13,381 research outputs found
Learned versus Hand-Designed Feature Representations for 3d Agglomeration
For image recognition and labeling tasks, recent results suggest that machine
learning methods that rely on manually specified feature representations may be
outperformed by methods that automatically derive feature representations based
on the data. Yet for problems that involve analysis of 3d objects, such as mesh
segmentation, shape retrieval, or neuron fragment agglomeration, there remains
a strong reliance on hand-designed feature descriptors. In this paper, we
evaluate a large set of hand-designed 3d feature descriptors alongside features
learned from the raw data using both end-to-end and unsupervised learning
techniques, in the context of agglomeration of 3d neuron fragments. By
combining unsupervised learning techniques with a novel dynamic pooling scheme,
we show how pure learning-based methods are for the first time competitive with
hand-designed 3d shape descriptors. We investigate data augmentation strategies
for dramatically increasing the size of the training set, and show how
combining both learned and hand-designed features leads to the highest
accuracy
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse
inputs with an application to depth upsampling from sparse laser scan data.
First, we show that traditional convolutional networks perform poorly when
applied to sparse data even when the location of missing data is provided to
the network. To overcome this problem, we propose a simple yet effective sparse
convolution layer which explicitly considers the location of missing data
during the convolution operation. We demonstrate the benefits of the proposed
network architecture in synthetic and real experiments with respect to various
baseline approaches. Compared to dense baselines, the proposed sparse
convolution network generalizes well to novel datasets and is invariant to the
level of sparsity in the data. For our evaluation, we derive a novel dataset
from the KITTI benchmark, comprising 93k depth annotated RGB images. Our
dataset allows for training and evaluating depth upsampling and depth
prediction techniques in challenging real-world settings and will be made
available upon publication
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