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Found 7 papers, 2 papers with code
RePAST: Relative Pose Attention Scene Representation Transformer
The Scene Representation Transformer (SRT) is a recent method to render novel views at interactive rates.
Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions
Namely, we extend NeuS, a state-of-the-art neural implicit function formulation, to represent multiple objects of a class (human heads in our case) simultaneously.
CycleGAN-Based Unpaired Speech Dereverberation
Typically, neural network-based speech dereverberation models are trained on paired data, composed of a dry utterance and its corresponding reverberant utterance.
Multi-sensor large-scale dataset for multi-view 3D reconstruction
We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms and for related tasks.
Unpaired Depth Super-Resolution in the Wild
We propose an unpaired learning method for depth super-resolution, which is based on a learnable degradation model, enhancement component and surface normal estimates as features to produce more accurate depth maps.
How Good MVSNets Are at Depth Fusion
We study the effects of the additional input to deep multi-view stereo methods in the form of low-quality sensor depth.
Deep Vectorization of Technical Drawings
We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images.
