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Found 6 papers, 0 papers with code
HINT: Learning Complete Human Neural Representations from Limited Viewpoints
To address this issue, we propose HINT, a NeRF-based algorithm able to learn a detailed and complete human model from limited viewing angles.
Gated Fields: Learning Scene Reconstruction from Gated Videos
We validate the method across day and night scenarios and find that Gated Fields compares favorably to RGB and LiDAR reconstruction methods.
Real-Time Environment Condition Classification for Autonomous Vehicles
Using the novel proposed dataset and hierarchy, we train RECNet, a deep learning model for the classification of environment conditions from a single RGB frame.
Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar
Neural fields have been broadly investigated as scene representations for the reproduction and novel generation of diverse outdoor scenes, including those autonomous vehicles and robots must handle.
Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues
We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images.
ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering
With data as bottleneck and most of today's training data relying on good weather conditions with inclement weather as outlier, we rely on an inverse rendering approach to reconstruct the scene content.
