83.54 117	89.79 159

PS-CasMVS		69.86 239	69.13 226	72.07 286	80.35 249	50.57 292	87.02 229	89.75 190	67.27 222	59.19 245	82.28 203	46.58 227	82.24 310	50.69 253	59.02 272	83.39 252

DTE-MVSNet		68.46 250	67.33 240	71.87 289	77.94 281	49.00 299	86.16 240	88.58 228	66.36 227	58.19 252	82.21 206	46.36 228	83.87 299	44.97 275	55.17 284	82.73 260

PMMVS		81.98 89	82.04 75	81.78 171	89.76 118	56.17 266	91.13 154	90.69 156	77.96 57	80.09 64	93.57 72	46.33 229	94.99 129	81.41 64	87.46 86	94.17 78

OPM-MVS		79.00 131	78.09 125	81.73 172	83.52 211	63.83 170	91.64 136	90.30 171	76.36 79	71.97 142	89.93 126	46.30 230	95.17 127	75.10 101	77.70 152	86.19 210

BH-RMVSNet		79.46 126	77.65 132	84.89 97	91.68 83	65.66 133	93.55 62	88.09 236	72.93 134	73.37 123	91.12 110	46.20 231	96.12 96	56.28 235	85.61 102	92.91 118

TR-MVS		78.77 138	77.37 140	82.95 134	90.49 105	60.88 214	93.67 58	90.07 181	70.08 191	74.51 114	91.37 108	45.69 232	95.70 115	60.12 220	80.32 133	92.29 131

Patchmatch-test1		75.00 197	71.80 210	84.58 106	86.63 171	70.08 24	81.06 273	89.19 209	71.60 166	70.01 159	77.16 263	45.53 233	88.63 272	51.79 250	73.27 181	95.02 54

semantic-postprocess				76.32 257	81.48 225	60.67 221		85.99 267	66.17 228	59.50 243	78.88 250	45.51 234	83.65 300	62.58 207	61.93 255	84.63 240

IterMVS		72.65 217	70.83 215	78.09 241	82.17 220	62.96 186	87.64 221	86.28 261	71.56 168	60.44 239	78.85 251	45.42 235	86.66 286	63.30 196	61.83 256	84.65 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		76.14 180	75.28 171	78.72 232	83.22 212	55.17 272	89.87 184	87.78 240	75.42 87	67.98 193	81.43 218	45.08 236	92.52 217	75.08 102	71.63 192	88.48 172

mvs-test1		78.74 139	77.95 128	81.14 186	83.22 212	57.13 262	93.96 48	87.78 240	75.42 87	72.68 129	90.80 113	45.08 236	94.54 145	75.08 102	77.49 156	91.74 138

v748		70.55 235	67.97 238	78.27 236	75.75 293	58.78 248	86.29 239	89.25 207	65.12 237	56.66 261	77.17 262	45.05 238	92.95 201	58.13 229	58.33 276	83.10 257

XVG-OURS-SEG-HR		74.70 200	73.08 196	79.57 215	78.25 278	57.33 261	80.49 276	87.32 246	63.22 247	68.76 181	90.12 125	44.89 239	91.59 237	70.55 138	74.09 177	89.79 159

v7n		71.31 225	68.65 228	79.28 223	76.40 288	60.77 217	86.71 235	89.45 200	64.17 241	58.77 250	78.24 252	44.59 240	93.54 191	57.76 230	61.75 258	83.52 248

pmmvs5		73.35 209	71.52 211	78.86 231	78.64 276	60.61 223	91.08 155	86.90 249	67.69 216	63.32 227	83.64 188	44.33 241	90.53 247	62.04 210	66.02 225	85.46 230

OpenMVS	70.45 11	78.54 144	75.92 156	86.41 55	85.93 184	71.68 11	92.74 84	92.51 95	66.49 226	64.56 216	91.96 100	43.88 242	98.10 25	54.61 239	90.65 67	89.44 164

