5 citations · 5 across the 10 of their papers we have counts for
15 papers
Complete Gaussian Splats from a Single Image with Denoising Diffusion Models
Ziwei Liao, Mohamed Sayed, Steven L. Waslander +3
Gaussian splatting typically requires dense observations of the scene and can fail to reconstruct occluded and unobserved areas. We propose a latent diffusion model to reconstruct…
MVSAnywhere: Zero-Shot Multi-View Stereo
Sergio Izquierdo, Mohamed Sayed, Michael Firman +6
Computing accurate depth from multiple views is a fundamental and longstanding challenge in computer vision. However, most existing approaches do not generalize well across differe…
DoubleTake: Geometry Guided Depth Estimation
Mohamed Sayed, Filippo Aleotti, Jamie Watson +5
Estimating depth from a sequence of posed RGB images is a fundamental computer vision task, with applications in augmented reality, path planning etc. Prior work typically makes us…
AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings
Jamie Watson, Filippo Aleotti, Mohamed Sayed +5
Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene…
Virtual Occlusions Through Implicit Depth
Jamie Watson, Mohamed Sayed, Zawar Qureshi +4
For augmented reality (AR), it is important that virtual assets appear to `sit among' real world objects. The virtual element should variously occlude and be occluded by real matte…
Removing Objects From Neural Radiance Fields
Silvan Weder, Guillermo Garcia-Hernando, Aron Monszpart +4
Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Befo…