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.

MSP-MVS		90.38 4	91.87 1	85.88 85	92.83 83	64.03 190	93.06 105	94.33 51	82.19 21	93.65 3	96.15 30	85.89 1	97.19 84	91.02 20	97.75 1	96.43 26
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS++		90.53 3	91.09 4	88.87 14	97.31 4	69.91 37	93.96 69	94.37 49	72.48 166	92.07 6	96.85 12	83.82 2	99.15 2	91.53 16	97.42 4	97.55 4

OPU-MVS				89.97 3	97.52 3	73.15 13	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

PC_three_1452									80.91 36	94.07 2	96.83 14	83.57 4	99.12 5	95.70 2	97.42 4	97.55 4

DPM-MVS		90.70 2	90.52 7	91.24 1	89.68 158	76.68 2	97.29 1	95.35 10	82.87 15	91.58 10	97.22 4	79.93 5	99.10 9	83.12 82	97.64 2	97.94 1

baseline2		83.68 91	83.42 82	84.48 135	87.37 214	66.00 135	90.06 226	95.93 7	79.71 48	69.08 220	90.39 163	77.92 6	96.28 122	78.91 119	81.38 166	91.16 191

GG-mvs-BLEND				86.53 65	91.91 113	69.67 45	75.02 343	94.75 31		78.67 121	90.85 154	77.91 7	94.56 191	72.25 166	93.74 47	95.36 57

gg-mvs-nofinetune		77.18 201	74.31 219	85.80 90	91.42 127	68.36 70	71.78 345	94.72 32	49.61 349	77.12 135	45.92 366	77.41 8	93.98 220	67.62 210	93.16 57	95.05 76

SED-MVS		89.94 8	90.36 9	88.70 16	96.45 14	69.38 47	96.89 4	94.44 43	71.65 196	92.11 4	97.21 5	76.79 9	99.11 6	92.34 8	95.36 14	97.62 2

test_241102_ONE					96.45 14	69.38 47		94.44 43	71.65 196	92.11 4	97.05 8	76.79 9	99.11 6

test_0728_THIRD									72.48 166	90.55 16	96.93 10	76.24 11	99.08 11	91.53 16	94.99 17	96.43 26

DPE-MVS		88.77 15	89.21 15	87.45 36	96.26 22	67.56 92	94.17 55	94.15 57	68.77 247	90.74 14	97.27 3	76.09 12	98.49 27	90.58 23	94.91 20	96.30 29
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + GP.		87.96 19	88.37 18	86.70 58	93.51 67	65.32 152	95.15 35	93.84 63	78.17 73	85.93 43	94.80 72	75.80 13	98.21 35	89.38 27	88.78 108	96.59 15

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 127	93.00 81	58.16 293	96.72 7	94.41 45	86.50 6	90.25 18	97.83 1	75.46 14	98.67 23	92.78 5	95.49 13	97.32 6

dcpmvs_2		87.37 28	87.55 28	86.85 49	95.04 36	68.20 77	90.36 217	90.66 195	79.37 52	81.20 86	93.67 102	74.73 15	96.55 118	90.88 21	92.00 75	95.82 44

MVSTER		82.47 108	82.05 105	83.74 152	92.68 90	69.01 56	91.90 156	93.21 91	79.83 44	72.14 187	85.71 222	74.72 16	94.72 182	75.72 138	72.49 230	87.50 233

test_241102_TWO								94.41 45	71.65 196	92.07 6	97.21 5	74.58 17	99.11 6	92.34 8	95.36 14	96.59 15

ETH3 D test6400		90.27 6	90.44 8	89.75 6	96.82 9	74.33 7	95.89 16	94.80 29	77.13 89	89.13 22	97.38 2	74.49 18	98.48 28	92.32 11	95.98 8	96.46 25

test_one_0601					96.32 20	69.74 42		94.18 55	71.42 208	90.67 15	96.85 12	74.45 19

DELS-MVS		90.05 7	90.09 10	89.94 4	93.14 78	73.88 8	97.01 3	94.40 47	88.32 2	85.71 46	94.91 69	74.11 20	98.91 17	87.26 51	95.94 9	97.03 10
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
patch_mono-2		89.71 10	90.99 5	85.85 88	96.04 26	63.70 198	95.04 41	95.19 13	86.74 5	91.53 11	95.15 59	73.86 21	97.58 63	93.38 3	92.00 75	96.28 32

DVP-MVS		89.41 12	89.73 13	88.45 21	96.40 17	69.99 33	96.64 8	94.52 39	71.92 182	90.55 16	96.93 10	73.77 22	99.08 11	91.91 14	94.90 21	96.29 30
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					96.40 17	69.99 33	96.76 6	94.33 51	71.92 182	91.89 8	97.11 7	73.77 22

ET-MVSNet_ETH3D		84.01 83	83.15 89	86.58 62	90.78 141	70.89 24	94.74 47	94.62 37	81.44 30	58.19 311	93.64 103	73.64 24	92.35 272	82.66 86	78.66 185	96.50 23

CSCG		86.87 38	86.26 45	88.72 15	95.05 35	70.79 25	93.83 83	95.33 11	68.48 251	77.63 128	94.35 86	73.04 25	98.45 29	84.92 70	93.71 49	96.92 11

tttt0517		79.50 160	78.53 159	82.41 186	87.22 216	61.43 243	89.75 236	94.76 30	69.29 239	67.91 238	88.06 196	72.92 26	95.63 149	62.91 251	73.90 219	90.16 201

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 44	96.26 27	72.84 27	99.38 1	92.64 6	95.93 10	97.08 9

iter_conf05		83.27 95	82.70 97	84.98 117	93.32 70	71.84 16	94.16 56	81.76 336	82.74 16	73.83 167	88.40 186	72.77 28	94.61 186	82.10 91	75.21 211	88.48 221

thisisatest0515		83.41 92	82.49 101	86.16 78	89.46 164	68.26 74	93.54 92	94.70 33	74.31 126	75.75 145	90.92 152	72.62 29	96.52 119	69.64 190	81.50 165	93.71 130

thisisatest0530		81.15 129	80.07 132	84.39 137	88.26 194	65.63 146	91.40 178	94.62 37	71.27 210	70.93 199	89.18 177	72.47 30	96.04 132	65.62 232	76.89 203	91.49 181

TSAR-MVS + MP.		88.11 18	88.64 16	86.54 64	91.73 118	68.04 80	90.36 217	93.55 77	82.89 14	91.29 12	92.89 120	72.27 31	96.03 133	87.99 40	94.77 25	95.54 51
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EPP-MVSNet		81.79 121	81.52 112	82.61 180	88.77 181	60.21 266	93.02 109	93.66 73	68.52 250	72.90 174	90.39 163	72.19 32	94.96 174	74.93 146	79.29 179	92.67 158

CostFormer		82.33 110	81.15 115	85.86 87	89.01 175	68.46 68	82.39 309	93.01 102	75.59 105	80.25 98	81.57 269	72.03 33	94.96 174	79.06 117	77.48 197	94.16 111

HPM-MVS++		89.37 13	89.95 12	87.64 29	95.10 34	68.23 76	95.24 32	94.49 41	82.43 19	88.90 24	96.35 24	71.89 34	98.63 24	88.76 36	96.40 6	96.06 36

CNVR-MVS		90.32 5	90.89 6	88.61 19	96.76 10	70.65 26	96.47 12	94.83 26	84.83 9	89.07 23	96.80 15	70.86 35	99.06 15	92.64 6	95.71 11	96.12 35

IB-MVS	77.80 4	82.18 112	80.46 130	87.35 38	89.14 172	70.28 31	95.59 25	95.17 15	78.85 63	70.19 208	85.82 220	70.66 36	97.67 55	72.19 169	66.52 270	94.09 115
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
baseline1		81.84 120	81.03 120	84.28 142	91.60 121	66.62 121	91.08 195	91.66 157	81.87 25	74.86 155	91.67 145	69.98 37	94.92 177	71.76 173	64.75 283	91.29 190

agg_prior1		87.02 36	87.26 33	86.28 76	94.16 50	66.97 112	94.08 63	93.31 88	71.85 189	84.49 59	95.39 45	68.91 38	96.75 111	88.84 35	94.32 38	95.13 72

alignmvs		87.28 29	86.97 38	88.24 23	91.30 130	71.14 23	95.61 24	93.56 76	79.30 53	87.07 33	95.25 54	68.43 39	96.93 105	87.87 41	84.33 148	96.65 13

PAPM		85.89 53	85.46 57	87.18 41	88.20 197	72.42 14	92.41 135	92.77 110	82.11 22	80.34 97	93.07 114	68.27 40	95.02 172	78.39 124	93.59 51	94.09 115

train_agg		87.21 31	87.42 31	86.60 60	94.18 46	67.28 101	94.16 56	93.51 78	71.87 187	85.52 48	95.33 47	68.19 41	97.27 82	89.09 31	94.90 21	95.25 69

test_8					94.19 45	67.19 104	94.15 59	93.42 85	71.87 187	85.38 51	95.35 46	68.19 41	96.95 102

ETH3D cwj APD-0.16		87.06 34	87.18 35	86.71 56	91.99 109	67.48 97	92.97 110	94.21 54	71.48 207	85.72 45	96.32 26	68.13 43	98.00 42	89.06 32	94.70 31	94.65 92

TEST9					94.18 46	67.28 101	94.16 56	93.51 78	71.75 194	85.52 48	95.33 47	68.01 44	97.27 82

test_prior3		87.38 27	87.70 24	86.42 69	94.71 39	67.35 99	95.10 37	93.10 100	75.40 110	85.25 54	95.61 40	67.94 45	96.84 107	87.47 46	94.77 25	95.05 76

test_prior2							95.10 37		75.40 110	85.25 54	95.61 40	67.94 45		87.47 46	94.77 25

WTY-MVS		86.32 45	85.81 52	87.85 25	92.82 85	69.37 49	95.20 33	95.25 12	82.71 17	81.91 79	94.73 73	67.93 47	97.63 60	79.55 111	82.25 159	96.54 18

Regformer-1		87.24 30	87.60 27	86.15 79	95.14 32	65.83 141	93.95 72	95.12 16	82.11 22	84.25 61	95.73 36	67.88 48	98.35 33	85.60 63	88.64 111	94.26 105

ETH3D-3000-0.1		87.61 24	87.89 21	86.75 55	93.58 64	67.21 103	94.31 53	94.14 58	72.92 157	87.13 30	96.62 17	67.81 49	97.94 43	90.13 24	94.42 36	95.09 74

APDe-MVS		87.54 25	87.84 22	86.65 59	96.07 25	66.30 129	94.84 46	93.78 64	69.35 237	88.39 25	96.34 25	67.74 50	97.66 58	90.62 22	93.44 53	96.01 39

tpm2		79.80 155	77.95 168	85.34 108	88.28 193	68.26 74	81.56 314	91.42 166	70.11 228	77.59 130	80.50 287	67.40 51	94.26 204	67.34 212	77.35 198	93.51 135

Regformer-2		87.00 37	87.43 30	85.71 95	95.14 32	64.73 169	93.95 72	94.95 23	81.69 27	84.03 66	95.73 36	67.35 52	98.19 37	85.40 65	88.64 111	94.20 107

miper_enhance_ethall		78.86 171	77.97 167	81.54 210	88.00 202	65.17 156	91.41 176	89.15 251	75.19 116	68.79 226	83.98 242	67.17 53	92.82 250	72.73 161	65.30 274	86.62 254

xxxxxxxxxxxxxcwj		87.14 32	87.19 34	86.99 47	93.84 56	67.89 84	95.05 39	84.72 316	78.19 71	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 64