AdaColmap		78.94 133	77.00 144	84.76 100	96.34 6	65.86 129	92.66 90	87.97 239	62.18 254	70.56 151	92.37 96	43.53 243	97.35 49	64.50 188	82.86 120	91.05 148

test_djsdf		73.76 208	72.56 202	77.39 249	77.00 286	53.93 277	89.07 198	90.69 156	65.80 230	63.92 221	82.03 211	43.14 244	92.67 213	72.83 113	68.53 211	85.57 228

GA-MVS		78.33 146	76.23 152	84.65 104	83.65 209	66.30 121	91.44 139	90.14 179	76.01 81	70.32 155	84.02 186	42.50 245	94.72 139	70.98 133	77.00 161	92.94 117

PLC	68.80 14	75.23 193	73.68 190	79.86 209	92.93 53	58.68 250	90.64 165	88.30 232	60.90 262	64.43 219	90.53 117	42.38 246	94.57 142	56.52 233	76.54 163	86.33 207
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1111		56.66 291	54.98 290	61.69 306	61.99 321	31.38 327	79.81 286	83.17 289	45.66 310	41.94 314	65.44 307	41.50 247	79.56 318	27.64 320	47.68 307	74.14 311

.test1245		46.52 301	49.68 297	37.02 323	61.99 321	31.38 327	79.81 286	83.17 289	45.66 310	41.94 314	65.44 307	41.50 247	79.56 318	27.64 320	0.01 339	0.13 338

V4		69.80 240	67.02 242	78.15 239	71.86 301	60.10 231	82.02 264	87.39 243	64.48 238	57.78 256	75.98 268	41.49 249	92.90 206	63.00 200	59.16 270	81.44 272

v52		69.80 240	67.01 243	78.15 239	71.84 302	60.10 231	82.02 264	87.39 243	64.48 238	57.80 255	75.97 269	41.47 250	92.90 206	63.00 200	59.13 271	81.45 271

Fast-Effi-MVS+-dtu		75.04 195	73.37 194	80.07 203	80.86 231	59.52 240	91.20 153	85.38 272	71.90 153	65.20 211	84.84 179	41.46 251	92.97 200	66.50 169	72.96 184	87.73 180

MS-PatchMatch		77.90 152	76.50 149	82.12 164	85.99 180	69.95 27	91.75 131	92.70 86	73.97 113	62.58 234	84.44 184	41.11 252	95.78 107	63.76 192	92.17 49	80.62 286

pcd1.5k->3k		31.17 310	31.85 308	29.12 325	81.48 225	0.00 346	0.00 337	91.79 121	0.00 341	0.00 342	0.00 343	41.05 253	0.00 344	0.00 341	72.34 190	87.36 191

XVG-OURS		74.25 203	72.46 204	79.63 213	78.45 277	57.59 259	80.33 278	87.39 243	63.86 243	68.76 181	89.62 129	40.50 254	91.72 235	69.00 149	74.25 175	89.58 162

VDD-MVS		83.06 72	81.81 78	86.81 38	90.86 103	67.70 67	95.40 19	91.50 132	75.46 86	81.78 52	92.34 97	40.09 255	97.13 61	86.85 30	82.04 126	95.60 29

DP-MVS		69.90 238	66.48 244	80.14 201	95.36 13	62.93 187	89.56 187	76.11 306	50.27 300	57.69 257	85.23 175	39.68 256	95.73 110	33.35 311	71.05 198	81.78 267

ADS-MVSNet2		66.90 259	63.44 262	77.26 252	88.06 152	60.70 220	68.01 313	75.56 311	57.57 277	64.48 217	69.87 300	38.68 257	84.10 294	40.87 288	67.89 216	86.97 197

ADS-MVSNet		68.54 249	64.38 259	81.03 191	88.06 152	66.90 90	68.01 313	84.02 280	57.57 277	64.48 217	69.87 300	38.68 257	89.21 271	40.87 288	67.89 216	86.97 197

LPG-MVS_test		75.82 186	74.58 177	79.56 216	84.31 201	59.37 242	90.44 168	89.73 193	69.49 195	64.86 213	88.42 136	38.65 259	94.30 159	72.56 116	72.76 185	85.01 235

