12 accepted papers
Transformers have emerged as powerful implicit rendering models, capable of performing geometric reasoning and producing photorealistic novel views in a single feedforward pass. A central challenge in these architectures is how to inject camera parameters into the transformer in a way that generalis…
In-orbit automated servicing is a promising path towards lowering the cost of satellite operations and reducing the amount of orbital debris. For this purpose, we present a pipeline for automated satellite docking port detection and state estimation using monocular vision data from standard RGB sens
Robots operating in low-light conditions with conventional cameras face significant challenges due to the low signal-to-noise ratio in the images. Previous work has demonstrated the use of burst-imaging techniques to partially overcome this issue. This study proposes a novel feature finder that enha
There are a multitude of emerging imaging technologies that could benefit robotics. However the need for bespoke models, calibration and low-level processing represents a key barrier to their adoption. In this work we present NOCaL, Neural Odometry and Calibration using Light fields, a semi-supervis…
Images captured under extremely low light conditions are noise-limited, which can cause existing robotic vision algorithms to fail. In this letter we develop an image processing technique for aiding 3D reconstruction from images acquired in low light conditions. Our technique, based on burst photogr
Curved refractive objects are common in the human environment, and have a complex visual appearance that can cause robotic vision algorithms to fail. Light-field cameras allow us to address this challenge by capturing the view-dependent appearance of such objects in a single exposure. We propose a n
While an exciting diversity of new imaging devices is emerging that could dramatically improve robotic perception, the challenges of calibrating and interpreting these cameras have limited their uptake in the robotics community. In this work we generalise techniques from unsupervised learning to all…
To be effective, robots will need to reliably operate in scenes with refractive objects in a variety of applications; however, refractive objects can cause many robotic vision algorithms, such as structure from motion, to become unreliable or even fail. We propose a novel method to distinguish betwe
Feature detectors and descriptors are key low-level vision tools that many higher-level tasks build on. Unfortunately these fail in the presence of challenging light transport effects including partial occlusion, low contrast, and reflective or refractive surfaces. Building on spatio-angular imaging…
Light field (LF) capture and processing are important in an expanding range of computer vision applications, offering rich textural and depth information and simplification of conventionally complex tasks. Although LF cameras are commercially available, no existing device offers wide field-of-view (…
This paper proposes the first derivation, implementation, and experimental validation of light field image-based visual servoing. Light field image Jacobians are derived based on a compact light field feature representation that is close to the form measured directly by light field cameras. We also
We describe an interactive approach for visual object analysis which exploits the ability of a robot to manipulate its environment. Knowledge of objects' mechanical properties is important in a host of robotics tasks, but their measurement can be impractical due to perceptual or mechanical limitatio