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.

DPM-MVS		97.86 7	97.25 17	99.68 1	98.25 102	99.10 1	99.76 12	97.78 62	96.61 4	98.15 31	99.53 7	93.62 14	100.00 1	91.79 137	99.80 23	99.94 14

OPU-MVS				99.49 2	99.64 20	98.51 2	99.77 9				99.19 32	95.12 6	99.97 20	99.90 1	99.92 3	99.99 1

PS-MVSNAJ		96.87 33	96.40 39	98.29 15	97.35 127	97.29 3	99.03 96	97.11 164	95.83 10	98.97 11	99.14 43	82.48 168	99.60 91	98.60 19	99.08 80	98.00 175

xiu_mvs_v2_base		96.66 37	96.17 47	98.11 24	97.11 136	96.96 4	99.01 99	97.04 171	95.51 16	98.86 13	99.11 50	82.19 174	99.36 120	98.59 21	98.14 107	98.00 175

MVS		93.92 108	92.28 132	98.83 4	95.69 181	96.82 5	96.22 272	98.17 31	84.89 238	84.34 217	98.61 99	79.32 196	99.83 57	93.88 112	99.43 64	99.86 28

WTY-MVS		95.97 59	95.11 76	98.54 10	97.62 119	96.65 6	99.44 48	98.74 13	92.25 68	95.21 98	98.46 111	86.56 111	99.46 111	95.00 94	92.69 170	99.50 82

MCST-MVS		98.18 2	97.95 7	98.86 3	99.85 3	96.60 7	99.70 17	97.98 43	97.18 2	95.96 82	99.33 21	92.62 22	100.00 1	98.99 13	99.93 1	99.98 6

DELS-MVS		97.12 24	96.60 35	98.68 8	98.03 110	96.57 8	99.84 3	97.84 52	96.36 8	95.20 99	98.24 116	88.17 73	99.83 57	96.11 72	99.60 52	99.64 67
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
ETH3 D test6400		97.67 10	97.33 16	98.69 7	99.69 9	96.43 9	99.63 25	97.73 71	91.05 90	98.66 19	99.53 7	90.59 38	99.71 73	99.32 8	99.80 23	99.91 18

HY-MVS	88.56 7	95.29 77	94.23 89	98.48 11	97.72 115	96.41 10	94.03 301	98.74 13	92.42 64	95.65 92	94.76 204	86.52 112	99.49 104	95.29 88	92.97 166	99.53 78

test_0728_SECOND				98.77 5	99.66 15	96.37 11	99.72 14	97.68 80					99.98 10	99.64 5	99.82 15	99.96 8

CNVR-MVS		98.46 1	98.38 1	98.72 6	99.80 4	96.19 12	99.80 8	97.99 42	97.05 3	99.41 2	99.59 2	92.89 21	100.00 1	98.99 13	99.90 5	99.96 8

CANet		97.00 27	96.49 37	98.55 9	98.86 88	96.10 13	99.83 5	97.52 115	95.90 9	97.21 53	98.90 76	82.66 165	99.93 35	98.71 16	98.80 94	99.63 69

canonicalmvs		95.02 83	93.96 100	98.20 17	97.53 124	95.92 14	98.71 126	96.19 219	91.78 76	95.86 87	98.49 107	79.53 194	99.03 139	96.12 71	91.42 193	99.66 65

MG-MVS		97.24 18	96.83 29	98.47 12	99.79 5	95.71 15	99.07 91	99.06 9	94.45 22	96.42 77	98.70 92	88.81 63	99.74 70	95.35 86	99.86 10	99.97 7

alignmvs		95.77 69	95.00 78	98.06 25	97.35 127	95.68 16	99.71 16	97.50 121	91.50 81	96.16 78	98.61 99	86.28 118	99.00 140	96.19 70	91.74 187	99.51 81

test_part2					99.54 36	95.42 17				98.13 32

DPE-MVS		98.11 5	98.00 5	98.44 13	99.50 43	95.39 18	99.29 66	97.72 73	94.50 20	98.64 20	99.54 3	93.32 15	99.97 20	99.58 7	99.90 5	99.95 11

IU-MVS					99.63 21	95.38 19		97.73 71	95.54 15	99.54 1				99.69 4	99.81 19	99.99 1

PAPM		96.35 47	95.94 55	97.58 40	94.10 228	95.25 20	98.93 106	98.17 31	94.26 23	93.94 119	98.72 89	89.68 54	97.88 181	96.36 67	99.29 74	99.62 71

SED-MVS		98.18 2	98.10 4	98.41 14	99.63 21	95.24 21	99.77 9	97.72 73	94.17 24	99.30 4	99.54 3	93.32 15	99.98 10	99.70 2	99.81 19	99.99 1

test_241102_ONE					99.63 21	95.24 21		97.72 73	94.16 26	99.30 4	99.49 10	93.32 15	99.98 10

xiu_mvs_v1_base_debu		94.73 89	93.98 97	96.99 65	95.19 196	95.24 21	98.62 142	96.50 198	92.99 48	97.52 47	98.83 80	72.37 241	99.15 132	97.03 50	96.74 126	96.58 204

xiu_mvs_v1_base		94.73 89	93.98 97	96.99 65	95.19 196	95.24 21	98.62 142	96.50 198	92.99 48	97.52 47	98.83 80	72.37 241	99.15 132	97.03 50	96.74 126	96.58 204

xiu_mvs_v1_base_debi		94.73 89	93.98 97	96.99 65	95.19 196	95.24 21	98.62 142	96.50 198	92.99 48	97.52 47	98.83 80	72.37 241	99.15 132	97.03 50	96.74 126	96.58 204

DVP-MVS		98.07 6	98.00 5	98.29 15	99.66 15	95.20 26	99.72 14	97.47 126	93.95 29	99.07 8	99.46 11	93.18 18	99.97 20	99.64 5	99.82 15	99.69 60

test0726					99.66 15	95.20 26	99.77 9	97.70 78	93.95 29	99.35 3	99.54 3	93.18 18

RRT_MVS		91.95 153	91.09 153	94.53 161	96.71 151	95.12 28	98.64 139	96.23 215	89.04 143	85.24 210	95.06 199	87.71 82	96.43 248	89.10 169	82.06 250	92.05 244

3Dnovator+	87.72 8	93.43 123	91.84 143	98.17 18	95.73 180	95.08 29	98.92 108	97.04 171	91.42 85	81.48 259	97.60 137	74.60 220	99.79 65	90.84 146	98.97 85	99.64 67

ETH3D-3000-0.1		97.29 16	97.01 22	98.12 22	99.18 69	94.97 30	99.47 40	97.52 115	89.85 120	98.79 16	99.46 11	90.41 44	99.69 75	98.78 15	99.67 38	99.70 57

thres600view7		93.18 132	92.00 140	96.75 83	97.62 119	94.92 31	99.07 91	99.36 2	87.96 180	90.47 165	96.78 170	83.29 156	98.71 151	82.93 235	90.47 202	96.61 202

SF-MVS		97.22 21	96.92 24	98.12 22	99.11 73	94.88 32	99.44 48	97.45 128	89.60 128	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 40	99.77 44

MVSFormer		94.71 92	94.08 95	96.61 92	95.05 208	94.87 33	97.77 214	96.17 220	86.84 205	98.04 37	98.52 103	85.52 125	95.99 275	89.83 154	98.97 85	98.96 121

lupinMVS		96.32 49	95.94 55	97.44 45	95.05 208	94.87 33	99.86 2	96.50 198	93.82 36	98.04 37	98.77 83	85.52 125	98.09 168	96.98 54	98.97 85	99.37 91

thres100view900		93.34 127	92.15 137	96.90 74	97.62 119	94.84 35	99.06 93	99.36 2	87.96 180	90.47 165	96.78 170	83.29 156	98.75 147	84.11 221	90.69 198	97.12 193

tfpn200view9		93.43 123	92.27 133	96.90 74	97.68 117	94.84 35	99.18 72	99.36 2	88.45 162	90.79 157	96.90 166	83.31 154	98.75 147	84.11 221	90.69 198	97.12 193

thres400		93.39 125	92.27 133	96.73 85	97.68 117	94.84 35	99.18 72	99.36 2	88.45 162	90.79 157	96.90 166	83.31 154	98.75 147	84.11 221	90.69 198	96.61 202

GG-mvs-BLEND				96.98 68	96.53 154	94.81 38	87.20 330	97.74 67		93.91 120	96.40 179	96.56 2	96.94 225	95.08 91	98.95 88	99.20 107

HPM-MVS++		97.72 9	97.59 9	98.14 19	99.53 41	94.76 39	99.19 70	97.75 65	95.66 13	98.21 30	99.29 22	91.10 28	99.99 5	97.68 43	99.87 7	99.68 61

thres200		93.69 115	92.59 128	96.97 69	97.76 114	94.74 40	99.35 61	99.36 2	89.23 137	91.21 154	96.97 163	83.42 153	98.77 145	85.08 207	90.96 196	97.39 187

ETH3D cwj APD-0.16		96.94 31	96.58 36	98.01 26	98.62 95	94.73 41	99.13 87	97.38 139	88.44 165	98.53 24	99.39 19	89.66 55	99.69 75	98.43 27	99.61 51	99.61 72

CANet_DTU		94.31 102	93.35 111	97.20 57	97.03 140	94.71 42	98.62 142	95.54 262	95.61 14	97.21 53	98.47 109	71.88 246	99.84 55	88.38 173	97.46 120	97.04 198

gg-mvs-nofinetune		90.00 187	87.71 207	96.89 79	96.15 169	94.69 43	85.15 336	97.74 67	68.32 337	92.97 133	60.16 347	96.10 3	96.84 227	93.89 111	98.87 89	99.14 110

baseline1		92.61 142	91.28 151	96.58 94	97.05 139	94.63 44	97.72 217	96.20 217	89.82 121	88.56 184	96.85 169	86.85 101	97.82 185	88.42 172	80.10 258	97.30 189

FMVSNet3		88.81 207	87.08 217	93.99 180	96.52 155	94.59 45	98.08 198	96.20 217	85.85 220	82.12 244	91.60 258	74.05 228	95.40 297	79.04 262	80.24 255	91.99 246

NCCC		98.12 4	98.11 3	98.13 20	99.76 6	94.46 46	99.81 6	97.88 48	96.54 5	98.84 14	99.46 11	92.55 23	99.98 10	98.25 34	99.93 1	99.94 14

test12				97.83 31	99.33 59	94.45 47		97.55 109		97.56 46		88.60 65	99.50 103		99.71 34	99.55 77

DeepC-MVS_fast	93.52 2	97.16 23	96.84 28	98.13 20	99.61 27	94.45 47	98.85 114	97.64 88	96.51 7	95.88 85	99.39 19	87.35 93	99.99 5	96.61 60	99.69 37	99.96 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 280x420		96.80 35	96.85 27	96.66 91	97.85 113	94.42 49	94.76 294	98.36 23	92.50 59	95.62 93	97.52 140	97.92 1	97.38 212	98.31 33	98.80 94	98.20 171

