1 citations · 3 across the 16 of their papers we have counts for
8 papers · 2 filters
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…
Invertible Neural Warp for NeRF
Shin-Fang Chng, Ravi Garg, Hemanth Saratchandran +1
This paper tackles the simultaneous optimization of pose and Neural Radiance Fields (NeRF). Departing from the conventional practice of using explicit global representations for ca…
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…
SeMoLi: What Moves Together Belongs Together
Jenny Seidenschwarz, Aljoša Ošep, Francesco Ferroni +2
We tackle semi-supervised object detection based on motion cues. Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to p…
Fast Kernel Scene Flow
Xueqian Li, Simon Lucey
In contrast to current state-of-the-art methods, such as NSFP [25], which employ deep implicit neural functions for modeling scene flow, we present a novel approach that utilizes c…
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…
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…
Convolutional Initialization for Data-Efficient Vision Transformers
Jianqiao Zheng, Xueqian Li, Simon Lucey
Training vision transformer networks on small datasets poses challenges. In contrast, convolutional neural networks (CNNs) can achieve state-of-the-art performance by leveraging th…