2martens
/
uni
mirror of https://github.com/2martens/uni.git
1
0
Fork 0
Code Releases Activity

[Masterproj] Polished paper review 

Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens 2018-06-12 13:57:20 +02:00
parent e7823fc5b3
commit 5b32039b09
1 changed files with 51 additions and 26 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

77
masterproj/seminar_report.tex
View File

@ -79,7 +79,7 @@ maxnames=2
\begin{document}
\title{Master project: seminar report template}
\title{Deep Sliding Shapes: A Review}
\author{Jim Martens}
\maketitle
@ -91,9 +91,11 @@ recognition network to find the actual objects. In the end it produces 3D
bounding boxes and outperforms 3D selective search and other state-of-the-art
solutions.
The paper is presenting the approach in an understandable manner. But the
reproducibility of Deep Sliding Shapes is suboptimal as key information for
such an endeavour is missing from the paper.
The introduced approach has a remarkable high-level structure that is
used in more recent networks as well. But the code implementation and the
provided implementation details or the lack thereof makes an independent
reproduction of the results and an adoption for other problems very difficult
if not impossible.
% Lists:
@ -360,10 +362,23 @@ Overall the paper provides many illustrating figures that make it far easier
to imagine the results of the introduced method and quite simply hydrate the
paper and make it friendlier to the eyes compared to an all text paper.
Lastly the paper provides many evaluation results that are understandable
Furthermore the paper provides many evaluation results that are understandable
largely without the main paper text and give a good overview over the performance
of the proposed method compared to others.
Aside from the paper writing skills the authors clearly posess, the presented
approach itself is also very good. It is an elegant idea to first reduce the
search volume by applying a region proposal network and then use an object recognition
network to do the heavy lifting. The usage of the 2D data is well thought of
as well. This abstract idea of dealing with 3D data has persisted and is somewhat
repeated by the Frustum Pointnet\cite{Qi2017}, which uses the results of a 2D
object detection network to determine the region in which the 3D object detection
takes place. The object detection network not only provides the region in form
of bounding boxes but also the classification of the detected objects in form
of a k vector. Though the specific implementation varies greatly the abstract
idea of region proposal, usage of 2D data and object detection/recognition at
the end is visible in both Deep Sliding Shapes and the Frustum Pointnet.
% subsection positive_aspect (end)
\subsection{Paper Weaknesses} % (fold)
@ -371,26 +386,17 @@ of the proposed method compared to others.
That said there are things to criticize about this paper. The information about
the network structure is spread over two figures and some sections of the paper
with no guarantees that no information is missing. Furthermore no information
regarding the training, validation and testing data split were available. While
this implementation information does not have to be inside the paper proper it
should have been inside appendices to make an independent replication of results
easier. Not directly a problem with the paper itself the decision to implement
a software framework from scratch rather than using a proven existing one like
Tensorflow makes it more difficult to utilize the pretrained models which are
indeed available.
The evaluation sections are inconsistent in their structure. The first section
about object proposal evaluation follows the rest of the paper and is written
in continuous text. It describes the compared methods and then discusses the
results. The second section regarding the object detecion evaluation however
is written completely different. There is no continuous text and the compared
methods are not really described. Instead the section is largely used to justify
the chosen design. This would not even be a problem if there were a introductory
text explaining their motivations for this kind of evaluation and guiding the
reader through the process. Currently there is no explanation given why
the detection evaluation starts with feature encoding and is followed by
design justification.
with no guarantees that no information is missing. The evaluation sections are
inconsistent in their structure. The first section about object proposal evaluation
follows the rest of the paper and is written in continuous text. It describes the
compared methods and then discusses the results. The second section regarding the
object detecion evaluation however is written completely different. There is no
continuous text and the compared methods are not really described. Instead the
section is largely used to justify the chosen design. This would not even be a
problem if there were a introductory text explaining their motivations for this
kind of evaluation and guiding the reader through the process. Currently there
is no explanation given why the detection evaluation starts with feature encoding
and is followed by design justification.
Furthermore the motivations for the used data sets NYUv2 and SUN RGB-D are
not quite clear. Which data set is used for what purpose and why? The text
@ -398,6 +404,16 @@ mentions in one sentence that the amodal bounding boxes are obtained from
SUN RGB-D without further explanation. It would have been advantageous
if the actual process of this "obtaining" was explained.
Lastly no information regarding the training, validation and testing data split were
available. While this implementation information does not have to be inside the
paper proper it should have been at least inside appendices to make an independent
replication of results possible. Not directly a problem with the paper itself the decision to
implement a software framework from scratch (Marvin framework) rather than using
a proven existing one like Tensorflow makes it more difficult to utilize the
pretrained models which are indeed available and more importantly to adapt Deep
Sliding Shapes to other data sets and problems. To top it all of, the available
Matlab "glue" code is not well documented.
% subsection negitive (end)
% section review (end)
@ -410,8 +426,17 @@ network and a joint 2D and 3D object recognitioin network. Experimental
results show that this approach delivers better results than previous
state-of-the-art methods.
The proposed approach introduced an important general structure for networks
working with 3D data and is roughly and on a high-level visible in more recent
network utilizing 3D data as well. In the practical sphere the custom code
framework and the badly documented code makes it very difficult to replicate the
results independently or even adapt Deep Sliding Shapes to other problems.
In short: Good theory, bad practical implementation.
In future work this method should be compared to other 3D centric object detection
approaches like Frustum Point Net\cite{Qi2017}.
approaches like Frustum Point Net\cite{Qi2017}. Especially a structural comparison
with other 3D approaches is interesting to see if there is a best practice structure
emerging for the handling of 3D data.
\newpage
\printbibliography