Abstract

In this paper, we present “BIKED,” a dataset composed of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of design information including assembly images, component images, numerical design parameters, and class labels. In this paper, we first discuss the processing of the dataset, and then highlight some prominent research questions that BIKED can help address. Of these questions, we further explore the following in detail: (1) How can we explore, understand, and visualize the current design space of bicycles and utilize this information? We apply unsupervised embedding methods to study the design space and identify key takeaways from this analysis. (2) When designing bikes using algorithms, under what conditions can machines understand the design of a given bike? We train a multitude of classifiers to understand designs, then examine the behavior of these classifiers through confusion matrices and permutation-based interpretability analysis. 3) Can machines learn to synthesize new bicycle designs by studying existing ones? We test Variational Autoencoders on random generation, interpolation, and extrapolation tasks after training on BIKED data. The dataset and code are available online.

References

1.
Deng
,
J.
,
Dong
,
W.
,
Socher
,
R.
,
Li
,
L.-J.
,
Li
,
K.
, and
Fei-Fei
,
L.
,
2009
, “
Imagenet: A Large-Scale Hierarchical Image Database
,”
2009 IEEE Conference on Computer Vision and Pattern Recognition
,
Miami, FL
, IEEE, pp.
248
255
.
2.
Krizhevsky
,
A.
, and
Hinton
,
G.
,
2009
, “
Learning Multiple Layers of Features From Tiny Images
”.
3.
LeCun
,
Y.
,
Bottou
,
L.
,
Bengio
,
Y.
, and
Haffner
,
P.
,
1998
, “
Gradient-Based Learning Applied to Document Recognition
,”
Proc. IEEE
,
86
(
11
), pp.
2278
2324
.
4.
Oke
,
O.
,
Bhalla
,
K.
,
Love
,
D. C.
, and
Siddiqui
,
S.
,
2015
, “
Tracking Global Bicycle Ownership Patterns
,”
J. Transp. Health
,
2
(
4
), pp.
490
501
.
5.
Oja
,
P.
,
Titze
,
S.
,
Bauman
,
A.
,
De Geus
,
B.
,
Krenn
,
P.
,
Reger-Nash
,
B.
, and
Kohlberger
,
T.
,
2011
, “
Health Benefits of Cycling: A Systematic Review
,”
Scand. J. Med. Sci. Sports
,
21
(
4
), pp.
496
509
.
6.
Hamilton
,
T. L.
, and
Wichman
,
C. J.
,
2018
, “
Bicycle Infrastructure and Traffic Congestion: Evidence From DC’s Capital Bikeshare
,”
J. Environ. Econ. Manage.
,
87
, pp.
72
93
.
7.
Edenhofer
,
O.
,
2015
,
Climate Change 2014: Mitigation of Climate Change
,
Vol. 3
,
Cambridge University Press
,
New York
.
8.
Lundberg
,
S. M.
, and
Lee
,
S.-I.
,
2017
, “
A Unified Approach to Interpreting Model Predictions
.”
Advances in Neural Information Processing Systems 30
,
I.
Guyon
,
U. V.
Luxburg
,
S.
Bengio
,
H.
Wallach
,
R.
Fergus
,
S.
Vishwanathan
, and
R.
Garnett
, eds.
Curran Associates, Inc.
, pp.
4765
4774
.
9.
Regenwetter
,
L.
,
Curry
,
B.
, and
Ahmed
,
F.
,
2021
, “
BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design
,”
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-21
,
Virtual
.
10.
Wilson
,
D. G.
, and
Schmidt
,
T.
,
2020
,
Bicycling Science
, 4th ed.,
MIT Press
,
Cambridge, MA
.
11.
Sharp
,
A.
,
1977
,
Bicycles and Tricycles: A Classic Treatise on Their Design and Construction
, 1st ed.,
Dover Publications Inc.
,
Mineola, NY
.
12.
Chowdhury
,
H.
,
Alam
,
F.
, and
Mainwaring
,
D.
,
2011
, “
A Full Scale Bicycle Aerodynamics Testing Methodology
,”
Procedia. Eng.
,
13
, pp.
94