LGP-MVS_train				79.56 216	84.31 201	59.37 242		89.73 193	69.49 195	64.86 213	88.42 136	38.65 259	94.30 159	72.56 116	72.76 185	85.01 235

VDDNet		80.50 104	78.26 123	87.21 29	86.19 177	69.79 30	94.48 34	91.31 139	60.42 265	79.34 71	90.91 111	38.48 261	96.56 89	82.16 56	81.05 130	95.27 40

ACMP	71.68 10	75.58 190	74.23 183	79.62 214	84.97 191	59.64 237	90.80 160	89.07 216	70.39 188	62.95 230	87.30 157	38.28 262	93.87 184	72.89 112	71.45 195	85.36 232
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		79.87 117	78.68 117	83.45 130	89.96 113	61.51 210	92.13 102	90.79 153	76.83 72	78.85 78	86.33 165	38.16 263	96.17 94	67.93 156	87.17 87	92.67 122
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
anonymousdsp		71.14 228	69.37 223	76.45 256	72.95 298	54.71 274	84.19 247	88.88 221	61.92 257	62.15 236	79.77 243	38.14 264	91.44 244	68.90 151	67.45 219	83.21 254

xiu_mvs_v1_base_debu		82.16 84	81.12 85	85.26 89	86.42 172	68.72 47	92.59 93	90.44 161	73.12 131	84.20 38	94.36 55	38.04 265	95.73 110	84.12 45	86.81 89	91.33 142

xiu_mvs_v1_base		82.16 84	81.12 85	85.26 89	86.42 172	68.72 47	92.59 93	90.44 161	73.12 131	84.20 38	94.36 55	38.04 265	95.73 110	84.12 45	86.81 89	91.33 142

xiu_mvs_v1_base_debi		82.16 84	81.12 85	85.26 89	86.42 172	68.72 47	92.59 93	90.44 161	73.12 131	84.20 38	94.36 55	38.04 265	95.73 110	84.12 45	86.81 89	91.33 142

PVSNet_0	68.08 15	71.81 220	68.32 234	82.27 158	84.68 193	62.31 199	88.68 202	90.31 170	75.84 82	57.93 254	80.65 232	37.85 268	94.19 164	69.94 141	29.05 327	90.31 155

Anonymous20231206		67.53 256	65.78 246	72.79 279	74.95 294	47.59 303	88.23 208	87.32 246	61.75 260	58.07 253	77.29 259	37.79 269	87.29 284	42.91 279	63.71 248	83.48 249

ACMM	69.62 13	74.34 201	72.73 199	79.17 228	84.25 203	57.87 254	90.36 171	89.93 186	63.17 248	65.64 210	86.04 169	37.79 269	94.10 170	65.89 176	71.52 194	85.55 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		78.18 147	75.77 158	85.41 84	87.14 165	69.11 39	92.96 77	91.15 144	66.71 224	70.47 152	86.07 167	37.49 271	96.48 90	70.15 140	79.80 135	90.65 151

LS3D		69.17 245	66.40 245	77.50 246	91.92 76	56.12 267	85.12 242	80.37 299	46.96 307	56.50 262	87.51 151	37.25 272	93.71 188	32.52 316	79.40 137	82.68 262

MDA-MVSNet_test_wron		63.78 275	60.16 276	74.64 265	78.15 279	60.41 224	83.49 252	84.03 279	56.17 288	39.17 319	71.59 297	37.22 273	83.24 305	42.87 281	48.73 305	80.26 289

YYNet1		63.76 276	60.14 277	74.62 266	78.06 280	60.19 230	83.46 254	83.99 283	56.18 287	39.25 318	71.56 298	37.18 274	83.34 303	42.90 280	48.70 306	80.32 288

FMVSNet5		68.04 252	65.66 248	75.18 263	84.43 199	57.89 253	83.54 251	86.26 262	61.83 259	53.64 278	73.30 277	37.15 275	85.08 290	48.99 259	61.77 257	82.56 263

test20.03		63.83 274	62.65 268	67.38 298	70.58 308	39.94 318	86.57 237	84.17 278	63.29 246	51.86 284	77.30 258	37.09 276	82.47 307	38.87 300	54.13 288	79.73 293

