arXiv subjects

Donald G. Dansereau

Publications and source records attributed to Donald G. Dansereau.

At least 19 recordsLinked to original sources

arXiv2026

Learning Underwater Active Perception in Simulation

When employing underwater vehicles for the autonomous inspection of assets, it is crucial to consider and assess the water conditions. These conditions significantly impact visibility and directly affect robotic operations. Turbidity can jeopardise the mission by preventing accurate visual documentation of inspected structures. Previous works have introduced methods to adapt to turbidity and backscattering, however, they also include manoeuvring and setup constraints. We propose a simple yet efficient approach to enable high-quality image acquisition of assets in a broad range of water conditions. This active perception framework includes a multi-layer perceptron (MLP) trained to predict image quality given a distance to a target and artificial light intensity. We generate a large synthetic dataset that includes ten water types with varying levels of turbidity and backscattering. For this, we modified the modelling software Blender to better account for the underwater light propagation properties. We validated the approach in simulation and demonstrate significant improvements in visual coverage and image quality compared to traditional methods. The project code is available on our project page at https://roboticimaging.org/Projects/ActiveUW/.

cs.CV↗

arXiv2026

A 3D Reconstruction Benchmark for Asset Inspection

Asset management requires accurate 3D models to inform the maintenance, repair, and assessment of buildings, maritime vessels, and other key structures as they age. These downstream applications rely on high-fidelity models produced from aerial surveys in close proximity to the asset, enabling operators to locate and characterise deterioration or damage and plan repairs. Captured images typically have high overlap between adjacent camera poses, sufficient detail at millimetre scale, and challenging visual appearances such as reflections and transparency. However, existing 3D reconstruction datasets lack examples of these conditions, making it difficult to benchmark methods for this task. We present a new dataset with ground truth depth maps, camera poses, and mesh models of three synthetic scenes with simulated inspection trajectories and varying levels of surface condition on non-Lambertian scene content. We evaluate state-of-the-art reconstruction methods on this dataset. Our results demonstrate that current approaches struggle significantly with the dense capture trajectories and complex surface conditions inherent to this domain, exposing a critical scalability gap and pointing toward new research directions for deployable 3D reconstruction in asset inspection. Project page: https://roboticimaging.org/Projects/asset-inspection-dataset/

cs.CV↗

arXiv2026

The TOLIMAN mission: A low-cost space telescope for high precision narrow-angle astrometry

The TOLIMAN project is engaged with the construction, launch and operation of a low-cost space telescope of unorthodox optical design. Its primary science goal targets an exhaustive search for temperate-orbit rocky planets around either star in the alpha Centauri AB binary within our nearest-neighbor star system. Despite their favorable proximity and brightness, the detection of terrestrial exoplanets around such nearby Sun-like stars remains problematic for contemporary instrumental approaches. By performing narrow-angle astrometric monitoring of binary stars at extreme precision, any exoplanets will betray their presence by way of gravitationally-induced perturbations on the binary orbit. Recovery of this signal is challenging for it amounts to only a few microarcseconds of angular deflection (at best), and so is normally thought to require a large (meter-class) instrument. By implementing an innovative optical and signal encoding architecture, the TOLIMAN space telescope aims to recover such signals with a telescope aperture of only 12.5cm. This paper gives an overview of key features of the mission; in particular the concepts underlying the optics to enable image registration at the extreme levels of precision required. An outline is also provided, sketching further mission components and systems incorporated into the 16U CubeSat spacecraft bus in which the science payload is housed - all of which are now under construction.

astro-ph.IM↗

arXiv2025

dLITE: Differentiable Lighting-Informed Trajectory Evaluation for On-Orbit Inspection

Visual inspection of space-borne assets is of increasing interest to spacecraft operators looking to plan maintenance, characterise damage, and extend the life of high-value satellites in orbit. The environment of Low Earth Orbit (LEO) presents unique challenges when planning inspection operations that maximise visibility, information, and data quality. Specular reflection of sunlight from spacecraft bodies, self-shadowing, and dynamic lighting in LEO significantly impact the quality of data captured throughout an orbit. This is exacerbated by the relative motion between spacecraft, which introduces variable imaging distances and attitudes during inspection. Planning inspection trajectories with the aide of simulation is a common approach. However, the ability to design and optimise an inspection trajectory specifically to improve the resulting image quality in proximity operations remains largely unexplored. In this work, we present $\partial$LITE, an end-to-end differentiable simulation pipeline for on-orbit inspection operations. We leverage state-of-the-art differentiable rendering tools and a custom orbit propagator to enable end-to-end optimisation of orbital parameters based on visual sensor data. $\partial$LITE enables us to automatically design non-obvious trajectories, vastly improving the quality and usefulness of attained data. To our knowledge, our differentiable inspection-planning pipeline is the first of its kind and provides new insights into modern computational approaches to spacecraft mission planning. Project page: https://appearance-aware.github.io/dlite/