1314		93.44 122	91.98 141	97.84 30	95.24 193	94.38 50	96.22 272	97.92 46	90.18 111	82.28 241	97.71 132	77.63 208	99.80 64	91.94 136	98.67 98	99.34 94

DP-MVS Recon		95.85 64	95.15 75	97.95 28	99.87 2	94.38 50	99.60 27	97.48 124	86.58 211	94.42 110	99.13 45	87.36 92	99.98 10	93.64 117	98.33 106	99.48 85

jason		95.40 76	94.86 79	97.03 61	92.91 259	94.23 52	99.70 17	96.30 209	93.56 42	96.73 71	98.52 103	81.46 183	97.91 178	96.08 73	98.47 103	98.96 121
jason: jason.
SMA-MVS		97.24 18	96.99 23	98.00 27	99.30 60	94.20 53	99.16 75	97.65 87	89.55 132	99.22 7	99.52 9	90.34 46	99.99 5	98.32 32	99.83 13	99.82 30
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
PAPR		96.35 47	95.82 60	97.94 29	99.63 21	94.19 54	99.42 53	97.55 109	92.43 61	93.82 123	99.12 46	87.30 94	99.91 38	94.02 109	99.06 81	99.74 51

ET-MVSNet_ETH3D		92.56 144	91.45 150	95.88 120	96.39 158	94.13 55	99.46 45	96.97 177	92.18 70	66.94 334	98.29 115	94.65 11	94.28 317	94.34 107	83.82 237	99.24 103

sss		94.85 86	93.94 102	97.58 40	96.43 157	94.09 56	98.93 106	99.16 8	89.50 133	95.27 97	97.85 123	81.50 181	99.65 84	92.79 131	94.02 159	98.99 118

CDPH-MVS		96.56 40	96.18 45	97.70 36	99.59 28	93.92 57	99.13 87	97.44 132	89.02 144	97.90 43	99.22 29	88.90 62	99.49 104	94.63 102	99.79 25	99.68 61

VNet		95.08 82	94.26 88	97.55 43	98.07 109	93.88 58	98.68 133	98.73 15	90.33 107	97.16 55	97.43 144	79.19 197	99.53 97	96.91 56	91.85 185	99.24 103

xxxxxxxxxxxxxcwj		97.51 12	97.42 13	97.78 34	99.34 53	93.85 59	99.65 23	95.45 267	95.69 11	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 40	99.77 44

save fliter					99.34 53	93.85 59	99.65 23	97.63 92	95.69 11

SD-MVS		97.51 12	97.40 14	97.81 32	99.01 79	93.79 61	99.33 64	97.38 139	93.73 38	98.83 15	99.02 57	90.87 33	99.88 44	98.69 17	99.74 28	99.77 44
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
APDe-MVS		97.53 11	97.47 10	97.70 36	99.58 29	93.63 62	99.56 32	97.52 115	93.59 41	98.01 39	99.12 46	90.80 35	99.55 94	99.26 10	99.79 25	99.93 17

APD-MVS		96.95 29	96.72 32	97.63 38	99.51 42	93.58 63	99.16 75	97.44 132	90.08 116	98.59 22	99.07 51	89.06 59	99.42 114	97.92 39	99.66 39	99.88 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		96.59 39	96.18 45	97.81 32	98.82 89	93.55 64	98.88 113	97.59 101	90.66 96	97.98 40	99.14 43	86.59 109	100.00 1	96.47 64	99.46 60	99.89 23

nrg030		90.23 180	88.87 188	94.32 168	91.53 277	93.54 65	98.79 123	95.89 242	88.12 176	84.55 215	94.61 206	78.80 201	96.88 226	92.35 134	75.21 282	92.53 228

OpenMVS	85.28 14	90.75 173	88.84 189	96.48 98	93.58 245	93.51 66	98.80 119	97.41 136	82.59 273	78.62 287	97.49 142	68.00 270	99.82 60	84.52 215	98.55 101	96.11 211

TSAR-MVS + MP.		97.44 15	97.46 11	97.39 49	99.12 72	93.49 67	98.52 153	97.50 121	94.46 21	98.99 10	98.64 96	91.58 25	99.08 138	98.49 24	99.83 13	99.60 73
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
QAPM		91.41 161	89.49 177	97.17 58	95.66 183	93.42 68	98.60 146	97.51 118	80.92 294	81.39 260	97.41 145	72.89 238	99.87 47	82.33 240	98.68 97	98.21 170

testtj		97.23 20	97.05 20	97.75 35	99.75 7	93.34 69	99.16 75	97.74 67	91.28 87	98.40 26	99.29 22	89.95 49	99.98 10	98.20 35	99.70 35	99.94 14

ZD-MVS					99.67 13	93.28 70		97.61 95	87.78 185	97.41 50	99.16 39	90.15 47	99.56 93	98.35 29	99.70 35

MSLP-MVS++		97.50 14	97.45 12	97.63 38	99.65 19	93.21 71	99.70 17	98.13 36	94.61 19	97.78 45	99.46 11	89.85 50	99.81 62	97.97 38	99.91 4	99.88 24

TEST9					99.57 33	93.17 72	99.38 56	97.66 82	89.57 130	98.39 27	99.18 35	90.88 32	99.66 80

train_agg		97.20 22	97.08 19	97.57 42	99.57 33	93.17 72	99.38 56	97.66 82	90.18 111	98.39 27	99.18 35	90.94 30	99.66 80	98.58 22	99.85 11	99.88 24

Regformer-1		96.97 28	96.80 30	97.47 44	99.46 47	93.11 74	98.89 111	97.94 44	92.89 52	96.90 58	99.02 57	89.78 51	99.53 97	97.06 49	99.26 76	99.75 48

EPNet		96.82 34	96.68 34	97.25 55	98.65 93	93.10 75	99.48 39	98.76 12	96.54 5	97.84 44	98.22 117	87.49 86	99.66 80	95.35 86	97.78 113	99.00 117
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_8					99.55 35	93.07 76	99.37 59	97.64 88	90.18 111	98.36 29	99.19 32	90.94 30	99.64 86

3Dnovator	87.35 11	93.17 133	91.77 145	97.37 51	95.41 190	93.07 76	98.82 117	97.85 51	91.53 80	82.56 235	97.58 139	71.97 245	99.82 60	91.01 143	99.23 78	99.22 106

cascas		90.93 170	89.33 181	95.76 125	95.69 181	93.03 78	98.99 101	96.59 190	80.49 296	86.79 202	94.45 207	65.23 288	98.60 154	93.52 119	92.18 180	95.66 214

test_yl		95.27 78	94.60 82	97.28 53	98.53 98	92.98 79	99.05 94	98.70 16	86.76 208	94.65 108	97.74 130	87.78 79	99.44 112	95.57 82	92.61 171	99.44 87

DCV-MVSNet		95.27 78	94.60 82	97.28 53	98.53 98	92.98 79	99.05 94	98.70 16	86.76 208	94.65 108	97.74 130	87.78 79	99.44 112	95.57 82	92.61 171	99.44 87

Regformer-2		96.94 31	96.78 31	97.42 46	99.46 47	92.97 81	98.89 111	97.93 45	92.86 54	96.88 59	99.02 57	89.74 53	99.53 97	97.03 50	99.26 76	99.75 48

MVSTER		92.71 138	92.32 131	93.86 183	97.29 129	92.95 82	99.01 99	96.59 190	90.09 115	85.51 208	94.00 214	94.61 12	96.56 238	90.77 148	83.03 243	92.08 242

旧先验1					98.97 80	92.90 83		97.74 67			99.15 41	91.05 29			99.33 70	99.60 73

MP-MVS-pluss		95.80 67	95.30 71	97.29 52	98.95 83	92.66 84	98.59 148	97.14 160	88.95 147	93.12 129	99.25 25	85.62 124	99.94 33	96.56 62	99.48 59	99.28 100
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
agg_prior1		97.12 24	97.03 21	97.38 50	99.54 36	92.66 84	99.35 61	97.64 88	90.38 105	97.98 40	99.17 37	90.84 34	99.61 89	98.57 23	99.78 27	99.87 27

agg_prior					99.54 36	92.66 84		97.64 88		97.98 40			99.61 89

MVS_Test		93.67 118	92.67 126	96.69 89	96.72 149	92.66 84	97.22 236	96.03 225	87.69 191	95.12 101	94.03 212	81.55 180	98.28 160	89.17 167	96.46 129	99.14 110

thisisatest0515		94.75 88	94.19 90	96.43 101	96.13 173	92.64 88	99.47 40	97.60 97	87.55 194	93.17 128	97.59 138	94.71 9	98.42 155	88.28 174	93.20 163	98.24 168

1121		95.19 80	94.45 85	97.42 46	98.88 86	92.58 89	96.22 272	97.75 65	85.50 226	96.86 62	99.01 61	88.59 67	99.90 40	87.64 182	99.60 52	99.79 34

FMVSNet2		86.90 233	84.79 251	93.24 192	95.11 203	92.54 90	97.67 220	95.86 244	82.94 267	80.55 265	91.17 267	62.89 295	95.29 299	77.23 273	79.71 262	91.90 247

新几何1				97.40 48	98.92 84	92.51 91		97.77 64	85.52 224	96.69 72	99.06 53	88.08 76	99.89 43	84.88 210	99.62 47	99.79 34

114514_t		94.06 104	93.05 118	97.06 60	99.08 76	92.26 92	98.97 103	97.01 175	82.58 274	92.57 135	98.22 117	80.68 187	99.30 127	89.34 163	99.02 83	99.63 69

test_prior4						92.00 93	99.41 54

test_prior3		97.07 26	97.09 18	97.01 62	99.58 29	91.77 94	99.57 30	97.57 106	91.43 83	98.12 34	98.97 63	90.43 40	99.49 104	98.33 30	99.81 19	99.79 34

test_prior				97.01 62	99.58 29	91.77 94		97.57 106					99.49 104			99.79 34

PHI-MVS		96.65 38	96.46 38	97.21 56	99.34 53	91.77 94	99.70 17	98.05 38	86.48 214	98.05 36	99.20 31	89.33 57	99.96 27	98.38 28	99.62 47	99.90 20

Regformer-3		96.50 42	96.36 41	96.91 73	99.34 53	91.72 97	98.71 126	97.90 47	92.48 60	96.00 79	98.95 70	88.60 65	99.52 100	96.44 65	98.83 91	99.49 83

ab-mvs		91.05 167	89.17 183	96.69 89	95.96 174	91.72 97	92.62 313	97.23 150	85.61 223	89.74 175	93.89 218	68.55 265	99.42 114	91.09 141	87.84 210	98.92 127

