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 69	92.83 57	64.03 170	93.06 76	94.52 32	82.19 19	93.65 1	96.15 12	85.89 1	97.19 59	91.02 10	97.75 1	96.29 16

GG-mvs-BLEND				86.53 50	91.91 79	69.67 34	75.02 314	94.75 27		78.67 83	90.85 114	77.91 2	94.56 148	72.25 121	93.74 31	95.36 36

gg-mvs-nofinetune		77.18 169	74.31 186	85.80 71	91.42 98	68.36 54	71.78 317	94.72 28	49.61 315	77.12 97	45.92 338	77.41 3	93.98 184	67.62 161	93.16 39	95.05 52

test_part1								94.26 41				77.03 4			95.18 8	96.11 19

ESAPD		89.08 8	89.53 8	87.72 20	96.29 7	68.16 60	94.96 31	94.26 41	68.52 212	90.78 3	97.23 2	77.03 4	98.90 7	91.52 6	95.18 8	96.11 19

TSAR-MVS + GP.		87.96 13	88.37 12	86.70 42	93.51 44	65.32 140	95.15 25	93.84 46	78.17 55	85.93 24	94.80 50	75.80 6	98.21 23	89.38 13	88.78 79	96.59 10

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 104	93.00 54	58.16 259	96.72 3	94.41 37	86.50 5	90.25 5	97.83 1	75.46 7	98.67 14	92.78 2	95.49 7	97.32 1

MVSTER		82.47 80	82.05 75	83.74 121	92.68 62	69.01 41	91.90 123	93.21 79	79.83 32	72.14 142	85.71 185	74.72 8	94.72 144	75.72 98	72.49 195	87.50 197

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 51	73.88 6	97.01 2	94.40 38	88.32 2	85.71 26	94.91 47	74.11 9	98.91 6	87.26 28	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 28	86.26 31	88.72 9	95.05 20	70.79 17	93.83 58	95.33 15	68.48 215	77.63 90	94.35 61	73.04 10	98.45 18	84.92 43	93.71 32	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 13	89.33 1	85.77 25	96.26 9	72.84 11	99.38 1	92.64 4	95.93 4	97.08 4

TSAR-MVS + MP.		88.11 12	88.64 10	86.54 48	91.73 86	68.04 63	90.36 176	93.55 58	82.89 14	91.29 2	92.89 89	72.27 12	96.03 102	87.99 22	94.77 15	95.54 33

EPP-MVSNet		81.79 91	81.52 82	82.61 144	88.77 151	60.21 235	93.02 78	93.66 54	68.52 212	72.90 129	90.39 122	72.19 13	94.96 134	74.93 107	79.29 142	92.67 126

CostFormer		82.33 82	81.15 85	85.86 68	89.01 146	68.46 51	82.39 277	93.01 89	75.59 84	80.25 65	81.57 230	72.03 14	94.96 134	79.06 80	77.48 161	94.16 83

HPM-MVS++		89.37 7	89.95 7	87.64 21	95.10 19	68.23 59	95.24 22	94.49 34	82.43 17	88.90 10	96.35 7	71.89 15	98.63 15	88.76 20	96.40 2	96.06 21

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 24	84.83 9	89.07 9	96.80 4	70.86 16	99.06 3	92.64 4	95.71 5	96.12 18

DWT-MVSNet_test		83.95 61	82.80 68	87.41 26	92.90 56	70.07 25	89.12 203	94.42 36	82.15 20	77.64 89	91.77 105	70.81 17	96.22 94	65.03 185	81.36 130	95.94 25

IB-MVS	77.80 4	82.18 84	80.46 95	87.35 28	89.14 143	70.28 22	95.59 17	95.17 17	78.85 47	70.19 162	85.82 183	70.66 18	97.67 35	72.19 124	66.52 237	94.09 88
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 25	87.26 24	86.28 61	94.16 33	66.97 88	94.08 44	93.31 75	71.85 160	84.49 37	95.39 26	68.91 19	96.75 85	88.84 19	94.32 22	95.13 49

PatchFormer-LS_test		83.14 71	81.81 79	87.12 33	92.34 66	69.92 28	88.64 210	93.32 74	82.07 23	74.87 114	91.62 109	68.91 19	96.08 101	66.07 176	78.45 150	95.37 35

alignmvs		87.28 21	86.97 26	88.24 15	91.30 101	71.14 16	95.61 16	93.56 57	79.30 38	87.07 19	95.25 34	68.43 21	96.93 79	87.87 23	84.33 114	96.65 8

PAPM		85.89 40	85.46 42	87.18 31	88.20 163	72.42 9	92.41 99	92.77 97	82.11 21	80.34 64	93.07 83	68.27 22	95.02 132	78.39 85	93.59 34	94.09 88

train_agg		87.21 23	87.42 22	86.60 45	94.18 29	67.28 79	94.16 38	93.51 59	71.87 158	85.52 28	95.33 28	68.19 23	97.27 56	89.09 15	94.90 12	95.25 45

test_8					94.19 28	67.19 81	94.15 40	93.42 70	71.87 158	85.38 30	95.35 27	68.19 23	96.95 76

TEST9					94.18 29	67.28 79	94.16 38	93.51 59	71.75 166	85.52 28	95.33 28	68.01 25	97.27 56

test_prior3		87.38 20	87.70 17	86.42 54	94.71 23	67.35 77	95.10 27	93.10 87	75.40 89	85.25 32	95.61 23	67.94 26	96.84 81	87.47 25	94.77 15	95.05 52

test_prior2							95.10 27		75.40 89	85.25 32	95.61 23	67.94 26		87.47 25	94.77 15

WTY-MVS		86.32 34	85.81 37	87.85 17	92.82 59	69.37 37	95.20 23	95.25 16	82.71 15	81.91 53	94.73 51	67.93 28	97.63 40	79.55 75	82.25 126	96.54 12

Regformer-1		87.24 22	87.60 19	86.15 63	95.14 17	65.83 133	93.95 51	95.12 18	82.11 21	84.25 39	95.73 19	67.88 29	98.35 21	85.60 38	88.64 80	94.26 77

APDe-MVS		87.54 18	87.84 15	86.65 43	96.07 11	66.30 123	94.84 34	93.78 47	69.35 200	88.39 12	96.34 8	67.74 30	97.66 38	90.62 11	93.44 36	96.01 24

tpm2		79.80 120	77.95 129	85.34 88	88.28 161	68.26 58	81.56 285	91.42 148	70.11 193	77.59 92	80.50 246	67.40 31	94.26 168	67.34 163	77.35 162	93.51 104

tpmp4_e23		78.85 135	76.55 152	85.77 73	89.25 139	68.39 53	81.63 284	91.38 150	70.40 190	75.21 112	79.22 262	67.37 32	94.79 139	58.98 229	75.51 173	94.13 85

Regformer-2		87.00 26	87.43 21	85.71 77	95.14 17	64.73 152	93.95 51	94.95 21	81.69 26	84.03 43	95.73 19	67.35 33	98.19 25	85.40 40	88.64 80	94.20 79

tfpn1000		75.25 198	74.00 192	79.03 236	90.30 115	57.56 267	88.55 211	93.36 73	64.14 255	65.17 224	89.76 138	67.06 34	91.46 257	34.54 322	73.09 190	88.06 189

HY-MVS	76.49 5	84.28 56	83.36 62	87.02 37	92.22 71	67.74 68	84.65 259	94.50 33	79.15 42	82.23 51	87.93 156	66.88 35	96.94 77	80.53 71	82.20 127	96.39 14

EPNet		87.84 16	88.38 11	86.23 62	93.30 46	66.05 127	95.26 21	94.84 23	87.09 3	88.06 13	94.53 54	66.79 36	97.34 52	83.89 50	91.68 56	95.29 40
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn_ndepth		76.45 182	75.22 176	80.14 205	90.97 105	58.92 253	90.11 181	93.24 78	65.96 235	67.37 208	90.52 120	66.67 37	92.29 231	37.71 307	74.44 180	89.21 169

FIs		79.47 126	79.41 109	79.67 217	85.95 194	59.40 247	91.68 135	93.94 44	78.06 56	68.96 183	88.28 149	66.61 38	91.77 241	66.20 175	74.99 178	87.82 194

NCCC		89.07 9	89.46 9	87.91 16	96.60 5	69.05 40	96.38 7	94.64 31	84.42 10	86.74 20	96.20 10	66.56 39	98.76 13	89.03 18	94.56 20	95.92 27

SD-MVS		87.49 19	87.49 20	87.50 25	93.60 41	68.82 46	93.90 55	92.63 104	76.86 71	87.90 14	95.76 18	66.17 40	97.63 40	89.06 17	91.48 60	96.05 22

agg_prior3		86.93 27	87.08 25	86.48 51	94.21 27	66.95 90	94.14 41	93.40 71	71.80 163	84.86 34	95.13 38	66.16 41	97.25 58	89.09 15	94.90 12	95.25 45

UniMVSNet_NR-MVSNet		78.15 151	77.55 135	79.98 210	84.46 211	60.26 233	92.25 101	93.20 81	77.50 64	68.88 184	86.61 174	66.10 42	92.13 234	66.38 172	62.55 263	87.54 196

CHOSEN 280x420		77.35 164	76.95 148	78.55 245	87.07 179	62.68 200	69.71 323	82.95 304	68.80 206	71.48 150	87.27 171	66.03 43	84.00 312	76.47 96	82.81 124	88.95 170

CANet		89.61 6	89.99 6	88.46 13	94.39 26	69.71 32	96.53 5	93.78 47	86.89 4	89.68 6	95.78 17	65.94 44	99.10 2	92.99 1	93.91 27	96.58 11

segment_acmp												65.94 44

Regformer-3		85.80 41	85.92 35	85.46 81	94.17 31	65.09 148	92.95 80	95.11 19	81.13 27	81.68 55	95.04 39	65.82 46	98.32 22	83.02 54	84.36 111	92.97 120

Vis-MVSNet (Re-imp)		79.24 129	79.57 104	78.24 251	88.46 157	52.29 297	90.41 175	89.12 226	74.24 106	69.13 179	91.91 103	65.77 47	90.09 273	59.00 228	88.09 84	92.33 133

FC-MVSNet-test		77.99 154	78.08 127	77.70 256	84.89 205	55.51 284	90.27 178	93.75 51	76.87 70	66.80 214	87.59 162	65.71 48	90.23 267	62.89 205	73.94 184	87.37 205

test12				87.09 35	94.60 25	68.86 44		92.91 93		82.67 50		65.44 49	97.55 43		93.69 33	94.84 60