cs.RO↗

arXiv2025

Light Field Based 6DoF Tracking of Previously Unobserved Objects

Object tracking is an important step in robotics and reautonomous driving pipelines, which has to generalize to previously unseen and complex objects. Existing high-performing methods often rely on pre-captured object views to build explicit reference models, which restricts them to a fixed set of known objects. However, such reference models can struggle with visually complex appearance, reducing the quality of tracking. In this work, we introduce an object tracking method based on light field images that does not depend on a pre-trained model, while being robust to complex visual behavior, such as reflections. We extract semantic and geometric features from light field inputs using vision foundation models and convert them into view-dependent Gaussian splats. These splats serve as a unified object representation, supporting differentiable rendering and pose optimization. We further introduce a light field object tracking dataset containing challenging reflective objects with precise ground truth poses. Experiments demonstrate that our method is competitive with state-of-the-art model-based trackers in these difficult cases, paving the way toward universal object tracking in robotic systems. Code/data available at https://github.com/nagonch/LiFT-6DoF.

cs.CV↗

arXiv2025

JOCA: Task-Driven Joint Optimisation of Camera Hardware and Adaptive Camera Control Algorithms

The quality of captured images strongly influences the performance of downstream perception tasks. Recent works on co-designing camera systems with perception tasks have shown improved task performance. However, most prior approaches focus on optimising fixed camera parameters set at manufacturing, while many parameters, such as exposure settings, require adaptive control at runtime. This paper introduces a method that jointly optimises camera hardware and adaptive camera control algorithms with downstream vision tasks. We present a unified optimisation framework that integrates gradient-based and derivative-free methods, enabling support for both continuous and discrete parameters, non-differentiable image formation processes, and neural network-based adaptive control algorithms. To address non-differentiable effects such as motion blur, we propose DF-Grad, a hybrid optimisation strategy that trains adaptive control networks using signals from a derivative-free optimiser alongside unsupervised task-driven learning. Experiments show that our method outperforms baselines that optimise static and dynamic parameters separately, particularly under challenging conditions such as low light and fast motion. These results demonstrate that jointly optimising hardware parameters and adaptive control algorithms improves perception performance and provides a unified approach to task-driven camera system design.

cs.CV↗

arXiv2025

Surf-NeRF: Surface Regularised Neural Radiance Fields

Neural Radiance Fields (NeRFs) provide a high fidelity, continuous scene representation that can realistically represent complex behaviour of light. Despite works like Ref-NeRF improving geometry through physics-inspired models, the ability for a NeRF to overcome shape-radiance ambiguity and converge to a representation consistent with real geometry remains limited. We demonstrate how both curriculum learning of a surface light field model and using a lattice-based hash encoding helps a NeRF converge towards a more geometrically accurate scene representation. We introduce four regularisation terms to impose geometric smoothness, consistency of normals, and a separation of Lambertian and specular appearance at geometry in the scene, conforming to physical models. Our approach yields 28% more accurate normals than traditional grid-based NeRF variants with reflection parameterisation. Our approach more accurately separates view-dependent appearance, conditioning a NeRF to have a geometric representation consistent with the captured scene. We demonstrate compatibility of our method with existing NeRF variants, as a key step in enabling radiance-based representations for geometry critical applications.

cs.CV↗

arXiv2024

Segment Anything in Light Fields for Real-Time Applications via Constrained Prompting

Segmented light field images can serve as a powerful representation in many of computer vision tasks exploiting geometry and appearance of objects, such as object pose tracking. In the light field domain, segmentation presents an additional objective of recognizing the same segment through all the views. Segment Anything Model 2 (SAM 2) allows producing semantically meaningful segments for monocular images and videos. However, using SAM 2 directly on light fields is highly ineffective due to unexploited constraints. In this work, we present a novel light field segmentation method that adapts SAM 2 to the light field domain without retraining or modifying the model. By utilizing the light field domain constraints, the method produces high quality and view-consistent light field masks, outperforming the SAM 2 video tracking baseline and working 7 times faster, with a real-time speed. We achieve this by exploiting the epipolar geometry cues to propagate the masks between the views, probing the SAM 2 latent space to estimate their occlusion, and further prompting SAM 2 for their refinement.