SF-MVS		87.03 35	87.09 36	86.84 50	92.70 89	67.45 98	93.64 87	93.76 67	70.78 220	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 64

HY-MVS	76.49 5	84.28 76	83.36 85	87.02 46	92.22 101	67.74 88	84.65 291	94.50 40	79.15 57	82.23 77	87.93 197	66.88 56	96.94 103	80.53 106	82.20 160	96.39 28

EPNet		87.84 22	88.38 17	86.23 77	93.30 71	66.05 133	95.26 31	94.84 25	87.09 3	88.06 26	94.53 77	66.79 57	97.34 75	83.89 78	91.68 80	95.29 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
9.14			87.63 25		93.86 55		94.41 51	94.18 55	72.76 160	86.21 38	96.51 19	66.64 58	97.88 49	90.08 25	94.04 41

FIs		79.47 161	79.41 147	79.67 254	85.95 236	59.40 276	91.68 168	93.94 61	78.06 74	68.96 223	88.28 189	66.61 59	91.77 284	66.20 226	74.99 212	87.82 230

NCCC		89.07 14	89.46 14	87.91 24	96.60 12	69.05 55	96.38 13	94.64 36	84.42 10	86.74 34	96.20 28	66.56 60	98.76 22	89.03 34	94.56 33	95.92 41

SD-MVS		87.49 26	87.49 29	87.50 35	93.60 63	68.82 61	93.90 76	92.63 118	76.86 92	87.90 27	95.76 35	66.17 61	97.63 60	89.06 32	91.48 84	96.05 37
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
UniMVSNet_NR-MVSNet		78.15 187	77.55 175	79.98 245	84.46 260	60.26 264	92.25 138	93.20 93	77.50 86	68.88 224	86.61 210	66.10 62	92.13 276	66.38 223	62.55 297	87.54 232

CHOSEN 280x420		77.35 199	76.95 187	78.55 270	87.07 219	62.68 223	69.71 351	82.95 332	68.80 246	71.48 195	87.27 207	66.03 63	84.00 345	76.47 135	82.81 157	88.95 212

CANet		89.61 11	89.99 11	88.46 20	94.39 44	69.71 43	96.53 11	93.78 64	86.89 4	89.68 19	95.78 34	65.94 64	99.10 9	92.99 4	93.91 44	96.58 17

segment_acmp												65.94 64

Regformer-3		85.80 54	85.92 50	85.46 102	94.17 48	65.09 162	92.95 112	95.11 17	81.13 33	81.68 81	95.04 60	65.82 66	98.32 34	83.02 83	84.36 145	92.97 152

Vis-MVSNet (Re-imp)		79.24 164	79.57 142	78.24 275	88.46 186	52.29 333	90.41 215	89.12 253	74.24 127	69.13 218	91.91 140	65.77 67	90.09 308	59.00 274	88.09 116	92.33 166

FC-MVSNet-test		77.99 189	78.08 165	77.70 279	84.89 253	55.51 318	90.27 220	93.75 70	76.87 91	66.80 254	87.59 201	65.71 68	90.23 305	62.89 252	73.94 217	87.37 237

SMA-MVS		88.14 16	88.29 19	87.67 28	93.21 75	68.72 63	93.85 79	94.03 60	74.18 128	91.74 9	96.67 16	65.61 69	98.42 32	89.24 30	96.08 7	95.88 43
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
test12				87.09 44	94.60 41	68.86 59		92.91 106		82.67 76		65.44 70	97.55 64		93.69 50	94.84 85

旧先验1					91.94 110	60.74 257		91.50 163			94.36 82	65.23 71			91.84 77	94.55 94

Regformer-4		85.45 58	85.69 55	84.73 125	94.17 48	63.23 207	92.95 112	94.83 26	80.66 38	81.29 84	95.04 60	65.12 72	98.08 40	82.74 85	84.36 145	92.88 156

1112_ss		80.56 140	79.83 139	82.77 175	88.65 182	60.78 253	92.29 137	88.36 277	72.58 163	72.46 183	94.95 64	65.09 73	93.42 235	66.38 223	77.71 190	94.10 114

MVSFormer		83.75 89	82.88 93	86.37 72	89.24 170	71.18 21	89.07 251	90.69 192	65.80 270	87.13 30	94.34 87	64.99 74	92.67 258	72.83 158	91.80 78	95.27 66

lupinMVS		87.74 23	87.77 23	87.63 33	89.24 170	71.18 21	96.57 10	92.90 107	82.70 18	87.13 30	95.27 52	64.99 74	95.80 138	89.34 28	91.80 78	95.93 40

tpmrst		80.57 139	79.14 154	84.84 123	90.10 150	68.28 73	81.70 312	89.72 233	77.63 84	75.96 144	79.54 300	64.94 76	92.71 255	75.43 139	77.28 200	93.55 134

ZD-MVS					96.63 11	65.50 150		93.50 80	70.74 221	85.26 53	95.19 58	64.92 77	97.29 78	87.51 45	93.01 59

miper_ehance_all_eth		77.60 195	76.44 192	81.09 225	85.70 241	64.41 179	90.65 209	88.64 272	72.31 172	67.37 248	82.52 255	64.77 78	92.64 262	70.67 182	65.30 274	86.24 260

Test_1112_low_res		79.56 159	78.60 158	82.43 183	88.24 196	60.39 263	92.09 145	87.99 286	72.10 180	71.84 190	87.42 204	64.62 79	93.04 239	65.80 230	77.30 199	93.85 128

test2506		83.29 94	82.92 92	84.37 138	88.39 190	63.18 210	92.01 150	91.35 169	77.66 82	78.49 122	91.42 146	64.58 80	95.09 170	73.19 154	89.23 104	94.85 82

DeepC-MVS_fast	79.48 2	87.95 20	88.00 20	87.79 27	95.86 29	68.32 71	95.74 20	94.11 59	83.82 12	83.49 69	96.19 29	64.53 81	98.44 30	83.42 81	94.88 24	96.61 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		86.62 43	86.66 42	86.50 66	96.95 8	65.70 143	94.41 51	93.45 82	67.74 254	86.19 39	96.39 23	64.38 82	97.91 46	87.33 49	93.14 58	95.90 42

MG-MVS		87.11 33	86.27 44	89.62 8	97.79 1	76.27 4	94.96 44	94.49 41	78.74 67	83.87 68	92.94 117	64.34 83	96.94 103	75.19 141	94.09 40	95.66 47

casdiffmvs		85.37 59	84.87 65	86.84 50	88.25 195	69.07 54	93.04 107	91.76 151	81.27 31	80.84 93	92.07 138	64.23 84	96.06 131	84.98 69	87.43 121	95.39 54

cl22		77.94 191	76.78 188	81.42 212	87.57 208	64.93 167	90.67 208	88.86 264	72.45 168	67.63 243	82.68 254	64.07 85	92.91 248	71.79 171	65.30 274	86.44 256

tpm		78.58 180	77.03 184	83.22 168	85.94 238	64.56 170	83.21 304	91.14 179	78.31 70	73.67 168	79.68 298	64.01 86	92.09 278	66.07 227	71.26 240	93.03 150

CDS-MVSNet		81.43 125	80.74 123	83.52 159	86.26 231	64.45 175	92.09 145	90.65 196	75.83 104	73.95 166	89.81 173	63.97 87	92.91 248	71.27 177	82.82 156	93.20 145
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_Test		84.16 81	83.20 86	87.05 45	91.56 123	69.82 39	89.99 231	92.05 136	77.77 79	82.84 73	86.57 211	63.93 88	96.09 128	74.91 147	89.18 106	95.25 69

APD-MVS		85.93 52	85.99 49	85.76 92	95.98 28	65.21 155	93.59 90	92.58 120	66.54 265	86.17 40	95.88 33	63.83 89	97.00 95	86.39 58	92.94 60	95.06 75
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs_anonymous		81.36 126	79.99 136	85.46 102	90.39 146	68.40 69	86.88 282	90.61 197	74.41 123	70.31 207	84.67 234	63.79 90	92.32 273	73.13 155	85.70 135	95.67 46

PVSNet_Blended_VisFu		83.97 84	83.50 77	85.39 106	90.02 151	66.59 123	93.77 84	91.73 152	77.43 88	77.08 137	89.81 173	63.77 91	96.97 100	79.67 110	88.21 114	92.60 160

baseline		85.01 64	84.44 69	86.71 56	88.33 192	68.73 62	90.24 222	91.82 150	81.05 35	81.18 87	92.50 127	63.69 92	96.08 130	84.45 73	86.71 129	95.32 60

CDPH-MVS		85.71 56	85.46 57	86.46 67	94.75 38	67.19 104	93.89 77	92.83 109	70.90 216	83.09 72	95.28 50	63.62 93	97.36 73	80.63 105	94.18 39	94.84 85

HyFIR lowres test		81.03 134	79.56 143	85.43 104	87.81 206	68.11 79	90.18 223	90.01 222	70.65 222	72.95 173	86.06 218	63.61 94	94.50 196	75.01 145	79.75 175	93.67 131

canonicalmvs		86.85 39	86.25 46	88.66 18	91.80 117	71.92 15	93.54 92	91.71 154	80.26 41	87.55 28	95.25 54	63.59 95	96.93 105	88.18 39	84.34 147	97.11 8

c3_l		76.83 207	75.47 205	80.93 229	85.02 251	64.18 188	90.39 216	88.11 283	71.66 195	66.65 255	81.64 267	63.58 96	92.56 263	69.31 196	62.86 296	86.04 267

SteuartSystems-ACMMP		86.82 41	86.90 39	86.58 62	90.42 144	66.38 126	96.09 15	93.87 62	77.73 80	84.01 67	95.66 38	63.39 97	97.94 43	87.40 48	93.55 52	95.42 52
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		83.77 88	83.67 75	84.06 146	92.79 88	63.56 203	91.76 164	94.81 28	79.65 49	77.87 125	94.09 94	63.35 98	97.90 47	79.35 113	79.36 177	90.74 195

UniMVSNet (Re)		77.58 196	76.78 188	79.98 245	84.11 266	60.80 252	91.76 164	93.17 96	76.56 98	69.93 214	84.78 233	63.32 99	92.36 271	64.89 238	62.51 299	86.78 249

PVSNet_BlendedMVS		83.38 93	83.43 80	83.22 168	93.76 58	67.53 94	94.06 64	93.61 74	79.13 58	81.00 91	85.14 227	63.19 100	97.29 78	87.08 52	73.91 218	84.83 289

PVSNet_Blended		86.73 42	86.86 40	86.31 75	93.76 58	67.53 94	96.33 14	93.61 74	82.34 20	81.00 91	93.08 112	63.19 100	97.29 78	87.08 52	91.38 86	94.13 113

PAPM_NR		82.97 101	81.84 108	86.37 72	94.10 52	66.76 118	87.66 272	92.84 108	69.96 230	74.07 164	93.57 105	63.10 102	97.50 66	70.66 183	90.58 96	94.85 82

nrg030		80.93 135	79.86 138	84.13 145	83.69 271	68.83 60	93.23 101	91.20 174	75.55 106	75.06 154	88.22 194	63.04 103	94.74 181	81.88 93	66.88 267	88.82 216