旧先验1					91.94 76	60.74 225		91.50 145			94.36 57	65.23 50			91.84 53	94.55 68

Regformer-4		85.45 44	85.69 40	84.73 102	94.17 31	63.23 185	92.95 80	94.83 24	80.66 29	81.29 57	95.04 39	65.12 51	98.08 28	82.74 55	84.36 111	92.88 124

conf0.01		74.95 204	73.61 197	78.96 237	89.65 126	56.94 273	87.72 227	93.45 62	65.14 243	65.68 216	89.99 129	65.09 52	91.67 243	35.16 314	70.61 206	88.27 183

conf0.002		74.95 204	73.61 197	78.96 237	89.65 126	56.94 273	87.72 227	93.45 62	65.14 243	65.68 216	89.99 129	65.09 52	91.67 243	35.16 314	70.61 206	88.27 183

thresconf0.02		74.92 207	73.61 197	78.85 240	89.65 126	56.94 273	87.72 227	93.45 62	65.14 243	65.68 216	89.99 129	65.09 52	91.67 243	35.16 314	70.61 206	87.94 190

tfpn_n400		74.92 207	73.61 197	78.85 240	89.65 126	56.94 273	87.72 227	93.45 62	65.14 243	65.68 216	89.99 129	65.09 52	91.67 243	35.16 314	70.61 206	87.94 190

tfpnconf		74.92 207	73.61 197	78.85 240	89.65 126	56.94 273	87.72 227	93.45 62	65.14 243	65.68 216	89.99 129	65.09 52	91.67 243	35.16 314	70.61 206	87.94 190

tfpnview11		74.92 207	73.61 197	78.85 240	89.65 126	56.94 273	87.72 227	93.45 62	65.14 243	65.68 216	89.99 129	65.09 52	91.67 243	35.16 314	70.61 206	87.94 190

1112_ss		80.56 104	79.83 101	82.77 138	88.65 152	60.78 222	92.29 100	88.36 244	72.58 139	72.46 138	94.95 42	65.09 52	93.42 200	66.38 172	77.71 153	94.10 87

MVSFormer		83.75 66	82.88 66	86.37 57	89.24 141	71.18 14	89.07 204	90.69 169	65.80 236	87.13 17	94.34 62	64.99 59	92.67 219	72.83 115	91.80 54	95.27 42

lupinMVS		87.74 17	87.77 16	87.63 23	89.24 141	71.18 14	96.57 4	92.90 94	82.70 16	87.13 17	95.27 32	64.99 59	95.80 108	89.34 14	91.80 54	95.93 26

tpmrst		80.57 103	79.14 116	84.84 100	90.10 118	68.28 57	81.70 281	89.72 208	77.63 62	75.96 104	79.54 260	64.94 61	92.71 217	75.43 100	77.28 164	93.55 103

Test_1112_low_res		79.56 125	78.60 120	82.43 150	88.24 162	60.39 231	92.09 107	87.99 251	72.10 154	71.84 145	87.42 165	64.62 62	93.04 204	65.80 180	77.30 163	93.85 99

DeepC-MVS_fast	79.48 2	87.95 14	88.00 14	87.79 19	95.86 14	68.32 55	95.74 12	94.11 43	83.82 12	83.49 46	96.19 11	64.53 63	98.44 19	83.42 53	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
MG-MVS		87.11 24	86.27 30	89.62 5	97.79 1	76.27 3	94.96 31	94.49 34	78.74 51	83.87 45	92.94 86	64.34 64	96.94 77	75.19 102	94.09 24	95.66 29

tpm		78.58 144	77.03 144	83.22 132	85.94 196	64.56 153	83.21 272	91.14 158	78.31 54	73.67 124	79.68 257	64.01 65	92.09 236	66.07 176	71.26 204	93.03 118

CDS-MVSNet		81.43 94	80.74 90	83.52 128	86.26 189	64.45 157	92.09 107	90.65 172	75.83 83	73.95 123	89.81 136	63.97 66	92.91 211	71.27 131	82.82 123	93.20 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_Test		84.16 58	83.20 63	87.05 36	91.56 90	69.82 29	89.99 185	92.05 124	77.77 59	82.84 49	86.57 175	63.93 67	96.09 99	74.91 108	89.18 77	95.25 45

APD-MVS		85.93 39	85.99 34	85.76 74	95.98 13	65.21 142	93.59 63	92.58 106	66.54 230	86.17 21	95.88 16	63.83 68	97.00 69	86.39 34	92.94 40	95.06 51
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs_anonymous		81.36 95	79.99 98	85.46 81	90.39 114	68.40 52	86.88 247	90.61 173	74.41 100	70.31 161	84.67 194	63.79 69	92.32 230	73.13 112	85.70 102	95.67 28

PVSNet_Blended_VisFu		83.97 60	83.50 55	85.39 86	90.02 119	66.59 113	93.77 59	91.73 135	77.43 66	77.08 99	89.81 136	63.77 70	96.97 74	79.67 74	88.21 83	92.60 128

CDPH-MVS		85.71 42	85.46 42	86.46 52	94.75 22	67.19 81	93.89 56	92.83 96	70.90 179	83.09 48	95.28 31	63.62 71	97.36 50	80.63 70	94.18 23	94.84 60

HyFIR lowres test		81.03 99	79.56 105	85.43 84	87.81 170	68.11 62	90.18 180	90.01 197	70.65 187	72.95 128	86.06 181	63.61 72	94.50 152	75.01 106	79.75 138	93.67 100

canonicalmvs		86.85 29	86.25 32	88.66 10	91.80 85	71.92 10	93.54 65	91.71 137	80.26 31	87.55 15	95.25 34	63.59 73	96.93 79	88.18 21	84.34 113	97.11 3

SteuartSystems-ACMMP		86.82 31	86.90 27	86.58 47	90.42 112	66.38 120	96.09 9	93.87 45	77.73 60	84.01 44	95.66 21	63.39 74	97.94 29	87.40 27	93.55 35	95.42 34
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		83.77 65	83.67 53	84.06 116	92.79 61	63.56 182	91.76 131	94.81 26	79.65 36	77.87 86	94.09 66	63.35 75	97.90 31	79.35 76	79.36 140	90.74 154

UniMVSNet (Re)		77.58 158	76.78 149	79.98 210	84.11 217	60.80 221	91.76 131	93.17 84	76.56 77	69.93 168	84.78 193	63.32 76	92.36 229	64.89 186	62.51 265	86.78 217

PVSNet_BlendedMVS		83.38 68	83.43 58	83.22 132	93.76 37	67.53 74	94.06 45	93.61 55	79.13 43	81.00 59	85.14 189	63.19 77	97.29 54	87.08 29	73.91 185	84.83 252

PVSNet_Blended		86.73 32	86.86 28	86.31 60	93.76 37	67.53 74	96.33 8	93.61 55	82.34 18	81.00 59	93.08 81	63.19 77	97.29 54	87.08 29	91.38 61	94.13 85

PAPM_NR		82.97 74	81.84 78	86.37 57	94.10 35	66.76 102	87.66 234	92.84 95	69.96 195	74.07 121	93.57 74	63.10 79	97.50 45	70.66 139	90.58 70	94.85 59

nrg030		80.93 100	79.86 100	84.13 115	83.69 221	68.83 45	93.23 72	91.20 155	75.55 85	75.06 113	88.22 154	63.04 80	94.74 143	81.88 61	66.88 234	88.82 173

EI-MVSNet-UG-set		83.14 71	82.96 64	83.67 126	92.28 69	63.19 189	91.38 145	94.68 29	79.22 40	76.60 101	93.75 71	62.64 81	97.76 33	78.07 87	78.01 151	90.05 161

DeepC-MVS	77.85 3	85.52 43	85.24 44	86.37 57	88.80 150	66.64 110	92.15 103	93.68 53	81.07 28	76.91 100	93.64 73	62.59 82	98.44 19	85.50 39	92.84 42	94.03 92
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 37	85.80 38	87.37 27	93.17 50	69.79 30	93.99 49	93.76 50	79.08 45	78.88 78	93.99 68	62.25 83	98.15 26	85.93 37	91.15 64	94.15 84

PHI-MVS		86.83 30	86.85 29	86.78 41	93.47 45	65.55 137	95.39 20	95.10 20	71.77 165	85.69 27	96.52 5	62.07 84	98.77 12	86.06 36	95.60 6	96.03 23

MP-MVS		85.02 47	84.97 46	85.17 93	92.60 63	64.27 167	93.24 71	92.27 113	73.13 130	79.63 71	94.43 55	61.90 85	97.17 60	85.00 42	92.56 45	94.06 91

jason		86.40 33	86.17 33	87.11 34	86.16 191	70.54 20	95.71 15	92.19 121	82.00 24	84.58 36	94.34 62	61.86 86	95.53 124	87.76 24	90.89 66	95.27 42
jason: jason.
PAPR		85.15 46	84.47 48	87.18 31	96.02 12	68.29 56	91.85 126	93.00 91	76.59 76	79.03 77	95.00 41	61.59 87	97.61 42	78.16 86	89.00 78	95.63 30

IS-MVSNet		80.14 112	79.41 109	82.33 157	87.91 168	60.08 239	91.97 115	88.27 247	72.90 135	71.44 151	91.73 108	61.44 88	93.66 195	62.47 210	86.53 97	93.24 111

EI-MVSNet		78.97 133	78.22 125	81.25 184	85.33 199	62.73 199	89.53 196	93.21 79	72.39 143	72.14 142	90.13 126	60.99 89	94.72 144	67.73 160	72.49 195	86.29 223

IterMVS-LS		76.49 180	75.18 177	80.43 201	84.49 210	62.74 198	90.64 170	88.80 235	72.40 142	65.16 225	81.72 229	60.98 90	92.27 232	67.74 159	64.65 255	86.29 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view600		76.93 171	75.50 171	81.23 185	91.44 94	62.00 207	89.94 186	96.56 1	70.68 183	68.54 190	87.31 166	60.79 91	94.19 169	38.90 301	75.31 174	87.48 198

view800		76.93 171	75.50 171	81.23 185	91.44 94	62.00 207	89.94 186	96.56 1	70.68 183	68.54 190	87.31 166	60.79 91	94.19 169	38.90 301	75.31 174	87.48 198

conf0.05thres1000		76.93 171	75.50 171	81.23 185	91.44 94	62.00 207	89.94 186	96.56 1	70.68 183	68.54 190	87.31 166	60.79 91	94.19 169	38.90 301	75.31 174	87.48 198

tfpn		76.93 171	75.50 171	81.23 185	91.44 94	62.00 207	89.94 186	96.56 1	70.68 183	68.54 190	87.31 166	60.79 91	94.19 169	38.90 301	75.31 174	87.48 198

tpm cat1		75.30 197	72.21 218	84.58 107	88.52 154	67.77 67	78.16 309	88.02 250	61.88 272	68.45 195	76.37 280	60.65 95	94.03 182	53.77 246	74.11 182	91.93 140

