I am a research fellow at Imperial College London. My work lies at the interection of computer vision and machine learning. My research mainly focuses on allowing machines to interpret and understand the 3D world around them.
I obtained my PhD from the University of Oxford Department of Computer Science. Before coming to the UK I did an MSc in electrical engineering at the University of the Witwatersrand in South Africa. I have received numerous international awards for my research including a best paper honourable mention at the Conference on Computer Vision and Pattern Recognition (CVPR). My research has been supported by a number of prestigious fellowships, including an EPSRC doctoral studentship, a Dyson Fellowship and most recently an Imperial College Early Career Fellowship.
Personal site: www.ronnieclark.co.uk
Lin S, Clark R, 2020, LaDDer: latent data distribution modelling with a generative prior, British Machine Vision Conference (BMVC), British Machine Vision Association
et al., Scalable uncertainty for computer vision with functional variationalinference, CVPR 2020, IEEE, Pages:12003-12013
et al., 2020, Learning meshes for dense visual SLAM, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE
et al., 2019, Learning object bounding boxes for 3D instance segmentation on point clouds, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Neural Information Processing Systems Foundation, Inc.
et al., 2018, CodeSLAM - Learning a compact, optimisable representation for dense visual SLAM, IEEE Computer Vision and Pattern Recognition 2018, IEEE, Pages:2560-2568
et al., 2018, Fusion++: Volumetric object-level SLAM, 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), International Conference on, IEEE, Pages:32-41, ISSN:2378-3826
et al., 2018, Learning to solve nonlinear least squares for monocular stereo, 15th European Conference on Computer Vision, Springer Nature Switzerland AG 2018, Pages:291-306, ISSN:0302-9743
et al., 2018, InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset, British Machine Vision Conference (BMVC), BMVC
et al., 2017, 3D Object reconstruction from a single depth view with adversarial learning, 16th IEEE International Conference on Computer Vision (ICCV), IEEE, Pages:679-688, ISSN:2473-9936
et al., VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization, IEEE International Conference on Computer Vision and Pattern Recognition
et al., VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem, Thirty-First AAAI Conference on Artificial Intelligence
et al., InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset