15 citations · 15 across the 10 of their papers we have counts for
11 papers
The Fourth Monocular Depth Estimation Challenge
Anton Obukhov, Matteo Poggi, Fabio Tosi +54
This paper presents the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), which focuses on zero-shot generalization to the SYNS-Patches benchmark, a…
The Third Monocular Depth Estimation Challenge
Jaime Spencer, Fabio Tosi, Matteo Poggi +38
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-…
Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTV
Jaime Spencer, Chris Russell, Simon Hadfield +1
Self-supervised learning is the key to unlocking generic computer vision systems. By eliminating the reliance on ground-truth annotations, it allows scaling to much larger data qua…
Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV
Jaime Spencer, Chris Russell, Simon Hadfield +1
Self-supervised monocular depth estimation (SS-MDE) has the potential to scale to vast quantities of data. Unfortunately, existing approaches limit themselves to the automotive dom…
The Second Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Michaela Trescakova +40
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, includin…
The Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Chris Russell +16
This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised monocular…
Medusa: Universal Feature Learning via Attentional Multitasking
Jaime Spencer, Richard Bowden, Simon Hadfield
Recent approaches to multi-task learning (MTL) have focused on modelling connections between tasks at the decoder level. This leads to a tight coupling between tasks, which need re…
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning
Jaime Spencer, Richard Bowden, Simon Hadfield
In the current monocular depth research, the dominant approach is to employ unsupervised training on large datasets, driven by warped photometric consistency. Such approaches lack…
Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance
Jaime Spencer, Richard Bowden, Simon Hadfield
"Like night and day" is a commonly used expression to imply that two things are completely different. Unfortunately, this tends to be the case for current visual feature representa…
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
Jaime Spencer, Richard Bowden, Simon Hadfield
How do computers and intelligent agents view the world around them? Feature extraction and representation constitutes one the basic building blocks towards answering this question.…
Localisation via Deep Imagination: learn the features not the map
Jaime Spencer, Oscar Mendez, Richard Bowden +1
How many times does a human have to drive through the same area to become familiar with it? To begin with, we might first build a mental model of our surroundings. Upon revisiting…