EI-MVSNet-UG-set		83.14 98	82.96 90	83.67 157	92.28 99	63.19 209	91.38 182	94.68 34	79.22 55	76.60 140	93.75 100	62.64 104	97.76 53	78.07 126	78.01 188	90.05 203

DeepC-MVS	77.85 3	85.52 57	85.24 59	86.37 72	88.80 180	66.64 120	92.15 141	93.68 72	81.07 34	76.91 138	93.64 103	62.59 105	98.44 30	85.50 64	92.84 62	94.03 119
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EIA-MVS		84.84 67	84.88 64	84.69 128	91.30 130	62.36 226	93.85 79	92.04 137	79.45 50	79.33 109	94.28 90	62.42 106	96.35 121	80.05 108	91.25 89	95.38 55

CS-MVS		85.80 54	86.65 43	83.27 167	92.00 108	58.92 284	95.31 30	91.86 146	79.97 43	84.82 56	95.40 44	62.26 107	95.51 160	86.11 60	92.08 74	95.37 56

MVS_111021_HR		86.19 48	85.80 53	87.37 37	93.17 77	69.79 40	93.99 68	93.76 67	79.08 60	78.88 117	93.99 97	62.25 108	98.15 38	85.93 62	91.15 90	94.15 112

PHI-MVS		86.83 40	86.85 41	86.78 54	93.47 68	65.55 148	95.39 29	95.10 18	71.77 193	85.69 47	96.52 18	62.07 109	98.77 21	86.06 61	95.60 12	96.03 38

MP-MVS		85.02 63	84.97 63	85.17 113	92.60 93	64.27 186	93.24 100	92.27 127	73.13 151	79.63 106	94.43 80	61.90 110	97.17 85	85.00 68	92.56 65	94.06 118
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
jason		86.40 44	86.17 47	87.11 43	86.16 233	70.54 28	95.71 23	92.19 134	82.00 24	84.58 58	94.34 87	61.86 111	95.53 159	87.76 42	90.89 92	95.27 66
jason: jason.
iter_conf_final		81.74 122	80.93 121	84.18 143	92.66 91	69.10 53	92.94 114	82.80 334	79.01 62	74.85 156	88.40 186	61.83 112	94.61 186	79.36 112	76.52 206	88.83 213

CS-MVS-test		86.14 49	87.01 37	83.52 159	92.63 92	59.36 279	95.49 26	91.92 142	80.09 42	85.46 50	95.53 43	61.82 113	95.77 141	86.77 56	93.37 54	95.41 53

PAPR		85.15 62	84.47 67	87.18 41	96.02 27	68.29 72	91.85 159	93.00 104	76.59 97	79.03 113	95.00 62	61.59 114	97.61 62	78.16 125	89.00 107	95.63 48

IS-MVSNet		80.14 147	79.41 147	82.33 187	87.91 203	60.08 268	91.97 154	88.27 280	72.90 158	71.44 196	91.73 144	61.44 115	93.66 230	62.47 255	86.53 131	93.24 143

cl____		76.07 214	74.67 210	80.28 237	85.15 247	61.76 237	90.12 224	88.73 268	71.16 211	65.43 261	81.57 269	61.15 116	92.95 243	66.54 220	62.17 301	86.13 265

DIV-MVS_self_test		76.07 214	74.67 210	80.28 237	85.14 248	61.75 238	90.12 224	88.73 268	71.16 211	65.42 262	81.60 268	61.15 116	92.94 247	66.54 220	62.16 303	86.14 263

EI-MVSNet		78.97 168	78.22 163	81.25 215	85.33 244	62.73 222	89.53 240	93.21 91	72.39 171	72.14 187	90.13 169	60.99 118	94.72 182	67.73 209	72.49 230	86.29 258

IterMVS-LS		76.49 210	75.18 208	80.43 234	84.49 259	62.74 221	90.64 210	88.80 265	72.40 170	65.16 264	81.72 265	60.98 119	92.27 274	67.74 208	64.65 285	86.29 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ETV-MVS		86.01 51	86.11 48	85.70 96	90.21 149	67.02 111	93.43 97	91.92 142	81.21 32	84.13 65	94.07 96	60.93 120	95.63 149	89.28 29	89.81 100	94.46 103

tpm cat1		75.30 230	72.21 248	84.58 132	88.52 183	67.77 87	78.16 337	88.02 285	61.88 303	68.45 232	76.37 323	60.65 121	94.03 218	53.77 291	74.11 215	91.93 176

TAMVS		80.37 143	79.45 146	83.13 170	85.14 248	63.37 204	91.23 189	90.76 191	74.81 121	72.65 177	88.49 183	60.63 122	92.95 243	69.41 194	81.95 162	93.08 149

ZNCC-MVS		85.33 60	85.08 61	86.06 80	93.09 80	65.65 145	93.89 77	93.41 86	73.75 139	79.94 101	94.68 75	60.61 123	98.03 41	82.63 88	93.72 48	94.52 98

thres100view900		78.37 183	77.01 185	82.46 182	91.89 114	63.21 208	91.19 193	96.33 1	72.28 174	70.45 204	87.89 198	60.31 124	95.32 164	45.16 323	77.58 193	88.83 213

thres600view7		78.00 188	76.66 190	82.03 202	91.93 111	63.69 199	91.30 187	96.33 1	72.43 169	70.46 203	87.89 198	60.31 124	94.92 177	42.64 335	76.64 204	87.48 234

CHOSEN 1792x2688		84.98 65	83.45 79	89.57 11	89.94 153	75.14 5	92.07 147	92.32 125	81.87 25	75.68 147	88.27 190	60.18 126	98.60 25	80.46 107	90.27 99	94.96 80

h-mvs33		83.01 100	82.56 100	84.35 139	89.34 165	62.02 231	92.72 120	93.76 67	81.45 28	82.73 74	92.25 136	60.11 127	97.13 88	87.69 43	62.96 294	93.91 124

hse-mvs2		81.12 132	81.11 119	81.16 218	86.52 226	57.48 304	89.40 243	91.16 176	81.45 28	82.73 74	90.49 161	60.11 127	94.58 188	87.69 43	60.41 320	91.41 184

tfpn200view9		78.79 174	77.43 177	82.88 173	92.21 102	64.49 172	92.05 148	96.28 4	73.48 146	71.75 192	88.26 191	60.07 129	95.32 164	45.16 323	77.58 193	88.83 213

thres400		78.68 177	77.43 177	82.43 183	92.21 102	64.49 172	92.05 148	96.28 4	73.48 146	71.75 192	88.26 191	60.07 129	95.32 164	45.16 323	77.58 193	87.48 234

test_part1		79.63 157	77.86 171	84.93 118	92.50 95	71.43 18	94.15 59	91.08 183	72.51 165	70.66 200	84.98 229	59.84 131	95.07 171	72.07 170	62.94 295	88.30 227

diffmvs		84.28 76	83.83 74	85.61 98	87.40 213	68.02 81	90.88 201	89.24 245	80.54 39	81.64 82	92.52 126	59.83 132	94.52 195	87.32 50	85.11 139	94.29 104

MVS		84.66 70	82.86 94	90.06 2	90.93 136	74.56 6	87.91 267	95.54 9	68.55 249	72.35 186	94.71 74	59.78 133	98.90 18	81.29 102	94.69 32	96.74 12

thres200		79.66 156	78.33 160	83.66 158	92.54 94	65.82 142	93.06 105	96.31 3	74.90 120	73.30 170	88.66 181	59.67 134	95.61 151	47.84 313	78.67 184	89.56 210

Effi-MVS+		83.82 87	82.76 95	86.99 47	89.56 161	69.40 46	91.35 184	86.12 305	72.59 162	83.22 71	92.81 124	59.60 135	96.01 135	81.76 94	87.80 118	95.56 50

eth_miper_zixun_eth		75.96 220	74.40 218	80.66 231	84.66 255	63.02 212	89.28 245	88.27 280	71.88 186	65.73 259	81.65 266	59.45 136	92.81 251	68.13 204	60.53 317	86.14 263

ACMMP_NAP		86.05 50	85.80 53	86.80 53	91.58 122	67.53 94	91.79 161	93.49 81	74.93 119	84.61 57	95.30 49	59.42 137	97.92 45	86.13 59	94.92 19	94.94 81

GST-MVS		84.63 71	84.29 71	85.66 97	92.82 85	65.27 153	93.04 107	93.13 98	73.20 149	78.89 114	94.18 93	59.41 138	97.85 50	81.45 98	92.48 68	93.86 127

UA-Net		80.02 150	79.65 141	81.11 221	89.33 167	57.72 298	86.33 285	89.00 260	77.44 87	81.01 90	89.15 178	59.33 139	95.90 136	61.01 262	84.28 150	89.73 207

NR-MVSNet		76.05 217	74.59 213	80.44 233	82.96 281	62.18 230	90.83 204	91.73 152	77.12 90	60.96 297	86.35 212	59.28 140	91.80 283	60.74 263	61.34 312	87.35 239

MP-MVS-pluss		85.24 61	85.13 60	85.56 99	91.42 127	65.59 147	91.54 173	92.51 122	74.56 122	80.62 94	95.64 39	59.15 141	97.00 95	86.94 54	93.80 45	94.07 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		84.73 68	84.40 70	85.72 93	93.75 60	65.01 163	93.50 94	93.19 94	72.19 176	79.22 110	94.93 66	59.04 142	97.67 55	81.55 96	92.21 69	94.49 101

#test#		84.98 65	84.74 66	85.72 93	93.75 60	65.01 163	94.09 62	93.19 94	73.55 145	79.22 110	94.93 66	59.04 142	97.67 55	82.66 86	92.21 69	94.49 101

MSLP-MVS++		86.27 46	85.91 51	87.35 38	92.01 107	68.97 58	95.04 41	92.70 112	79.04 61	81.50 83	96.50 20	58.98 144	96.78 109	83.49 80	93.93 43	96.29 30

Patchmatch-test		65.86 303	60.94 315	80.62 232	83.75 270	58.83 285	58.91 365	75.26 354	44.50 361	50.95 340	77.09 317	58.81 145	87.90 324	35.13 355	64.03 289	95.12 73

EPNet_dtu		78.80 173	79.26 151	77.43 284	88.06 199	49.71 346	91.96 155	91.95 141	77.67 81	76.56 141	91.28 150	58.51 146	90.20 306	56.37 281	80.95 169	92.39 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DROMVSNet		84.53 72	85.04 62	83.01 171	89.34 165	61.37 244	94.42 50	91.09 181	77.91 77	83.24 70	94.20 92	58.37 147	95.40 161	85.35 66	91.41 85	92.27 172

VNet		86.20 47	85.65 56	87.84 26	93.92 54	69.99 33	95.73 22	95.94 6	78.43 69	86.00 42	93.07 114	58.22 148	97.00 95	85.22 67	84.33 148	96.52 19

TESTMET0.1,1		82.41 109	81.98 107	83.72 155	88.08 198	63.74 195	92.70 122	93.77 66	79.30 53	77.61 129	87.57 202	58.19 149	94.08 211	73.91 153	86.68 130	93.33 140

原ACMM1				84.42 136	93.21 75	64.27 186		93.40 87	65.39 273	79.51 107	92.50 127	58.11 150	96.69 113	65.27 237	93.96 42	92.32 167

sam_mvs1												57.85 151				94.68 89

CR-MVSNet		73.79 247	70.82 259	82.70 177	83.15 278	67.96 82	70.25 348	84.00 324	73.67 143	69.97 212	72.41 338	57.82 152	89.48 312	52.99 294	73.13 222	90.64 197

