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Found 10 papers, 3 papers with code
ViewFusion: Towards Multi-View Consistency via Interpolated Denoising
Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images.
Divide and Conquer: Rethinking the Training Paradigm of Neural Radiance Fields
Neural radiance fields (NeRFs) have exhibited potential in synthesizing high-fidelity views of 3D scenes but the standard training paradigm of NeRF presupposes an equal importance for each image in the training set.
BLiRF: Bandlimited Radiance Fields for Dynamic Scene Modeling
Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem.
Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation
This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism.
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task.
Localising In Complex Scenes Using Balanced Adversarial Adaptation
Domain adaptation and generative modelling have collectively mitigated the expensive nature of data collection and labelling by leveraging the rich abundance of accurate, labelled data in simulation environments.
EMPNet: Neural Localisation and Mapping Using Embedded Memory Points
Continuously estimating an agent's state space and a representation of its surroundings has proven vital towards full autonomy.
Parallel Optimal Transport GAN
We demonstrate that operating in a low dimension representation of the data distribution benefits from convergence rate gains in estimating the Wasserstein distance, resulting in more stable GAN training.
Traversing Latent Space using Decision Ferns
The practice of transforming raw data to a feature space so that inference can be performed in that space has been popular for many years.
Generative Adversarial Forests for Better Conditioned Adversarial Learning
In recent times, many of the breakthroughs in various vision-related tasks have revolved around improving learning of deep models; these methods have ranged from network architectural improvements such as Residual Networks, to various forms of regularisation such as Batch Normalisation.
