Search Results for author:
Found 14 papers, 7 papers with code
OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment
The task of motion prediction is pivotal for autonomous driving systems, providing crucial data to choose a vehicle behavior strategy within its surroundings.
uSF: Learning Neural Semantic Field with Uncertainty
Recently, there has been an increased interest in NeRF methods which reconstruct differentiable representation of three-dimensional scenes.
Interactive Semantic Map Representation for Skill-based Visual Object Navigation
Visual object navigation using learning methods is one of the key tasks in mobile robotics.
SegmATRon: Embodied Adaptive Semantic Segmentation for Indoor Environment
This paper presents an adaptive transformer model named SegmATRon for embodied image semantic segmentation.
Recurrent Action Transformer with Memory
One solution to this problem is to enhance transformers with memory mechanisms.
Rethinking Voxelization and Classification for 3D Object Detection
The main challenge in 3D object detection from LiDAR point clouds is achieving real-time performance without affecting the reliability of the network.
HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images
To study the main stages of solving the place recognition problem on the HPointLoc dataset, we proposed a new modular approach named as PNTR.
Quantum-machine-learning channel discrimination
Quantum channel discrimination with a variational quantum classifier (ii) allows one to operate even with random and mixed input states and simple variational circuits.
Real-time 3D Object Detection using Feature Map Flow
In this paper, we present a real-time 3D detection approach considering time-spatial feature map aggregation from different time steps of deep neural model inference (named feature map flow, FMF).
On the origin of electron accumulation layer at clean InAs(111) surfaces
In this paper, we provide a comprehensive theoretical analysis of the electronic structure of InAs(111) surfaces with a special attention paid to the energy region close to the fundamental bandgap.
Materials Science
Certified variational quantum algorithms for eigenstate preparation
Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form.
Variational Quantum Eigensolver for Frustrated Quantum Systems
Hybrid quantum-classical algorithms have been proposed as a potentially viable application of quantum computers.
Experimental neural network enhanced quantum tomography
We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to a SPAM-agnostic protocol with idealized measurements.
Mimicking graphene with polaritonic spin vortices
Exploring the properties of strongly correlated systems through quantum simulation with photons, cold atoms or polaritons represents an active area of research.
Mesoscale and Nanoscale Physics