TAMVS		80.37 107	79.45 108	83.13 134	85.14 202	63.37 183	91.23 152	90.76 168	74.81 98	72.65 132	88.49 145	60.63 96	92.95 207	69.41 148	81.95 129	93.08 117

tfpn111		78.00 152	76.62 151	82.13 167	91.89 80	63.21 186	91.19 156	96.33 5	72.28 145	70.45 156	87.89 157	60.31 97	94.91 138	42.61 288	76.64 166	88.27 183

conf200view11		78.32 149	77.01 145	82.27 160	91.89 80	63.21 186	91.19 156	96.33 5	72.28 145	70.45 156	87.89 157	60.31 97	95.32 127	45.16 275	77.58 156	88.27 183

thres100view900		78.37 147	77.01 145	82.46 146	91.89 80	63.21 186	91.19 156	96.33 5	72.28 145	70.45 156	87.89 157	60.31 97	95.32 127	45.16 275	77.58 156	88.83 171

thres600view7		78.00 152	76.66 150	82.03 173	91.93 77	63.69 178	91.30 150	96.33 5	72.43 141	70.46 155	87.89 157	60.31 97	94.92 137	42.64 287	76.64 166	87.48 198

CHOSEN 1792x2688		84.98 48	83.45 57	89.57 6	89.94 121	75.14 4	92.07 109	92.32 111	81.87 25	75.68 105	88.27 150	60.18 101	98.60 16	80.46 72	90.27 73	94.96 57

tfpn200view9		78.79 138	77.43 138	82.88 136	92.21 72	64.49 154	92.05 110	96.28 10	73.48 125	71.75 147	88.26 151	60.07 102	95.32 127	45.16 275	77.58 156	88.83 171

thres400		78.68 141	77.43 138	82.43 150	92.21 72	64.49 154	92.05 110	96.28 10	73.48 125	71.75 147	88.26 151	60.07 102	95.32 127	45.16 275	77.58 156	87.48 198

MVS		84.66 52	82.86 67	90.06 1	90.93 106	74.56 5	87.91 223	95.54 14	68.55 211	72.35 141	94.71 52	59.78 104	98.90 7	81.29 69	94.69 19	96.74 7

thres200		79.66 122	78.33 122	83.66 127	92.54 64	65.82 134	93.06 76	96.31 9	74.90 97	73.30 126	88.66 143	59.67 105	95.61 118	47.84 266	78.67 147	89.56 167

Effi-MVS+		83.82 64	82.76 69	86.99 38	89.56 134	69.40 36	91.35 147	86.12 278	72.59 138	83.22 47	92.81 91	59.60 106	96.01 104	81.76 62	87.80 86	95.56 32

ACMMP_Plus		86.05 38	85.80 38	86.80 40	91.58 89	67.53 74	91.79 128	93.49 61	74.93 96	84.61 35	95.30 30	59.42 107	97.92 30	86.13 35	94.92 11	94.94 58

UA-Net		80.02 115	79.65 103	81.11 191	89.33 137	57.72 263	86.33 252	89.00 232	77.44 65	81.01 58	89.15 140	59.33 108	95.90 105	61.01 217	84.28 116	89.73 165

NR-MVSNet		76.05 187	74.59 181	80.44 200	82.96 230	62.18 206	90.83 164	91.73 135	77.12 67	60.96 251	86.35 176	59.28 109	91.80 240	60.74 218	61.34 276	87.35 207

MP-MVS-pluss		85.24 45	85.13 45	85.56 78	91.42 98	65.59 136	91.54 140	92.51 108	74.56 99	80.62 61	95.64 22	59.15 110	97.00 69	86.94 31	93.80 28	94.07 90
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		84.73 50	84.40 50	85.72 75	93.75 39	65.01 149	93.50 66	93.19 82	72.19 150	79.22 74	94.93 44	59.04 111	97.67 35	81.55 64	92.21 48	94.49 74

#test#		84.98 48	84.74 47	85.72 75	93.75 39	65.01 149	94.09 43	93.19 82	73.55 124	79.22 74	94.93 44	59.04 111	97.67 35	82.66 56	92.21 48	94.49 74

MSLP-MVS++		86.27 35	85.91 36	87.35 28	92.01 75	68.97 43	95.04 29	92.70 99	79.04 46	81.50 56	96.50 6	58.98 113	96.78 83	83.49 52	93.93 26	96.29 16

Patchmatch-test		65.86 277	60.94 288	80.62 199	83.75 220	58.83 254	58.91 340	75.26 326	44.50 330	50.95 304	77.09 278	58.81 114	87.90 294	35.13 320	64.03 258	95.12 50

EPNet_dtu		78.80 137	79.26 113	77.43 261	88.06 165	49.71 310	91.96 116	91.95 129	77.67 61	76.56 102	91.28 111	58.51 115	90.20 268	56.37 236	80.95 133	92.39 132
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VNet		86.20 36	85.65 41	87.84 18	93.92 36	69.99 26	95.73 14	95.94 12	78.43 53	86.00 23	93.07 83	58.22 116	97.00 69	85.22 41	84.33 114	96.52 13

TESTMET0.1,1		82.41 81	81.98 77	83.72 124	88.08 164	63.74 175	92.70 88	93.77 49	79.30 38	77.61 91	87.57 163	58.19 117	94.08 177	73.91 111	86.68 94	93.33 109

原ACMM1				84.42 111	93.21 49	64.27 167		93.40 71	65.39 239	79.51 72	92.50 93	58.11 118	96.69 87	65.27 184	93.96 25	92.32 134

sam_mvs1												57.85 119				94.68 64

CR-MVSNet		73.79 219	70.82 228	82.70 140	83.15 228	67.96 64	70.25 320	84.00 294	73.67 122	69.97 166	72.41 299	57.82 120	89.48 282	52.99 250	73.13 188	90.64 156

Patchmtry		67.53 269	63.93 273	78.34 246	82.12 235	64.38 161	68.72 324	84.00 294	48.23 320	59.24 258	72.41 299	57.82 120	89.27 284	46.10 272	56.68 295	81.36 289

patchmatchnet-post											67.62 317	57.62 122	90.25 264

PCF-MVS	73.15 9	79.29 128	77.63 134	84.29 113	86.06 192	65.96 129	87.03 242	91.10 159	69.86 196	69.79 169	90.64 116	57.54 123	96.59 88	64.37 192	82.29 125	90.32 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.14 97	80.01 97	84.51 110	90.24 117	65.86 131	94.12 42	89.15 225	73.81 118	75.37 111	88.26 151	57.26 124	94.53 151	66.97 167	84.92 106	93.15 114

1121		81.25 96	80.05 96	84.87 99	92.30 68	64.31 164	87.91 223	91.39 149	59.44 285	79.94 67	92.91 87	57.09 125	97.01 67	66.63 168	92.81 43	93.29 110

PatchT		69.11 259	65.37 264	80.32 202	82.07 236	63.68 179	67.96 329	87.62 255	50.86 313	69.37 176	65.18 323	57.09 125	88.53 290	41.59 291	66.60 236	88.74 174

testdata				81.34 183	89.02 145	57.72 263		89.84 201	58.65 289	85.32 31	94.09 66	57.03 127	93.28 201	69.34 149	90.56 71	93.03 118

PatchmatchNet		77.46 159	74.63 180	85.96 66	89.55 135	70.35 21	79.97 298	89.55 211	72.23 148	70.94 152	76.91 279	57.03 127	92.79 215	54.27 243	81.17 131	94.74 62
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
region2R		84.36 54	84.03 52	85.36 87	93.54 43	64.31 164	93.43 69	92.95 92	72.16 153	78.86 79	94.84 49	56.97 129	97.53 44	81.38 67	92.11 52	94.24 78

新几何1				84.73 102	92.32 67	64.28 166		91.46 147	59.56 284	79.77 69	92.90 88	56.95 130	96.57 90	63.40 197	92.91 41	93.34 107

diffmvs		80.18 110	78.55 121	85.07 94	88.56 153	66.93 91	86.70 250	88.62 239	70.42 189	78.69 82	85.26 187	56.93 131	94.77 140	68.68 155	83.09 120	93.51 104

MVS_0304		88.39 10	88.35 13	88.50 12	93.01 53	70.11 23	95.90 10	92.20 119	86.27 6	88.70 11	95.92 15	56.76 132	99.02 4	92.68 3	93.76 30	96.37 15

WR-MVS		76.76 178	75.74 163	79.82 215	84.60 207	62.27 205	92.60 93	92.51 108	76.06 80	67.87 201	85.34 186	56.76 132	90.24 266	62.20 211	63.69 262	86.94 215

HPM-MVS		83.25 69	82.95 65	84.17 114	92.25 70	62.88 196	90.91 162	91.86 131	70.30 192	77.12 97	93.96 69	56.75 134	96.28 93	82.04 60	91.34 63	93.34 107

sss		82.71 78	82.38 73	83.73 123	89.25 139	59.58 245	92.24 102	94.89 22	77.96 57	79.86 68	92.38 97	56.70 135	97.05 64	77.26 92	80.86 134	94.55 68

ACMMPR		84.37 53	84.06 51	85.28 89	93.56 42	64.37 162	93.50 66	93.15 85	72.19 150	78.85 80	94.86 48	56.69 136	97.45 46	81.55 64	92.20 50	94.02 93

FMVSNet3		77.73 157	76.04 158	82.80 137	91.20 103	68.99 42	91.87 124	91.99 126	73.35 128	67.04 210	83.19 207	56.62 137	92.14 233	59.80 224	69.34 217	87.28 209

Patchmatch-RL test		68.17 264	64.49 270	79.19 232	71.22 318	53.93 291	70.07 322	71.54 335	69.22 201	56.79 274	62.89 327	56.58 138	88.61 287	69.53 147	52.61 304	95.03 55

test_post											23.01 347	56.49 139	92.67 219

RPMNet		69.58 255	65.21 265	82.70 140	83.15 228	67.96 64	70.25 320	86.15 277	46.83 323	69.97 166	65.10 324	56.48 140	89.48 282	35.79 313	73.13 188	90.64 156

DU-MVS		76.86 175	75.84 161	79.91 212	82.96 230	60.26 233	91.26 151	91.54 142	76.46 78	68.88 184	86.35 176	56.16 141	92.13 234	66.38 172	62.55 263	87.35 207

Baseline_NR-MVSNet		73.99 217	72.83 210	77.48 260	80.78 246	59.29 250	91.79 128	84.55 289	68.85 205	68.99 182	80.70 243	56.16 141	92.04 237	62.67 208	60.98 278	81.11 295

