88 citations · 161 across the 19 of their papers we have counts for
19 papers · 1 filter
Approximating Continuous Convolutions for Deep Network Compression
Theo W. Costain, Victor Adrian Prisacariu
We present ApproxConv, a novel method for compressing the layers of a convolutional neural network. Reframing conventional discrete convolution as continuous convolution of paramet…
Map-free Visual Relocalization: Metric Pose Relative to a Single Image
Eduardo Arnold, Jamie Wynn, Sara Vicente +5
Can we relocalize in a scene represented by a single reference image? Standard visual relocalization requires hundreds of images and scale calibration to build a scene-specific 3D…
BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion
Kejie Li, Yansong Tang, Victor Adrian Prisacariu +1
Dense 3D reconstruction from a stream of depth images is the key to many mixed reality and robotic applications. Although methods based on Truncated Signed Distance Function (TSDF)…
Few-shot Semantic Segmentation with Self-supervision from Pseudo-classes
Yiwen Li, Gratianus Wesley Putra Data, Yunguan Fu +2
Despite the success of deep learning methods for semantic segmentation, few-shot semantic segmentation remains a challenging task due to the limited training data and the generalis…
Ray-ONet: Efficient 3D Reconstruction From A Single RGB Image
Wenjing Bian, Zirui Wang, Kejie Li +1
We propose Ray-ONet to reconstruct detailed 3D models from monocular images efficiently. By predicting a series of occupancy probabilities along a ray that is back-projected from a…
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
Jamie Watson, Oisin Mac Aodha, Victor Prisacariu +2
Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications,…