PVSNet	73.49 8	80.05 113	78.63 118	84.31 111	90.92 101	64.97 149	92.47 96	91.05 148	79.18 41	72.43 137	90.51 118	37.05 277	94.06 173	68.06 154	86.00 99	93.90 94

EU-MVSNet		64.01 273	63.01 265	67.02 299	74.40 296	38.86 322	83.27 256	86.19 263	45.11 313	54.27 271	81.15 227	36.91 278	80.01 315	48.79 261	57.02 278	82.19 265

FMVSNet1		72.71 215	69.91 219	81.10 188	83.60 210	65.11 143	90.01 176	90.32 167	63.92 242	63.56 225	80.25 238	36.35 279	91.54 239	54.46 240	66.75 222	86.64 203

testpf		57.17 288	56.93 285	57.88 310	79.13 271	42.40 311	34.23 333	85.97 268	52.64 293	47.66 299	66.50 304	36.33 280	79.65 317	53.60 245	56.31 282	51.60 326

CMPMVS	48.56 21	66.77 260	64.41 258	73.84 271	70.65 307	50.31 293	77.79 296	85.73 271	45.54 312	44.76 307	82.14 207	35.40 281	90.14 256	63.18 198	74.54 174	81.07 281
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		67.57 255	64.76 254	76.00 260	72.82 300	53.37 279	88.71 201	86.78 250	53.19 292	57.58 258	78.03 254	35.33 282	92.41 220	55.56 237	54.88 286	82.21 264

test2356		64.16 272	63.28 263	66.81 300	69.37 312	39.86 320	87.76 218	86.02 266	59.83 269	53.54 279	73.23 278	34.94 283	80.67 314	39.66 292	65.20 232	79.89 291

PatchMatch-RL		72.06 218	69.98 217	78.28 235	89.51 123	55.70 269	83.49 252	83.39 287	61.24 261	63.72 224	82.76 196	34.77 284	93.03 199	53.37 247	77.59 153	86.12 212

LTVRE_ROB	59.60 19	66.27 262	63.54 261	74.45 267	84.00 206	51.55 286	67.08 316	83.53 284	58.78 274	54.94 266	80.31 236	34.54 285	93.23 196	40.64 290	68.03 214	78.58 301
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
F-COLMAP		70.66 233	68.44 232	77.32 250	86.37 175	55.91 268	88.00 211	86.32 260	56.94 283	57.28 259	88.07 145	33.58 286	92.49 218	51.02 252	68.37 212	83.55 246

pmmvs-eth3d		65.53 266	62.32 270	75.19 262	69.39 311	59.59 238	82.80 261	83.43 285	62.52 253	51.30 288	72.49 282	32.86 287	87.16 285	55.32 238	50.73 297	78.83 299

MDA-MVSNet-bldmvs		61.54 281	57.70 282	73.05 276	79.53 264	57.00 264	83.08 259	81.23 294	57.57 277	34.91 322	72.45 284	32.79 288	86.26 289	35.81 306	41.95 315	75.89 308

MIMVSNet		71.64 223	68.44 232	81.23 181	81.97 223	64.44 156	73.05 302	88.80 222	69.67 194	64.59 215	74.79 274	32.79 288	87.82 281	53.99 242	76.35 164	91.42 141

UnsupCasMVSNet_eth		65.79 265	63.10 264	73.88 270	70.71 306	50.29 294	81.09 272	89.88 187	72.58 139	49.25 294	74.77 275	32.57 290	87.43 283	55.96 236	41.04 317	83.90 244

N_pmnet		50.55 297	49.11 299	54.88 314	77.17 285	4.02 343	84.36 246	2.00 344	48.59 303	45.86 303	68.82 302	32.22 291	82.80 306	31.58 317	51.38 295	77.81 304

test_0402		64.54 269	61.09 274	74.92 264	84.10 205	60.75 218	87.95 212	79.71 301	52.03 295	52.41 282	77.20 260	32.21 292	91.64 236	23.14 326	61.03 263	72.36 313

