14 citations · 37 across the 9 of their papers we have counts for
9 papers
Lagrangian Hashing for Compressed Neural Field Representations
Shrisudhan Govindarajan, Zeno Sambugaro, Akhmedkhan +7
We present Lagrangian Hashing, a representation for neural fields combining the characteristics of fast training NeRF methods that rely on Eulerian grids (i.e.~InstantNGP), with th…
OpenScene: 3D Scene Understanding with Open Vocabularies
Songyou Peng, Kyle Genova, Chiyu "Max" Jiang +3
Traditional 3D scene understanding approaches rely on labeled 3D datasets to train a model for a single task with supervision. We propose OpenScene, an alternative approach where a…
NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization
Shitao Tang, Sicong Tang, Andrea Tagliasacchi +2
This paper presents an end-to-end neural mapping method for camera localization, dubbed NeuMap, encoding a whole scene into a grid of latent codes, with which a Transformer-based a…
Volume Rendering Digest (for NeRF)
Andrea Tagliasacchi, Ben Mildenhall
Neural Radiance Fields employ simple volume rendering as a way to overcome the challenges of differentiating through ray-triangle intersections by leveraging a probabilistic notion…
NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds
Weiwei Sun, Daniel Rebain, Renjie Liao +4
We introduce a method for instance proposal generation for 3D point clouds. Existing techniques typically directly regress proposals in a single feed-forward step, leading to inacc…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…