TSAR-MVS + GP.		96.95 29	96.91 25	97.07 59	98.88 86	91.62 99	99.58 29	96.54 196	95.09 18	96.84 65	98.63 98	91.16 26	99.77 67	99.04 12	96.42 131	99.81 31

PVSNet_BlendedMVS		93.36 126	93.20 115	93.84 184	98.77 90	91.61 100	99.47 40	98.04 39	91.44 82	94.21 114	92.63 244	83.50 150	99.87 47	97.41 45	83.37 241	90.05 305

PVSNet_Blended		95.94 61	95.66 66	96.75 83	98.77 90	91.61 100	99.88 1	98.04 39	93.64 40	94.21 114	97.76 128	83.50 150	99.87 47	97.41 45	97.75 114	98.79 139

PCF-MVS	89.78 5	91.26 162	89.63 175	96.16 112	95.44 189	91.58 102	95.29 290	96.10 223	85.07 233	82.75 231	97.45 143	78.28 204	99.78 66	80.60 255	95.65 148	97.12 193
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SteuartSystems-ACMMP		97.25 17	97.34 15	97.01 62	97.38 126	91.46 103	99.75 13	97.66 82	94.14 28	98.13 32	99.26 24	92.16 24	99.66 80	97.91 40	99.64 43	99.90 20
Skip Steuart: Steuart Systems R&D Blog.
Regformer-4		96.45 45	96.33 43	96.81 80	99.34 53	91.44 104	98.71 126	97.88 48	92.43 61	95.97 81	98.95 70	88.42 69	99.51 101	96.40 66	98.83 91	99.49 83

VPNet		88.30 214	86.57 223	93.49 188	91.95 270	91.35 105	98.18 189	97.20 156	88.61 155	84.52 216	94.89 201	62.21 298	96.76 232	89.34 163	72.26 312	92.36 231

GST-MVS		95.97 59	95.66 66	96.90 74	99.49 45	91.22 106	99.45 47	97.48 124	89.69 124	95.89 84	98.72 89	86.37 117	99.95 30	94.62 103	99.22 79	99.52 79

test222					98.32 101	91.21 107	98.08 198	97.58 103	83.74 253	95.87 86	99.02 57	86.74 104			99.64 43	99.81 31

ZNCC-MVS		96.09 55	95.81 62	96.95 72	99.42 49	91.19 108	99.55 33	97.53 113	89.72 123	95.86 87	98.94 75	86.59 109	99.97 20	95.13 90	99.56 55	99.68 61

zzz-MVS		96.21 53	95.96 54	96.96 70	99.29 61	91.19 108	98.69 131	97.45 128	92.58 56	94.39 111	99.24 27	86.43 115	99.99 5	96.22 68	99.40 68	99.71 55

MTAPA		96.09 55	95.80 63	96.96 70	99.29 61	91.19 108	97.23 235	97.45 128	92.58 56	94.39 111	99.24 27	86.43 115	99.99 5	96.22 68	99.40 68	99.71 55

MDTV_nov1_ep13_2view						91.17 111	91.38 320		87.45 196	93.08 130		86.67 107		87.02 187		98.95 125

FIs		90.70 174	89.87 173	93.18 193	92.29 264	91.12 112	98.17 191	98.25 26	89.11 141	83.44 223	94.82 203	82.26 172	96.17 268	87.76 180	82.76 245	92.25 234

1112_ss		92.71 138	91.55 149	96.20 108	95.56 185	91.12 112	98.48 161	94.69 293	88.29 171	86.89 199	98.50 105	87.02 98	98.66 152	84.75 211	89.77 205	98.81 137

PVSNet_Blended_VisFu		94.67 93	94.11 93	96.34 106	97.14 134	91.10 114	99.32 65	97.43 134	92.10 72	91.53 148	96.38 182	83.29 156	99.68 78	93.42 122	96.37 132	98.25 167

Test_1112_low_res		92.27 148	90.97 156	96.18 109	95.53 187	91.10 114	98.47 163	94.66 294	88.28 172	86.83 201	93.50 230	87.00 99	98.65 153	84.69 212	89.74 206	98.80 138

LFMVS		92.23 149	90.84 160	96.42 102	98.24 103	91.08 116	98.24 184	96.22 216	83.39 260	94.74 106	98.31 113	61.12 303	98.85 142	94.45 106	92.82 167	99.32 95

ETV-MVS		96.00 57	96.00 53	96.00 116	96.56 153	91.05 117	99.63 25	96.61 187	93.26 46	97.39 51	98.30 114	86.62 108	98.13 165	98.07 37	97.57 115	98.82 136

VPA-MVSNet		89.10 197	87.66 208	93.45 189	92.56 261	91.02 118	97.97 204	98.32 24	86.92 204	86.03 205	92.01 250	68.84 264	97.10 220	90.92 144	75.34 281	92.23 236

MVS_111021_HR		96.69 36	96.69 33	96.72 87	98.58 97	91.00 119	99.14 84	99.45 1	93.86 35	95.15 100	98.73 87	88.48 68	99.76 68	97.23 48	99.56 55	99.40 90

HFP-MVS		96.42 46	96.26 44	96.90 74	99.69 9	90.96 120	99.47 40	97.81 57	90.54 101	96.88 59	99.05 54	87.57 83	99.96 27	95.65 77	99.72 30	99.78 38

#test#		96.48 43	96.34 42	96.90 74	99.69 9	90.96 120	99.53 36	97.81 57	90.94 94	96.88 59	99.05 54	87.57 83	99.96 27	95.87 76	99.72 30	99.78 38

UniMVSNet (Re)		89.50 195	88.32 201	93.03 195	92.21 266	90.96 120	98.90 110	98.39 22	89.13 140	83.22 224	92.03 248	81.69 179	96.34 259	86.79 192	72.53 308	91.81 248

IB-MVS	89.43 6	92.12 150	90.83 162	95.98 118	95.40 191	90.78 123	99.81 6	98.06 37	91.23 89	85.63 207	93.66 224	90.63 37	98.78 144	91.22 140	71.85 315	98.36 163
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
Effi-MVS+		93.87 111	93.15 116	96.02 115	95.79 177	90.76 124	96.70 256	95.78 246	86.98 202	95.71 90	97.17 155	79.58 192	98.01 176	94.57 104	96.09 139	99.31 96

DeepC-MVS	91.02 4	94.56 98	93.92 103	96.46 99	97.16 133	90.76 124	98.39 174	97.11 164	93.92 31	88.66 183	98.33 112	78.14 205	99.85 54	95.02 93	98.57 100	98.78 141
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
diffmvs		94.59 97	94.19 90	95.81 123	95.54 186	90.69 126	98.70 130	95.68 254	91.61 78	95.96 82	97.81 125	80.11 189	98.06 172	96.52 63	95.76 145	98.67 147

NR-MVSNet		87.74 224	86.00 232	92.96 197	91.46 278	90.68 127	96.65 257	97.42 135	88.02 179	73.42 313	93.68 222	77.31 209	95.83 285	84.26 217	71.82 316	92.36 231

XVS		96.47 44	96.37 40	96.77 81	99.62 25	90.66 128	99.43 51	97.58 103	92.41 65	96.86 62	98.96 68	87.37 89	99.87 47	95.65 77	99.43 64	99.78 38

X-MVStestdata		90.69 175	88.66 194	96.77 81	99.62 25	90.66 128	99.43 51	97.58 103	92.41 65	96.86 62	29.59 358	87.37 89	99.87 47	95.65 77	99.43 64	99.78 38

ACMMPR		96.28 51	96.14 51	96.73 85	99.68 12	90.47 130	99.47 40	97.80 59	90.54 101	96.83 67	99.03 56	86.51 113	99.95 30	95.65 77	99.72 30	99.75 48

EI-MVSNet-Vis-set		95.76 70	95.63 70	96.17 111	99.14 71	90.33 131	98.49 160	97.82 54	91.92 73	94.75 105	98.88 78	87.06 97	99.48 109	95.40 85	97.17 124	98.70 146

region2R		96.30 50	96.17 47	96.70 88	99.70 8	90.31 132	99.46 45	97.66 82	90.55 100	97.07 56	99.07 51	86.85 101	99.97 20	95.43 84	99.74 28	99.81 31

TESTMET0.1,1		93.82 112	93.26 114	95.49 132	95.21 195	90.25 133	99.15 81	97.54 112	89.18 139	91.79 141	94.87 202	89.13 58	97.63 200	86.21 196	96.29 136	98.60 150

baseline2		94.04 105	93.80 106	94.74 154	93.07 257	90.25 133	98.12 194	98.16 33	89.86 119	86.53 203	96.95 164	95.56 5	98.05 173	91.44 139	94.53 154	95.93 212

PVSNet	87.13 12	93.69 115	92.83 123	96.28 107	97.99 111	90.22 135	99.38 56	98.93 10	91.42 85	93.66 124	97.68 133	71.29 253	99.64 86	87.94 179	97.20 123	98.98 119

MSP-MVS		97.77 8	98.18 2	96.53 97	99.54 36	90.14 136	99.41 54	97.70 78	95.46 17	98.60 21	99.19 32	95.71 4	99.49 104	98.15 36	99.85 11	99.95 11

PAPM_NR		95.43 73	95.05 77	96.57 95	99.42 49	90.14 136	98.58 150	97.51 118	90.65 98	92.44 137	98.90 76	87.77 81	99.90 40	90.88 145	99.32 71	99.68 61

MP-MVS		96.00 57	95.82 60	96.54 96	99.47 46	90.13 138	99.36 60	97.41 136	90.64 99	95.49 94	98.95 70	85.51 127	99.98 10	96.00 75	99.59 54	99.52 79
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
原ACMM1				96.18 109	99.03 78	90.08 139		97.63 92	88.98 145	97.00 57	98.97 63	88.14 75	99.71 73	88.23 175	99.62 47	98.76 143

UniMVSNet_NR-MVSNet		89.60 192	88.55 198	92.75 203	92.17 267	90.07 140	98.74 125	98.15 34	88.37 168	83.21 225	93.98 215	82.86 162	95.93 279	86.95 189	72.47 309	92.25 234

DU-MVS		88.83 205	87.51 209	92.79 201	91.46 278	90.07 140	98.71 126	97.62 94	88.87 151	83.21 225	93.68 222	74.63 218	95.93 279	86.95 189	72.47 309	92.36 231

baseline		93.91 109	93.30 112	95.72 126	95.10 206	90.07 140	97.48 225	95.91 239	91.03 91	93.54 125	97.68 133	79.58 192	98.02 175	94.27 108	95.14 150	99.08 114

API-MVS		94.78 87	94.18 92	96.59 93	99.21 68	90.06 143	98.80 119	97.78 62	83.59 257	93.85 121	99.21 30	83.79 147	99.97 20	92.37 133	99.00 84	99.74 51