API-MVS		82.28 83	80.53 94	87.54 24	96.13 10	70.59 19	93.63 61	91.04 162	65.72 238	75.45 110	92.83 90	56.11 143	98.89 10	64.10 193	89.75 76	93.15 114

MPTG		84.73 50	84.47 48	85.50 79	91.89 80	65.16 143	91.55 139	92.23 114	75.32 91	80.53 62	95.21 36	56.06 144	97.16 61	84.86 44	92.55 46	94.18 80

MTAPA		83.91 62	83.38 61	85.50 79	91.89 80	65.16 143	81.75 280	92.23 114	75.32 91	80.53 62	95.21 36	56.06 144	97.16 61	84.86 44	92.55 46	94.18 80

JIA-IIPM		66.06 276	62.45 282	76.88 267	81.42 242	54.45 290	57.49 341	88.67 237	49.36 316	63.86 235	46.86 337	56.06 144	90.25 264	49.53 260	68.83 221	85.95 234

v148		76.19 184	74.47 185	81.36 182	80.05 274	64.44 158	91.75 133	90.23 186	73.68 121	67.13 209	80.84 242	55.92 147	93.86 191	68.95 152	61.73 272	85.76 240

WR-MVS_H		70.59 246	69.94 230	72.53 293	81.03 243	51.43 301	87.35 239	92.03 125	67.38 226	60.23 254	80.70 243	55.84 148	83.45 316	46.33 271	58.58 289	82.72 276

XVS		83.87 63	83.47 56	85.05 95	93.22 47	63.78 173	92.92 82	92.66 102	73.99 111	78.18 84	94.31 64	55.25 149	97.41 47	79.16 78	91.58 58	93.95 95

X-MVStestdata		76.86 175	74.13 189	85.05 95	93.22 47	63.78 173	92.92 82	92.66 102	73.99 111	78.18 84	10.19 353	55.25 149	97.41 47	79.16 78	91.58 58	93.95 95

BH-w/o		80.49 106	79.30 112	84.05 117	90.83 110	64.36 163	93.60 62	89.42 215	74.35 105	69.09 180	90.15 125	55.23 151	95.61 118	64.61 188	86.43 99	92.17 138

CP-MVS		83.71 67	83.40 60	84.65 105	93.14 51	63.84 171	94.59 35	92.28 112	71.03 177	77.41 93	94.92 46	55.21 152	96.19 95	81.32 68	90.70 68	93.91 97

PGM-MVS		83.25 69	82.70 71	84.92 97	92.81 60	64.07 169	90.44 173	92.20 119	71.28 175	77.23 96	94.43 55	55.17 153	97.31 53	79.33 77	91.38 61	93.37 106

v1		77.29 165	75.57 168	82.42 153	80.61 260	66.73 103	91.96 116	90.42 177	74.41 100	69.46 173	82.12 221	55.14 154	94.40 158	71.00 132	65.04 248	86.13 226

divwei89l23v2f112		77.28 166	75.57 168	82.42 153	80.62 257	66.72 105	91.96 116	90.42 177	74.41 100	69.46 173	82.12 221	55.11 155	94.40 158	71.00 132	65.04 248	86.12 227

v1141		77.28 166	75.57 168	82.42 153	80.63 256	66.73 103	91.96 116	90.42 177	74.41 100	69.46 173	82.12 221	55.09 156	94.40 158	70.99 134	65.05 247	86.12 227

tpmvs		72.88 225	69.76 233	82.22 164	90.98 104	67.05 86	78.22 308	88.30 245	63.10 263	64.35 233	74.98 287	55.09 156	94.27 166	43.25 281	69.57 216	85.34 248

v18		71.94 231	69.43 234	79.50 223	80.74 247	66.82 95	88.16 217	86.66 264	68.95 204	55.55 277	72.66 294	55.03 158	90.15 269	64.78 187	52.30 305	81.54 283

v6		77.39 161	75.71 164	82.44 147	80.67 252	66.82 95	91.94 120	90.18 189	74.19 107	69.60 170	82.50 214	55.00 159	94.44 153	71.68 127	65.60 240	86.05 230

v1neww		77.39 161	75.71 164	82.44 147	80.69 250	66.83 93	91.94 120	90.18 189	74.19 107	69.60 170	82.51 211	54.99 160	94.44 153	71.68 127	65.60 240	86.05 230

v7new		77.39 161	75.71 164	82.44 147	80.69 250	66.83 93	91.94 120	90.18 189	74.19 107	69.60 170	82.51 211	54.99 160	94.44 153	71.68 127	65.60 240	86.05 230

v8		75.35 196	73.26 207	81.61 180	80.67 252	66.82 95	89.54 195	89.27 219	71.65 167	63.30 241	80.30 250	54.99 160	94.06 178	67.33 164	62.33 266	83.94 258

sam_mvs												54.91 163

v16		71.81 232	69.26 236	79.47 224	80.66 254	66.81 99	87.93 221	86.63 266	68.70 209	55.35 278	72.51 295	54.75 164	90.12 271	64.51 189	52.28 306	81.47 284

v17		71.77 234	69.20 237	79.46 225	80.62 257	66.81 99	87.93 221	86.63 266	68.71 208	55.25 279	72.49 296	54.72 165	90.11 272	64.50 190	51.97 307	81.47 284

EPMVS		78.49 146	75.98 159	86.02 64	91.21 102	69.68 33	80.23 294	91.20 155	75.25 93	72.48 137	78.11 267	54.65 166	93.69 194	57.66 234	83.04 121	94.69 63

v15		71.40 236	68.75 239	79.35 226	80.39 261	66.70 107	87.57 236	86.64 265	68.66 210	54.68 281	72.00 303	54.50 167	89.98 274	63.69 195	50.66 312	81.38 288

ab-mvs		80.18 110	78.31 123	85.80 71	88.44 158	65.49 139	83.00 274	92.67 101	71.82 162	77.36 94	85.01 190	54.50 167	96.59 88	76.35 97	75.63 172	95.32 39

DP-MVS Recon		82.73 76	81.65 81	85.98 65	97.31 3	67.06 85	95.15 25	91.99 126	69.08 203	76.50 103	93.89 70	54.48 169	98.20 24	70.76 138	85.66 103	92.69 125

XXY-MVS		77.94 155	76.44 154	82.43 150	82.60 232	64.44 158	92.01 112	91.83 133	73.59 123	70.00 165	85.82 183	54.43 170	94.76 141	69.63 145	68.02 228	88.10 188

MDTV_nov1_ep13_2view						59.90 241	80.13 296		67.65 223	72.79 130		54.33 171		59.83 223		92.58 129

V14		71.29 238	68.61 241	79.31 227	80.34 265	66.65 109	87.39 238	86.61 268	68.41 216	54.49 283	71.91 304	54.25 172	89.96 275	63.50 196	50.62 313	81.33 290

Test By Simon												54.21 173

V9		71.16 239	68.46 243	79.27 229	80.26 268	66.60 111	87.21 241	86.56 269	68.17 217	54.26 286	71.81 306	54.00 174	89.93 276	63.28 199	50.57 314	81.27 291

MAR-MVS		84.18 57	83.43 58	86.44 53	96.25 9	65.93 130	94.28 37	94.27 40	74.41 100	79.16 76	95.61 23	53.99 175	98.88 11	69.62 146	93.26 38	94.50 73
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		80.10 113	79.56 105	81.72 177	86.93 182	61.17 217	92.70 88	91.54 142	71.51 173	75.62 106	86.94 172	53.83 176	92.38 227	72.21 122	84.76 109	91.60 143

test0.0.03 1		72.76 226	72.71 212	72.88 291	80.25 269	47.99 315	91.22 153	89.45 213	71.51 173	62.51 248	87.66 161	53.83 176	85.06 305	50.16 257	67.84 231	85.58 242

v2v482		77.42 160	75.65 167	82.73 139	80.38 262	67.13 84	91.85 126	90.23 186	75.09 94	69.37 176	83.39 205	53.79 178	94.44 153	71.77 125	65.00 251	86.63 221

v12		71.02 243	68.29 248	79.22 231	80.18 271	66.53 116	87.01 244	86.54 271	67.90 219	54.00 289	71.70 307	53.66 179	89.91 277	63.09 201	50.51 315	81.21 292

pcd_1.5k_mvsjas		4.46 334	5.95 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	53.55 180	0.00 358	0.00 355	0.00 355	0.00 356

PS-MVSNAJss		77.26 168	76.31 155	80.13 207	80.64 255	59.16 251	90.63 172	91.06 160	72.80 136	68.58 189	84.57 196	53.55 180	93.96 185	72.97 113	71.96 198	87.27 210

PS-MVSNAJ		88.14 11	87.61 18	89.71 4	92.06 74	76.72 1	95.75 11	93.26 77	83.86 11	89.55 7	96.06 13	53.55 180	97.89 32	91.10 8	93.31 37	94.54 70

v13		70.90 244	68.15 249	79.15 235	80.08 272	66.45 118	86.83 248	86.50 272	67.62 225	53.78 291	71.61 309	53.51 183	89.87 279	62.89 205	50.50 316	81.14 294

mPP-MVS		82.96 75	82.44 72	84.52 109	92.83 57	62.92 194	92.76 85	91.85 132	71.52 172	75.61 108	94.24 65	53.48 184	96.99 72	78.97 81	90.73 67	93.64 102

xiu_mvs_v2_base		87.92 15	87.38 23	89.55 7	91.41 100	76.43 2	95.74 12	93.12 86	83.53 13	89.55 7	95.95 14	53.45 185	97.68 34	91.07 9	92.62 44	94.54 70

test_post1							78.95 302				20.70 351	53.05 186	91.50 256	60.43 220

MDTV_nov1_ep13			72.61 213		89.06 144	68.48 50	80.33 292	90.11 193	71.84 161	71.81 146	75.92 284	53.01 187	93.92 187	48.04 265	73.38 186

v11		71.05 242	68.32 246	79.23 230	80.34 265	66.57 114	87.01 244	86.55 270	68.11 218	54.40 284	71.66 308	52.94 188	89.91 277	62.71 207	51.12 310	81.21 292

test222					89.77 124	61.60 214	89.55 194	89.42 215	56.83 298	77.28 95	92.43 96	52.76 189			91.14 65	93.09 116

v1144		76.73 179	74.88 179	82.27 160	80.23 270	66.60 111	91.68 135	90.21 188	73.69 120	69.06 181	81.89 226	52.73 190	94.40 158	69.21 150	65.23 244	85.80 237

v7		76.83 177	75.01 178	82.29 159	80.35 263	66.70 107	91.68 135	89.97 198	73.47 127	69.22 178	82.22 218	52.52 191	94.43 157	69.73 144	65.96 239	85.74 241

v10		74.77 211	72.54 215	81.46 181	80.33 267	66.71 106	89.15 202	89.08 228	70.94 178	63.08 242	79.86 255	52.52 191	94.04 181	65.70 181	62.17 267	83.64 260

