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Found 10 papers, 4 papers with code
PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration
Camera-LiDAR extrinsic calibration is a critical task for multi-sensor fusion in autonomous systems, such as self-driving vehicles and mobile robots.
Learning to Estimate Two Dense Depths from LiDAR and Event Data
In this work, we propose to address these issues by fusing information from an event camera and a LiDAR using a learning-based approach to estimate accurate dense depth maps.
Decentralized cooperative perception for autonomous vehicles: Learning to value the unknown
In particular, we propose Locally Predictable VAE (LP-VAE), which appears to be producing better belief states for predictions than state-of-the-art models, both as a standalone model and in the context of DRL.
Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras
As an answer to these points, we propose an optimized framework for computing optical flow in real-time with both low- and high-resolution event cameras.
Continuous Conditional Random Field Convolution for Point Cloud Segmentation
Furthermore, we build an encoder-decoder network based on the proposed continuous CRF graph convolution (CRFConv), in which the CRFConv embedded in the decoding layers can restore the details of high-level features that were lost in the encoding stage to enhance the location ability of the network, thereby benefiting segmentation.
Efficient exact computation of the conjunctive and disjunctive decompositions of D-S Theory for information fusion: Translation and extension
With it, we are able to reduce these computations up to a linear complexity in the number of focal sets in some cases.
Fusion of neural networks, for LIDAR-based evidential road mapping
So as to reach satisfactory results, the system fuses road detection results obtained from three variants of RoadSeg, processing different LIDAR features.
Focal points and their implications for Möbius Transforms and Dempster-Shafer Theory
Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a much higher computational burden.
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.
Explicit Induction Bias for Transfer Learning with Convolutional Networks
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.
