Core Concepts
Combining regression-based and generative approaches, latentSplat introduces variational 3D Gaussians for efficient and high-quality 3D reconstruction.
Abstract
The content introduces latentSplat, a method for scalable generalizable 3D reconstruction using variational feature Gaussians. It combines regression and generative models to handle uncertainty in reconstructions efficiently. The approach outperforms previous methods in quality, scalability, and generalization to novel views.
Directory:
Introduction
Goal of 3D reconstruction from images.
Need for strong priors due to underconstrained nature.
Existing Approaches
Regression-based vs. generative approaches.
Importance of probabilistic modeling in uncertain regions.
Autoencoding Variational Gaussians
Description of the method's core representation.
Sampling and rendering semantic Gaussians.
Encoding Reference Views
Overview of the encoder architecture.
Decoding
Rendering RGB colors and features into pixel space.
Training
Loss functions used for training the model.
Experiments
Results on object-centric and scene-level reconstructions.
Efficiency Comparison
Time and memory requirements compared to baselines.
Ablations Study
Impact of different design choices on performance.
Stats
We present latentSplat, a method for scalable generalizable 3D reconstruction from two reference views (left).
We show that latentSplat outperforms previous works in reconstruction quality and generalization.