DSMNet-mixed		56.78 289	54.44 291	63.79 305	63.21 318	29.44 331	64.43 319	64.10 330	42.12 321	51.32 287	71.60 296	31.76 293	75.04 324	36.23 304	65.20 232	86.87 201

Test4		76.45 178	73.45 193	85.45 82	76.07 292	67.61 70	88.38 207	90.83 152	76.71 74	53.06 280	79.65 246	31.61 294	94.35 157	78.47 81	86.22 98	94.40 72

MSDG		69.54 243	65.73 247	80.96 192	85.11 190	63.71 175	84.19 247	83.28 288	56.95 282	54.50 268	84.03 185	31.50 295	96.03 100	42.87 281	69.13 207	83.14 256

RPSCF		64.24 271	61.98 272	71.01 290	76.10 290	45.00 307	75.83 299	75.94 308	46.94 308	58.96 248	84.59 182	31.40 296	82.00 311	47.76 265	60.33 268	86.04 218

jajsoiax		73.05 210	71.51 212	77.67 244	77.46 283	54.83 273	88.81 200	90.04 183	69.13 199	62.85 232	83.51 190	31.16 297	92.75 210	70.83 134	69.80 200	85.43 231

MVS-HIRNet		60.25 283	55.55 289	74.35 268	84.37 200	56.57 265	71.64 304	74.11 315	34.44 325	45.54 305	42.24 327	31.11 298	89.81 266	40.36 291	76.10 165	76.67 307

SixPastTwentyTwo		64.92 267	61.78 273	74.34 269	78.74 274	49.76 295	83.42 255	79.51 302	62.86 250	50.27 291	77.35 257	30.92 299	90.49 248	45.89 271	47.06 308	82.78 258

LP		56.71 290	51.64 294	71.91 288	80.08 258	60.33 226	61.72 321	75.61 310	43.87 318	43.76 311	60.30 317	30.46 300	84.05 295	22.94 327	46.06 310	71.34 315

mvs_tets		72.71 215	71.11 213	77.52 245	77.41 284	54.52 275	88.45 206	89.76 189	68.76 204	62.70 233	83.26 193	29.49 301	92.71 211	70.51 139	69.62 202	85.34 233

K. test v3		63.09 277	59.61 279	73.53 273	76.26 289	49.38 298	83.27 256	77.15 305	64.35 240	47.77 297	72.32 287	28.73 302	87.79 282	49.93 257	36.69 322	83.41 251

UnsupCasMVSNet_bld		61.60 280	57.71 281	73.29 275	68.73 313	51.64 285	78.61 290	89.05 217	57.20 281	46.11 300	61.96 315	28.70 303	88.60 274	50.08 256	38.90 320	79.63 294

lessismore_v0				73.72 272	72.93 299	47.83 302		61.72 333		45.86 303	73.76 276	28.63 304	89.81 266	47.75 266	31.37 326	83.53 247

new-patchmatchnet		59.30 287	56.48 287	67.79 296	65.86 315	44.19 308	82.47 262	81.77 292	59.94 268	43.65 312	66.20 306	27.67 305	81.68 312	39.34 293	41.40 316	77.50 305

testing_2		71.09 229	67.32 241	82.40 154	69.82 309	66.52 115	83.64 250	90.77 154	72.21 146	45.12 306	71.07 299	27.60 306	93.74 187	75.71 97	69.96 199	86.95 199

ACMH	63.93 17	68.62 247	64.81 253	80.03 204	85.22 188	63.25 182	87.72 219	84.66 275	60.83 263	51.57 286	79.43 248	27.29 307	94.96 130	41.76 284	64.84 239	81.88 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		64.68 268	62.17 271	72.21 284	76.08 291	47.35 304	80.67 275	81.02 296	56.19 286	51.60 285	79.66 245	27.05 308	88.56 275	53.60 245	53.63 289	80.71 285

