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Found 6 papers, 3 papers with code
Multi-Space Neural Radiance Fields
Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry or distorted rendering.
Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections
To remedy this issue, we present a novel surface reconstruction framework, NeuS-HSR, based on implicit neural rendering.
Transfusion: A Novel SLAM Method Focused on Transparent Objects
In this paper, we propose a novel SLAM approach called transfusion that allows transparent object existence and recovery in the video input.
SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Weights Flipping
The channel redundancy in feature maps of convolutional neural networks (CNNs) results in the large consumption of memories and computational resources.
DeepBlindness: Fast Blindness Map Estimation and Blindness Type Classification for Outdoor Scene from Single Color Image
Outdoor vision robotic systems and autonomous cars suffer from many image-quality issues, particularly haze, defocus blur, and motion blur, which we will define generically as "blindness issues".
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image
In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth.