EPMVS		92.59 143	91.59 148	95.59 131	97.22 131	90.03 144	91.78 318	98.04 39	90.42 104	91.66 144	90.65 280	86.49 114	97.46 209	81.78 247	96.31 134	99.28 100

thisisatest0530		94.00 106	93.52 109	95.43 135	95.76 179	90.02 145	98.99 101	97.60 97	86.58 211	91.74 142	97.36 146	94.78 8	98.34 156	86.37 195	92.48 174	97.94 177

CNLPA		93.64 119	92.74 124	96.36 105	98.96 82	90.01 146	99.19 70	95.89 242	86.22 217	89.40 178	98.85 79	80.66 188	99.84 55	88.57 171	96.92 125	99.24 103

EI-MVSNet-UG-set		95.43 73	95.29 72	95.86 121	99.07 77	89.87 147	98.43 165	97.80 59	91.78 76	94.11 116	98.77 83	86.25 119	99.48 109	94.95 96	96.45 130	98.22 169

FC-MVSNet-test		90.22 181	89.40 179	92.67 206	91.78 274	89.86 148	97.89 206	98.22 28	88.81 152	82.96 230	94.66 205	81.90 178	95.96 277	85.89 202	82.52 248	92.20 238

casdiffmvs		93.98 107	93.43 110	95.61 130	95.07 207	89.86 148	98.80 119	95.84 245	90.98 92	92.74 134	97.66 135	79.71 191	98.10 167	94.72 100	95.37 149	98.87 131

CS-MVS		95.85 64	95.86 58	95.82 122	96.80 146	89.78 150	99.84 3	96.60 188	92.60 55	96.81 69	98.70 92	85.04 134	98.25 161	97.90 41	98.43 104	99.42 89

PGM-MVS		95.85 64	95.65 68	96.45 100	99.50 43	89.77 151	98.22 185	98.90 11	89.19 138	96.74 70	98.95 70	85.91 123	99.92 36	93.94 110	99.46 60	99.66 65

RRT_test8_iter05		91.04 168	90.40 170	92.95 198	96.20 167	89.75 152	98.97 103	96.38 204	88.52 158	82.00 249	93.51 229	90.69 36	96.73 233	90.43 150	76.91 276	92.38 230

XXY-MVS		87.75 222	86.02 231	92.95 198	90.46 290	89.70 153	97.71 219	95.90 240	84.02 249	80.95 261	94.05 209	67.51 274	97.10 220	85.16 206	78.41 265	92.04 245

mvs_anonymous		92.50 145	91.65 147	95.06 145	96.60 152	89.64 154	97.06 241	96.44 202	86.64 210	84.14 218	93.93 216	82.49 167	96.17 268	91.47 138	96.08 140	99.35 92

CP-MVS		96.22 52	96.15 50	96.42 102	99.67 13	89.62 155	99.70 17	97.61 95	90.07 117	96.00 79	99.16 39	87.43 87	99.92 36	96.03 74	99.72 30	99.70 57

WR-MVS		88.54 212	87.22 216	92.52 207	91.93 272	89.50 156	98.56 151	97.84 52	86.99 200	81.87 253	93.81 219	74.25 227	95.92 281	85.29 205	74.43 290	92.12 240

DWT-MVSNet_test		94.36 100	93.95 101	95.62 128	96.99 141	89.47 157	96.62 258	97.38 139	90.96 93	93.07 131	97.27 147	93.73 13	98.09 168	85.86 203	93.65 161	99.29 98

CDS-MVSNet		93.47 121	93.04 119	94.76 152	94.75 219	89.45 158	98.82 117	97.03 173	87.91 182	90.97 156	96.48 177	89.06 59	96.36 253	89.50 158	92.81 169	98.49 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mPP-MVS		95.90 63	95.75 64	96.38 104	99.58 29	89.41 159	99.26 67	97.41 136	90.66 96	94.82 104	98.95 70	86.15 120	99.98 10	95.24 89	99.64 43	99.74 51

HPM-MVS		95.41 75	95.22 74	95.99 117	99.29 61	89.14 160	99.17 74	97.09 168	87.28 198	95.40 95	98.48 108	84.93 136	99.38 118	95.64 81	99.65 40	99.47 86
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
AdaColmap		93.82 112	93.06 117	96.10 113	99.88 1	89.07 161	98.33 177	97.55 109	86.81 207	90.39 167	98.65 95	75.09 217	99.98 10	93.32 123	97.53 118	99.26 102

SR-MVS		96.13 54	96.16 49	96.07 114	99.42 49	89.04 162	98.59 148	97.33 144	90.44 103	96.84 65	99.12 46	86.75 103	99.41 116	97.47 44	99.44 63	99.76 47

PatchmatchNet		92.05 152	91.04 155	95.06 145	96.17 168	89.04 162	91.26 322	97.26 145	89.56 131	90.64 161	90.56 286	88.35 71	97.11 218	79.53 258	96.07 141	99.03 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
miper_enhance_ethall		90.33 178	89.70 174	92.22 210	97.12 135	88.93 164	98.35 176	95.96 229	88.60 156	83.14 229	92.33 246	87.38 88	96.18 267	86.49 194	77.89 268	91.55 259

pmmvs4		87.58 227	86.17 230	91.80 221	89.58 300	88.92 165	97.25 233	95.28 277	82.54 275	80.49 266	93.17 236	75.62 215	96.05 274	82.75 236	78.90 263	90.42 296

SCA		90.64 176	89.25 182	94.83 151	94.95 212	88.83 166	96.26 269	97.21 152	90.06 118	90.03 171	90.62 282	66.61 280	96.81 229	83.16 231	94.36 156	98.84 132

GBi-Net		86.67 238	84.96 245	91.80 221	95.11 203	88.81 167	96.77 250	95.25 278	82.94 267	82.12 244	90.25 293	62.89 295	94.97 304	79.04 262	80.24 255	91.62 253

test1		86.67 238	84.96 245	91.80 221	95.11 203	88.81 167	96.77 250	95.25 278	82.94 267	82.12 244	90.25 293	62.89 295	94.97 304	79.04 262	80.24 255	91.62 253

FMVSNet1		83.94 278	81.32 286	91.80 221	91.94 271	88.81 167	96.77 250	95.25 278	77.98 306	78.25 292	90.25 293	50.37 335	94.97 304	73.27 304	77.81 272	91.62 253

CHOSEN 1792x2688		94.35 101	93.82 105	95.95 119	97.40 125	88.74 170	98.41 168	98.27 25	92.18 70	91.43 149	96.40 179	78.88 198	99.81 62	93.59 118	97.81 110	99.30 97

UGNet		91.91 154	90.85 159	95.10 142	97.06 138	88.69 171	98.01 202	98.24 27	92.41 65	92.39 138	93.61 225	60.52 304	99.68 78	88.14 176	97.25 122	96.92 200
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
TranMVSNet+NR-MVSNet		87.75 222	86.31 227	92.07 215	90.81 286	88.56 172	98.33 177	97.18 157	87.76 186	81.87 253	93.90 217	72.45 240	95.43 295	83.13 233	71.30 319	92.23 236

BH-RMVSNet		91.25 164	89.99 172	95.03 147	96.75 148	88.55 173	98.65 137	94.95 287	87.74 188	87.74 189	97.80 126	68.27 267	98.14 164	80.53 256	97.49 119	98.41 157

MDTV_nov1_ep13			90.47 169		96.14 170	88.55 173	91.34 321	97.51 118	89.58 129	92.24 139	90.50 290	86.99 100	97.61 202	77.64 272	92.34 176

UA-Net		93.30 128	92.62 127	95.34 138	96.27 162	88.53 175	95.88 282	96.97 177	90.90 95	95.37 96	97.07 159	82.38 171	99.10 137	83.91 225	94.86 153	98.38 160

HPM-MVS_fast		94.89 84	94.62 81	95.70 127	99.11 73	88.44 176	99.14 84	97.11 164	85.82 221	95.69 91	98.47 109	83.46 152	99.32 126	93.16 125	99.63 46	99.35 92

Vis-MVSNet		92.64 140	91.85 142	95.03 147	95.12 202	88.23 177	98.48 161	96.81 181	91.61 78	92.16 140	97.22 151	71.58 251	98.00 177	85.85 204	97.81 110	98.88 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
gm-plane-assit					94.69 220	88.14 178			88.22 173		97.20 152		98.29 159	90.79 147

ACMMP		94.67 93	94.30 87	95.79 124	99.25 64	88.13 179	98.41 168	98.67 19	90.38 105	91.43 149	98.72 89	82.22 173	99.95 30	93.83 114	95.76 145	99.29 98
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
tfpnnormal		83.65 279	81.35 285	90.56 246	91.37 280	88.06 180	97.29 230	97.87 50	78.51 305	76.20 298	90.91 270	64.78 289	96.47 245	61.71 334	73.50 301	87.13 330

HyFIR lowres test		93.68 117	93.29 113	94.87 149	97.57 123	88.04 181	98.18 189	98.47 21	87.57 193	91.24 153	95.05 200	85.49 128	97.46 209	93.22 124	92.82 167	99.10 112

TR-MVS		90.77 172	89.44 178	94.76 152	96.31 161	88.02 182	97.92 205	95.96 229	85.52 224	88.22 187	97.23 150	66.80 279	98.09 168	84.58 214	92.38 175	98.17 172

GA-MVS		90.10 185	88.69 193	94.33 167	92.44 263	87.97 183	99.08 90	96.26 213	89.65 125	86.92 198	93.11 237	68.09 268	96.96 223	82.54 239	90.15 203	98.05 173

testing_2		80.92 291	77.24 300	91.98 216	78.88 346	87.83 184	93.96 302	95.72 250	84.27 246	56.20 343	80.42 337	38.64 349	96.40 250	87.20 185	79.85 260	91.72 249

APD-MVS_3200maxsize		95.64 72	95.65 68	95.62 128	99.24 65	87.80 185	98.42 166	97.22 151	88.93 149	96.64 75	98.98 62	85.49 128	99.36 120	96.68 57	99.27 75	99.70 57

MVS_111021_LR		95.78 68	95.94 55	95.28 140	98.19 106	87.69 186	98.80 119	99.26 7	93.39 43	95.04 102	98.69 94	84.09 145	99.76 68	96.96 55	99.06 81	98.38 160

VDDNet		90.08 186	88.54 199	94.69 156	94.41 225	87.68 187	98.21 187	96.40 203	76.21 315	93.33 127	97.75 129	54.93 322	98.77 145	94.71 101	90.96 196	97.61 184

TAMVS		92.62 141	92.09 139	94.20 172	94.10 228	87.68 187	98.41 168	96.97 177	87.53 195	89.74 175	96.04 187	84.77 140	96.49 244	88.97 170	92.31 177	98.42 156

cl-mvsnet2		89.57 193	88.79 191	91.91 217	97.94 112	87.62 189	97.98 203	96.51 197	85.03 234	82.37 240	91.79 254	83.65 148	96.50 242	85.96 199	77.89 268	91.61 256

v2v482		87.27 230	85.76 235	91.78 225	89.59 299	87.58 190	98.56 151	95.54 262	84.53 242	82.51 236	91.78 255	73.11 235	96.47 245	82.07 243	74.14 296	91.30 270