ACMH+	65.35 16	67.65 254	64.55 255	76.96 253	84.59 195	57.10 263	88.08 210	80.79 297	58.59 276	53.00 281	81.09 228	26.63 309	92.95 201	46.51 268	61.69 260	80.82 283

OpenMVS_ROB	61.12 18	66.39 261	62.92 266	76.80 255	76.51 287	57.77 255	89.22 193	83.41 286	55.48 289	53.86 276	77.84 255	26.28 310	93.95 181	34.90 309	68.76 209	78.68 300

COLMAP_ROB	57.96 20	62.98 278	59.65 278	72.98 277	81.44 227	53.00 281	83.75 249	75.53 312	48.34 305	48.81 295	81.40 220	24.14 311	90.30 249	32.95 313	60.52 267	75.65 309
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		60.16 284	57.33 284	68.67 294	69.71 310	44.13 309	78.92 289	84.21 277	55.05 290	44.63 308	71.85 291	23.91 312	81.54 313	32.63 315	55.03 285	80.35 287

testgi		64.48 270	62.87 267	69.31 293	71.24 303	40.62 317	85.49 241	79.92 300	65.36 234	54.18 273	83.49 191	23.74 313	84.55 292	41.60 285	60.79 266	82.77 259

ITE_SJBPF				70.43 291	74.44 295	47.06 305		77.32 304	60.16 267	54.04 274	83.53 189	23.30 314	84.01 297	43.07 278	61.58 261	80.21 290

EG-PatchMatch MVS		68.55 248	65.41 250	77.96 242	78.69 275	62.93 187	89.86 185	89.17 210	60.55 264	50.27 291	77.73 256	22.60 315	94.06 173	47.18 267	72.65 187	76.88 306

tmp_tt		22.26 315	23.75 314	17.80 328	5.23 342	12.06 342	35.26 332	39.48 339	2.82 338	18.94 329	44.20 326	22.23 316	24.64 340	36.30 303	9.31 336	16.69 333

USDC		67.43 258	64.51 256	76.19 258	77.94 281	55.29 271	78.38 292	85.00 273	73.17 129	48.36 296	80.37 235	21.23 317	92.48 219	52.15 249	64.02 246	80.81 284

XVG-ACMP-BASELINE		68.04 252	65.53 249	75.56 261	74.06 297	52.37 282	78.43 291	85.88 269	62.03 255	58.91 249	81.21 226	20.38 318	91.15 245	60.69 217	68.18 213	83.16 255

AllTest		61.66 279	58.06 280	72.46 281	79.57 262	51.42 288	80.17 281	68.61 325	51.25 297	45.88 301	81.23 222	19.86 319	86.58 287	38.98 294	57.01 279	79.39 295

TestCases				72.46 281	79.57 262	51.42 288		68.61 325	51.25 297	45.88 301	81.23 222	19.86 319	86.58 287	38.98 294	57.01 279	79.39 295

test1235678		55.73 292	52.74 292	64.68 304	60.16 324	35.56 325	81.65 268	81.46 293	51.27 296	38.93 320	62.82 314	17.44 321	78.58 322	30.87 318	50.09 304	79.89 291

pmmvs3		55.51 293	51.50 296	67.53 297	57.90 326	50.93 291	80.37 277	73.66 316	40.63 322	44.15 310	64.75 311	16.30 322	78.97 320	44.77 276	40.98 318	72.69 312

Anonymous20231211		53.57 296	49.43 298	66.00 302	65.01 316	42.08 312	80.95 274	72.60 318	38.46 323	41.65 316	64.48 312	15.72 323	84.23 293	25.78 323	40.24 319	71.68 314

TDRefinement		55.28 294	51.58 295	66.39 301	59.53 325	46.15 306	76.23 298	72.80 317	44.60 315	42.49 313	76.28 267	15.29 324	82.39 308	33.20 312	43.75 313	70.62 317

new_pmnet		49.31 298	46.44 300	57.93 309	62.84 319	40.74 316	68.47 312	62.96 332	36.48 324	35.09 321	57.81 319	14.97 325	72.18 325	32.86 314	46.44 309	60.88 324