cs.CV↗

arXiv2024

TaCOS: Task-Specific Camera Optimization with Simulation

The performance of perception tasks is heavily influenced by imaging systems. However, designing cameras with high task performance is costly, requiring extensive camera knowledge and experimentation with physical hardware. Additionally, cameras and perception tasks are mostly designed in isolation, whereas recent methods that jointly design cameras and tasks have shown improved performance. Therefore, we present a novel end-to-end optimization approach that co-designs cameras with specific vision tasks. This method combines derivative-free and gradient-based optimizers to support both continuous and discrete camera parameters within manufacturing constraints. We leverage recent computer graphics techniques and physical camera characteristics to simulate the cameras in virtual environments, making the design process cost-effective. We validate our simulations against physical cameras and provide a procedurally generated virtual environment. Our experiments demonstrate that our method designs cameras that outperform common off-the-shelf options, and more efficiently compared to the state-of-the-art approach, requiring only 2 minutes to design a camera on an example experiment compared with 67 minutes for the competing method. Designed to support the development of cameras under manufacturing constraints, multiple cameras, and unconventional cameras, we believe this approach can advance the fully automated design of cameras.

cs.CV↗

arXiv2024

LBurst: Learning-Based Robotic Burst Feature Extraction for 3D Reconstruction in Low Light

Drones have revolutionized the fields of aerial imaging, mapping, and disaster recovery. However, the deployment of drones in low-light conditions is constrained by the image quality produced by their on-board cameras. In this paper, we present a learning architecture for improving 3D reconstructions in low-light conditions by finding features in a burst. Our approach enhances visual reconstruction by detecting and describing high quality true features and less spurious features in low signal-to-noise ratio images. We demonstrate that our method is capable of handling challenging scenes in millilux illumination, making it a significant step towards drones operating at night and in extremely low-light applications such as underground mining and search and rescue operations.

cs.CV↗

arXiv2024

Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera

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 sensing or an event camera. Rather than taking snapshots of the environment, an event camera has independent pixels that asynchronously respond to light changes, offering advantages such as high dynamic range, low power consumption and latency, etc. This work focuses on satellite-agnostic operations (only a geometric knowledge of the actual port is required) using the recently released Lockheed Martin Mission Augmentation Port (LM-MAP) as the target. By leveraging shallow data-driven techniques to preprocess the incoming data to highlight the LM-MAP's reflective navigational aids and then using basic geometric models for state estimation, we present a lightweight and data-efficient pipeline that can be used independently with either RGB or event cameras. We demonstrate the soundness of the pipeline and perform a quantitative comparison of the two modalities based on data collected with a photometrically accurate test bench that includes a robotic arm to simulate the target satellite's uncontrolled motion.

cs.RO↗

arXiv2024

Adapting CNNs for Fisheye Cameras without Retraining

The majority of image processing approaches assume images are in or can be rectified to a perspective projection. However, in many applications it is beneficial to use non conventional cameras, such as fisheye cameras, that have a larger field of view (FOV). The issue arises that these large-FOV images can't be rectified to a perspective projection without significant cropping of the original image. To address this issue we propose Rectified Convolutions (RectConv); a new approach for adapting pre-trained convolutional networks to operate with new non-perspective images, without any retraining. Replacing the convolutional layers of the network with RectConv layers allows the network to see both rectified patches and the entire FOV. We demonstrate RectConv adapting multiple pre-trained networks to perform segmentation and detection on fisheye imagery from two publicly available datasets. Our approach requires no additional data or training, and operates directly on the native image as captured from the camera. We believe this work is a step toward adapting the vast resources available for perspective images to operate across a broad range of camera geometries.

cs.CV↗

arXiv2023

The Need for Inherently Privacy-Preserving Vision in Trustworthy Autonomous Systems