CLD-MVS		82.73 76	82.35 74	83.86 119	87.90 169	67.65 71	95.45 18	92.18 122	85.06 8	72.58 134	92.27 100	52.46 193	95.78 109	84.18 46	79.06 143	88.16 187
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		75.86 190	74.52 184	79.89 213	82.44 233	60.64 228	91.37 146	91.37 151	76.63 75	67.65 203	86.21 179	52.37 194	91.55 251	61.84 213	60.81 279	87.48 198

VPA-MVSNet		79.03 131	78.00 128	82.11 171	85.95 194	64.48 156	93.22 73	94.66 30	75.05 95	74.04 122	84.95 191	52.17 195	93.52 197	74.90 109	67.04 233	88.32 182

APD-MVS_3200maxsize		81.64 92	81.32 84	82.59 145	92.36 65	58.74 256	91.39 143	91.01 163	63.35 259	79.72 70	94.62 53	51.82 196	96.14 97	79.71 73	87.93 85	92.89 123

dp		75.01 202	72.09 219	83.76 120	89.28 138	66.22 126	79.96 299	89.75 203	71.16 176	67.80 202	77.19 275	51.81 197	92.54 222	50.39 256	71.44 203	92.51 131

v144192		76.05 187	74.03 190	82.12 168	79.50 279	66.55 115	91.39 143	89.71 209	72.30 144	68.17 196	81.33 234	51.75 198	94.03 182	67.94 157	64.19 257	85.77 238

BH-untuned		78.68 141	77.08 143	83.48 130	89.84 123	63.74 175	92.70 88	88.59 240	71.57 170	66.83 213	88.65 144	51.75 198	95.39 125	59.03 227	84.77 108	91.32 149

HQP2-MVS												51.63 200

HQP-MVS		81.14 97	80.64 92	82.64 143	87.54 172	63.66 180	94.06 45	91.70 138	79.80 33	74.18 117	90.30 123	51.63 200	95.61 118	77.63 90	78.90 144	88.63 175

V42		76.46 181	74.55 183	82.19 165	79.14 284	67.82 66	90.26 179	89.42 215	73.75 119	68.63 188	81.89 226	51.31 202	94.09 176	71.69 126	64.84 252	84.66 253

TransMVSNet (Re)		70.07 250	67.66 251	77.31 264	80.62 257	59.13 252	91.78 130	84.94 287	65.97 234	60.08 255	80.44 247	50.78 203	91.87 239	48.84 262	45.46 325	80.94 297

HQP_MVS		80.34 108	79.75 102	82.12 168	86.94 180	62.42 201	93.13 74	91.31 152	78.81 49	72.53 135	89.14 141	50.66 204	95.55 122	76.74 93	78.53 148	88.39 180

plane_prior6					87.23 176	62.32 203						50.66 204

ACMMP		81.49 93	80.67 91	83.93 118	91.71 87	62.90 195	92.13 104	92.22 118	71.79 164	71.68 149	93.49 76	50.32 206	96.96 75	78.47 83	84.22 118	91.93 140
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 89	81.52 82	83.51 129	88.42 159	62.88 196	89.77 192	88.93 233	76.78 73	75.55 109	93.10 79	50.31 207	95.38 126	83.82 51	87.02 90	92.26 137

1314		80.70 102	78.95 117	85.94 67	87.77 171	67.56 73	87.91 223	92.55 107	72.17 152	67.44 204	93.09 80	50.27 208	97.04 66	71.68 127	87.64 87	93.23 112

CP-MVSNet		70.50 248	69.91 231	72.26 296	80.71 249	51.00 304	87.23 240	90.30 184	67.84 220	59.64 256	82.69 210	50.23 209	82.30 323	51.28 253	59.28 283	83.46 265

LCM-MVSNet-Re		72.93 223	71.84 220	76.18 272	88.49 155	48.02 314	80.07 297	70.17 336	73.96 114	52.25 297	80.09 254	49.98 210	88.24 292	67.35 162	84.23 117	92.28 136

Vis-MVSNet		80.92 101	79.98 99	83.74 121	88.48 156	61.80 212	93.44 68	88.26 248	73.96 114	77.73 87	91.76 106	49.94 211	94.76 141	65.84 179	90.37 72	94.65 66
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		75.98 189	73.92 193	82.15 166	79.73 275	66.24 125	91.22 153	89.75 203	72.67 137	68.49 194	81.42 232	49.86 212	94.27 166	67.08 165	65.02 250	85.95 234

test-mter		79.96 116	79.38 111	81.72 177	86.93 182	61.17 217	92.70 88	91.54 142	73.85 116	75.62 106	86.94 172	49.84 213	92.38 227	72.21 122	84.76 109	91.60 143

cdsmvs_eth3d_5k		19.86 329	26.47 325	0.00 345	0.00 359	0.00 360	0.00 351	93.45 62	0.00 355	0.00 356	95.27 32	49.56 214	0.00 358	0.00 355	0.00 355	0.00 356

3Dnovator+	73.60 7	82.10 88	80.60 93	86.60 45	90.89 108	66.80 101	95.20 23	93.44 69	74.05 110	67.42 205	92.49 94	49.46 215	97.65 39	70.80 137	91.68 56	95.33 37

MVP-Stereo		77.12 170	76.23 156	79.79 216	81.72 238	66.34 122	89.29 198	90.88 164	70.56 188	62.01 250	82.88 208	49.34 216	94.13 174	65.55 182	93.80 28	78.88 314
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OMC-MVS		78.67 143	77.91 131	80.95 197	85.76 198	57.40 268	88.49 212	88.67 237	73.85 116	72.43 139	92.10 101	49.29 217	94.55 149	72.73 117	77.89 152	90.91 153

VPNet		78.82 136	77.53 136	82.70 140	84.52 209	66.44 119	93.93 53	92.23 114	80.46 30	72.60 133	88.38 148	49.18 218	93.13 203	72.47 120	63.97 260	88.55 177

CVMVSNet		74.04 216	74.27 187	73.33 287	85.33 199	43.94 324	89.53 196	88.39 243	54.33 305	70.37 159	90.13 126	49.17 219	84.05 309	61.83 214	79.36 140	91.99 139

v1921920		75.63 194	73.49 204	82.06 172	79.38 280	66.35 121	91.07 161	89.48 212	71.98 155	67.99 197	81.22 237	49.16 220	93.90 188	66.56 170	64.56 256	85.92 236

pm-mvs1		72.89 224	71.09 226	78.26 250	79.10 286	57.62 265	90.80 165	89.30 218	67.66 222	62.91 244	81.78 228	49.11 221	92.95 207	60.29 221	58.89 288	84.22 256

pmmvs4		73.92 218	71.81 221	80.25 204	79.17 283	65.24 141	87.43 237	87.26 261	67.64 224	63.46 239	83.91 200	48.96 222	91.53 255	62.94 204	65.49 243	83.96 257

TAPA-MVS	70.22 12	74.94 206	73.53 203	79.17 233	90.40 113	52.07 298	89.19 201	89.61 210	62.69 265	70.07 163	92.67 92	48.89 223	94.32 163	38.26 306	79.97 136	91.12 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator	73.91 6	82.69 79	80.82 89	88.31 14	89.57 133	71.26 13	92.60 93	94.39 39	78.84 48	67.89 200	92.48 95	48.42 224	98.52 17	68.80 154	94.40 21	95.15 48

CPTT-MVS		79.59 124	79.16 115	80.89 198	91.54 92	59.80 242	92.10 106	88.54 242	60.42 279	72.96 127	93.28 78	48.27 225	92.80 214	78.89 82	86.50 98	90.06 160

GBi-Net		75.65 192	73.83 194	81.10 192	88.85 147	65.11 145	90.01 182	90.32 180	70.84 180	67.04 210	80.25 251	48.03 226	91.54 252	59.80 224	69.34 217	86.64 218

test1		75.65 192	73.83 194	81.10 192	88.85 147	65.11 145	90.01 182	90.32 180	70.84 180	67.04 210	80.25 251	48.03 226	91.54 252	59.80 224	69.34 217	86.64 218

FMVSNet2		76.07 186	74.01 191	82.26 163	88.85 147	67.66 70	91.33 148	91.61 140	70.84 180	65.98 215	82.25 217	48.03 226	92.00 238	58.46 230	68.73 223	87.10 211

LFMVS		84.34 55	82.73 70	89.18 8	94.76 21	73.25 8	94.99 30	91.89 130	71.90 156	82.16 52	93.49 76	47.98 229	97.05 64	82.55 57	84.82 107	97.25 2

QAPM		79.95 117	77.39 140	87.64 21	89.63 132	71.41 12	93.30 70	93.70 52	65.34 241	67.39 207	91.75 107	47.83 230	98.96 5	57.71 233	89.81 74	92.54 130

HPM-MVS_fast		80.25 109	79.55 107	82.33 157	91.55 91	59.95 240	91.32 149	89.16 224	65.23 242	74.71 115	93.07 83	47.81 231	95.74 111	74.87 110	88.23 82	91.31 150

CANet_DTU		84.09 59	83.52 54	85.81 70	90.30 115	66.82 95	91.87 124	89.01 231	85.27 7	86.09 22	93.74 72	47.71 232	96.98 73	77.90 89	89.78 75	93.65 101

abl_6		79.82 119	79.20 114	81.70 179	89.85 122	58.34 258	88.47 213	90.07 194	62.56 266	77.71 88	93.08 81	47.65 233	96.78 83	77.94 88	85.45 105	89.99 162

v1240		75.21 200	72.98 209	81.88 174	79.20 282	66.00 128	90.75 167	89.11 227	71.63 168	67.41 206	81.22 237	47.36 234	93.87 189	65.46 183	64.72 254	85.77 238

PEN-MVS		69.46 257	68.56 242	72.17 298	79.27 281	49.71 310	86.90 246	89.24 221	67.24 228	59.08 260	82.51 211	47.23 235	83.54 315	48.42 264	57.12 291	83.25 268

DI_MVS_plusplus_test		79.78 121	77.50 137	86.62 44	80.90 244	69.46 35	90.69 168	91.97 128	77.00 68	59.07 261	82.34 215	46.82 236	95.88 106	82.14 59	86.59 96	94.53 72

