1 citations · 3 across the 16 of their papers we have counts for
17 papers
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…
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…
Efficient Learning With Sine-Activated Low-rank Matrices
Yiping Ji, Hemanth Saratchandran, Cameron Gordon +2
Low-rank decomposition has emerged as a vital tool for enhancing parameter efficiency in neural network architectures, gaining traction across diverse applications in machine learn…
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…
D'OH: Decoder-Only Random Hypernetworks for Implicit Neural Representations
Cameron Gordon, Lachlan Ewen MacDonald, Hemanth Saratchandran +1
Deep implicit functions have been found to be an effective tool for efficiently encoding all manner of natural signals. Their attractiveness stems from their ability to compactly r…
A Sampling Theory Perspective on Activations for Implicit Neural Representations
Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko +2
Implicit Neural Representations (INRs) have gained popularity for encoding signals as compact, differentiable entities. While commonly using techniques like Fourier positional enco…
Analyzing the Neural Tangent Kernel of Periodically Activated Coordinate Networks
Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey
Recently, neural networks utilizing periodic activation functions have been proven to demonstrate superior performance in vision tasks compared to traditional ReLU-activated networ…
Architectural Strategies for the optimization of Physics-Informed Neural Networks
Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey
Physics-informed neural networks (PINNs) offer a promising avenue for tackling both forward and inverse problems in partial differential equations (PDEs) by incorporating deep lear…
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…
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…
Multi-Body Neural Scene Flow
Kavisha Vidanapathirana, Shin-Fang Chng, Xueqian Li +1
The test-time optimization of scene flow - using a coordinate network as a neural prior - has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art pe…
Robust Point Cloud Processing through Positional Embedding
Jianqiao Zheng, Xueqian Li, Sameera Ramasinghe +1
End-to-end trained per-point embeddings are an essential ingredient of any state-of-the-art 3D point cloud processing such as detection or alignment. Methods like PointNet, or the…