99
.
13.
Chowdhury
,
H.
, and
Alam
,
F.
,
2012
, “
Bicycle Aerodynamics: An Experimental Evaluation Methodology
,”
Sports Eng.
,
15
(
2
), pp.
73
80
.
14.
Malizia
,
F.
, and
Blocken
,
B.
,
2020
, “
Bicycle Aerodynamics: History, State-of-the-Art and Future Perspectives
,”
J. Wind Eng. Ind. Aerodyn.
,
200
, p.
104134
.
15.
Suppapitnarm
,
A.
,
Parks
,
G.
,
Shea
,
K.
, and
Clarkson
,
P.
,
2004
, “
Conceptual Design of Bicycle Frames by Multiobjective Shape Annealing
,”
Eng. Optim.
,
36
(
2
), pp.
165
188
.
16.
Laios
,
L.
, and
Giannatsis
,
J.
,
2010
, “
Ergonomic Evaluation and Redesign of Children Bicycles Based on Anthropometric Data
,”
Appl. Ergon.
,
41
(
3
), pp.
428
435
.
17.
Swart
,
J.
, and
Holliday
,
W.
,
2019
, “
Cycling Biomechanics Optimization–the (r) Evolution of Bicycle Fitting
,”
Curr. Sports Med. Rep.
,
18
(
12
), pp.
490
496
.
18.
Chang
,
A. X.
,
Funkhouser
,
T.
,
Guibas
,
L.
,
Hanrahan
,
P.
,
Huang
,
Q.
,
Li
,
Z.
,
Savarese
,
S.
, et al
,
2015
, “
Shapenet: An Information-Rich 3D Model Repository
,” arXiv preprint.
19.
Wu
,
Z.
,
Song
,
S.
,
Khosla
,
A.
,
Yu
,
F.
,
Zhang
,
L.
,
Tang
,
X.
, and
Xiao
,
J.
,
2015
, “
3D Shapenets: A Deep Representation for Volumetric Shapes
,”
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
,
Boston, MA
.
20.
Gao
,
L.
,
Yang
,
J.
,
Wu
,
T.
,
Yuan
,
Y.-J.
,
Fu
,
H.
,
Lai
,
Y.-K.
, and
Zhang
,
H.
,
2019
, “
SDM-NET: Deep Generative Network for Structured Deformable Mesh
,”
ACM Trans. Graph. (TOG)
,
38
(
6
), pp.
1
15
.
21.
Mo
,
K.
,
Guerrero
,
P.
,
Yi
,
L.
,
Su
,
H.
,
Wonka
,
P.
,
Mitra
,
N.
, and
Guibas
,
L. J.
,
2019
, “
Structurenet: Hierarchical Graph Networks for 3D Shape Generation
,”
ACM Transactions on Graphics
,
38
(
6
).
22.
Li
,
J.
,
Xu
,
K.
,
Chaudhuri
,
S.
,
Yumer
,
E.
,
Zhang
,
H.
, and
Guibas
,
L.
,
2017
, “
Grass: Generative Recursive Autoencoders for Shape Structures
,”
ACM Trans. Graph. (TOG)
,
36
(
4
), pp.
1
14
.
23.
Sosnovik
,
I.
, and
Oseledets
,
I.
,
2019
, “
Neural Networks for Topology Optimization
,”
Russian J. Numer. Anal. Math. Modell.
,
34
(
4
), pp.
215
223
.
24.
Nie
,
Z.
,
Lin
,
T.
,
Jiang
,
H.
, and
Kara
,
L. B.
,
2021
, “
Topologygan: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
,”
ASME J. Mech. Des.
,
143
(
3
), p. 031715.
25.
Van der Maaten
,
L.
, and
Hinton
,
G.
,
2008
, “
Visualizing Data Using T-SNE
,”
J. Mach. Learn. Res.
,
9
(
11)
, pp.
2579
2605
.
26.
Kingma
,
D. P.
, and
Welling
,
M.
,
2013
, “
Auto-Encoding Variational Bayes
.” arXiv preprint arXiv:1312.6114.
27.
Kingma
,
D. P.
, and
Welling
,
M.
,
2019
, “
An Introduction to Variational Autoencoders
.” arXiv preprint arXiv:1906.02691.
28.
Chen
,
W.
, and
Ahmed
,
F.
,
2021
, “
Padgan: Learning to Generate High-quality Novel Designs
,”
ASME J. Mech. Des.
,
143
(
3
), p.
031703
.
29.
Xu
,
L.
,
Skoularidou
,
M.
,
Cuesta-Infante
,
A.
, and
Veeramachaneni
,
K.
,
2019
, “
Modeling Tabular Data Using Conditional Gan.
Advances in Neural Information Processing Systems 32
,
Vancouver, BC
.
30.
Goodfellow
,
I. J.
,
Pouget-Abadie
,
J.
,
Mirza
,
M.
,
Xu
,
B.
,
Warde-Farley
,
D.
,
Ozair
,
S.
,
Courville
,
A.
, and
Bengio
,
Y.
,
2014
, “
Generative Adversarial Networks
.” arXiv preprint arXiv:1406.2661.
31.
Kingma
,
D. P.
, and
Ba
,
J.
,
2014
, “
Adam: A Method for Stochastic Optimization
,” arXiv preprint.
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