-
Notifications
You must be signed in to change notification settings - Fork 13
/
index.html
289 lines (240 loc) · 8.66 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
<!-- saved from url=-->
<html><head><meta http-equiv="Content-Type" content="text/html; charset=windows-1252"><script src="jsapi" type="text/javascript"></script>
<script type="text/javascript">google.load("jquery", "1.3.2");</script>
<style type="text/css">
body {
font-family: "HelveticaNeue-Light", "Helvetica Neue Light", "Helvetica Neue", Helvetica, Arial, "Lucida Grande", sans-serif;
font-weight:300;
font-size:18px;
margin-left: auto;
margin-right: auto;
width: 1100px;
}
h1 {
font-weight:300;
}
.disclaimerbox {
background-color: #eee;
border: 1px solid #eeeeee;
border-radius: 10px ;
-moz-border-radius: 10px ;
-webkit-border-radius: 10px ;
padding: 20px;
}
video.header-vid {
height: 140px;
border: 1px solid black;
border-radius: 10px ;
-moz-border-radius: 10px ;
-webkit-border-radius: 10px ;
}
img.header-img {
height: 140px;
border: 1px solid black;
border-radius: 10px ;
-moz-border-radius: 10px ;
-webkit-border-radius: 10px ;
}
img.rounded {
border: 1px solid #eeeeee;
border-radius: 10px ;
-moz-border-radius: 10px ;
-webkit-border-radius: 10px ;
}
a:link,a:visited
{
color: #1367a7;
text-decoration: none;
}
a:hover {
color: #208799;
}
td.dl-link {
height: 160px;
text-align: center;
font-size: 22px;
}
.layered-paper-big { /* modified from: http://css-tricks.com/snippets/css/layered-paper/ */
box-shadow:
0px 0px 1px 1px rgba(0,0,0,0.35), /* The top layer shadow */
5px 5px 0 0px #fff, /* The second layer */
5px 5px 1px 1px rgba(0,0,0,0.35), /* The second layer shadow */
10px 10px 0 0px #fff, /* The third layer */
10px 10px 1px 1px rgba(0,0,0,0.35), /* The third layer shadow */
15px 15px 0 0px #fff, /* The fourth layer */
15px 15px 1px 1px rgba(0,0,0,0.35), /* The fourth layer shadow */
20px 20px 0 0px #fff, /* The fifth layer */
20px 20px 1px 1px rgba(0,0,0,0.35), /* The fifth layer shadow */
25px 25px 0 0px #fff, /* The fifth layer */
25px 25px 1px 1px rgba(0,0,0,0.35); /* The fifth layer shadow */
margin-left: 10px;
margin-right: 45px;
}
.layered-paper { /* modified from: http://css-tricks.com/snippets/css/layered-paper/ */
box-shadow:
0px 0px 1px 1px rgba(0,0,0,0.35), /* The top layer shadow */
5px 5px 0 0px #fff, /* The second layer */
5px 5px 1px 1px rgba(0,0,0,0.35), /* The second layer shadow */
10px 10px 0 0px #fff, /* The third layer */
10px 10px 1px 1px rgba(0,0,0,0.35); /* The third layer shadow */
margin-top: 5px;
margin-left: 10px;
margin-right: 30px;
margin-bottom: 5px;
}
.vert-cent {
position: relative;
top: 50%;
transform: translateY(-50%);
}
hr
{
border: 0;
height: 1.5px;
background-image: linear-gradient(to right, rgba(0, 0, 0, 0), rgba(0, 0, 0, 0.75), rgba(0, 0, 0, 0));
}
</style>
<title>Selective Sensor Fusion for Neural Visual-Inertial Odometry</title>
<meta property="og:title" content="Selective Sensor Fusion for Neural Visual-Inertial Odometry">
</head>
<body>
<br>
<table align="center" width="900px">
<tbody><tr>
<td width="600px">
<center>
<a href="logo.jpg"><img src="logo.jpg" height="100px"></a><br>
</center>
</td>
</tr>
</tbody></table>
<br>
<br>
<center>
<span style="font-size:36px">Selective Sensor Fusion for Neural Visual-Inertial Odometry</span>
</center>
<br>
<table align="center" width="1100px">
<tbody><tr>
<td align="center" width="120px">
<center>
<span style="font-size:18px"><a href="http://www.cs.ox.ac.uk/people/changhao.chen/website/">Changhao Chen</a></span>
</center>
</td>
<td align="center" width="120px">
<center>
<span style="font-size:18px"><a href="http://www.cs.ox.ac.uk/people/stefano.rosa/">Stefano Rosa</a></span>
</center>
</td>
<td align="center" width="120px">
<center>
<span style="font-size:18px"><a href="https://sites.google.com/view/yishumiao">Yishu Miao</a></span>
</center>
</td>
<td align="center" width="120px">
<center>