Vision is a popular and effective sensor for robotics from which we can derive rich information about the environment: the geometry and semantics of the scene, as well as the age, gender, identity, activity and even emotional state of humans within that scene. This raises important questions about the reach, lifespan, and potential misuse of this information. This paper is a call to action to consider privacy in the context of robotic vision. We propose a specific form privacy preservation in which no images are captured or could be reconstructed by an attacker even with full remote access. We present a set of principles by which such systems can be designed, and through a case study in localisation demonstrate in simulation a specific implementation that delivers an important robotic capability in an inherently privacy-preserving manner. This is a first step, and we hope to inspire future works that expand the range of applications open to sighted robotic systems.

cs.CV↗

arXiv2022

NOCaL: Calibration-Free Semi-Supervised Learning of Odometry and Camera Intrinsics

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-supervised learning architecture capable of interpreting previously unseen cameras without calibration. NOCaL learns to estimate camera parameters, relative pose, and scene appearance. It employs a scene-rendering hypernetwork pretrained on a large number of existing cameras and scenes, and adapts to previously unseen cameras using a small supervised training set to enforce metric scale. We demonstrate NOCaL on rendered and captured imagery using conventional cameras, demonstrating calibration-free odometry and novel view synthesis. This work represents a key step toward automating the interpretation of general camera geometries and emerging imaging technologies.

cs.RO↗

arXiv2022

BuFF: Burst Feature Finder for Light-Constrained 3D Reconstruction

Robots operating at night using conventional vision cameras face significant challenges in reconstruction due to noise-limited images. Previous work has demonstrated that burst-imaging techniques can be used to partially overcome this issue. In this paper, we develop a novel feature detector that operates directly on image bursts that enhances vision-based reconstruction under extremely low-light conditions. Our approach finds keypoints with well-defined scale and apparent motion within each burst by jointly searching in a multi-scale and multi-motion space. Because we describe these features at a stage where the images have higher signal-to-noise ratio, the detected features are more accurate than the state-of-the-art on conventional noisy images and burst-merged images and exhibit high precision, recall, and matching performance. We show improved feature performance and camera pose estimates and demonstrate improved structure-from-motion performance using our feature detector in challenging light-constrained scenes. Our feature finder provides a significant step towards robots operating in low-light scenarios and applications including night-time operations.

cs.RO↗

arXiv2022

Semantically Accurate Super-Resolution Generative Adversarial Networks

This work addresses the problems of semantic segmentation and image super-resolution by jointly considering the performance of both in training a Generative Adversarial Network (GAN). We propose a novel architecture and domain-specific feature loss, allowing super-resolution to operate as a pre-processing step to increase the performance of downstream computer vision tasks, specifically semantic segmentation. We demonstrate this approach using Nearmap's aerial imagery dataset which covers hundreds of urban areas at 5-7 cm per pixel resolution. We show the proposed approach improves perceived image quality as well as quantitative segmentation accuracy across all prediction classes, yielding an average accuracy improvement of 11.8% and 108% at 4x and 32x super-resolution, compared with state-of-the art single-network methods. This work demonstrates that jointly considering image-based and task-specific losses can improve the performance of both, and advances the state-of-the-art in semantic-aware super-resolution of aerial imagery.

cs.CV↗

arXiv2021

Burst Imaging for Light-Constrained Structure-From-Motion

Images captured under extremely low light conditions are noise-limited, which can cause existing robotic vision algorithms to fail. In this paper we develop an image processing technique for aiding 3D reconstruction from images acquired in low light conditions. Our technique, based on burst photography, uses direct methods for image registration within bursts of short exposure time images to improve the robustness and accuracy of feature-based structure-from-motion (SfM). We demonstrate improved SfM performance in challenging light-constrained scenes, including quantitative evaluations that show improved feature performance and camera pose estimates. Additionally, we show that our method converges more frequently to correct reconstructions than the state-of-the-art. Our method is a significant step towards allowing robots to operate in low light conditions, with potential applications to robots operating in environments such as underground mines and night time operation.

cs.RO↗

arXiv2021

Refractive Light-Field Features for Curved Transparent Objects in Structure from Motion

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 novel image feature for light fields that detects and describes the patterns of light refracted through curved transparent objects. We derive characteristic points based on these features allowing them to be used in place of conventional 2D features. Using our features, we demonstrate improved structure-from-motion performance in challenging scenes containing refractive objects, including quantitative evaluations that show improved camera pose estimates and 3D reconstructions. Additionally, our methods converge 15-35% more frequently than the state-of-the-art. Our method is a critical step towards allowing robots to operate around refractive objects, with applications in manufacturing, quality assurance, pick-and-place, and domestic robots working with acrylic, glass and other transparent materials.

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