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Found 12 papers, 2 papers with code
An N-Point Linear Solver for Line and Motion Estimation with Event Cameras
To recover the full linear camera velocity we fuse observations from multiple lines with a novel velocity averaging scheme that relies on a geometrically-motivated residual, and thus solves the problem more efficiently than previous schemes which minimize an algebraic residual.
A 5-Point Minimal Solver for Event Camera Relative Motion Estimation
Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene.
VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM
Event cameras have recently gained in popularity as they hold strong potential to complement regular cameras in situations of high dynamics or challenging illumination.
Globally-Optimal Contrast Maximisation for Event Cameras
The practical validity of our approach is demonstrated by a successful application to three different event camera motion estimation problems.
Globally-Optimal Event Camera Motion Estimation
The practical validity of our approach is supported by a highly successful application to AGV motion estimation with a downward facing event camera, a challenging scenario in which the sensor experiences fronto-parallel motion in front of noisy, fast moving textures.
FP-Loc: Lightweight and Drift-free Floor Plan-assisted LiDAR Localization
Our approach relies on robust ceiling and ground plane detection, which solves part of the pose and supports the segmentation of vertical structure elements such as walls and pillars.
Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles
We encountered this difficulty by introducing the first globally-optimal, correspondence-less solution to plane-based Ackermann motion estimation.
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding
We define a maximum traceable distance metric, through which we learn to what extent the text contrastive learning benefits from the historical information of negative samples.
ESOD:Edge-based Task Scheduling for Object Detection
In detail, we train a DNN model (termed as pre-model) to predict which object detection model to use for the coming task and offloads to which edge servers by physical characteristics of the image task (e. g., brightness, saturation).
Visual Odometry with an Event Camera Using Continuous Ray Warping and Volumetric Contrast Maximization
We present a new solution to tracking and mapping with an event camera.
Learning to Remove: Towards Isotropic Pre-trained BERT Embedding
We train the weights on word similarity tasks and show that processed embedding is more isotropic.
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
We experimentally show that how two mainstream compression techniques, data quantization and pruning, perform on these network architectures and the implications of compression techniques to the model storage size, inference time, energy consumption and performance metrics.