ADS-MVSNet		88.99 198	87.30 213	94.07 176	96.21 164	87.56 191	87.15 331	96.78 183	83.01 265	89.91 173	87.27 319	78.87 199	97.01 222	74.20 297	92.27 178	97.64 180

PLC	91.07 3	94.23 103	94.01 96	94.87 149	99.17 70	87.49 192	99.25 68	96.55 195	88.43 166	91.26 152	98.21 119	85.92 122	99.86 52	89.77 157	97.57 115	97.24 191
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MAR-MVS		94.43 99	94.09 94	95.45 134	99.10 75	87.47 193	98.39 174	97.79 61	88.37 168	94.02 118	99.17 37	78.64 203	99.91 38	92.48 132	98.85 90	98.96 121
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
tpmrst		92.78 137	92.16 136	94.65 157	96.27 162	87.45 194	91.83 317	97.10 167	89.10 142	94.68 107	90.69 277	88.22 72	97.73 196	89.78 156	91.80 186	98.77 142

DP-MVS		88.75 209	86.56 224	95.34 138	98.92 84	87.45 194	97.64 221	93.52 315	70.55 329	81.49 258	97.25 148	74.43 223	99.88 44	71.14 312	94.09 158	98.67 147

Fast-Effi-MVS+		91.72 156	90.79 163	94.49 162	95.89 175	87.40 196	99.54 35	95.70 252	85.01 236	89.28 180	95.68 191	77.75 207	97.57 206	83.22 230	95.06 151	98.51 153

MIMVSNet		84.48 270	81.83 280	92.42 208	91.73 275	87.36 197	85.52 334	94.42 300	81.40 288	81.91 251	87.58 316	51.92 331	92.81 326	73.84 300	88.15 209	97.08 197

IS-MVSNet		93.00 135	92.51 129	94.49 162	96.14 170	87.36 197	98.31 180	95.70 252	88.58 157	90.17 169	97.50 141	83.02 160	97.22 215	87.06 186	96.07 141	98.90 128

testdata				95.26 141	98.20 104	87.28 199		97.60 97	85.21 229	98.48 25	99.15 41	88.15 74	98.72 150	90.29 151	99.45 62	99.78 38

test-LLR		93.11 134	92.68 125	94.40 165	94.94 213	87.27 200	99.15 81	97.25 146	90.21 109	91.57 145	94.04 210	84.89 137	97.58 203	85.94 200	96.13 137	98.36 163

test-mter		93.27 130	92.89 122	94.40 165	94.94 213	87.27 200	99.15 81	97.25 146	88.95 147	91.57 145	94.04 210	88.03 77	97.58 203	85.94 200	96.13 137	98.36 163

SR-MVS-dyc-post		95.75 71	95.86 58	95.41 136	99.22 66	87.26 202	98.40 171	97.21 152	89.63 126	96.67 73	98.97 63	86.73 105	99.36 120	96.62 58	99.31 72	99.60 73

RE-MVS-def			95.70 65		99.22 66	87.26 202	98.40 171	97.21 152	89.63 126	96.67 73	98.97 63	85.24 133		96.62 58	99.31 72	99.60 73

test1172		95.92 62	96.07 52	95.46 133	99.42 49	87.24 204	98.51 156	97.24 148	90.29 108	96.56 76	99.12 46	86.73 105	99.36 120	97.33 47	99.42 67	99.78 38

v1144		86.83 235	85.31 242	91.40 227	89.75 297	87.21 205	98.31 180	95.45 267	83.22 262	82.70 233	90.78 273	73.36 231	96.36 253	79.49 259	74.69 288	90.63 293

OMC-MVS		93.90 110	93.62 108	94.73 155	98.63 94	87.00 206	98.04 201	96.56 194	92.19 69	92.46 136	98.73 87	79.49 195	99.14 135	92.16 135	94.34 157	98.03 174

abl_6		94.63 95	94.48 84	95.09 143	98.61 96	86.96 207	98.06 200	96.97 177	89.31 136	95.86 87	98.56 101	79.82 190	99.64 86	94.53 105	98.65 99	98.66 149

miper_ehance_all_eth		88.94 200	88.12 204	91.40 227	95.32 192	86.93 208	97.85 210	95.55 261	84.19 247	81.97 250	91.50 260	84.16 144	95.91 282	84.69 212	77.89 268	91.36 267

v8		86.11 247	84.45 256	91.10 232	89.99 293	86.85 209	97.24 234	95.36 275	81.99 282	79.89 274	89.86 302	74.53 222	96.39 251	78.83 266	72.32 311	90.05 305

CPTT-MVS		94.60 96	94.43 86	95.09 143	99.66 15	86.85 209	99.44 48	97.47 126	83.22 262	94.34 113	98.96 68	82.50 166	99.55 94	94.81 97	99.50 58	98.88 129

v10		85.73 256	84.01 262	90.87 240	90.03 292	86.73 211	97.20 237	95.22 285	81.25 290	79.85 275	89.75 303	73.30 234	96.28 265	76.87 277	72.64 307	89.61 312

Vis-MVSNet (Re-imp)		93.26 131	93.00 121	94.06 177	96.14 170	86.71 212	98.68 133	96.70 184	88.30 170	89.71 177	97.64 136	85.43 131	96.39 251	88.06 178	96.32 133	99.08 114

EIA-MVS		95.11 81	95.27 73	94.64 158	96.34 160	86.51 213	99.59 28	96.62 186	92.51 58	94.08 117	98.64 96	86.05 121	98.24 162	95.07 92	98.50 102	99.18 108

CSCG		94.87 85	94.71 80	95.36 137	99.54 36	86.49 214	99.34 63	98.15 34	82.71 272	90.15 170	99.25 25	89.48 56	99.86 52	94.97 95	98.82 93	99.72 54

tttt0517		93.30 128	93.01 120	94.17 173	95.57 184	86.47 215	98.51 156	97.60 97	85.99 219	90.55 162	97.19 153	94.80 7	98.31 157	85.06 208	91.86 184	97.74 179

dp		90.16 184	88.83 190	94.14 174	96.38 159	86.42 216	91.57 319	97.06 170	84.76 240	88.81 182	90.19 298	84.29 143	97.43 211	75.05 290	91.35 195	98.56 151

v1192		86.32 245	84.71 252	91.17 231	89.53 302	86.40 217	98.13 192	95.44 269	82.52 276	82.42 238	90.62 282	71.58 251	96.33 260	77.23 273	74.88 285	90.79 285

HQP5-MVS						86.39 218

HQP-MVS		91.50 158	91.23 152	92.29 209	93.95 232	86.39 218	99.16 75	96.37 205	93.92 31	87.57 190	96.67 173	73.34 232	97.77 189	93.82 115	86.29 216	92.72 224

PatchMatch-RL		91.47 159	90.54 167	94.26 170	98.20 104	86.36 220	96.94 245	97.14 160	87.75 187	88.98 181	95.75 190	71.80 248	99.40 117	80.92 252	97.39 121	97.02 199

LS3D		90.19 182	88.72 192	94.59 160	98.97 80	86.33 221	96.90 247	96.60 188	74.96 319	84.06 220	98.74 86	75.78 214	99.83 57	74.93 291	97.57 115	97.62 183

CR-MVSNet		88.83 205	87.38 212	93.16 194	93.47 247	86.24 222	84.97 338	94.20 305	88.92 150	90.76 159	86.88 323	84.43 141	94.82 309	70.64 313	92.17 181	98.41 157

RPMNet		85.07 262	81.88 279	94.64 158	93.47 247	86.24 222	84.97 338	97.21 152	64.85 343	90.76 159	78.80 341	80.95 186	99.27 128	53.76 344	92.17 181	98.41 157

NP-MVS					93.94 235	86.22 224					96.67 173

BH-w/o		92.32 146	91.79 144	93.91 182	96.85 143	86.18 225	99.11 89	95.74 249	88.13 175	84.81 212	97.00 162	77.26 210	97.91 178	89.16 168	98.03 108	97.64 180

cl_fuxian		88.19 217	87.23 215	91.06 233	94.97 211	86.17 226	97.72 217	95.38 273	83.43 259	81.68 257	91.37 262	82.81 163	95.72 288	84.04 224	73.70 298	91.29 271

MSDG		88.29 215	86.37 226	94.04 178	96.90 142	86.15 227	96.52 261	94.36 302	77.89 310	79.22 282	96.95 164	69.72 259	99.59 92	73.20 305	92.58 173	96.37 209

CLD-MVS		91.06 166	90.71 164	92.10 214	94.05 231	86.10 228	99.55 33	96.29 212	94.16 26	84.70 213	97.17 155	69.62 260	97.82 185	94.74 99	86.08 221	92.39 229
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
V42		87.00 232	85.68 237	90.98 236	89.91 294	86.08 229	98.32 179	95.61 257	83.67 256	82.72 232	90.67 278	74.00 229	96.53 240	81.94 246	74.28 293	90.32 298

HQP_MVS		91.26 162	90.95 157	92.16 213	93.84 239	86.07 230	99.02 97	96.30 209	93.38 44	86.99 196	96.52 175	72.92 236	97.75 194	93.46 120	86.17 219	92.67 226

plane_prior						86.07 230	99.14 84		93.81 37						86.26 218

plane_prior6					93.92 236	86.02 232						72.92 236

plane_prior3						85.91 233			93.65 39	86.99 196

CostFormer		92.89 136	92.48 130	94.12 175	94.99 210	85.89 234	92.89 311	97.00 176	86.98 202	95.00 103	90.78 273	90.05 48	97.51 207	92.92 129	91.73 188	98.96 121

EI-MVSNet		89.87 189	89.38 180	91.36 229	94.32 226	85.87 235	97.61 222	96.59 190	85.10 231	85.51 208	97.10 157	81.30 185	96.56 238	83.85 227	83.03 243	91.64 251

IterMVS-LS		88.34 213	87.44 210	91.04 234	94.10 228	85.85 236	98.10 196	95.48 265	85.12 230	82.03 248	91.21 266	81.35 184	95.63 291	83.86 226	75.73 280	91.63 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDD-MVS		91.24 165	90.18 171	94.45 164	97.08 137	85.84 237	98.40 171	96.10 223	86.99 200	93.36 126	98.16 120	54.27 324	99.20 129	96.59 61	90.63 201	98.31 166

plane_prior7					93.84 239	85.73 238

EPP-MVSNet		93.75 114	93.67 107	94.01 179	95.86 176	85.70 239	98.67 135	97.66 82	84.46 243	91.36 151	97.18 154	91.16 26	97.79 187	92.93 128	93.75 160	98.53 152

v144192		86.40 243	84.89 248	90.91 237	89.48 303	85.59 240	98.21 187	95.43 270	82.45 277	82.62 234	90.58 285	72.79 239	96.36 253	78.45 268	74.04 297	90.79 285