TinyColmap		60.32 282	56.42 288	72.00 287	78.78 273	53.18 280	78.36 293	75.64 309	52.30 294	41.59 317	75.82 271	14.76 326	88.35 277	35.84 305	54.71 287	74.46 310

LF4IMVS		54.01 295	52.12 293	59.69 308	62.41 320	39.91 319	68.59 311	68.28 327	42.96 319	44.55 309	75.18 272	14.09 327	68.39 328	41.36 287	51.68 294	70.78 316

PM-MVS		59.40 285	56.59 286	67.84 295	63.63 317	41.86 314	76.76 297	63.22 331	59.01 273	51.07 289	72.27 288	11.72 328	83.25 304	61.34 213	50.28 303	78.39 302

no-one		44.13 303	38.39 304	61.34 307	45.91 334	41.94 313	61.67 322	75.07 314	45.05 314	20.07 328	40.68 330	11.58 329	79.82 316	30.18 319	15.30 330	62.26 323

testus		59.36 286	57.51 283	64.90 303	66.72 314	37.56 323	84.98 243	81.09 295	57.46 280	47.72 298	72.76 279	11.43 330	78.78 321	36.56 302	58.91 273	78.36 303

ambc				69.61 292	61.38 323	41.35 315	49.07 330	85.86 270		50.18 293	66.40 305	10.16 331	88.14 279	45.73 272	44.20 312	79.32 297

FPMVS		45.64 302	43.10 303	53.23 316	51.42 329	36.46 324	64.97 318	71.91 320	29.13 327	27.53 325	61.55 316	9.83 332	65.01 332	16.00 332	55.58 283	58.22 325

ANet_high		40.27 305	35.20 306	55.47 313	34.74 338	34.47 326	63.84 320	71.56 321	48.42 304	18.80 330	41.08 328	9.52 333	64.45 333	20.18 329	8.66 337	67.49 321

test12356		47.51 299	44.82 301	55.56 312	52.53 327	21.09 338	71.45 305	76.03 307	44.14 317	30.69 323	58.18 318	9.01 334	76.14 323	26.95 322	34.43 325	69.46 319

testmv		46.98 300	43.53 302	57.35 311	47.75 332	30.41 330	74.99 301	77.69 303	42.84 320	28.03 324	53.36 320	8.18 335	71.18 326	24.36 325	34.55 323	70.46 318

EMVS		23.76 314	23.20 315	25.46 327	41.52 336	16.90 341	60.56 324	38.79 341	14.62 334	8.99 337	20.24 338	7.35 336	45.82 338	7.25 337	9.46 335	13.64 335

E-PMN		24.61 312	24.00 313	26.45 326	43.74 335	18.44 340	60.86 323	39.66 338	15.11 333	9.53 336	22.10 334	6.52 337	46.94 337	8.31 336	10.14 333	13.98 334

DeepMVS_CX				34.71 324	51.45 328	24.73 337		28.48 343	31.46 326	17.49 331	52.75 321	5.80 338	42.60 339	18.18 331	19.42 328	36.81 330

Gipuma		34.91 307	31.44 309	45.30 319	70.99 305	39.64 321	19.85 336	72.56 319	20.10 332	16.16 332	21.47 335	5.08 339	71.16 327	13.07 333	43.70 314	25.08 332
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		40.54 304	35.79 305	54.76 315	36.92 337	30.81 329	51.41 328	69.02 324	22.07 329	24.63 326	45.37 325	4.56 340	65.81 330	33.67 310	34.50 324	67.67 320

PMMVS2		37.93 306	33.61 307	50.92 317	46.31 333	24.76 336	60.55 325	50.05 335	28.94 328	20.93 327	47.59 322	4.41 341	65.13 331	25.14 324	18.55 329	62.87 322

