Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research. Kaolin provides efficient implementations of differentiable 3D modules for use in deep learning systems. With functionality to load and preprocess several popular 3D datasets, and native functions to manipulate meshes, pointclouds, signed distance functions, and voxel grids, Kaolin mitigates the need to write wasteful boilerplate code. Kaolin packages together several differentiable graphics modules including rendering, lighting, shading, and view warping. Kaolin also supports an array of loss functions and evaluation metrics for seamless evaluation and provides visualization functionality to render the 3D results. Importantly, we curate a comprehensive model zoo comprising many state-of-the-art 3D deep learning architectures, to serve as a starting point for future research endeavours. Kaolin is available as open-source software at https://github.com/NVIDIAGameWorks/kaolin/.
Code (6)
Tasks
Deep LearningSimilar Papers 제목 키워드 기반
Torchmeta: A Meta-Learning library for PyTorch
The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research. They offer a way to get a fair comparison between different algorithms, and th…
Meta-LearningiSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations
Core computations in Graph Neural Network (GNN) training and inference are often mapped to sparse matrix operations such as sparse-dense matrix multiplication (SpMM). These sparse operations are harder to optimize by man…
CPUGraph Neural NetworkEasyVolcap: Accelerating Neural Volumetric Video Research
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations. When acquired, such volumography can be viewed from any viewpoint and times…
Accelerating 3D Deep Learning with PyTorch3D
Deep learning has significantly improved 2D image recognition. Extending into 3D may advance many new applications including autonomous vehicles, virtual and augmented reality, authoring 3D content, and even improving 2D…
Autonomous VehiclesDeep LearningPyTorch Tabular: A Framework for Deep Learning with Tabular Data
In spite of showing unreasonable effectiveness in modalities like Text and Image, Deep Learning has always lagged Gradient Boosting in tabular data - both in popularity and performance. But recently there have been newer…
Deep Learning