CNLPA		74.31 214	72.30 217	80.32 202	91.49 93	61.66 213	90.85 163	80.72 311	56.67 299	63.85 236	90.64 116	46.75 237	90.84 260	53.79 245	75.99 171	88.47 179

test_normal		79.66 122	77.36 142	86.54 48	80.72 248	69.21 38	90.68 169	92.16 123	76.99 69	58.63 265	82.03 224	46.70 238	95.86 107	81.74 63	86.63 95	94.56 67

114514_t		79.17 130	77.67 132	83.68 125	95.32 16	65.53 138	92.85 84	91.60 141	63.49 258	67.92 199	90.63 118	46.65 239	95.72 116	67.01 166	83.54 119	89.79 163

PS-CasMVS		69.86 252	69.13 238	72.07 299	80.35 263	50.57 306	87.02 243	89.75 203	67.27 227	59.19 259	82.28 216	46.58 240	82.24 324	50.69 255	59.02 286	83.39 267

DTE-MVSNet		68.46 263	67.33 253	71.87 302	77.94 295	49.00 313	86.16 254	88.58 241	66.36 232	58.19 266	82.21 219	46.36 241	83.87 313	44.97 279	55.17 298	82.73 275

PMMVS		81.98 90	82.04 76	81.78 175	89.76 125	56.17 280	91.13 159	90.69 169	77.96 57	80.09 66	93.57 74	46.33 242	94.99 133	81.41 66	87.46 88	94.17 82

OPM-MVS		79.00 132	78.09 126	81.73 176	83.52 224	63.83 172	91.64 138	90.30 184	76.36 79	71.97 144	89.93 135	46.30 243	95.17 131	75.10 103	77.70 154	86.19 225

BH-RMVSNet		79.46 127	77.65 133	84.89 98	91.68 88	65.66 135	93.55 64	88.09 249	72.93 134	73.37 125	91.12 112	46.20 244	96.12 98	56.28 237	85.61 104	92.91 122

TR-MVS		78.77 139	77.37 141	82.95 135	90.49 111	60.88 220	93.67 60	90.07 194	70.08 194	74.51 116	91.37 110	45.69 245	95.70 117	60.12 222	80.32 135	92.29 135

Patchmatch-test1		75.00 203	71.80 222	84.58 107	86.63 184	70.08 24	81.06 287	89.19 222	71.60 169	70.01 164	77.16 277	45.53 246	88.63 286	51.79 252	73.27 187	95.02 56

semantic-postprocess				76.32 270	81.48 239	60.67 227		85.99 280	66.17 233	59.50 257	78.88 263	45.51 247	83.65 314	62.58 209	61.93 268	84.63 255

IterMVS		72.65 229	70.83 227	78.09 254	82.17 234	62.96 191	87.64 235	86.28 274	71.56 171	60.44 253	78.85 264	45.42 248	86.66 300	63.30 198	61.83 269	84.65 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		76.14 185	75.28 175	78.72 244	83.22 226	55.17 286	89.87 190	87.78 253	75.42 87	67.98 198	81.43 231	45.08 249	92.52 223	75.08 104	71.63 199	88.48 178

mvs-test1		78.74 140	77.95 129	81.14 190	83.22 226	57.13 270	93.96 50	87.78 253	75.42 87	72.68 131	90.80 115	45.08 249	94.54 150	75.08 104	77.49 160	91.74 142

v748		70.55 247	67.97 250	78.27 249	75.75 307	58.78 255	86.29 253	89.25 220	65.12 249	56.66 275	77.17 276	45.05 251	92.95 207	58.13 231	58.33 290	83.10 272

XVG-OURS-SEG-HR		74.70 212	73.08 208	79.57 220	78.25 292	57.33 269	80.49 290	87.32 259	63.22 261	68.76 186	90.12 128	44.89 252	91.59 250	70.55 140	74.09 183	89.79 163

v7n		71.31 237	68.65 240	79.28 228	76.40 302	60.77 223	86.71 249	89.45 213	64.17 254	58.77 264	78.24 266	44.59 253	93.54 196	57.76 232	61.75 271	83.52 263

pmmvs5		73.35 221	71.52 223	78.86 239	78.64 290	60.61 229	91.08 160	86.90 262	67.69 221	63.32 240	83.64 201	44.33 254	90.53 261	62.04 212	66.02 238	85.46 245

OpenMVS	70.45 11	78.54 145	75.92 160	86.41 56	85.93 197	71.68 11	92.74 86	92.51 108	66.49 231	64.56 229	91.96 102	43.88 255	98.10 27	54.61 241	90.65 69	89.44 168

AdaColmap		78.94 134	77.00 147	84.76 101	96.34 6	65.86 131	92.66 92	87.97 252	62.18 268	70.56 153	92.37 98	43.53 256	97.35 51	64.50 190	82.86 122	91.05 152

tfpnnormal		70.10 249	67.36 252	78.32 247	83.45 225	60.97 219	88.85 206	92.77 97	64.85 250	60.83 252	78.53 265	43.52 257	93.48 198	31.73 330	61.70 273	80.52 302

test_djsdf		73.76 220	72.56 214	77.39 262	77.00 300	53.93 291	89.07 204	90.69 169	65.80 236	63.92 234	82.03 224	43.14 258	92.67 219	72.83 115	68.53 224	85.57 243

GA-MVS		78.33 148	76.23 156	84.65 105	83.65 222	66.30 123	91.44 141	90.14 192	76.01 81	70.32 160	84.02 199	42.50 259	94.72 144	70.98 135	77.00 165	92.94 121

PLC	68.80 14	75.23 199	73.68 196	79.86 214	92.93 55	58.68 257	90.64 170	88.30 245	60.90 276	64.43 232	90.53 119	42.38 260	94.57 147	56.52 235	76.54 168	86.33 222
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
1111		56.66 304	54.98 303	61.69 319	61.99 335	31.38 341	79.81 300	83.17 302	45.66 324	41.94 328	65.44 321	41.50 261	79.56 332	27.64 334	47.68 321	74.14 327

.test1245		46.52 314	49.68 310	37.02 336	61.99 335	31.38 341	79.81 300	83.17 302	45.66 324	41.94 328	65.44 321	41.50 261	79.56 332	27.64 334	0.01 353	0.13 354

V4		69.80 253	67.02 255	78.15 252	71.86 315	60.10 237	82.02 278	87.39 256	64.48 251	57.78 270	75.98 282	41.49 263	92.90 212	63.00 202	59.16 284	81.44 287

v52		69.80 253	67.01 256	78.15 252	71.84 316	60.10 237	82.02 278	87.39 256	64.48 251	57.80 269	75.97 283	41.47 264	92.90 212	63.00 202	59.13 285	81.45 286

Fast-Effi-MVS+-dtu		75.04 201	73.37 206	80.07 208	80.86 245	59.52 246	91.20 155	85.38 285	71.90 156	65.20 223	84.84 192	41.46 265	92.97 206	66.50 171	72.96 191	87.73 195

MS-PatchMatch		77.90 156	76.50 153	82.12 168	85.99 193	69.95 27	91.75 133	92.70 99	73.97 113	62.58 247	84.44 197	41.11 266	95.78 109	63.76 194	92.17 51	80.62 301

pcd1.5k->3k		31.17 323	31.85 321	29.12 338	81.48 239	0.00 360	0.00 351	91.79 134	0.00 355	0.00 356	0.00 357	41.05 267	0.00 358	0.00 355	72.34 197	87.36 206

XVG-OURS		74.25 215	72.46 216	79.63 218	78.45 291	57.59 266	80.33 292	87.39 256	63.86 257	68.76 186	89.62 139	40.50 268	91.72 242	69.00 151	74.25 181	89.58 166

VDD-MVS		83.06 73	81.81 79	86.81 39	90.86 109	67.70 69	95.40 19	91.50 145	75.46 86	81.78 54	92.34 99	40.09 269	97.13 63	86.85 32	82.04 128	95.60 31

DP-MVS		69.90 251	66.48 257	80.14 205	95.36 15	62.93 192	89.56 193	76.11 319	50.27 314	57.69 271	85.23 188	39.68 270	95.73 112	33.35 324	71.05 205	81.78 282

ADS-MVSNet2		66.90 272	63.44 275	77.26 265	88.06 165	60.70 226	68.01 327	75.56 324	57.57 291	64.48 230	69.87 314	38.68 271	84.10 308	40.87 293	67.89 229	86.97 212

ADS-MVSNet		68.54 262	64.38 272	81.03 195	88.06 165	66.90 92	68.01 327	84.02 293	57.57 291	64.48 230	69.87 314	38.68 271	89.21 285	40.87 293	67.89 229	86.97 212

LPG-MVS_test		75.82 191	74.58 182	79.56 221	84.31 214	59.37 248	90.44 173	89.73 206	69.49 198	64.86 226	88.42 146	38.65 273	94.30 164	72.56 118	72.76 192	85.01 250

LGP-MVS_train				79.56 221	84.31 214	59.37 248		89.73 206	69.49 198	64.86 226	88.42 146	38.65 273	94.30 164	72.56 118	72.76 192	85.01 250

VDDNet		80.50 105	78.26 124	87.21 30	86.19 190	69.79 30	94.48 36	91.31 152	60.42 279	79.34 73	90.91 113	38.48 275	96.56 91	82.16 58	81.05 132	95.27 42

ACMP	71.68 10	75.58 195	74.23 188	79.62 219	84.97 204	59.64 243	90.80 165	89.07 229	70.39 191	62.95 243	87.30 170	38.28 276	93.87 189	72.89 114	71.45 202	85.36 247
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		79.87 118	78.68 118	83.45 131	89.96 120	61.51 215	92.13 104	90.79 166	76.83 72	78.85 80	86.33 178	38.16 277	96.17 96	67.93 158	87.17 89	92.67 126
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		71.14 240	69.37 235	76.45 269	72.95 312	54.71 288	84.19 261	88.88 234	61.92 271	62.15 249	79.77 256	38.14 278	91.44 258	68.90 153	67.45 232	83.21 269

xiu_mvs_v1_base_debu		82.16 85	81.12 86	85.26 90	86.42 185	68.72 47	92.59 95	90.44 174	73.12 131	84.20 40	94.36 57	38.04 279	95.73 112	84.12 47	86.81 91	91.33 146

xiu_mvs_v1_base		82.16 85	81.12 86	85.26 90	86.42 185	68.72 47	92.59 95	90.44 174	73.12 131	84.20 40	94.36 57	38.04 279	95.73 112	84.12 47	86.81 91	91.33 146

xiu_mvs_v1_base_debi		82.16 85	81.12 86	85.26 90	86.42 185	68.72 47	92.59 95	90.44 174	73.12 131	84.20 40	94.36 57	38.04 279	95.73 112	84.12 47	86.81 91	91.33 146