Patchmtry		67.53 295	63.93 300	78.34 271	82.12 288	64.38 180	68.72 352	84.00 324	48.23 354	59.24 304	72.41 338	57.82 152	89.27 313	46.10 320	56.68 332	81.36 326

patchmatchnet-post											67.62 351	57.62 154	90.25 301

PCF-MVS	73.15 9	79.29 163	77.63 174	84.29 141	86.06 234	65.96 137	87.03 278	91.10 180	69.86 232	69.79 215	90.64 156	57.54 155	96.59 114	64.37 241	82.29 158	90.32 199
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.14 130	80.01 135	84.51 134	90.24 148	65.86 139	94.12 61	89.15 251	73.81 138	75.37 153	88.26 191	57.26 156	94.53 194	66.97 216	84.92 140	93.15 146

miper_lstm_enhance		73.05 251	71.73 253	77.03 290	83.80 269	58.32 291	81.76 310	88.88 262	69.80 233	61.01 296	78.23 307	57.19 157	87.51 330	65.34 236	59.53 322	85.27 285

1121		81.25 128	80.05 133	84.87 122	92.30 98	64.31 183	87.91 267	91.39 167	59.44 318	79.94 101	92.91 118	57.09 158	97.01 93	66.63 217	92.81 63	93.29 141

PatchT		69.11 280	65.37 291	80.32 235	82.07 289	63.68 200	67.96 357	87.62 290	50.86 346	69.37 216	65.18 354	57.09 158	88.53 318	41.59 338	66.60 269	88.74 217

testdata				81.34 214	89.02 174	57.72 298		89.84 226	58.65 322	85.32 52	94.09 94	57.03 160	93.28 236	69.34 195	90.56 97	93.03 150

PatchmatchNet		77.46 197	74.63 212	85.96 83	89.55 162	70.35 30	79.97 328	89.55 236	72.23 175	70.94 198	76.91 319	57.03 160	92.79 253	54.27 288	81.17 167	94.74 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_yl		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 241	81.09 88	92.88 121	57.00 162	97.44 68	81.11 103	81.76 163	96.23 33

DCV-MVSNet		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 241	81.09 88	92.88 121	57.00 162	97.44 68	81.11 103	81.76 163	96.23 33

region2R		84.36 74	84.03 73	85.36 107	93.54 66	64.31 183	93.43 97	92.95 105	72.16 179	78.86 118	94.84 71	56.97 164	97.53 65	81.38 100	92.11 73	94.24 106

新几何1				84.73 125	92.32 97	64.28 185		91.46 165	59.56 317	79.77 104	92.90 119	56.95 165	96.57 116	63.40 246	92.91 61	93.34 138

WR-MVS		76.76 208	75.74 202	79.82 251	84.60 256	62.27 229	92.60 128	92.51 122	76.06 101	67.87 240	85.34 225	56.76 166	90.24 304	62.20 256	63.69 293	86.94 247

HPM-MVS		83.25 96	82.95 91	84.17 144	92.25 100	62.88 219	90.91 198	91.86 146	70.30 226	77.12 135	93.96 98	56.75 167	96.28 122	82.04 92	91.34 88	93.34 138
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		82.71 106	82.38 103	83.73 154	89.25 169	59.58 274	92.24 139	94.89 24	77.96 75	79.86 103	92.38 132	56.70 168	97.05 90	77.26 131	80.86 170	94.55 94

ACMMPR		84.37 73	84.06 72	85.28 109	93.56 65	64.37 181	93.50 94	93.15 97	72.19 176	78.85 119	94.86 70	56.69 169	97.45 67	81.55 96	92.20 71	94.02 120

FMVSNet3		77.73 194	76.04 197	82.80 174	91.20 133	68.99 57	91.87 157	91.99 139	73.35 148	67.04 250	83.19 250	56.62 170	92.14 275	59.80 270	69.34 250	87.28 241

Patchmatch-RL test		68.17 289	64.49 297	79.19 262	71.22 355	53.93 326	70.07 350	71.54 362	69.22 240	56.79 320	62.89 357	56.58 171	88.61 315	69.53 193	52.61 341	95.03 79

test_post											23.01 373	56.49 172	92.67 258

RPMNet		70.42 271	65.68 287	84.63 131	83.15 278	67.96 82	70.25 348	90.45 198	46.83 357	69.97 212	65.10 355	56.48 173	95.30 167	35.79 354	73.13 222	90.64 197

DU-MVS		76.86 203	75.84 200	79.91 247	82.96 281	60.26 264	91.26 188	91.54 160	76.46 99	68.88 224	86.35 212	56.16 174	92.13 276	66.38 223	62.55 297	87.35 239

Baseline_NR-MVSNet		73.99 244	72.83 239	77.48 283	80.78 298	59.29 280	91.79 161	84.55 319	68.85 245	68.99 222	80.70 283	56.16 174	92.04 279	62.67 253	60.98 314	81.11 327

API-MVS		82.28 111	80.53 128	87.54 34	96.13 24	70.59 27	93.63 88	91.04 186	65.72 272	75.45 152	92.83 123	56.11 176	98.89 19	64.10 242	89.75 103	93.15 146

zzz-MVS		84.73 68	84.47 67	85.50 100	91.89 114	65.16 157	91.55 172	92.23 128	75.32 112	80.53 95	95.21 56	56.06 177	97.16 86	84.86 71	92.55 66	94.18 108

MTAPA		83.91 85	83.38 84	85.50 100	91.89 114	65.16 157	81.75 311	92.23 128	75.32 112	80.53 95	95.21 56	56.06 177	97.16 86	84.86 71	92.55 66	94.18 108

JIA-IIPM		66.06 302	62.45 309	76.88 294	81.42 294	54.45 325	57.49 366	88.67 270	49.36 350	63.86 278	46.86 365	56.06 177	90.25 301	49.53 303	68.83 254	85.95 270

v148		76.19 212	74.47 217	81.36 213	80.05 309	64.44 176	91.75 166	90.23 212	73.68 142	67.13 249	80.84 282	55.92 180	93.86 227	68.95 200	61.73 308	85.76 276

WR-MVS_H		70.59 269	69.94 264	72.53 322	81.03 295	51.43 337	87.35 276	92.03 138	67.38 259	60.23 300	80.70 283	55.84 181	83.45 349	46.33 319	58.58 327	82.72 312

AUN-MVS		78.37 183	77.43 177	81.17 217	86.60 225	57.45 305	89.46 242	91.16 176	74.11 129	74.40 159	90.49 161	55.52 182	94.57 189	74.73 150	60.43 319	91.48 182

XVS		83.87 86	83.47 78	85.05 114	93.22 73	63.78 193	92.92 115	92.66 115	73.99 131	78.18 123	94.31 89	55.25 183	97.41 70	79.16 115	91.58 82	93.95 122

X-MVStestdata		76.86 203	74.13 223	85.05 114	93.22 73	63.78 193	92.92 115	92.66 115	73.99 131	78.18 123	10.19 378	55.25 183	97.41 70	79.16 115	91.58 82	93.95 122

BH-w/o		80.49 142	79.30 150	84.05 147	90.83 140	64.36 182	93.60 89	89.42 240	74.35 125	69.09 219	90.15 168	55.23 185	95.61 151	64.61 239	86.43 133	92.17 174

CP-MVS		83.71 90	83.40 83	84.65 129	93.14 78	63.84 191	94.59 48	92.28 126	71.03 214	77.41 131	94.92 68	55.21 186	96.19 124	81.32 101	90.70 94	93.91 124

PGM-MVS		83.25 96	82.70 97	84.92 119	92.81 87	64.07 189	90.44 213	92.20 133	71.28 209	77.23 134	94.43 80	55.17 187	97.31 77	79.33 114	91.38 86	93.37 137

tpmvs		72.88 255	69.76 267	82.22 192	90.98 135	67.05 109	78.22 336	88.30 278	63.10 290	64.35 275	74.98 330	55.09 188	94.27 202	43.25 329	69.57 249	85.34 283

v8		75.35 228	73.26 235	81.61 209	80.67 300	66.82 115	89.54 239	89.27 244	71.65 196	63.30 284	80.30 291	54.99 189	94.06 213	67.33 213	62.33 300	83.94 295

sam_mvs												54.91 190

EPMVS		78.49 182	75.98 198	86.02 81	91.21 132	69.68 44	80.23 324	91.20 174	75.25 114	72.48 182	78.11 308	54.65 191	93.69 229	57.66 278	83.04 154	94.69 88

ab-mvs		80.18 146	78.31 161	85.80 90	88.44 187	65.49 151	83.00 306	92.67 114	71.82 191	77.36 132	85.01 228	54.50 192	96.59 114	76.35 136	75.63 210	95.32 60

KD-MVS_2432*1600		69.03 281	66.37 283	77.01 291	85.56 242	61.06 248	81.44 315	90.25 210	67.27 260	58.00 314	76.53 321	54.49 193	87.63 328	48.04 310	35.77 365	82.34 318

miper_refine_blended		69.03 281	66.37 283	77.01 291	85.56 242	61.06 248	81.44 315	90.25 210	67.27 260	58.00 314	76.53 321	54.49 193	87.63 328	48.04 310	35.77 365	82.34 318

DP-MVS Recon		82.73 104	81.65 111	85.98 82	97.31 4	67.06 108	95.15 35	91.99 139	69.08 244	76.50 142	93.89 99	54.48 195	98.20 36	70.76 181	85.66 136	92.69 157

GeoE		78.90 170	77.43 177	83.29 166	88.95 176	62.02 231	92.31 136	86.23 303	70.24 227	71.34 197	89.27 176	54.43 196	94.04 216	63.31 247	80.81 171	93.81 129

XXY-MVS		77.94 191	76.44 192	82.43 183	82.60 283	64.44 176	92.01 150	91.83 149	73.59 144	70.00 211	85.82 220	54.43 196	94.76 179	69.63 191	68.02 261	88.10 229

MDTV_nov1_ep13_2view						59.90 270	80.13 326		67.65 257	72.79 175		54.33 198		59.83 269		92.58 161

Test By Simon												54.21 199

MAR-MVS		84.18 80	83.43 80	86.44 68	96.25 23	65.93 138	94.28 54	94.27 53	74.41 123	79.16 112	95.61 40	53.99 200	98.88 20	69.62 192	93.26 56	94.50 100
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		80.10 148	79.56 143	81.72 206	86.93 222	61.17 245	92.70 122	91.54 160	71.51 205	75.62 148	86.94 208	53.83 201	92.38 269	72.21 167	84.76 143	91.60 179

test0.0.03 1		72.76 256	72.71 242	72.88 320	80.25 307	47.99 352	91.22 190	89.45 238	71.51 205	62.51 291	87.66 200	53.83 201	85.06 340	50.16 300	67.84 264	85.58 277

v2v482		77.42 198	75.65 204	82.73 176	80.38 303	67.13 107	91.85 159	90.23 212	75.09 117	69.37 216	83.39 248	53.79 203	94.44 197	71.77 172	65.00 280	86.63 253

SR-MVS		82.81 103	82.58 99	83.50 162	93.35 69	61.16 247	92.23 140	91.28 173	64.48 278	81.27 85	95.28 50	53.71 204	95.86 137	82.87 84	88.77 109	93.49 136

pcd_1.5k_mvsjas		4.46 350	5.95 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	53.55 205	0.00 383	0.00 381	0.00 380	0.00 379

