Search Results for author:
Found 10 papers, 1 papers with code
Cascaded and Generalizable Neural Radiance Fields for Fast View Synthesis
Moreover, our method can leverage a denser set of reference images of a single scene to produce accurate novel views without relying on additional explicit representations and still maintains the high-speed rendering of the pre-trained model.
Fast Neural Architecture Search for Lightweight Dense Prediction Networks
We present LDP, a lightweight dense prediction neural architecture search (NAS) framework.
StressNAS: Affect State and Stress Detection Using Neural Architecture Search
Smartwatches have rapidly evolved towards capabilities to accurately capture physiological signals.
Lightweight Monocular Depth with a Novel Neural Architecture Search Method
This paper presents a novel neural architecture search method, called LiDNAS, for generating lightweight monocular depth estimation models.
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss
In addition, we introduce a normalized Hessian loss term invariant to scaling and shear along the depth direction, which is shown to substantially improve the accuracy.
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion
In this paper, we propose enhancing monocular depth estimation by adding 3D points as depth guidance.
RGBD-Net: Predicting color and depth images for novel views synthesis
We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware generator network.
Sequential View Synthesis with Transformer
This paper addresses the problem of novel view synthesis by means of neural rendering, where we are interested in predicting the novel view at an arbitrary camera pose based on a given set of input images from other viewpoints.
Guiding Monocular Depth Estimation Using Depth-Attention Volume
Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D interpretations.
Predicting Novel Views Using Generative Adversarial Query Network
The problem of predicting a novel view of the scene using an arbitrary number of observations is a challenging problem for computers as well as for humans.
