7 papers
3D-LFM: Lifting Foundation Model
Mosam Dabhi, Laszlo A. Jeni, Simon Lucey
The lifting of 3D structure and camera from 2D landmarks is at the cornerstone of the entire discipline of computer vision. Traditional methods have been confined to specific rigid…
Weight Conditioning for Smooth Optimization of Neural Networks
Hemanth Saratchandran, Thomas X. Wang, Simon Lucey
In this article, we introduce a novel normalization technique for neural network weight matrices, which we term weight conditioning. This approach aims to narrow the gap between th…
From Activation to Initialization: Scaling Insights for Optimizing Neural Fields
Hemanth Saratchandran, Sameera Ramasinghe, Simon Lucey
In the realm of computer vision, Neural Fields have gained prominence as a contemporary tool harnessing neural networks for signal representation. Despite the remarkable progress i…
DARB-Splatting: Generalizing Splatting with Decaying Anisotropic Radial Basis Functions
Hashiru Pramuditha, Vinasirajan Viruthshaan, Vishagar Arunan +4
Splatting-based 3D reconstruction methods have gained popularity with the advent of 3D Gaussian Splatting, efficiently synthesizing high-quality novel views. These methods commonly…
Preconditioners for the Stochastic Training of Neural Fields
Shin-Fang Chng, Hemanth Saratchandran, Simon Lucey
Neural fields encode continuous multidimensional signals as neural networks, enabling diverse applications in computer vision, robotics, and geometry. While Adam is effective for s…
SMORE: Simultaneous Map and Object REconstruction
Nathaniel Chodosh, Anish Madan, Simon Lucey +1
We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. Depth-based reconstructions tend to focus on small-scale objects or large-scale SLAM…