PS-MVSNAJss		77.26 200	76.31 194	80.13 242	80.64 301	59.16 281	90.63 212	91.06 184	72.80 159	68.58 230	84.57 236	53.55 205	93.96 221	72.97 156	71.96 234	87.27 242

PS-MVSNAJ		88.14 16	87.61 26	89.71 7	92.06 104	76.72 1	95.75 19	93.26 90	83.86 11	89.55 20	96.06 31	53.55 205	97.89 48	91.10 18	93.31 55	94.54 96

mPP-MVS		82.96 102	82.44 102	84.52 133	92.83 83	62.92 217	92.76 118	91.85 148	71.52 204	75.61 150	94.24 91	53.48 208	96.99 98	78.97 118	90.73 93	93.64 133

xiu_mvs_v2_base		87.92 21	87.38 32	89.55 12	91.41 129	76.43 3	95.74 20	93.12 99	83.53 13	89.55 20	95.95 32	53.45 209	97.68 54	91.07 19	92.62 64	94.54 96

test_post1							78.95 330				20.70 376	53.05 210	91.50 293	60.43 265

MDTV_nov1_ep13			72.61 243		89.06 173	68.48 67	80.33 322	90.11 216	71.84 190	71.81 191	75.92 327	53.01 211	93.92 223	48.04 310	73.38 220

test222					89.77 156	61.60 240	89.55 238	89.42 240	56.83 331	77.28 133	92.43 131	52.76 212			91.14 91	93.09 148

v1144		76.73 209	74.88 209	82.27 189	80.23 308	66.60 122	91.68 168	90.21 214	73.69 141	69.06 221	81.89 262	52.73 213	94.40 198	69.21 197	65.23 277	85.80 273

v10		74.77 236	72.54 245	81.46 211	80.33 306	66.71 119	89.15 250	89.08 255	70.94 215	63.08 285	79.86 296	52.52 214	94.04 216	65.70 231	62.17 301	83.64 297

CLD-MVS		82.73 104	82.35 104	83.86 150	87.90 204	67.65 91	95.45 27	92.18 135	85.06 8	72.58 179	92.27 135	52.46 215	95.78 139	84.18 74	79.06 180	88.16 228
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		75.86 221	74.52 216	79.89 248	82.44 284	60.64 260	91.37 183	91.37 168	76.63 96	67.65 242	86.21 216	52.37 216	91.55 288	61.84 258	60.81 315	87.48 234

VPA-MVSNet		79.03 166	78.00 166	82.11 200	85.95 236	64.48 174	93.22 102	94.66 35	75.05 118	74.04 165	84.95 230	52.17 217	93.52 232	74.90 148	67.04 266	88.32 226

APD-MVS_3200maxsize		81.64 123	81.32 114	82.59 181	92.36 96	58.74 286	91.39 180	91.01 187	63.35 286	79.72 105	94.62 76	51.82 218	96.14 126	79.71 109	87.93 117	92.89 155

dp		75.01 234	72.09 249	83.76 151	89.28 168	66.22 132	79.96 329	89.75 228	71.16 211	67.80 241	77.19 316	51.81 219	92.54 264	50.39 299	71.44 239	92.51 163

v144192		76.05 217	74.03 224	82.12 197	79.50 314	66.55 124	91.39 180	89.71 234	72.30 173	68.17 233	81.33 274	51.75 220	94.03 218	67.94 206	64.19 287	85.77 274

BH-untuned		78.68 177	77.08 183	83.48 163	89.84 155	63.74 195	92.70 122	88.59 273	71.57 202	66.83 253	88.65 182	51.75 220	95.39 162	59.03 273	84.77 142	91.32 188

HQP2-MVS												51.63 222

HQP-MVS		81.14 130	80.64 126	82.64 179	87.54 209	63.66 201	94.06 64	91.70 155	79.80 45	74.18 160	90.30 165	51.63 222	95.61 151	77.63 129	78.90 181	88.63 218

V42		76.46 211	74.55 215	82.19 193	79.14 320	67.82 86	90.26 221	89.42 240	73.75 139	68.63 229	81.89 262	51.31 224	94.09 210	71.69 174	64.84 281	84.66 290

test1172		81.90 119	81.83 109	82.13 196	93.23 72	57.52 303	91.61 171	90.98 188	64.32 280	80.20 99	95.00 62	51.26 225	95.61 151	81.73 95	88.13 115	93.26 142

SR-MVS-dyc-post		81.06 133	80.70 124	82.15 194	92.02 105	58.56 288	90.90 199	90.45 198	62.76 292	78.89 114	94.46 78	51.26 225	95.61 151	78.77 121	86.77 127	92.28 169

CL-MVSNet_self_test		69.92 274	68.09 276	75.41 302	73.25 350	55.90 316	90.05 227	89.90 224	69.96 230	61.96 294	76.54 320	51.05 227	87.64 327	49.51 304	50.59 346	82.70 314

TransMVSNet (Re)		70.07 273	67.66 277	77.31 287	80.62 302	59.13 283	91.78 163	84.94 315	65.97 269	60.08 301	80.44 288	50.78 228	91.87 281	48.84 306	45.46 354	80.94 329

HQP_MVS		80.34 144	79.75 140	82.12 197	86.94 220	62.42 224	93.13 103	91.31 170	78.81 65	72.53 180	89.14 179	50.66 229	95.55 157	76.74 132	78.53 186	88.39 224

plane_prior6					87.23 215	62.32 227						50.66 229

ACMMP		81.49 124	80.67 125	83.93 149	91.71 119	62.90 218	92.13 142	92.22 132	71.79 192	71.68 194	93.49 107	50.32 231	96.96 101	78.47 123	84.22 152	91.93 176
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		82.02 117	81.52 112	83.51 161	88.42 188	62.88 219	89.77 235	88.93 261	76.78 95	75.55 151	93.10 110	50.31 232	95.38 163	83.82 79	87.02 123	92.26 173

1314		80.70 138	78.95 155	85.94 84	87.77 207	67.56 92	87.91 267	92.55 121	72.17 178	67.44 244	93.09 111	50.27 233	97.04 92	71.68 175	87.64 119	93.23 144

CP-MVSNet		70.50 270	69.91 265	72.26 325	80.71 299	51.00 340	87.23 277	90.30 208	67.84 253	59.64 302	82.69 253	50.23 234	82.30 357	51.28 296	59.28 323	83.46 302

LCM-MVSNet-Re		72.93 253	71.84 251	76.18 299	88.49 184	48.02 351	80.07 327	70.17 363	73.96 134	52.25 333	80.09 295	49.98 235	88.24 321	67.35 211	84.23 151	92.28 169

Vis-MVSNet		80.92 136	79.98 137	83.74 152	88.48 185	61.80 235	93.44 96	88.26 282	73.96 134	77.73 126	91.76 142	49.94 236	94.76 179	65.84 229	90.37 98	94.65 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		75.98 219	73.92 226	82.15 194	79.73 310	66.24 131	91.22 190	89.75 228	72.67 161	68.49 231	81.42 272	49.86 237	94.27 202	67.08 214	65.02 279	85.95 270

test-mter		79.96 151	79.38 149	81.72 206	86.93 222	61.17 245	92.70 122	91.54 160	73.85 136	75.62 148	86.94 208	49.84 238	92.38 269	72.21 167	84.76 143	91.60 179

cdsmvs_eth3d_5k		19.86 345	26.47 344	0.00 364	0.00 387	0.00 388	0.00 375	93.45 82	0.00 382	0.00 383	95.27 52	49.56 239	0.00 383	0.00 381	0.00 380	0.00 379

3Dnovator+	73.60 7	82.10 116	80.60 127	86.60 60	90.89 138	66.80 117	95.20 33	93.44 84	74.05 130	67.42 245	92.49 129	49.46 240	97.65 59	70.80 180	91.68 80	95.33 58

MVP-Stereo		77.12 202	76.23 195	79.79 252	81.72 291	66.34 128	89.29 244	90.88 189	70.56 224	62.01 293	82.88 251	49.34 241	94.13 208	65.55 234	93.80 45	78.88 346
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
RE-MVS-def			80.48 129		92.02 105	58.56 288	90.90 199	90.45 198	62.76 292	78.89 114	94.46 78	49.30 242		78.77 121	86.77 127	92.28 169

OMC-MVS		78.67 179	77.91 170	80.95 228	85.76 240	57.40 306	88.49 259	88.67 270	73.85 136	72.43 184	92.10 137	49.29 243	94.55 192	72.73 161	77.89 189	90.91 194

VPNet		78.82 172	77.53 176	82.70 177	84.52 258	66.44 125	93.93 74	92.23 128	80.46 40	72.60 178	88.38 188	49.18 244	93.13 238	72.47 165	63.97 291	88.55 220

CVMVSNet		74.04 243	74.27 220	73.33 316	85.33 244	43.94 362	89.53 240	88.39 276	54.33 338	70.37 205	90.13 169	49.17 245	84.05 343	61.83 259	79.36 177	91.99 175

v1921920		75.63 226	73.49 232	82.06 201	79.38 315	66.35 127	91.07 197	89.48 237	71.98 181	67.99 234	81.22 277	49.16 246	93.90 224	66.56 219	64.56 286	85.92 272

pm-mvs1		72.89 254	71.09 257	78.26 274	79.10 321	57.62 301	90.80 205	89.30 243	67.66 256	62.91 287	81.78 264	49.11 247	92.95 243	60.29 267	58.89 325	84.22 293

pmmvs4		73.92 245	71.81 252	80.25 239	79.17 318	65.24 154	87.43 275	87.26 294	67.64 258	63.46 282	83.91 243	48.96 248	91.53 292	62.94 250	65.49 273	83.96 294

TAPA-MVS	70.22 12	74.94 235	73.53 231	79.17 263	90.40 145	52.07 334	89.19 249	89.61 235	62.69 294	70.07 209	92.67 125	48.89 249	94.32 199	38.26 349	79.97 173	91.12 192
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator	73.91 6	82.69 107	80.82 122	88.31 22	89.57 160	71.26 20	92.60 128	94.39 48	78.84 64	67.89 239	92.48 130	48.42 250	98.52 26	68.80 202	94.40 37	95.15 71

CPTT-MVS		79.59 158	79.16 153	80.89 230	91.54 125	59.80 271	92.10 144	88.54 275	60.42 310	72.96 172	93.28 109	48.27 251	92.80 252	78.89 120	86.50 132	90.06 202

GBi-Net		75.65 224	73.83 227	81.10 222	88.85 177	65.11 159	90.01 228	90.32 204	70.84 217	67.04 250	80.25 292	48.03 252	91.54 289	59.80 270	69.34 250	86.64 250

test1		75.65 224	73.83 227	81.10 222	88.85 177	65.11 159	90.01 228	90.32 204	70.84 217	67.04 250	80.25 292	48.03 252	91.54 289	59.80 270	69.34 250	86.64 250

FMVSNet2		76.07 214	74.01 225	82.26 191	88.85 177	67.66 90	91.33 185	91.61 158	70.84 217	65.98 258	82.25 258	48.03 252	92.00 280	58.46 275	68.73 256	87.10 244

LFMVS		84.34 75	82.73 96	89.18 13	94.76 37	73.25 10	94.99 43	91.89 145	71.90 184	82.16 78	93.49 107	47.98 255	97.05 90	82.55 89	84.82 141	97.25 7

