806 research outputs found
Neighbourhood-insensitive point cloud normal estimation network
We introduce a novel self-attention-based normal estimation network that is able to focus softly on relevant points and adjust the softness by learning a temperature parameter, making it able to work naturally and effectively within a large neighbourhood range. As a result, our model outperforms all existing normal estimation algorithms by a large margin, achieving 94.1% accuracy in comparison with the previous state of the art of 91.2%, with a 25x smaller model and 12x faster inference time. We also use point-to-plane Iterative Closest Point (ICP) as an application case to show that our normal estimations lead to faster convergence than normal estimations from other methods, without manually fine-tuning neighbourhood range parameters. Code available at https://code.active.vision
Neighbourhood-Insensitive Point Cloud Normal Estimation Network
We introduce a novel self-attention-based normal estimation network that is
able to focus softly on relevant points and adjust the softness by learning a
temperature parameter, making it able to work naturally and effectively within
a large neighbourhood range. As a result, our model outperforms all existing
normal estimation algorithms by a large margin, achieving 94.1% accuracy in
comparison with the previous state of the art of 91.2%, with a 25x smaller
model and 12x faster inference time. We also use point-to-plane Iterative
Closest Point (ICP) as an application case to show that our normal estimations
lead to faster convergence than normal estimations from other methods, without
manually fine-tuning neighbourhood range parameters. Code available at
https://code.active.vision.Comment: Accepted in BMVC 2020 as oral presentation. Code available at
https://code.active.vision and project page at http://ninormal.active.visio
Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
We present a relocalization pipeline, which combines an absolute pose
regression (APR) network with a novel view synthesis based direct matching
module, offering superior accuracy while maintaining low inference time. Our
contribution is twofold: i) we design a direct matching module that supplies a
photometric supervision signal to refine the pose regression network via
differentiable rendering; ii) we modify the rotation representation from the
classical quaternion to SO(3) in pose regression, removing the need for
balancing rotation and translation loss terms. As a result, our network
Direct-PoseNet achieves state-of-the-art performance among all other
single-image APR methods on the 7-Scenes benchmark and the LLFF dataset
Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving
Risk assessment is a crucial component of collision warning and avoidance
systems in intelligent vehicles. To accurately detect potential vehicle
collisions, reachability-based formal approaches have been developed to ensure
driving safety, but suffer from over-conservatism, potentially leading to
false-positive risk events in complicated real-world applications. In this
work, we combine two reachability analysis techniques, i.e., backward reachable
set (BRS) and stochastic forward reachable set (FRS), and propose an integrated
probabilistic collision detection framework in highway driving. Within the
framework, we can firstly use a BRS to formally check whether a two-vehicle
interaction is safe; otherwise, a prediction-based stochastic FRS is employed
to estimate a collision probability at each future time step. In doing so, the
framework can not only identify non-risky events with guaranteed safety, but
also provide accurate collision risk estimation in safety-critical events. To
construct the stochastic FRS, we develop a neural network-based acceleration
model for surrounding vehicles, and further incorporate confidence-aware
dynamic belief to improve the prediction accuracy. Extensive experiments are
conducted to validate the performance of the acceleration prediction model
based on naturalistic highway driving data, and the efficiency and
effectiveness of the framework with the infused confidence belief are tested
both in naturalistic and simulated highway scenarios. The proposed risk
assessment framework is promising in real-world applications.Comment: Under review at Engineering. arXiv admin note: text overlap with
arXiv:2205.0135
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