OPM-MVS		89.76 190	89.15 184	91.57 226	90.53 289	85.58 241	98.11 195	95.93 234	92.88 53	86.05 204	96.47 178	67.06 278	97.87 182	89.29 166	86.08 221	91.26 273

tpm2		91.77 155	91.09 153	93.82 185	94.83 217	85.56 242	92.51 314	97.16 159	84.00 250	93.83 122	90.66 279	87.54 85	97.17 216	87.73 181	91.55 191	98.72 144

cl-mvsnet_		87.82 219	86.79 222	90.89 239	94.88 215	85.43 243	97.81 211	95.24 281	82.91 271	80.71 264	91.22 265	81.97 177	95.84 284	81.34 249	75.06 283	91.40 266

cl-mvsnet1		87.82 219	86.81 221	90.87 240	94.87 216	85.39 244	97.81 211	95.22 285	82.92 270	80.76 263	91.31 264	81.99 175	95.81 286	81.36 248	75.04 284	91.42 265

tpm cat1		88.89 201	87.27 214	93.76 186	95.79 177	85.32 245	90.76 326	97.09 168	76.14 316	85.72 206	88.59 312	82.92 161	98.04 174	76.96 276	91.43 192	97.90 178

v1921920		86.02 248	84.44 257	90.77 242	89.32 305	85.20 246	98.10 196	95.35 276	82.19 280	82.25 242	90.71 275	70.73 254	96.30 264	76.85 278	74.49 289	90.80 284

pm-mvs1		84.68 266	82.78 272	90.40 250	89.58 300	85.18 247	97.31 229	94.73 291	81.93 284	76.05 300	92.01 250	65.48 287	96.11 272	78.75 267	69.14 323	89.91 308

TAPA-MVS	87.50 9	90.35 177	89.05 185	94.25 171	98.48 100	85.17 248	98.42 166	96.58 193	82.44 278	87.24 195	98.53 102	82.77 164	98.84 143	59.09 339	97.88 109	98.72 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1240		85.77 255	84.11 260	90.73 243	89.26 306	85.15 249	97.88 208	95.23 284	81.89 285	82.16 243	90.55 287	69.60 261	96.31 261	75.59 288	74.87 286	90.72 290

ppachtmachnet_test		83.63 280	81.57 283	89.80 264	89.01 307	85.09 250	97.13 239	94.50 297	78.84 302	76.14 299	91.00 269	69.78 258	94.61 314	63.40 330	74.36 291	89.71 311

Anonymous20240529		87.66 225	85.58 238	93.92 181	97.59 122	85.01 251	98.13 192	97.13 162	66.69 341	88.47 185	96.01 188	55.09 321	99.51 101	87.00 188	84.12 233	97.23 192

EPNet_dtu		92.28 147	92.15 137	92.70 204	97.29 129	84.84 252	98.64 139	97.82 54	92.91 51	93.02 132	97.02 161	85.48 130	95.70 289	72.25 309	94.89 152	97.55 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-untuned		91.46 160	90.84 160	93.33 191	96.51 156	84.83 253	98.84 116	95.50 264	86.44 216	83.50 222	96.70 172	75.49 216	97.77 189	86.78 193	97.81 110	97.40 186

tpmvs		89.16 196	87.76 205	93.35 190	97.19 132	84.75 254	90.58 328	97.36 142	81.99 282	84.56 214	89.31 309	83.98 146	98.17 163	74.85 293	90.00 204	97.12 193

PVSNet_0	83.28 16	87.31 229	85.16 243	93.74 187	94.78 218	84.59 255	98.91 109	98.69 18	89.81 122	78.59 289	93.23 234	61.95 299	99.34 125	94.75 98	55.72 344	97.30 189

Anonymous20231211		84.72 265	82.65 276	90.91 237	97.71 116	84.55 256	97.28 231	96.67 185	66.88 340	79.18 283	90.87 272	58.47 308	96.60 236	82.61 238	74.20 294	91.59 258

test0.0.03 1		88.96 199	88.61 195	90.03 260	91.09 283	84.43 257	98.97 103	97.02 174	90.21 109	80.29 268	96.31 183	84.89 137	91.93 337	72.98 306	85.70 224	93.73 219

PS-MVSNAJss		89.54 194	89.05 185	91.00 235	88.77 310	84.36 258	97.39 226	95.97 227	88.47 159	81.88 252	93.80 220	82.48 168	96.50 242	89.34 163	83.34 242	92.15 239

pmmvs5		85.87 250	84.40 259	90.30 252	88.53 314	84.23 259	98.60 146	93.71 312	81.53 287	80.29 268	92.02 249	64.51 290	95.52 293	82.04 245	78.34 266	91.15 275

Anonymous202405211		88.84 203	87.03 218	94.27 169	98.14 108	84.18 260	98.44 164	95.58 260	76.79 314	89.34 179	96.88 168	53.42 328	99.54 96	87.53 184	87.12 214	99.09 113

v148		86.38 244	85.06 244	90.37 251	89.47 304	84.10 261	98.52 153	95.48 265	83.80 252	80.93 262	90.22 296	74.60 220	96.31 261	80.92 252	71.55 317	90.69 291

TransMVSNet (Re)		81.97 286	79.61 294	89.08 281	89.70 298	84.01 262	97.26 232	91.85 333	78.84 302	73.07 318	91.62 257	67.17 277	95.21 301	67.50 320	59.46 340	88.02 321

FMVSNet5		82.29 284	80.54 289	87.52 296	93.79 242	84.01 262	93.73 304	92.47 324	76.92 313	74.27 310	86.15 328	63.69 294	89.24 342	69.07 315	74.79 287	89.29 315

our_test_3		84.47 271	82.80 270	89.50 273	89.01 307	83.90 264	97.03 242	94.56 296	81.33 289	75.36 307	90.52 288	71.69 249	94.54 315	68.81 316	76.84 277	90.07 303

MVP-Stereo		86.61 240	85.83 234	88.93 285	88.70 312	83.85 265	96.07 277	94.41 301	82.15 281	75.64 304	91.96 252	67.65 273	96.45 247	77.20 275	98.72 96	86.51 333
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS		85.81 253	84.67 253	89.22 278	93.51 246	83.67 266	96.32 266	94.80 289	85.09 232	78.69 285	90.17 299	66.57 282	93.17 323	79.48 260	77.42 274	90.81 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
USDC		84.74 264	82.93 268	90.16 255	91.73 275	83.54 267	95.00 292	93.30 317	88.77 153	73.19 314	93.30 232	53.62 327	97.65 199	75.88 286	81.54 253	89.30 314

D2MVS		87.96 218	87.39 211	89.70 267	91.84 273	83.40 268	98.31 180	98.49 20	88.04 178	78.23 293	90.26 292	73.57 230	96.79 231	84.21 218	83.53 239	88.90 319

Baseline_NR-MVSNet		85.83 252	84.82 250	88.87 286	88.73 311	83.34 269	98.63 141	91.66 334	80.41 297	82.44 237	91.35 263	74.63 218	95.42 296	84.13 220	71.39 318	87.84 322

WR-MVS_H		86.53 242	85.49 240	89.66 270	91.04 284	83.31 270	97.53 224	98.20 30	84.95 237	79.64 276	90.90 271	78.01 206	95.33 298	76.29 283	72.81 305	90.35 297

test_part1		82.56 283	80.00 292	90.23 253	91.26 281	83.25 271	96.62 258	95.60 258	64.00 345	75.63 305	89.93 300	53.97 326	96.16 270	82.26 242	70.73 321	91.29 271

LTVRE_ROB	81.71 19	84.59 268	82.72 274	90.18 254	92.89 260	83.18 272	93.15 309	94.74 290	78.99 301	75.14 308	92.69 242	65.64 286	97.63 200	69.46 314	81.82 252	89.74 309
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
PatchT		85.44 259	83.19 266	92.22 210	93.13 256	83.00 273	83.80 344	96.37 205	70.62 328	90.55 162	79.63 340	84.81 139	94.87 307	58.18 341	91.59 190	98.79 139

anonymousdsp		86.69 237	85.75 236	89.53 272	86.46 330	82.94 274	96.39 263	95.71 251	83.97 251	79.63 277	90.70 276	68.85 263	95.94 278	86.01 197	84.02 234	89.72 310

ACMH	83.09 17	84.60 267	82.61 277	90.57 245	93.18 255	82.94 274	96.27 267	94.92 288	81.01 292	72.61 321	93.61 225	56.54 313	97.79 187	74.31 296	81.07 254	90.99 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS-SCA-FT		85.73 256	84.64 254	89.00 283	93.46 249	82.90 276	96.27 267	94.70 292	85.02 235	78.62 287	90.35 291	66.61 280	93.33 322	79.38 261	77.36 275	90.76 287

F-COLMAP		92.07 151	91.75 146	93.02 196	98.16 107	82.89 277	98.79 123	95.97 227	86.54 213	87.92 188	97.80 126	78.69 202	99.65 84	85.97 198	95.93 143	96.53 207

Patchmatch-test		86.25 246	84.06 261	92.82 200	94.42 224	82.88 278	82.88 345	94.23 304	71.58 326	79.39 280	90.62 282	89.00 61	96.42 249	63.03 331	91.37 194	99.16 109

Patchmtry		83.61 281	81.64 281	89.50 273	93.36 251	82.84 279	84.10 341	94.20 305	69.47 334	79.57 278	86.88 323	84.43 141	94.78 310	68.48 318	74.30 292	90.88 282

CP-MVSNet		86.54 241	85.45 241	89.79 265	91.02 285	82.78 280	97.38 228	97.56 108	85.37 227	79.53 279	93.03 238	71.86 247	95.25 300	79.92 257	73.43 303	91.34 268

AUN-MVS		90.17 183	89.50 176	92.19 212	96.21 164	82.67 281	97.76 216	97.53 113	88.05 177	91.67 143	96.15 184	83.10 159	97.47 208	88.11 177	66.91 329	96.43 208

eth_miper_zixun_eth		87.76 221	87.00 219	90.06 257	94.67 221	82.65 282	97.02 244	95.37 274	84.19 247	81.86 255	91.58 259	81.47 182	95.90 283	83.24 229	73.61 299	91.61 256

MS-PatchMatch		86.75 236	85.92 233	89.22 278	91.97 269	82.47 283	96.91 246	96.14 222	83.74 253	77.73 294	93.53 228	58.19 309	97.37 214	76.75 279	98.35 105	87.84 322

test_djsdf		88.26 216	87.73 206	89.84 263	88.05 319	82.21 284	97.77 214	96.17 220	86.84 205	82.41 239	91.95 253	72.07 244	95.99 275	89.83 154	84.50 230	91.32 269