PVSNet_0	68.08 15	71.81 232	68.32 246	82.27 160	84.68 206	62.31 204	88.68 209	90.31 183	75.84 82	57.93 268	80.65 245	37.85 282	94.19 169	69.94 143	29.05 341	90.31 159

Anonymous20231206		67.53 269	65.78 259	72.79 292	74.95 308	47.59 317	88.23 216	87.32 259	61.75 274	58.07 267	77.29 273	37.79 283	87.29 298	42.91 283	63.71 261	83.48 264

ACMM	69.62 13	74.34 213	72.73 211	79.17 233	84.25 216	57.87 261	90.36 176	89.93 199	63.17 262	65.64 222	86.04 182	37.79 283	94.10 175	65.89 178	71.52 201	85.55 244
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		78.18 150	75.77 162	85.41 85	87.14 178	69.11 39	92.96 79	91.15 157	66.71 229	70.47 154	86.07 180	37.49 285	96.48 92	70.15 142	79.80 137	90.65 155

LS3D		69.17 258	66.40 258	77.50 259	91.92 78	56.12 281	85.12 256	80.37 312	46.96 321	56.50 276	87.51 164	37.25 286	93.71 193	32.52 329	79.40 139	82.68 277

MDA-MVSNet_test_wron		63.78 288	60.16 289	74.64 278	78.15 293	60.41 230	83.49 266	84.03 292	56.17 302	39.17 333	71.59 311	37.22 287	83.24 319	42.87 285	48.73 319	80.26 305

YYNet1		63.76 289	60.14 290	74.62 279	78.06 294	60.19 236	83.46 268	83.99 296	56.18 301	39.25 332	71.56 312	37.18 288	83.34 317	42.90 284	48.70 320	80.32 304

FMVSNet5		68.04 265	65.66 261	75.18 276	84.43 212	57.89 260	83.54 265	86.26 275	61.83 273	53.64 292	73.30 291	37.15 289	85.08 304	48.99 261	61.77 270	82.56 278

test20.03		63.83 287	62.65 281	67.38 311	70.58 322	39.94 332	86.57 251	84.17 291	63.29 260	51.86 298	77.30 272	37.09 290	82.47 321	38.87 305	54.13 302	79.73 309

PVSNet	73.49 8	80.05 114	78.63 119	84.31 112	90.92 107	64.97 151	92.47 98	91.05 161	79.18 41	72.43 139	90.51 121	37.05 291	94.06 178	68.06 156	86.00 101	93.90 98

EU-MVSNet		64.01 286	63.01 278	67.02 312	74.40 310	38.86 336	83.27 270	86.19 276	45.11 327	54.27 285	81.15 240	36.91 292	80.01 329	48.79 263	57.02 292	82.19 280

FMVSNet1		72.71 227	69.91 231	81.10 192	83.60 223	65.11 145	90.01 182	90.32 180	63.92 256	63.56 238	80.25 251	36.35 293	91.54 252	54.46 242	66.75 235	86.64 218

testpf		57.17 301	56.93 298	57.88 323	79.13 285	42.40 325	34.23 347	85.97 281	52.64 307	47.66 313	66.50 318	36.33 294	79.65 331	53.60 247	56.31 296	51.60 342

CMPMVS	48.56 21	66.77 273	64.41 271	73.84 284	70.65 321	50.31 307	77.79 310	85.73 284	45.54 326	44.76 321	82.14 220	35.40 295	90.14 270	63.18 200	74.54 179	81.07 296
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		67.57 268	64.76 267	76.00 273	72.82 314	53.37 293	88.71 208	86.78 263	53.19 306	57.58 272	78.03 268	35.33 296	92.41 226	55.56 239	54.88 300	82.21 279

test2356		64.16 285	63.28 276	66.81 313	69.37 326	39.86 334	87.76 226	86.02 279	59.83 283	53.54 293	73.23 292	34.94 297	80.67 328	39.66 297	65.20 245	79.89 307

PatchMatch-RL		72.06 230	69.98 229	78.28 248	89.51 136	55.70 283	83.49 266	83.39 300	61.24 275	63.72 237	82.76 209	34.77 298	93.03 205	53.37 249	77.59 155	86.12 227

LTVRE_ROB	59.60 19	66.27 275	63.54 274	74.45 280	84.00 219	51.55 300	67.08 330	83.53 297	58.78 288	54.94 280	80.31 249	34.54 299	93.23 202	40.64 295	68.03 227	78.58 317
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		70.66 245	68.44 244	77.32 263	86.37 188	55.91 282	88.00 219	86.32 273	56.94 297	57.28 273	88.07 155	33.58 300	92.49 224	51.02 254	68.37 225	83.55 261

pmmvs-eth3d		65.53 279	62.32 283	75.19 275	69.39 325	59.59 244	82.80 275	83.43 298	62.52 267	51.30 302	72.49 296	32.86 301	87.16 299	55.32 240	50.73 311	78.83 315

MDA-MVSNet-bldmvs		61.54 294	57.70 295	73.05 289	79.53 278	57.00 272	83.08 273	81.23 307	57.57 291	34.91 336	72.45 298	32.79 302	86.26 303	35.81 312	41.95 329	75.89 324

MIMVSNet		71.64 235	68.44 244	81.23 185	81.97 237	64.44 158	73.05 316	88.80 235	69.67 197	64.59 228	74.79 288	32.79 302	87.82 295	53.99 244	76.35 169	91.42 145

UnsupCasMVSNet_eth		65.79 278	63.10 277	73.88 283	70.71 320	50.29 308	81.09 286	89.88 200	72.58 139	49.25 308	74.77 289	32.57 304	87.43 297	55.96 238	41.04 331	83.90 259

N_pmnet		50.55 310	49.11 312	54.88 327	77.17 299	4.02 357	84.36 260	2.00 357	48.59 317	45.86 317	68.82 316	32.22 305	82.80 320	31.58 331	51.38 309	77.81 320

test_0402		64.54 282	61.09 287	74.92 277	84.10 218	60.75 224	87.95 220	79.71 314	52.03 309	52.41 296	77.20 274	32.21 306	91.64 249	23.14 340	61.03 277	72.36 329

DSMNet-mixed		56.78 302	54.44 304	63.79 318	63.21 332	29.44 345	64.43 333	64.10 343	42.12 335	51.32 301	71.60 310	31.76 307	75.04 338	36.23 310	65.20 245	86.87 216

Test4		76.45 182	73.45 205	85.45 83	76.07 306	67.61 72	88.38 215	90.83 165	76.71 74	53.06 294	79.65 259	31.61 308	94.35 162	78.47 83	86.22 100	94.40 76

MSDG		69.54 256	65.73 260	80.96 196	85.11 203	63.71 177	84.19 261	83.28 301	56.95 296	54.50 282	84.03 198	31.50 309	96.03 102	42.87 285	69.13 220	83.14 271

RPSCF		64.24 284	61.98 285	71.01 303	76.10 304	45.00 321	75.83 313	75.94 321	46.94 322	58.96 262	84.59 195	31.40 310	82.00 325	47.76 267	60.33 282	86.04 233

jajsoiax		73.05 222	71.51 224	77.67 257	77.46 297	54.83 287	88.81 207	90.04 196	69.13 202	62.85 245	83.51 203	31.16 311	92.75 216	70.83 136	69.80 213	85.43 246

MVS-HIRNet		60.25 296	55.55 302	74.35 281	84.37 213	56.57 279	71.64 318	74.11 328	34.44 339	45.54 319	42.24 341	31.11 312	89.81 280	40.36 296	76.10 170	76.67 323

SixPastTwentyTwo		64.92 280	61.78 286	74.34 282	78.74 288	49.76 309	83.42 269	79.51 315	62.86 264	50.27 305	77.35 271	30.92 313	90.49 262	45.89 273	47.06 322	82.78 273

LP		56.71 303	51.64 307	71.91 301	80.08 272	60.33 232	61.72 335	75.61 323	43.87 332	43.76 325	60.30 331	30.46 314	84.05 309	22.94 341	46.06 324	71.34 331

mvs_tets		72.71 227	71.11 225	77.52 258	77.41 298	54.52 289	88.45 214	89.76 202	68.76 207	62.70 246	83.26 206	29.49 315	92.71 217	70.51 141	69.62 215	85.34 248

K. test v3		63.09 290	59.61 292	73.53 286	76.26 303	49.38 312	83.27 270	77.15 318	64.35 253	47.77 311	72.32 301	28.73 316	87.79 296	49.93 259	36.69 336	83.41 266

UnsupCasMVSNet_bld		61.60 293	57.71 294	73.29 288	68.73 327	51.64 299	78.61 304	89.05 230	57.20 295	46.11 314	61.96 329	28.70 317	88.60 288	50.08 258	38.90 334	79.63 310

lessismore_v0				73.72 285	72.93 313	47.83 316		61.72 346		45.86 317	73.76 290	28.63 318	89.81 280	47.75 268	31.37 340	83.53 262

new-patchmatchnet		59.30 300	56.48 300	67.79 309	65.86 329	44.19 322	82.47 276	81.77 305	59.94 282	43.65 326	66.20 320	27.67 319	81.68 326	39.34 298	41.40 330	77.50 321

testing_2		71.09 241	67.32 254	82.40 156	69.82 323	66.52 117	83.64 264	90.77 167	72.21 149	45.12 320	71.07 313	27.60 320	93.74 192	75.71 99	69.96 212	86.95 214

ACMH	63.93 17	68.62 260	64.81 266	80.03 209	85.22 201	63.25 184	87.72 227	84.66 288	60.83 277	51.57 300	79.43 261	27.29 321	94.96 134	41.76 289	64.84 252	81.88 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		64.68 281	62.17 284	72.21 297	76.08 305	47.35 318	80.67 289	81.02 309	56.19 300	51.60 299	79.66 258	27.05 322	88.56 289	53.60 247	53.63 303	80.71 300

ACMH+	65.35 16	67.65 267	64.55 268	76.96 266	84.59 208	57.10 271	88.08 218	80.79 310	58.59 290	53.00 295	81.09 241	26.63 323	92.95 207	46.51 270	61.69 274	80.82 298

OpenMVS_ROB	61.12 18	66.39 274	62.92 279	76.80 268	76.51 301	57.77 262	89.22 199	83.41 299	55.48 303	53.86 290	77.84 269	26.28 324	93.95 186	34.90 321	68.76 222	78.68 316

COLMAP_ROB	57.96 20	62.98 291	59.65 291	72.98 290	81.44 241	53.00 295	83.75 263	75.53 325	48.34 319	48.81 309	81.40 233	24.14 325	90.30 263	32.95 326	60.52 281	75.65 325
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		60.16 297	57.33 297	68.67 307	69.71 324	44.13 323	78.92 303	84.21 290	55.05 304	44.63 322	71.85 305	23.91 326	81.54 327	32.63 328	55.03 299	80.35 303

testgi		64.48 283	62.87 280	69.31 306	71.24 317	40.62 331	85.49 255	79.92 313	65.36 240	54.18 287	83.49 204	23.74 327	84.55 306	41.60 290	60.79 280	82.77 274