PMVS	26.43 22	31.84 309	28.16 311	42.89 320	25.87 341	27.58 334	50.92 329	49.78 337	21.37 331	14.17 334	40.81 329	2.01 342	66.62 329	9.61 335	38.88 321	34.49 331
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 313	19.77 317	38.09 322	34.56 339	26.92 335	26.57 334	38.87 340	11.73 336	11.37 335	27.44 332	1.37 343	50.42 336	11.41 334	14.60 331	36.93 329
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		32.77 308	29.98 310	41.11 321	48.05 330	29.17 332	65.82 317	50.02 336	21.42 330	14.74 333	37.19 331	1.11 344	55.11 335	19.75 330	11.77 332	39.06 328

wuyk23d		11.30 317	10.95 318	12.33 329	48.05 330	19.89 339	25.89 335	1.92 345	3.58 337	3.12 339	1.37 340	0.64 345	15.77 341	6.23 338	7.77 338	1.35 336

wuykxyi23d		29.03 311	23.09 316	46.84 318	31.67 340	28.82 333	43.46 331	57.72 334	14.39 335	7.52 338	20.84 336	0.64 345	60.29 334	21.57 328	10.04 334	51.40 327

test123		6.92 320	9.21 321	0.08 330	0.03 344	0.05 344	81.65 268	0.01 347	0.02 340	0.14 341	0.85 342	0.03 347	0.02 342	0.12 340	0.00 341	0.16 337

testmvs		7.23 319	9.62 320	0.06 331	0.04 343	0.02 345	84.98 243	0.02 346	0.03 339	0.18 340	1.21 341	0.01 348	0.02 342	0.14 339	0.01 339	0.13 338

sosnet-low-res		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 341	0.00 340

sosnet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 341	0.00 340

uncertanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 341	0.00 340

Regformer		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 341	0.00 340

ab-mvs-re		7.91 318	10.55 319	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	94.95 40	0.00 349	0.00 344	0.00 341	0.00 341	0.00 340

uanet		0.00 322	0.00 323	0.00 332	0.00 345	0.00 346	0.00 337	0.00 348	0.00 341	0.00 342	0.00 343	0.00 349	0.00 344	0.00 341	0.00 341	0.00 340

ESAPD								94.66 27

MTGPA								92.23 101

MTMP								32.52 342

gm-plane-assit					88.42 146	67.04 85			78.62 52		91.83 102		97.37 47	76.57 93

test9_res														89.41 10	94.96 8	95.29 38

agg_prior2														86.41 31	94.75 16	95.33 35

agg_prior					94.16 31	66.97 86		93.31 64		84.49 35			96.75 83

test_prior4						67.18 81	93.92 52

test_prior				86.42 53	94.71 21	67.35 75		93.10 75					96.84 79			95.05 50

旧先验2							92.00 112		59.37 272	87.54 14			93.47 193	75.39 99

新几何2							91.41 140

无先验							92.71 85	92.61 92	62.03 255				97.01 65	66.63 166		93.97 90

原ACMM2							92.01 110

testdata2													96.09 97	61.26 214

testdata1							89.21 194		77.55 63

plane_prior7					86.94 167	61.51 210

plane_prior5								91.31 139					95.55 120	76.74 91	78.53 146	88.39 174

plane_prior4											89.14 131

plane_prior3						61.95 206			79.09 44	72.53 133

plane_prior2							93.13 72		78.81 49

plane_prior1					87.15 164

plane_prior						62.42 196	93.85 55		79.38 37						78.80 144

n2								0.00 348

nn								0.00 348

door-mid								66.01 329

test11								93.01 77

door								66.57 328

HQP5-MVS						63.66 178

HQP-NCC					87.54 159		94.06 43		79.80 33	74.18 115

ACMP_Plane					87.54 159		94.06 43		79.80 33	74.18 115

BP-MVS														77.63 88

HQP4-MVS										74.18 115			95.61 116			88.63 169

HQP3-MVS								91.70 125							78.90 142

NP-MVS					87.41 162	63.04 185					90.30 120

ACMMP++_ref															71.63 192

ACMMP++															69.72 201

High-resolution multi-view benchmark