QAPM		79.95 152	77.39 181	87.64 29	89.63 159	71.41 19	93.30 99	93.70 71	65.34 275	67.39 247	91.75 143	47.83 256	98.96 16	57.71 277	89.81 100	92.54 162

HPM-MVS_fast		80.25 145	79.55 145	82.33 187	91.55 124	59.95 269	91.32 186	89.16 250	65.23 276	74.71 157	93.07 114	47.81 257	95.74 142	74.87 149	88.23 113	91.31 189

CANet_DTU		84.09 82	83.52 76	85.81 89	90.30 147	66.82 115	91.87 157	89.01 259	85.27 7	86.09 41	93.74 101	47.71 258	96.98 99	77.90 128	89.78 102	93.65 132

abl_6		79.82 154	79.20 152	81.70 208	89.85 154	58.34 290	88.47 260	90.07 217	62.56 295	77.71 127	93.08 112	47.65 259	96.78 109	77.94 127	85.45 138	89.99 204

v1240		75.21 232	72.98 237	81.88 203	79.20 317	66.00 135	90.75 207	89.11 254	71.63 200	67.41 246	81.22 277	47.36 260	93.87 225	65.46 235	64.72 284	85.77 274

PEN-MVS		69.46 278	68.56 272	72.17 327	79.27 316	49.71 346	86.90 281	89.24 245	67.24 263	59.08 307	82.51 256	47.23 261	83.54 348	48.42 308	57.12 328	83.25 305

CNLPA		74.31 241	72.30 247	80.32 235	91.49 126	61.66 239	90.85 203	80.72 340	56.67 332	63.85 279	90.64 156	46.75 262	90.84 296	53.79 290	75.99 209	88.47 223

114514_t		79.17 165	77.67 172	83.68 156	95.32 31	65.53 149	92.85 117	91.60 159	63.49 285	67.92 237	90.63 158	46.65 263	95.72 147	67.01 215	83.54 153	89.79 205

PS-CasMVS		69.86 276	69.13 270	72.07 328	80.35 305	50.57 342	87.02 279	89.75 228	67.27 260	59.19 306	82.28 257	46.58 264	82.24 358	50.69 298	59.02 324	83.39 304

DTE-MVSNet		68.46 287	67.33 279	71.87 330	77.94 333	49.00 349	86.16 286	88.58 274	66.36 267	58.19 311	82.21 259	46.36 265	83.87 346	44.97 326	55.17 335	82.73 311

test1111		80.84 137	80.02 134	83.33 165	87.87 205	60.76 255	92.62 127	86.86 296	77.86 78	75.73 146	91.39 148	46.35 266	94.70 185	72.79 160	88.68 110	94.52 98

ECVR-MVS		81.29 127	80.38 131	84.01 148	88.39 190	61.96 233	92.56 133	86.79 297	77.66 82	76.63 139	91.42 146	46.34 267	95.24 168	74.36 151	89.23 104	94.85 82

PMMVS		81.98 118	82.04 106	81.78 204	89.76 157	56.17 313	91.13 194	90.69 192	77.96 75	80.09 100	93.57 105	46.33 268	94.99 173	81.41 99	87.46 120	94.17 110

OPM-MVS		79.00 167	78.09 164	81.73 205	83.52 274	63.83 192	91.64 170	90.30 208	76.36 100	71.97 189	89.93 172	46.30 269	95.17 169	75.10 142	77.70 191	86.19 262
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
BH-RMVSNet		79.46 162	77.65 173	84.89 120	91.68 120	65.66 144	93.55 91	88.09 284	72.93 156	73.37 169	91.12 151	46.20 270	96.12 127	56.28 282	85.61 137	92.91 154

mvsmamba		76.85 205	75.71 203	80.25 239	83.07 280	59.16 281	91.44 174	80.64 341	76.84 93	67.95 236	86.33 214	46.17 271	94.24 205	76.06 137	72.92 225	87.36 238

TR-MVS		78.77 175	77.37 182	82.95 172	90.49 143	60.88 251	93.67 86	90.07 217	70.08 229	74.51 158	91.37 149	45.69 272	95.70 148	60.12 268	80.32 172	92.29 168

IterMVS-SCA-FT		71.55 265	69.97 263	76.32 297	81.48 292	60.67 259	87.64 273	85.99 306	66.17 268	59.50 303	78.88 302	45.53 273	83.65 347	62.58 254	61.93 304	84.63 292

SCA		75.82 222	72.76 240	85.01 116	86.63 224	70.08 32	81.06 318	89.19 248	71.60 201	70.01 210	77.09 317	45.53 273	90.25 301	60.43 265	73.27 221	94.68 89

IterMVS		72.65 260	70.83 258	78.09 277	82.17 287	62.96 214	87.64 273	86.28 301	71.56 203	60.44 299	78.85 303	45.42 275	86.66 334	63.30 248	61.83 305	84.65 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		76.14 213	75.28 207	78.72 269	83.22 276	55.17 320	89.87 232	87.78 288	75.42 108	67.98 235	81.43 271	45.08 276	92.52 265	75.08 143	71.63 235	88.48 221

mvs-test1		78.74 176	77.95 168	81.14 219	83.22 276	57.13 308	93.96 69	87.78 288	75.42 108	72.68 176	90.80 155	45.08 276	94.54 193	75.08 143	77.49 196	91.74 178

XVG-OURS-SEG-HR		74.70 237	73.08 236	79.57 257	78.25 330	57.33 307	80.49 320	87.32 292	63.22 288	68.76 227	90.12 171	44.89 278	91.59 287	70.55 184	74.09 216	89.79 205

v7n		71.31 266	68.65 271	79.28 261	76.40 341	60.77 254	86.71 283	89.45 238	64.17 281	58.77 310	78.24 306	44.59 279	93.54 231	57.76 276	61.75 307	83.52 300

pmmvs5		73.35 249	71.52 254	78.86 267	78.64 327	60.61 261	91.08 195	86.90 295	67.69 255	63.32 283	83.64 244	44.33 280	90.53 298	62.04 257	66.02 272	85.46 280

OpenMVS	70.45 11	78.54 181	75.92 199	86.41 71	85.93 239	71.68 17	92.74 119	92.51 122	66.49 266	64.56 270	91.96 139	43.88 281	98.10 39	54.61 286	90.65 95	89.44 211

AdaColmap		78.94 169	77.00 186	84.76 124	96.34 19	65.86 139	92.66 126	87.97 287	62.18 298	70.56 201	92.37 133	43.53 282	97.35 74	64.50 240	82.86 155	91.05 193

tfpnnormal		70.10 272	67.36 278	78.32 272	83.45 275	60.97 250	88.85 253	92.77 110	64.85 277	60.83 298	78.53 304	43.52 283	93.48 233	31.73 365	61.70 309	80.52 334

test_djsdf		73.76 248	72.56 244	77.39 285	77.00 339	53.93 326	89.07 251	90.69 192	65.80 270	63.92 277	82.03 261	43.14 284	92.67 258	72.83 158	68.53 257	85.57 278

GA-MVS		78.33 185	76.23 195	84.65 129	83.65 272	66.30 129	91.44 174	90.14 215	76.01 102	70.32 206	84.02 241	42.50 285	94.72 182	70.98 178	77.00 202	92.94 153

PLC	68.80 14	75.23 231	73.68 230	79.86 250	92.93 82	58.68 287	90.64 210	88.30 278	60.90 307	64.43 274	90.53 159	42.38 286	94.57 189	56.52 280	76.54 205	86.33 257
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		73.80 246	72.02 250	79.15 265	79.15 319	62.97 213	88.58 258	90.07 217	72.94 155	59.22 305	78.30 305	42.31 287	92.70 257	65.59 233	72.00 233	81.79 324

Fast-Effi-MVS+-dtu		75.04 233	73.37 233	80.07 243	80.86 297	59.52 275	91.20 192	85.38 310	71.90 184	65.20 263	84.84 232	41.46 288	92.97 241	66.50 222	72.96 224	87.73 231

MS-PatchMatch		77.90 193	76.50 191	82.12 197	85.99 235	69.95 36	91.75 166	92.70 112	73.97 133	62.58 290	84.44 237	41.11 289	95.78 139	63.76 245	92.17 72	80.62 333

our_test_3		68.29 288	64.69 294	79.11 266	78.92 322	64.85 168	88.40 262	85.06 313	60.32 312	52.68 331	76.12 325	40.81 290	89.80 311	44.25 328	55.65 333	82.67 316

XVG-OURS		74.25 242	72.46 246	79.63 255	78.45 329	57.59 302	80.33 322	87.39 291	63.86 283	68.76 227	89.62 175	40.50 291	91.72 285	69.00 199	74.25 214	89.58 208

VDD-MVS		83.06 99	81.81 110	86.81 52	90.86 139	67.70 89	95.40 28	91.50 163	75.46 107	81.78 80	92.34 134	40.09 292	97.13 88	86.85 55	82.04 161	95.60 49

DP-MVS		69.90 275	66.48 281	80.14 241	95.36 30	62.93 215	89.56 237	76.11 349	50.27 348	57.69 317	85.23 226	39.68 293	95.73 143	33.35 359	71.05 241	81.78 325

test_low_dy_conf_001		74.54 238	73.33 234	78.16 276	80.91 296	55.04 321	89.24 246	89.06 257	69.33 238	64.21 276	85.60 224	39.35 294	92.96 242	71.28 176	71.03 242	86.49 255

RRT_MVS		74.44 239	72.97 238	78.84 268	82.36 286	57.66 300	89.83 234	88.79 267	70.61 223	64.58 269	84.89 231	39.24 295	92.65 261	70.11 187	66.34 271	86.21 261

ppachtmachnet_test		67.72 292	63.70 301	79.77 253	78.92 322	66.04 134	88.68 256	82.90 333	60.11 314	55.45 322	75.96 326	39.19 296	90.55 297	39.53 344	52.55 342	82.71 313

bld_raw_conf005		75.32 229	73.81 229	79.87 249	82.38 285	58.28 292	90.86 202	78.04 346	75.22 115	66.60 256	85.65 223	38.81 297	94.20 206	74.18 152	72.54 229	87.14 243

ADS-MVSNet2		66.90 298	63.44 303	77.26 288	88.06 199	60.70 258	68.01 355	75.56 352	57.57 324	64.48 271	69.87 347	38.68 298	84.10 342	40.87 340	67.89 262	86.97 245

ADS-MVSNet		68.54 286	64.38 299	81.03 226	88.06 199	66.90 114	68.01 355	84.02 323	57.57 324	64.48 271	69.87 347	38.68 298	89.21 314	40.87 340	67.89 262	86.97 245

LPG-MVS_test		75.82 222	74.58 214	79.56 258	84.31 263	59.37 277	90.44 213	89.73 231	69.49 235	64.86 265	88.42 184	38.65 300	94.30 200	72.56 163	72.76 226	85.01 287

LGP-MVS_train				79.56 258	84.31 263	59.37 277		89.73 231	69.49 235	64.86 265	88.42 184	38.65 300	94.30 200	72.56 163	72.76 226	85.01 287

VDDNet		80.50 141	78.26 162	87.21 40	86.19 232	69.79 40	94.48 49	91.31 170	60.42 310	79.34 108	90.91 153	38.48 302	96.56 117	82.16 90	81.05 168	95.27 66