PS-CasMVS		85.81 253	84.58 255	89.49 275	90.77 287	82.11 285	97.20 237	97.36 142	84.83 239	79.12 284	92.84 241	67.42 275	95.16 302	78.39 269	73.25 304	91.21 274

v7n		84.42 272	82.75 273	89.43 276	88.15 317	81.86 286	96.75 253	95.67 255	80.53 295	78.38 291	89.43 307	69.89 257	96.35 258	73.83 301	72.13 313	90.07 303

jajsoiax		87.35 228	86.51 225	89.87 261	87.75 324	81.74 287	97.03 242	95.98 226	88.47 159	80.15 270	93.80 220	61.47 300	96.36 253	89.44 161	84.47 231	91.50 260

MVS-HIRNet		79.01 299	75.13 308	90.66 244	93.82 241	81.69 288	85.16 335	93.75 311	54.54 346	74.17 311	59.15 349	57.46 311	96.58 237	63.74 329	94.38 155	93.72 220

tpm		89.67 191	88.95 187	91.82 220	92.54 262	81.43 289	92.95 310	95.92 235	87.81 184	90.50 164	89.44 306	84.99 135	95.65 290	83.67 228	82.71 246	98.38 160

PMMVS		93.62 120	93.90 104	92.79 201	96.79 147	81.40 290	98.85 114	96.81 181	91.25 88	96.82 68	98.15 121	77.02 211	98.13 165	93.15 126	96.30 135	98.83 135

mvs_tets		87.09 231	86.22 228	89.71 266	87.87 320	81.39 291	96.73 255	95.90 240	88.19 174	79.99 272	93.61 225	59.96 306	96.31 261	89.40 162	84.34 232	91.43 264

ACMM	86.95 13	88.77 208	88.22 203	90.43 249	93.61 244	81.34 292	98.50 158	95.92 235	87.88 183	83.85 221	95.20 198	67.20 276	97.89 180	86.90 191	84.90 227	92.06 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PEN-MVS		85.21 261	83.93 263	89.07 282	89.89 296	81.31 293	97.09 240	97.24 148	84.45 244	78.66 286	92.68 243	68.44 266	94.87 307	75.98 285	70.92 320	91.04 278

XVG-OURS		90.83 171	90.49 168	91.86 218	95.23 194	81.25 294	95.79 287	95.92 235	88.96 146	90.02 172	98.03 122	71.60 250	99.35 124	91.06 142	87.78 211	94.98 215

miper_lstm_enhance		86.90 233	86.20 229	89.00 283	94.53 223	81.19 295	96.74 254	95.24 281	82.33 279	80.15 270	90.51 289	81.99 175	94.68 313	80.71 254	73.58 300	91.12 276

pmmvs-eth3d		78.71 302	76.16 305	86.38 303	80.25 343	81.19 295	94.17 299	92.13 329	77.97 307	66.90 335	82.31 332	55.76 315	92.56 330	73.63 303	62.31 337	85.38 336

XVG-OURS-SEG-HR		90.95 169	90.66 166	91.83 219	95.18 199	81.14 297	95.92 279	95.92 235	88.40 167	90.33 168	97.85 123	70.66 256	99.38 118	92.83 130	88.83 207	94.98 215

ACMP	87.39 10	88.71 210	88.24 202	90.12 256	93.91 237	81.06 298	98.50 158	95.67 255	89.43 134	80.37 267	95.55 192	65.67 285	97.83 184	90.55 149	84.51 229	91.47 261
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		88.86 202	88.47 200	90.06 257	93.35 252	80.95 299	98.22 185	95.94 232	87.73 189	83.17 227	96.11 185	66.28 283	97.77 189	90.19 152	85.19 225	91.46 262

LGP-MVS_train				90.06 257	93.35 252	80.95 299		95.94 232	87.73 189	83.17 227	96.11 185	66.28 283	97.77 189	90.19 152	85.19 225	91.46 262

UniMVSNet_ETH3D		85.65 258	83.79 264	91.21 230	90.41 291	80.75 301	95.36 289	95.78 246	78.76 304	81.83 256	94.33 208	49.86 336	96.66 234	84.30 216	83.52 240	96.22 210

MVS_0304		84.13 276	82.66 275	88.52 288	93.07 257	80.15 302	95.81 286	98.21 29	79.27 299	86.85 200	86.40 326	41.33 346	94.69 312	76.36 282	86.69 215	90.73 289

MDA-MVSNet_test_wron		79.65 297	77.05 301	87.45 297	87.79 323	80.13 303	96.25 270	94.44 298	73.87 323	51.80 345	87.47 318	68.04 269	92.12 335	66.02 325	67.79 327	90.09 301

YYNet1		79.64 298	77.04 302	87.43 298	87.80 322	79.98 304	96.23 271	94.44 298	73.83 324	51.83 344	87.53 317	67.96 271	92.07 336	66.00 326	67.75 328	90.23 300

DTE-MVSNet		84.14 275	82.80 270	88.14 291	88.95 309	79.87 305	96.81 249	96.24 214	83.50 258	77.60 295	92.52 245	67.89 272	94.24 318	72.64 308	69.05 324	90.32 298

ACMH+	83.78 15	84.21 273	82.56 278	89.15 280	93.73 243	79.16 306	96.43 262	94.28 303	81.09 291	74.00 312	94.03 212	54.58 323	97.67 197	76.10 284	78.81 264	90.63 293

ADS-MVSNet2		87.62 226	86.88 220	89.86 262	96.21 164	79.14 307	87.15 331	92.99 318	83.01 265	89.91 173	87.27 319	78.87 199	92.80 327	74.20 297	92.27 178	97.64 180

COLMAP_ROB	82.69 18	84.54 269	82.82 269	89.70 267	96.72 149	78.85 308	95.89 280	92.83 321	71.55 327	77.54 296	95.89 189	59.40 307	99.14 135	67.26 321	88.26 208	91.11 277
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
AllTest		84.97 263	83.12 267	90.52 247	96.82 144	78.84 309	95.89 280	92.17 327	77.96 308	75.94 301	95.50 193	55.48 317	99.18 130	71.15 310	87.14 212	93.55 221

TestCases				90.52 247	96.82 144	78.84 309		92.17 327	77.96 308	75.94 301	95.50 193	55.48 317	99.18 130	71.15 310	87.14 212	93.55 221

TinyColmap		80.42 294	77.94 296	87.85 293	92.09 268	78.58 311	93.74 303	89.94 343	74.99 318	69.77 324	91.78 255	46.09 340	97.58 203	65.17 328	77.89 268	87.38 325

MDA-MVSNet-bldmvs		77.82 306	74.75 310	87.03 300	88.33 315	78.52 312	96.34 265	92.85 320	75.57 317	48.87 347	87.89 314	57.32 312	92.49 331	60.79 335	64.80 333	90.08 302

test_0402		78.81 301	76.33 304	86.26 304	91.18 282	78.44 313	95.88 282	91.34 338	68.55 335	70.51 323	89.91 301	52.65 330	94.99 303	47.14 347	79.78 261	85.34 338

Fast-Effi-MVS+-dtu		88.84 203	88.59 197	89.58 271	93.44 250	78.18 314	98.65 137	94.62 295	88.46 161	84.12 219	95.37 197	68.91 262	96.52 241	82.06 244	91.70 189	94.06 218

pmmvs6		79.90 296	77.31 299	87.67 295	84.17 335	78.13 315	95.86 284	93.68 313	67.94 338	72.67 320	89.62 305	50.98 334	95.75 287	74.80 294	66.04 330	89.14 317

DeepPCF-MVS	93.56 1	96.55 41	97.84 8	92.68 205	98.71 92	78.11 316	99.70 17	97.71 77	98.18 1	97.36 52	99.76 1	90.37 45	99.94 33	99.27 9	99.54 57	99.99 1

OpenMVS_ROB	73.86 20	77.99 305	75.06 309	86.77 302	83.81 337	77.94 317	96.38 264	91.53 337	67.54 339	68.38 327	87.13 322	43.94 342	96.08 273	55.03 343	81.83 251	86.29 334

EG-PatchMatch MVS		79.92 295	77.59 297	86.90 301	87.06 328	77.90 318	96.20 275	94.06 307	74.61 320	66.53 336	88.76 311	40.40 348	96.20 266	67.02 322	83.66 238	86.61 331

XVG-ACMP-BASELINE		85.86 251	84.95 247	88.57 287	89.90 295	77.12 319	94.30 297	95.60 258	87.40 197	82.12 244	92.99 240	53.42 328	97.66 198	85.02 209	83.83 235	90.92 281

ITE_SJBPF				87.93 292	92.26 265	76.44 320		93.47 316	87.67 192	79.95 273	95.49 195	56.50 314	97.38 212	75.24 289	82.33 249	89.98 307

UnsupCasMVSNet_bld		73.85 312	70.14 315	84.99 311	79.44 344	75.73 321	88.53 329	95.24 281	70.12 332	61.94 340	74.81 342	41.41 345	93.62 320	68.65 317	51.13 348	85.62 335

MIMVSNet1		75.92 309	73.30 312	83.81 316	81.29 341	75.57 322	92.26 315	92.05 330	73.09 325	67.48 333	86.18 327	40.87 347	87.64 345	55.78 342	70.68 322	88.21 320

UnsupCasMVSNet_eth		78.90 300	76.67 303	85.58 309	82.81 339	74.94 323	91.98 316	96.31 208	84.64 241	65.84 337	87.71 315	51.33 332	92.23 333	72.89 307	56.50 343	89.56 313

testgi		82.29 284	81.00 288	86.17 305	87.24 326	74.84 324	97.39 226	91.62 335	88.63 154	75.85 303	95.42 196	46.07 341	91.55 338	66.87 324	79.94 259	92.12 240

mvs-test1		91.57 157	92.20 135	89.70 267	95.15 200	74.34 325	99.51 37	95.40 271	91.92 73	91.02 155	97.25 148	74.27 225	98.08 171	89.45 159	95.83 144	96.67 201

pmmvs3		72.86 313	69.76 317	82.17 320	73.86 348	74.19 326	94.20 298	89.01 346	64.23 344	67.72 330	80.91 336	41.48 344	88.65 344	62.40 332	54.02 346	83.68 341

TDRefinement		78.01 304	75.31 307	86.10 306	70.06 350	73.84 327	93.59 307	91.58 336	74.51 321	73.08 317	91.04 268	49.63 337	97.12 217	74.88 292	59.47 339	87.33 327

JIA-IIPM		85.97 249	84.85 249	89.33 277	93.23 254	73.68 328	85.05 337	97.13 162	69.62 333	91.56 147	68.03 345	88.03 77	96.96 223	77.89 271	93.12 164	97.34 188

CVMVSNet		90.30 179	90.91 158	88.46 290	94.32 226	73.58 329	97.61 222	97.59 101	90.16 114	88.43 186	97.10 157	76.83 212	92.86 324	82.64 237	93.54 162	98.93 126