<span style="font-size:18px"><a href="http://www.cs.ox.ac.uk/people/xiaoxuan.lu/">Chris Xiaoxuan Lu</a></span>
</center>
</td>
<td align="center" width="120px">
<center>
<span style="font-size:18px"><a href="">Wei Wu</a></span>
</center>
</td>
<td align="center" width="120px">
<center>
<span style="font-size:18px"><a href="http://www.cs.ox.ac.uk/people/andrew.markham/">Andrew Markham</span>
</center>
</td>
<td align="center" width="100px">
<center>
<span style="font-size:18px"><a href="http://www.cs.ox.ac.uk/people/niki.trigoni/">Niki Trigoni</a></span>
</center>
</td>
</tr>
</tbody></table>
<br>
<table align="center" width="700px">
<tbody><tr>
<td align="center" width="100px">
<center>
<span style="font-size:20px">In CVPR 2019</span>
</center>
</td>
</tr>
</tbody></table>
<br>
<table align="center" width="400px">
<tbody><tr>
<td><span style="font-size:14pt"><center>
<a href="https://arxiv.org/abs/1903.01534"><font color="Gray">[Paper (2.4MB)]</font></a>
</center></span></td>
<td><span style="font-size:14pt"><center>
<a href=""><font color="Gray">[Supplementary (0.8MB)]</font></a>
</center></span></td>
</tr>
</tbody></table>
<br>
<table align="center" width="900px">
<tbody><tr>
<td width="600px">
<center>
<a href="framework.png"><img src="framework.png" height="300px"></a><br>
</center>
</td>
</tr>
</tbody></table>
<br>
<br>
<hr>
<!-- <table align=center width=550px> -->
<center><h1>Abstract</h1></center><table align="center" width="1100">
Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data.
We propose a novel end-to-end selective sensor fusion framework for monocular VIO, which fuses monocular images and inertial measurements in order to estimate the trajectory whilst improving robustness to real-life issues, such as missing and corrupted data or bad sensor synchronization.
In particular, we propose two fusion modalities based on different masking strategies: deterministic soft fusion and stochastic hard fusion, and we compare with previously proposed direct fusion baselines.
During testing, the network is able to selectively process the features of the available sensor modalities and produce a trajectory at scale. We present a thorough investigation on the performances on three public autonomous driving, Micro Aerial Vehicle (MAV) and hand-held VIO datasets.
The results demonstrate the effectiveness of the fusion strategies, which offer better performances compared to direct fusion, particularly in presence of corrupted data.
In addition, we study the interpretability of the fusion networks by visualising the masking layers in different scenarios and with varying data corruption, revealing interesting correlations between the fusion networks and imperfect sensory input data.
<br>
<br>
<hr>
<!-- <table align=center width=550px> -->
<center><h1>Visualization of Masks</h1></center><table align="center" width="1200">
<table align="center" width="900px">
<tbody><tr>
<td width="600px">
<center>
<a href="mask_visual"><img src="mask_visual.jpg" height="300px"></a><br>
</center>
</td>
</tr>
</tbody></table>
</table>
<br>
<hr>
<a name="results"></a>
<center><h1>Supplemental video</h1></center>
<!-- Our Differentiable Ray Consistency (DRC) formulation allows us to learn single-view 3D reconstruction using varying kinds of multi-view observations and can be applied to diverse settings.-->
<table align="center" width="500px">
<tbody><tr height="300px">
<td valign="top" width="400px">
<center>
<iframe width="640" height="360" src="https://www.youtube.com/embed/w8y5L-1hgbw" frameborder="0" allowfullscreen=""></iframe>
</center>
</td>
</tr>
</tbody></table>
<br>
<hr>
<table align="center" width="1100px">
<tbody><tr>
<td>
<left>
<center><h1>Acknowledgements</h1></center>
We would like to thank the anonymous reviewers for their valuable comments. We also thank our colleagues for their help.</a>
</left>
</td>
</tr>
</tbody></table>
<br><br>
</body></html>