ACMP	71.68 10	75.58 227	74.23 221	79.62 256	84.97 252	59.64 272	90.80 205	89.07 256	70.39 225	62.95 286	87.30 206	38.28 303	93.87 225	72.89 157	71.45 238	85.36 282
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		79.87 153	78.68 156	83.45 164	89.96 152	61.51 241	92.13 142	90.79 190	76.83 94	78.85 119	86.33 214	38.16 304	96.17 125	67.93 207	87.17 122	92.67 158
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		71.14 267	69.37 269	76.45 296	72.95 351	54.71 323	84.19 293	88.88 262	61.92 302	62.15 292	79.77 297	38.14 305	91.44 294	68.90 201	67.45 265	83.21 306

xiu_mvs_v1_base_debu		82.16 113	81.12 116	85.26 110	86.42 227	68.72 63	92.59 130	90.44 201	73.12 152	84.20 62	94.36 82	38.04 306	95.73 143	84.12 75	86.81 124	91.33 185

xiu_mvs_v1_base		82.16 113	81.12 116	85.26 110	86.42 227	68.72 63	92.59 130	90.44 201	73.12 152	84.20 62	94.36 82	38.04 306	95.73 143	84.12 75	86.81 124	91.33 185

xiu_mvs_v1_base_debi		82.16 113	81.12 116	85.26 110	86.42 227	68.72 63	92.59 130	90.44 201	73.12 152	84.20 62	94.36 82	38.04 306	95.73 143	84.12 75	86.81 124	91.33 185

PVSNet_0	68.08 15	71.81 262	68.32 275	82.27 189	84.68 254	62.31 228	88.68 256	90.31 207	75.84 103	57.93 316	80.65 286	37.85 309	94.19 207	69.94 188	29.05 370	90.31 200

Anonymous20231206		67.53 295	65.78 285	72.79 321	74.95 345	47.59 354	88.23 263	87.32 292	61.75 305	58.07 313	77.29 314	37.79 310	87.29 332	42.91 331	63.71 292	83.48 301

ACMM	69.62 13	74.34 240	72.73 241	79.17 263	84.25 265	57.87 296	90.36 217	89.93 223	63.17 289	65.64 260	86.04 219	37.79 310	94.10 209	65.89 228	71.52 237	85.55 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		78.18 186	75.77 201	85.41 105	87.14 218	69.11 52	92.96 111	91.15 178	66.71 264	70.47 202	86.07 217	37.49 312	96.48 120	70.15 186	79.80 174	90.65 196

LS3D		69.17 279	66.40 282	77.50 282	91.92 112	56.12 314	85.12 288	80.37 342	46.96 355	56.50 321	87.51 203	37.25 313	93.71 228	32.52 364	79.40 176	82.68 315

MDA-MVSNet_test_wron		63.78 313	60.16 316	74.64 306	78.15 331	60.41 262	83.49 298	84.03 322	56.17 335	39.17 364	71.59 344	37.22 314	83.24 352	42.87 333	48.73 348	80.26 337

YYNet1		63.76 314	60.14 317	74.62 307	78.06 332	60.19 267	83.46 300	83.99 326	56.18 334	39.25 363	71.56 345	37.18 315	83.34 350	42.90 332	48.70 349	80.32 336

FMVSNet5		68.04 290	65.66 288	75.18 304	84.43 261	57.89 295	83.54 297	86.26 302	61.83 304	53.64 329	73.30 335	37.15 316	85.08 339	48.99 305	61.77 306	82.56 317

test20.03		63.83 312	62.65 308	67.38 341	70.58 359	39.94 367	86.57 284	84.17 321	63.29 287	51.86 334	77.30 313	37.09 317	82.47 355	38.87 348	54.13 339	79.73 340

PVSNet	73.49 8	80.05 149	78.63 157	84.31 140	90.92 137	64.97 165	92.47 134	91.05 185	79.18 56	72.43 184	90.51 160	37.05 318	94.06 213	68.06 205	86.00 134	93.90 126

EU-MVSNet		64.01 311	63.01 305	67.02 342	74.40 348	38.86 371	83.27 302	86.19 304	45.11 359	54.27 325	81.15 280	36.91 319	80.01 362	48.79 307	57.02 329	82.19 322

Anonymous20231211		73.08 250	70.39 261	81.13 220	90.62 142	63.33 205	91.40 178	90.06 220	51.84 343	64.46 273	80.67 285	36.49 320	94.07 212	63.83 244	64.17 288	85.98 269

FMVSNet1		72.71 258	69.91 265	81.10 222	83.60 273	65.11 159	90.01 228	90.32 204	63.92 282	63.56 281	80.25 292	36.35 321	91.54 289	54.46 287	66.75 268	86.64 250

Anonymous20240529		76.84 206	74.15 222	84.88 121	91.02 134	64.95 166	93.84 82	91.09 181	53.57 339	73.00 171	87.42 204	35.91 322	97.32 76	69.14 198	72.41 232	92.36 165

CMPMVS	48.56 21	66.77 299	64.41 298	73.84 313	70.65 358	50.31 343	77.79 338	85.73 309	45.54 358	44.76 356	82.14 260	35.40 323	90.14 307	63.18 249	74.54 213	81.07 328
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		67.57 294	64.76 293	76.00 300	72.82 353	53.37 328	88.71 255	86.78 298	53.19 340	57.58 318	78.03 309	35.33 324	92.41 268	55.56 284	54.88 337	82.21 321

PatchMatch-RL		72.06 261	69.98 262	78.28 273	89.51 163	55.70 317	83.49 298	83.39 330	61.24 306	63.72 280	82.76 252	34.77 325	93.03 240	53.37 293	77.59 192	86.12 266

LTVRE_ROB	59.60 19	66.27 301	63.54 302	74.45 308	84.00 268	51.55 336	67.08 358	83.53 327	58.78 321	54.94 323	80.31 290	34.54 326	93.23 237	40.64 342	68.03 260	78.58 349
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
UniMVSNet_ETH3D		72.74 257	70.53 260	79.36 260	78.62 328	56.64 311	85.01 289	89.20 247	63.77 284	64.84 267	84.44 237	34.05 327	91.86 282	63.94 243	70.89 243	89.57 209

bld_raw_dy_0_64		71.59 264	69.71 268	77.22 289	77.82 335	58.12 294	87.71 271	73.66 356	68.01 252	61.90 295	84.29 239	33.68 328	88.43 319	69.91 189	70.43 244	85.11 286

F-COLMAP		70.66 268	68.44 273	77.32 286	86.37 230	55.91 315	88.00 265	86.32 300	56.94 330	57.28 319	88.07 195	33.58 329	92.49 266	51.02 297	68.37 258	83.55 298

pmmvs-eth3d		65.53 305	62.32 310	75.19 303	69.39 361	59.59 273	82.80 307	83.43 328	62.52 296	51.30 338	72.49 336	32.86 330	87.16 333	55.32 285	50.73 345	78.83 347

MDA-MVSNet-bldmvs		61.54 320	57.70 324	73.05 318	79.53 313	57.00 310	83.08 305	81.23 337	57.57 324	34.91 366	72.45 337	32.79 331	86.26 337	35.81 353	41.95 358	75.89 355

MIMVSNet		71.64 263	68.44 273	81.23 216	81.97 290	64.44 176	73.05 344	88.80 265	69.67 234	64.59 268	74.79 331	32.79 331	87.82 325	53.99 289	76.35 207	91.42 183

UnsupCasMVSNet_eth		65.79 304	63.10 304	73.88 312	70.71 357	50.29 344	81.09 317	89.88 225	72.58 163	49.25 345	74.77 332	32.57 333	87.43 331	55.96 283	41.04 360	83.90 296

N_pmnet		50.55 331	49.11 334	54.88 349	77.17 338	4.02 385	84.36 292	2.00 384	48.59 351	45.86 353	68.82 349	32.22 334	82.80 354	31.58 366	51.38 344	77.81 351

test_0402		64.54 308	61.09 314	74.92 305	84.10 267	60.75 256	87.95 266	79.71 344	52.03 342	52.41 332	77.20 315	32.21 335	91.64 286	23.14 368	61.03 313	72.36 360

DSMNet-mixed		56.78 327	54.44 330	63.79 344	63.21 367	29.44 375	64.43 360	64.10 370	42.12 363	51.32 337	71.60 343	31.76 336	75.04 364	36.23 351	65.20 278	86.87 248

MSDG		69.54 277	65.73 286	80.96 227	85.11 250	63.71 197	84.19 293	83.28 331	56.95 329	54.50 324	84.03 240	31.50 337	96.03 133	42.87 333	69.13 253	83.14 308

RPSCF		64.24 310	61.98 312	71.01 332	76.10 343	45.00 359	75.83 341	75.94 350	46.94 356	58.96 308	84.59 235	31.40 338	82.00 359	47.76 314	60.33 321	86.04 267

jajsoiax		73.05 251	71.51 255	77.67 280	77.46 336	54.83 322	88.81 254	90.04 221	69.13 243	62.85 288	83.51 246	31.16 339	92.75 254	70.83 179	69.80 246	85.43 281

MVS-HIRNet		60.25 323	55.55 329	74.35 309	84.37 262	56.57 312	71.64 346	74.11 355	34.44 366	45.54 355	42.24 369	31.11 340	89.81 309	40.36 343	76.10 208	76.67 354

SixPastTwentyTwo		64.92 306	61.78 313	74.34 310	78.74 325	49.76 345	83.42 301	79.51 345	62.86 291	50.27 341	77.35 312	30.92 341	90.49 299	45.89 321	47.06 351	82.78 309

KD-MVS_self_test		60.87 321	58.60 321	67.68 339	66.13 364	39.93 368	75.63 342	84.70 317	57.32 327	49.57 344	68.45 350	29.55 342	82.87 353	48.09 309	47.94 350	80.25 338

mvs_tets		72.71 258	71.11 256	77.52 281	77.41 337	54.52 324	88.45 261	89.76 227	68.76 248	62.70 289	83.26 249	29.49 343	92.71 255	70.51 185	69.62 248	85.34 283

Anonymous202405211		77.96 190	75.33 206	85.87 86	93.73 62	64.52 171	94.85 45	85.36 311	62.52 296	76.11 143	90.18 167	29.43 344	97.29 78	68.51 203	77.24 201	95.81 45

K. test v3		63.09 315	59.61 319	73.53 315	76.26 342	49.38 348	83.27 302	77.15 348	64.35 279	47.77 349	72.32 340	28.73 345	87.79 326	49.93 302	36.69 364	83.41 303

UnsupCasMVSNet_bld		61.60 319	57.71 323	73.29 317	68.73 362	51.64 335	78.61 332	89.05 258	57.20 328	46.11 350	61.96 359	28.70 346	88.60 316	50.08 301	38.90 362	79.63 341

lessismore_v0				73.72 314	72.93 352	47.83 353		61.72 373		45.86 353	73.76 333	28.63 347	89.81 309	47.75 315	31.37 369	83.53 299

new-patchmatchnet		59.30 326	56.48 327	67.79 338	65.86 365	44.19 360	82.47 308	81.77 335	59.94 315	43.65 360	66.20 353	27.67 348	81.68 360	39.34 345	41.40 359	77.50 352

ACMH	63.93 17	68.62 284	64.81 292	80.03 244	85.22 246	63.25 206	87.72 270	84.66 318	60.83 308	51.57 336	79.43 301	27.29 349	94.96 174	41.76 336	64.84 281	81.88 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		64.68 307	62.17 311	72.21 326	76.08 344	47.35 355	80.67 319	81.02 338	56.19 333	51.60 335	79.66 299	27.05 350	88.56 317	53.60 292	53.63 340	80.71 332

