2,409 research outputs found
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
201
Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation
Rapid and low power computation of optical flow (OF) is potentially useful in
robotics. The dynamic vision sensor (DVS) event camera produces quick and
sparse output, and has high dynamic range, but conventional OF algorithms are
frame-based and cannot be directly used with event-based cameras. Previous DVS
OF methods do not work well with dense textured input and are designed for
implementation in logic circuits. This paper proposes a new block-matching
based DVS OF algorithm which is inspired by motion estimation methods used for
MPEG video compression. The algorithm was implemented both in software and on
FPGA. For each event, it computes the motion direction as one of 9 directions.
The speed of the motion is set by the sample interval. Results show that the
Average Angular Error can be improved by 30\% compared with previous methods.
The OF can be calculated on FPGA with 50\,MHz clock in 0.2\,us per event (11
clock cycles), 20 times faster than a Java software implementation running on a
desktop PC. Sample data is shown that the method works on scenes dominated by
edges, sparse features, and dense texture.Comment: Published in ISCAS 201
Cascading Convolutional Temporal Colour Constancy
Computational Colour Constancy (CCC) consists of estimating the colour of one
or more illuminants in a scene and using them to remove unwanted chromatic
distortions. Much research has focused on illuminant estimation for CCC on
single images, with few attempts of leveraging the temporal information
intrinsic in sequences of correlated images (e.g., the frames in a video), a
task known as Temporal Colour Constancy (TCC). The state-of-the-art for TCC is
TCCNet, a deep-learning architecture that uses a ConvLSTM for aggregating the
encodings produced by CNN submodules for each image in a sequence. We extend
this architecture with different models obtained by (i) substituting the TCCNet
submodules with C4, the state-of-the-art method for CCC targeting images; (ii)
adding a cascading strategy to perform an iterative improvement of the estimate
of the illuminant. We tested our models on the recently released TCC benchmark
and achieved results that surpass the state-of-the-art. Analyzing the impact of
the number of frames involved in illuminant estimation on performance, we show
that it is possible to reduce inference time by training the models on few
selected frames from the sequences while retaining comparable accuracy
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