ITE_SJBPF				70.43 304	74.44 309	47.06 319		77.32 317	60.16 281	54.04 288	83.53 202	23.30 328	84.01 311	43.07 282	61.58 275	80.21 306

EG-PatchMatch MVS		68.55 261	65.41 263	77.96 255	78.69 289	62.93 192	89.86 191	89.17 223	60.55 278	50.27 305	77.73 270	22.60 329	94.06 178	47.18 269	72.65 194	76.88 322

tmp_tt		22.26 328	23.75 327	17.80 341	5.23 356	12.06 356	35.26 346	39.48 352	2.82 352	18.94 343	44.20 340	22.23 330	24.64 354	36.30 309	9.31 350	16.69 349

USDC		67.43 271	64.51 269	76.19 271	77.94 295	55.29 285	78.38 306	85.00 286	73.17 129	48.36 310	80.37 248	21.23 331	92.48 225	52.15 251	64.02 259	80.81 299

XVG-ACMP-BASELINE		68.04 265	65.53 262	75.56 274	74.06 311	52.37 296	78.43 305	85.88 282	62.03 269	58.91 263	81.21 239	20.38 332	91.15 259	60.69 219	68.18 226	83.16 270

AllTest		61.66 292	58.06 293	72.46 294	79.57 276	51.42 302	80.17 295	68.61 338	51.25 311	45.88 315	81.23 235	19.86 333	86.58 301	38.98 299	57.01 293	79.39 311

TestCases				72.46 294	79.57 276	51.42 302		68.61 338	51.25 311	45.88 315	81.23 235	19.86 333	86.58 301	38.98 299	57.01 293	79.39 311

test1235678		55.73 305	52.74 305	64.68 317	60.16 338	35.56 339	81.65 282	81.46 306	51.27 310	38.93 334	62.82 328	17.44 335	78.58 336	30.87 332	50.09 318	79.89 307

pmmvs3		55.51 306	51.50 309	67.53 310	57.90 340	50.93 305	80.37 291	73.66 329	40.63 336	44.15 324	64.75 325	16.30 336	78.97 334	44.77 280	40.98 332	72.69 328

Anonymous20231211		53.57 309	49.43 311	66.00 315	65.01 330	42.08 326	80.95 288	72.60 331	38.46 337	41.65 330	64.48 326	15.72 337	84.23 307	25.78 337	40.24 333	71.68 330

TDRefinement		55.28 307	51.58 308	66.39 314	59.53 339	46.15 320	76.23 312	72.80 330	44.60 329	42.49 327	76.28 281	15.29 338	82.39 322	33.20 325	43.75 327	70.62 333

new_pmnet		49.31 311	46.44 313	57.93 322	62.84 333	40.74 330	68.47 326	62.96 345	36.48 338	35.09 335	57.81 333	14.97 339	72.18 339	32.86 327	46.44 323	60.88 340

TinyColmap		60.32 295	56.42 301	72.00 300	78.78 287	53.18 294	78.36 307	75.64 322	52.30 308	41.59 331	75.82 285	14.76 340	88.35 291	35.84 311	54.71 301	74.46 326

LF4IMVS		54.01 308	52.12 306	59.69 321	62.41 334	39.91 333	68.59 325	68.28 340	42.96 333	44.55 323	75.18 286	14.09 341	68.39 342	41.36 292	51.68 308	70.78 332

PM-MVS		59.40 298	56.59 299	67.84 308	63.63 331	41.86 328	76.76 311	63.22 344	59.01 287	51.07 303	72.27 302	11.72 342	83.25 318	61.34 215	50.28 317	78.39 318

no-one		44.13 316	38.39 317	61.34 320	45.91 348	41.94 327	61.67 336	75.07 327	45.05 328	20.07 342	40.68 344	11.58 343	79.82 330	30.18 333	15.30 344	62.26 339

testus		59.36 299	57.51 296	64.90 316	66.72 328	37.56 337	84.98 257	81.09 308	57.46 294	47.72 312	72.76 293	11.43 344	78.78 335	36.56 308	58.91 287	78.36 319

ambc				69.61 305	61.38 337	41.35 329	49.07 344	85.86 283		50.18 307	66.40 319	10.16 345	88.14 293	45.73 274	44.20 326	79.32 313

FPMVS		45.64 315	43.10 316	53.23 329	51.42 343	36.46 338	64.97 332	71.91 333	29.13 341	27.53 339	61.55 330	9.83 346	65.01 346	16.00 346	55.58 297	58.22 341

ANet_high		40.27 318	35.20 319	55.47 326	34.74 352	34.47 340	63.84 334	71.56 334	48.42 318	18.80 344	41.08 342	9.52 347	64.45 347	20.18 343	8.66 351	67.49 337

test12356		47.51 312	44.82 314	55.56 325	52.53 341	21.09 352	71.45 319	76.03 320	44.14 331	30.69 337	58.18 332	9.01 348	76.14 337	26.95 336	34.43 339	69.46 335

testmv		46.98 313	43.53 315	57.35 324	47.75 346	30.41 344	74.99 315	77.69 316	42.84 334	28.03 338	53.36 334	8.18 349	71.18 340	24.36 339	34.55 337	70.46 334

EMVS		23.76 327	23.20 328	25.46 340	41.52 350	16.90 355	60.56 338	38.79 354	14.62 348	8.99 351	20.24 352	7.35 350	45.82 352	7.25 351	9.46 349	13.64 351

E-PMN		24.61 325	24.00 326	26.45 339	43.74 349	18.44 354	60.86 337	39.66 351	15.11 347	9.53 350	22.10 348	6.52 351	46.94 351	8.31 350	10.14 347	13.98 350

DeepMVS_CX				34.71 337	51.45 342	24.73 351		28.48 356	31.46 340	17.49 345	52.75 335	5.80 352	42.60 353	18.18 345	19.42 342	36.81 346

Gipuma		34.91 320	31.44 322	45.30 332	70.99 319	39.64 335	19.85 350	72.56 332	20.10 346	16.16 346	21.47 349	5.08 353	71.16 341	13.07 347	43.70 328	25.08 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		40.54 317	35.79 318	54.76 328	36.92 351	30.81 343	51.41 342	69.02 337	22.07 343	24.63 340	45.37 339	4.56 354	65.81 344	33.67 323	34.50 338	67.67 336

PMMVS2		37.93 319	33.61 320	50.92 330	46.31 347	24.76 350	60.55 339	50.05 348	28.94 342	20.93 341	47.59 336	4.41 355	65.13 345	25.14 338	18.55 343	62.87 338

PMVS	26.43 22	31.84 322	28.16 324	42.89 333	25.87 355	27.58 348	50.92 343	49.78 350	21.37 345	14.17 348	40.81 343	2.01 356	66.62 343	9.61 349	38.88 335	34.49 347
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 326	19.77 330	38.09 335	34.56 353	26.92 349	26.57 348	38.87 353	11.73 350	11.37 349	27.44 346	1.37 357	50.42 350	11.41 348	14.60 345	36.93 345
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		32.77 321	29.98 323	41.11 334	48.05 344	29.17 346	65.82 331	50.02 349	21.42 344	14.74 347	37.19 345	1.11 358	55.11 349	19.75 344	11.77 346	39.06 344

wuyk23d		11.30 330	10.95 331	12.33 342	48.05 344	19.89 353	25.89 349	1.92 358	3.58 351	3.12 353	1.37 354	0.64 359	15.77 355	6.23 352	7.77 352	1.35 352

wuykxyi23d		29.03 324	23.09 329	46.84 331	31.67 354	28.82 347	43.46 345	57.72 347	14.39 349	7.52 352	20.84 350	0.64 359	60.29 348	21.57 342	10.04 348	51.40 343

test123		6.92 333	9.21 334	0.08 343	0.03 358	0.05 358	81.65 282	0.01 360	0.02 354	0.14 355	0.85 356	0.03 361	0.02 356	0.12 354	0.00 355	0.16 353

testmvs		7.23 332	9.62 333	0.06 344	0.04 357	0.02 359	84.98 257	0.02 359	0.03 353	0.18 354	1.21 355	0.01 362	0.02 356	0.14 353	0.01 353	0.13 354

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 355	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 355	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 355	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 355	0.00 356

ab-mvs-re		7.91 331	10.55 332	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	94.95 42	0.00 363	0.00 358	0.00 355	0.00 355	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 355	0.00 356

GSMVS																94.68 64

test_part3							94.96 31		68.52 212		97.23 2		98.90 7	91.52 6

test_part2					96.29 7	68.16 60				90.78 3

MTGPA								92.23 114

MTMP								32.52 355

gm-plane-assit					88.42 159	67.04 87			78.62 52		91.83 104		97.37 49	76.57 95

test9_res														89.41 12	94.96 10	95.29 40

agg_prior2														86.41 33	94.75 18	95.33 37

agg_prior					94.16 33	66.97 88		93.31 75		84.49 37			96.75 85

test_prior4						67.18 83	93.92 54

test_prior				86.42 54	94.71 23	67.35 77		93.10 87					96.84 81			95.05 52

旧先验2							92.00 114		59.37 286	87.54 16			93.47 199	75.39 101

新几何2							91.41 142

无先验							92.71 87	92.61 105	62.03 269				97.01 67	66.63 168		93.97 94

原ACMM2							92.01 112

testdata2													96.09 99	61.26 216

testdata1							89.21 200		77.55 63

plane_prior7					86.94 180	61.51 215

plane_prior5								91.31 152					95.55 122	76.74 93	78.53 148	88.39 180

plane_prior4											89.14 141

plane_prior3						61.95 211			79.09 44	72.53 135

plane_prior2							93.13 74		78.81 49

plane_prior1					87.15 177

plane_prior						62.42 201	93.85 57		79.38 37						78.80 146

n2								0.00 361

nn								0.00 361

door-mid								66.01 342

test11								93.01 89

door								66.57 341

HQP5-MVS						63.66 180

HQP-NCC					87.54 172		94.06 45		79.80 33	74.18 117

ACMP_Plane					87.54 172		94.06 45		79.80 33	74.18 117

BP-MVS														77.63 90

HQP4-MVS										74.18 117			95.61 118			88.63 175

HQP3-MVS								91.70 138							78.90 144

NP-MVS					87.41 175	63.04 190					90.30 123

ACMMP++_ref															71.63 199

ACMMP++															69.72 214

High-resolution multi-view benchmark