88 research outputs found
Elliptically symmetric distributions for directional data of arbitrary dimension
We formulate a class of angular Gaussian distributions that allows different
degrees of isotropy for directional random variables of arbitrary dimension.
Through a series of novel reparameterization, this distribution family is
indexed by parameters with meaningful statistical interpretations that can
range over the entire real space of an adequate dimension. The new
parameterization greatly simplifies maximum likelihood estimation of all model
parameters, which in turn leads to theoretically sound and numerically stable
inference procedures to infer key features of the distribution. Byproducts from
the likelihood-based inference are used to develop graphical and numerical
diagnostic tools for assessing goodness of fit of this distribution in a data
application. Simulation study and application to data from a hydrogeology study
are used to demonstrate implementation and performance of the inference
procedures and diagnostics methods.Comment: 22 pages, 15 figure
Learnable Graph Matching: A Practical Paradigm for Data Association
Data association is at the core of many computer vision tasks, e.g., multiple
object tracking, image matching, and point cloud registration. Existing methods
usually solve the data association problem by network flow optimization,
bipartite matching, or end-to-end learning directly. Despite their popularity,
we find some defects of the current solutions: they mostly ignore the
intra-view context information; besides, they either train deep association
models in an end-to-end way and hardly utilize the advantage of
optimization-based assignment methods, or only use an off-the-shelf neural
network to extract features. In this paper, we propose a general learnable
graph matching method to address these issues. Especially, we model the
intra-view relationships as an undirected graph. Then data association turns
into a general graph matching problem between graphs. Furthermore, to make
optimization end-to-end differentiable, we relax the original graph matching
problem into continuous quadratic programming and then incorporate training
into a deep graph neural network with KKT conditions and implicit function
theorem. In MOT task, our method achieves state-of-the-art performance on
several MOT datasets. For image matching, our method outperforms
state-of-the-art methods with half training data and iterations on a popular
indoor dataset, ScanNet. Code will be available at
https://github.com/jiaweihe1996/GMTracker.Comment: Submitted to TPAMI on Mar 21, 2022. arXiv admin note: substantial
text overlap with arXiv:2103.1617
Object as Query: Lifting any 2D Object Detector to 3D Detection
3D object detection from multi-view images has drawn much attention over the
past few years. Existing methods mainly establish 3D representations from
multi-view images and adopt a dense detection head for object detection, or
employ object queries distributed in 3D space to localize objects. In this
paper, we design Multi-View 2D Objects guided 3D Object Detector (MV2D), which
can lift any 2D object detector to multi-view 3D object detection. Since 2D
detections can provide valuable priors for object existence, MV2D exploits 2D
detectors to generate object queries conditioned on the rich image semantics.
These dynamically generated queries help MV2D to recall objects in the field of
view and show a strong capability of localizing 3D objects. For the generated
queries, we design a sparse cross attention module to force them to focus on
the features of specific objects, which suppresses interference from noises.
The evaluation results on the nuScenes dataset demonstrate the dynamic object
queries and sparse feature aggregation can promote 3D detection capability.
MV2D also exhibits a state-of-the-art performance among existing methods. We
hope MV2D can serve as a new baseline for future research.Comment: technical repor
TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios
Time-to-Contact (TTC) estimation is a critical task for assessing collision
risk and is widely used in various driver assistance and autonomous driving
systems. The past few decades have witnessed development of related theories
and algorithms. The prevalent learning-based methods call for a large-scale TTC
dataset in real-world scenarios. In this work, we present a large-scale object
oriented TTC dataset in the driving scene for promoting the TTC estimation by a
monocular camera. To collect valuable samples and make data with different TTC
values relatively balanced, we go through thousands of hours of driving data
and select over 200K sequences with a preset data distribution. To augment the
quantity of small TTC cases, we also generate clips using the latest Neural
rendering methods. Additionally, we provide several simple yet effective TTC
estimation baselines and evaluate them extensively on the proposed dataset to
demonstrate their effectiveness. The proposed dataset is publicly available at
https://open-dataset.tusen.ai/TSTTC.Comment: 19 pages, 9 figure
Mip-Splatting: Alias-free 3D Gaussian Splatting
Recently, 3D Gaussian Splatting has demonstrated impressive novel view
synthesis results, reaching high fidelity and efficiency. However, strong
artifacts can be observed when changing the sampling rate, \eg, by changing
focal length or camera distance. We find that the source for this phenomenon
can be attributed to the lack of 3D frequency constraints and the usage of a 2D
dilation filter. To address this problem, we introduce a 3D smoothing filter
which constrains the size of the 3D Gaussian primitives based on the maximal
sampling frequency induced by the input views, eliminating high-frequency
artifacts when zooming in. Moreover, replacing 2D dilation with a 2D Mip
filter, which simulates a 2D box filter, effectively mitigates aliasing and
dilation issues. Our evaluation, including scenarios such a training on
single-scale images and testing on multiple scales, validates the effectiveness
of our approach.Comment: Project page: https://niujinshuchong.github.io/mip-splatting
- …