Anonymous20231206		80.76 292	79.42 295	84.79 313	84.78 333	72.98 330	96.53 260	92.97 319	79.56 298	74.33 309	88.83 310	61.27 302	92.15 334	60.59 336	75.92 279	89.24 316

new_pmnet		76.02 308	73.71 311	82.95 318	83.88 336	72.85 331	91.26 322	92.26 326	70.44 330	62.60 339	81.37 334	47.64 339	92.32 332	61.85 333	72.10 314	83.68 341

LCM-MVSNet-Re		88.59 211	88.61 195	88.51 289	95.53 187	72.68 332	96.85 248	88.43 347	88.45 162	73.14 315	90.63 281	75.82 213	94.38 316	92.95 127	95.71 147	98.48 155

new-patchmatchnet		74.80 311	72.40 314	81.99 322	78.36 347	72.20 333	94.44 295	92.36 325	77.06 311	63.47 338	79.98 339	51.04 333	88.85 343	60.53 337	54.35 345	84.92 339

Effi-MVS+-dtu		89.97 188	90.68 165	87.81 294	95.15 200	71.98 334	97.87 209	95.40 271	91.92 73	87.57 190	91.44 261	74.27 225	96.84 227	89.45 159	93.10 165	94.60 217

test20.03		78.51 303	77.48 298	81.62 323	83.07 338	71.03 335	96.11 276	92.83 321	81.66 286	69.31 325	89.68 304	57.53 310	87.29 346	58.65 340	68.47 325	86.53 332

SixPastTwentyTwo		82.63 282	81.58 282	85.79 307	88.12 318	71.01 336	95.17 291	92.54 323	84.33 245	72.93 319	92.08 247	60.41 305	95.61 292	74.47 295	74.15 295	90.75 288

OurMVSNet-221017-0		84.13 276	83.59 265	85.77 308	87.81 321	70.24 337	94.89 293	93.65 314	86.08 218	76.53 297	93.28 233	61.41 301	96.14 271	80.95 251	77.69 273	90.93 280

K. test v3		81.04 290	79.77 293	84.83 312	87.41 325	70.23 338	95.60 288	93.93 309	83.70 255	67.51 332	89.35 308	55.76 315	93.58 321	76.67 280	68.03 326	90.67 292

Patchmatch-RL test		81.90 288	80.13 290	87.23 299	80.71 342	70.12 339	84.07 342	88.19 348	83.16 264	70.57 322	82.18 333	87.18 95	92.59 329	82.28 241	62.78 334	98.98 119

lessismore_v0				85.08 310	85.59 331	69.28 340		90.56 341		67.68 331	90.21 297	54.21 325	95.46 294	73.88 299	62.64 335	90.50 295

CL-MVSNet_2432*1600		77.47 307	75.88 306	82.24 319	81.59 340	68.93 341	92.83 312	94.02 308	77.03 312	73.14 315	83.39 330	55.44 319	90.42 340	67.95 319	57.53 342	87.38 325

LF4IMVS		81.94 287	81.17 287	84.25 315	87.23 327	68.87 342	93.35 308	91.93 332	83.35 261	75.40 306	93.00 239	49.25 338	96.65 235	78.88 265	78.11 267	87.22 329

EU-MVSNet		84.19 274	84.42 258	83.52 317	88.64 313	67.37 343	96.04 278	95.76 248	85.29 228	78.44 290	93.18 235	70.67 255	91.48 339	75.79 287	75.98 278	91.70 250

PM-MVS		74.88 310	72.85 313	80.98 325	78.98 345	64.75 344	90.81 325	85.77 350	80.95 293	68.23 329	82.81 331	29.08 351	92.84 325	76.54 281	62.46 336	85.36 337

RPSCF		85.33 260	85.55 239	84.67 314	94.63 222	62.28 345	93.73 304	93.76 310	74.38 322	85.23 211	97.06 160	64.09 291	98.31 157	80.98 250	86.08 221	93.41 223

DSMNet-mixed		81.60 289	81.43 284	82.10 321	84.36 334	60.79 346	93.63 306	86.74 349	79.00 300	79.32 281	87.15 321	63.87 293	89.78 341	66.89 323	91.92 183	95.73 213

CMPMVS	58.40 21	80.48 293	80.11 291	81.59 324	85.10 332	59.56 347	94.14 300	95.95 231	68.54 336	60.71 341	93.31 231	55.35 320	97.87 182	83.06 234	84.85 228	87.33 327
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Gipuma		54.77 318	52.22 322	62.40 333	86.50 329	59.37 348	50.20 352	90.35 342	36.52 350	41.20 350	49.49 351	18.33 355	81.29 348	32.10 350	65.34 331	46.54 350
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ambc				79.60 326	72.76 349	56.61 349	76.20 347	92.01 331		68.25 328	80.23 338	23.34 352	94.73 311	73.78 302	60.81 338	87.48 324

PMMVS2		58.97 317	55.07 320	70.69 330	62.72 351	55.37 350	85.97 333	80.52 353	49.48 347	45.94 348	68.31 344	15.73 357	80.78 349	49.79 346	37.12 349	75.91 344

ANet_high		50.71 320	46.17 323	64.33 332	44.27 358	52.30 351	76.13 348	78.73 354	64.95 342	27.37 353	55.23 350	14.61 358	67.74 352	36.01 349	18.23 352	72.95 346

DeepMVS_CX				76.08 327	90.74 288	51.65 352		90.84 340	86.47 215	57.89 342	87.98 313	35.88 350	92.60 328	65.77 327	65.06 332	83.97 340

LCM-MVSNet		60.07 316	56.37 319	71.18 328	54.81 356	48.67 353	82.17 346	89.48 345	37.95 349	49.13 346	69.12 343	13.75 359	81.76 347	59.28 338	51.63 347	83.10 343

MVE	44.00 22	41.70 322	37.64 327	53.90 336	49.46 357	43.37 354	65.09 351	66.66 357	26.19 354	25.77 355	48.53 352	3.58 362	63.35 354	26.15 352	27.28 350	54.97 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
FPMVS		61.57 315	60.32 318	65.34 331	60.14 354	42.44 355	91.02 324	89.72 344	44.15 348	42.63 349	80.93 335	19.02 353	80.59 350	42.50 348	72.76 306	73.00 345

tmp_tt		53.66 319	52.86 321	56.05 334	32.75 360	41.97 356	73.42 349	76.12 356	21.91 355	39.68 351	96.39 181	42.59 343	65.10 353	78.00 270	14.92 354	61.08 347

E-PMN		41.02 323	40.93 325	41.29 337	61.97 352	33.83 357	84.00 343	65.17 358	27.17 352	27.56 352	46.72 353	17.63 356	60.41 355	19.32 353	18.82 351	29.61 351

PMVS	41.42 23	45.67 321	42.50 324	55.17 335	34.28 359	32.37 358	66.24 350	78.71 355	30.72 351	22.04 356	59.59 348	4.59 360	77.85 351	27.49 351	58.84 341	55.29 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		39.96 324	39.88 326	40.18 338	59.57 355	32.12 359	84.79 340	64.57 359	26.27 353	26.14 354	44.18 356	18.73 354	59.29 356	17.03 354	17.67 353	29.12 352

N_pmnet		70.19 314	69.87 316	71.12 329	88.24 316	30.63 360	95.85 285	28.70 360	70.18 331	68.73 326	86.55 325	64.04 292	93.81 319	53.12 345	73.46 302	88.94 318

wuyk23d		16.71 327	16.73 331	16.65 339	60.15 353	25.22 361	41.24 353	5.17 361	6.56 356	5.48 359	3.61 359	3.64 361	22.72 357	15.20 355	9.52 355	1.99 355

test123		16.58 328	19.47 330	7.91 340	3.59 362	5.37 362	94.32 296	1.39 363	2.49 358	13.98 358	44.60 355	2.91 363	2.65 358	11.35 357	0.57 357	15.70 353

testmvs		18.81 326	23.05 329	6.10 341	4.48 361	2.29 363	97.78 213	3.00 362	3.27 357	18.60 357	62.71 346	1.53 364	2.49 359	14.26 356	1.80 356	13.50 354

uanet_test		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

cdsmvs_eth3d_5k		22.52 325	30.03 328	0.00 342	0.00 363	0.00 364	0.00 354	97.17 158	0.00 359	0.00 360	98.77 83	74.35 224	0.00 360	0.00 358	0.00 358	0.00 356

pcd_1.5k_mvsjas		6.87 330	9.16 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	82.48 168	0.00 360	0.00 358	0.00 358	0.00 356

sosnet-low-res		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

sosnet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uncertanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

Regformer		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

ab-mvs-re		8.21 329	10.94 332	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	98.50 105	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

uanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 358	0.00 356

test_241102_TWO								97.72 73	94.17 24	99.23 6	99.54 3	93.14 20	99.98 10	99.70 2	99.82 15	99.99 1

9.14			96.87 26		99.34 53		99.50 38	97.49 123	89.41 135	98.59 22	99.43 16	89.78 51	99.69 75	98.69 17	99.62 47

test_0728_THIRD									93.01 47	99.07 8	99.46 11	94.66 10	99.97 20	99.25 11	99.82 15	99.95 11

GSMVS																98.84 132

sam_mvs1												88.39 70				98.84 132

sam_mvs												87.08 96

MTGPA								97.45 128

test_post1							90.74 327				41.37 357	85.38 132	96.36 253	83.16 231

test_post											46.00 354	87.37 89	97.11 218

patchmatchnet-post											84.86 329	88.73 64	96.81 229

MTMP							99.21 69	91.09 339

test9_res														98.60 19	99.87 7	99.90 20

agg_prior2														97.84 42	99.87 7	99.91 18

test_prior2							99.57 30		91.43 83	98.12 34	98.97 63	90.43 40		98.33 30	99.81 19

旧先验2							98.67 135		85.75 222	98.96 12			98.97 141	93.84 113

新几何2							98.26 183

无先验							98.52 153	97.82 54	87.20 199				99.90 40	87.64 182		99.85 29

原ACMM2							98.69 131

testdata2													99.88 44	84.16 219

segment_acmp												90.56 39

testdata1							97.89 206		92.43 61

plane_prior5								96.30 209					97.75 194	93.46 120	86.17 219	92.67 226

plane_prior4											96.52 175

plane_prior2							99.02 97		93.38 44

plane_prior1					93.90 238

n2								0.00 364

nn								0.00 364

door-mid								84.90 352

test11								97.68 80

door								85.30 351

HQP-NCC					93.95 232		99.16 75		93.92 31	87.57 190

ACMP_Plane					93.95 232		99.16 75		93.92 31	87.57 190

BP-MVS														93.82 115

HQP4-MVS										87.57 190			97.77 189			92.72 224

HQP3-MVS								96.37 205							86.29 216

HQP2-MVS												73.34 232

ACMMP++_ref															82.64 247

ACMMP++															83.83 235

Test By Simon												83.62 149

High-resolution multi-view benchmark