166 research outputs found
Matching neural paths: transfer from recognition to correspondence search
Many machine learning tasks require finding per-part correspondences between
objects. In this work we focus on low-level correspondences - a highly
ambiguous matching problem. We propose to use a hierarchical semantic
representation of the objects, coming from a convolutional neural network, to
solve this ambiguity. Training it for low-level correspondence prediction
directly might not be an option in some domains where the ground-truth
correspondences are hard to obtain. We show how transfer from recognition can
be used to avoid such training. Our idea is to mark parts as "matching" if
their features are close to each other at all the levels of convolutional
feature hierarchy (neural paths). Although the overall number of such paths is
exponential in the number of layers, we propose a polynomial algorithm for
aggregating all of them in a single backward pass. The empirical validation is
done on the task of stereo correspondence and demonstrates that we achieve
competitive results among the methods which do not use labeled target domain
data.Comment: Accepted at NIPS 201
Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements
We present an approach to depth estimation that fuses information from a
stereo pair with sparse range measurements derived from a LIDAR sensor or a
range camera. The goal of this work is to exploit the complementary strengths
of the two sensor modalities, the accurate but sparse range measurements and
the ambiguous but dense stereo information. These two sources are effectively
and efficiently fused by combining ideas from anisotropic diffusion and
semi-global matching.
We evaluate our approach on the KITTI 2015 and Middlebury 2014 datasets,
using randomly sampled ground truth range measurements as our sparse depth
input. We achieve significant performance improvements with a small fraction of
range measurements on both datasets. We also provide qualitative results from
our platform using the PMDTec Monstar sensor. Our entire pipeline runs on an
NVIDIA TX-2 platform at 5Hz on 1280x1024 stereo images with 128 disparity
levels.Comment: 7 pages, 5 figures, 2 table
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