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Found 6 papers, 0 papers with code
A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshes
The question of representation of 3D geometry is of vital importance when it comes to leveraging the recent advances in the field of machine learning for geometry processing tasks.
A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization
In addition, we show that careful sampling is important both for the input geometry and in our point cloud generation process to improve results.
3D Shape Generation with Grid-based Implicit Functions
To remedy these issues, we propose to train the GAN on grids (i. e. each cell covers a part of a shape).
Localized Latent Updates for Fine-Tuning Vision-Language Models
Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for improved performance on specific datasets.
Adaptive Voronoi NeRFs
Neural Radiance Fields (NeRFs) learn to represent a 3D scene from just a set of registered images.
Partial Symmetry Detection for 3D Geometry using Contrastive Learning with Geodesic Point Cloud Patches
To our knowledge, we are the first to propose a self-supervised data-driven method for partial extrinsic symmetry detection.