ACMH+	65.35 16	67.65 293	64.55 295	76.96 293	84.59 257	57.10 309	88.08 264	80.79 339	58.59 323	53.00 330	81.09 281	26.63 351	92.95 243	46.51 317	61.69 310	80.82 330

OpenMVS_ROB	61.12 18	66.39 300	62.92 306	76.80 295	76.51 340	57.77 297	89.22 247	83.41 329	55.48 336	53.86 328	77.84 310	26.28 352	93.95 222	34.90 356	68.76 255	78.68 348

MVS_0304		68.99 283	67.23 280	74.28 311	80.36 304	52.54 331	87.01 280	86.36 299	59.89 316	66.22 257	73.56 334	24.25 353	88.03 323	57.34 279	70.11 245	82.27 320

COLMAP_ROB	57.96 20	62.98 316	59.65 318	72.98 319	81.44 293	53.00 330	83.75 296	75.53 353	48.34 353	48.81 346	81.40 273	24.14 354	90.30 300	32.95 361	60.52 318	75.65 356
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		60.16 324	57.33 325	68.67 336	69.71 360	44.13 361	78.92 331	84.21 320	55.05 337	44.63 357	71.85 342	23.91 355	81.54 361	32.63 363	55.03 336	80.35 335

testgi		64.48 309	62.87 307	69.31 335	71.24 354	40.62 366	85.49 287	79.92 343	65.36 274	54.18 326	83.49 247	23.74 356	84.55 341	41.60 337	60.79 316	82.77 310

ITE_SJBPF				70.43 333	74.44 347	47.06 356		77.32 347	60.16 313	54.04 327	83.53 245	23.30 357	84.01 344	43.07 330	61.58 311	80.21 339

EG-PatchMatch MVS		68.55 285	65.41 290	77.96 278	78.69 326	62.93 215	89.86 233	89.17 249	60.55 309	50.27 341	77.73 311	22.60 358	94.06 213	47.18 316	72.65 228	76.88 353

tmp_tt		22.26 344	23.75 346	17.80 360	5.23 384	12.06 384	35.26 371	39.48 379	2.82 378	18.94 371	44.20 368	22.23 359	24.64 379	36.30 350	9.31 376	16.69 373

USDC		67.43 297	64.51 296	76.19 298	77.94 333	55.29 319	78.38 334	85.00 314	73.17 150	48.36 347	80.37 289	21.23 360	92.48 267	52.15 295	64.02 290	80.81 331

Anonymous20240521		62.09 317	59.08 320	71.10 331	67.19 363	48.72 350	83.91 295	85.23 312	50.38 347	47.84 348	71.22 346	20.74 361	85.51 338	46.47 318	58.75 326	79.06 345

XVG-ACMP-BASELINE		68.04 290	65.53 289	75.56 301	74.06 349	52.37 332	78.43 333	85.88 307	62.03 300	58.91 309	81.21 279	20.38 362	91.15 295	60.69 264	68.18 259	83.16 307

AllTest		61.66 318	58.06 322	72.46 323	79.57 311	51.42 338	80.17 325	68.61 365	51.25 344	45.88 351	81.23 275	19.86 363	86.58 335	38.98 346	57.01 330	79.39 342

TestCases				72.46 323	79.57 311	51.42 338		68.61 365	51.25 344	45.88 351	81.23 275	19.86 363	86.58 335	38.98 346	57.01 330	79.39 342

pmmvs3		55.51 328	51.50 333	67.53 340	57.90 372	50.93 341	80.37 321	73.66 356	40.63 364	44.15 359	64.75 356	16.30 365	78.97 363	44.77 327	40.98 361	72.69 358

TDRefinement		55.28 329	51.58 332	66.39 343	59.53 371	46.15 358	76.23 340	72.80 358	44.60 360	42.49 361	76.28 324	15.29 366	82.39 356	33.20 360	43.75 356	70.62 362

new_pmnet		49.31 332	46.44 335	57.93 346	62.84 368	40.74 365	68.47 354	62.96 372	36.48 365	35.09 365	57.81 361	14.97 367	72.18 366	32.86 362	46.44 352	60.88 366

TinyColmap		60.32 322	56.42 328	72.00 329	78.78 324	53.18 329	78.36 335	75.64 351	52.30 341	41.59 362	75.82 328	14.76 368	88.35 320	35.84 352	54.71 338	74.46 357

EGC-MVSNET		42.35 334	38.09 337	55.11 348	74.57 346	46.62 357	71.63 347	55.77 374	0.04 379	0.24 380	62.70 358	14.24 369	74.91 365	17.59 371	46.06 353	43.80 368

LF4IMVS		54.01 330	52.12 331	59.69 345	62.41 369	39.91 369	68.59 353	68.28 367	42.96 362	44.55 358	75.18 329	14.09 370	68.39 368	41.36 339	51.68 343	70.78 361

PM-MVS		59.40 325	56.59 326	67.84 337	63.63 366	41.86 363	76.76 339	63.22 371	59.01 320	51.07 339	72.27 341	11.72 371	83.25 351	61.34 260	50.28 347	78.39 350

ambc				69.61 334	61.38 370	41.35 364	49.07 369	85.86 308		50.18 343	66.40 352	10.16 372	88.14 322	45.73 322	44.20 355	79.32 344

FPMVS		45.64 333	43.10 336	53.23 351	51.42 375	36.46 372	64.97 359	71.91 360	29.13 368	27.53 368	61.55 360	9.83 373	65.01 372	16.00 372	55.58 334	58.22 367

ANet_high		40.27 336	35.20 339	55.47 347	34.74 381	34.47 373	63.84 361	71.56 361	48.42 352	18.80 372	41.08 370	9.52 374	64.45 373	20.18 369	8.66 377	67.49 364

test_method		38.59 337	35.16 340	48.89 353	54.33 373	21.35 380	45.32 370	53.71 375	7.41 376	28.74 367	51.62 363	8.70 375	52.87 374	33.73 357	32.89 368	72.47 359

EMVS		23.76 343	23.20 347	25.46 359	41.52 379	16.90 383	60.56 363	38.79 381	14.62 374	8.99 378	20.24 377	7.35 376	45.82 377	7.25 377	9.46 375	13.64 375

E-PMN		24.61 341	24.00 345	26.45 358	43.74 378	18.44 382	60.86 362	39.66 378	15.11 373	9.53 377	22.10 374	6.52 377	46.94 376	8.31 376	10.14 374	13.98 374

DeepMVS_CX				34.71 357	51.45 374	24.73 379		28.48 383	31.46 367	17.49 373	52.75 362	5.80 378	42.60 378	18.18 370	19.42 371	36.81 370

Gipuma		34.91 339	31.44 342	45.30 354	70.99 356	39.64 370	19.85 374	72.56 359	20.10 372	16.16 374	21.47 375	5.08 379	71.16 367	13.07 373	43.70 357	25.08 372
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		40.54 335	35.79 338	54.76 350	36.92 380	30.81 374	51.41 367	69.02 364	22.07 370	24.63 369	45.37 367	4.56 380	65.81 370	33.67 358	34.50 367	67.67 363

PMMVS2		37.93 338	33.61 341	50.92 352	46.31 377	24.76 378	60.55 364	50.05 376	28.94 369	20.93 370	47.59 364	4.41 381	65.13 371	25.14 367	18.55 372	62.87 365

PMVS	26.43 22	31.84 340	28.16 343	42.89 355	25.87 383	27.58 376	50.92 368	49.78 377	21.37 371	14.17 375	40.81 371	2.01 382	66.62 369	9.61 375	38.88 363	34.49 371
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 342	19.77 348	38.09 356	34.56 382	26.92 377	26.57 372	38.87 380	11.73 375	11.37 376	27.44 372	1.37 383	50.42 375	11.41 374	14.60 373	36.93 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 346	10.95 349	12.33 361	48.05 376	19.89 381	25.89 373	1.92 385	3.58 377	3.12 379	1.37 379	0.64 384	15.77 380	6.23 378	7.77 378	1.35 376

test123		6.92 349	9.21 352	0.08 362	0.03 386	0.05 386	81.65 313	0.01 387	0.02 381	0.14 382	0.85 381	0.03 385	0.02 381	0.12 380	0.00 380	0.16 377

testmvs		7.23 348	9.62 351	0.06 363	0.04 385	0.02 387	84.98 290	0.02 386	0.03 380	0.18 381	1.21 380	0.01 386	0.02 381	0.14 379	0.01 379	0.13 378

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 380	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 380	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 380	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 380	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 380	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 380	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 380	0.00 379

ab-mvs-re		7.91 347	10.55 350	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	94.95 64	0.00 387	0.00 383	0.00 381	0.00 380	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 380	0.00 379

FOURS1					93.95 53	61.77 236	93.96 69	91.92 142	62.14 299	86.57 35

MSC_two_6792asdad				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

No_MVS				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

eth-test2					0.00 387

eth-test					0.00 387

IU-MVS					96.46 13	69.91 37		95.18 14	80.75 37	95.28 1				92.34 8	95.36 14	96.47 24

save fliter					93.84 56	67.89 84	95.05 39	92.66 115	78.19 71

test_0728_SECOND				88.70 16	96.45 14	70.43 29	96.64 8	94.37 49					99.15 2	91.91 14	94.90 21	96.51 20

GSMVS																94.68 89

test_part2					96.29 21	68.16 78				90.78 13

MTGPA								92.23 128

MTMP							93.77 84	32.52 382

gm-plane-assit					88.42 188	67.04 110			78.62 68		91.83 141		97.37 72	76.57 134

test9_res														89.41 26	94.96 18	95.29 62

agg_prior2														86.41 57	94.75 30	95.33 58

agg_prior					94.16 50	66.97 112		93.31 88		84.49 59			96.75 111

test_prior4						67.18 106	93.92 75

test_prior				86.42 69	94.71 39	67.35 99		93.10 100					96.84 107			95.05 76

旧先验2							92.00 153		59.37 319	87.54 29			93.47 234	75.39 140

新几何2							91.41 176

无先验							92.71 121	92.61 119	62.03 300				97.01 93	66.63 217		93.97 121

原ACMM2							92.01 150

testdata2													96.09 128	61.26 261

testdata1							89.21 248		77.55 85

plane_prior7					86.94 220	61.51 241

plane_prior5								91.31 170					95.55 157	76.74 132	78.53 186	88.39 224

plane_prior4											89.14 179

plane_prior3						61.95 234			79.09 59	72.53 180

plane_prior2							93.13 103		78.81 65

plane_prior1					87.15 217

plane_prior						62.42 224	93.85 79		79.38 51						78.80 183

n2								0.00 388

nn								0.00 388

door-mid								66.01 369

test11								93.01 102

door								66.57 368

HQP5-MVS						63.66 201

HQP-NCC					87.54 209		94.06 64		79.80 45	74.18 160

ACMP_Plane					87.54 209		94.06 64		79.80 45	74.18 160

BP-MVS														77.63 129

HQP4-MVS										74.18 160			95.61 151			88.63 218

HQP3-MVS								91.70 155							78.90 181

NP-MVS					87.41 212	63.04 211					90.30 165

ACMMP++_ref															71.63 235

ACMMP++															69.72 247

High-resolution multi-view benchmark