5 citations · 7 across the 8 of their papers we have counts for
13 papers
PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes
Ahmed Abdelreheem, Filippo Aleotti, Jamie Watson +6
We introduce the novel task of Language-Guided Object Placement in Real 3D Scenes. Our model is given a 3D scene's point cloud, a 3D asset, and a textual prompt broadly describing…
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…
Morpheus: Text-Driven 3D Gaussian Splat Shape and Color Stylization
Jamie Wynn, Zawar Qureshi, Jakub Powierza +2
Exploring real-world spaces using novel-view synthesis is fun, and reimagining those worlds in a different style adds another layer of excitement. Stylized worlds can also be used…
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…
A versatile robotic hand with 3D perception, force sensing for autonomous manipulation
Nikolaus Correll, Dylan Kriegman, Stephen Otto +1
We describe a force-controlled robotic gripper with built-in tactile and 3D perception. We also describe a complete autonomous manipulation pipeline consisting of object detection,…
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…
SimpleRecon: 3D Reconstruction Without 3D Convolutions
Mohamed Sayed, John Gibson, Jamie Watson +3
Traditionally, 3D indoor scene reconstruction from posed images happens in two phases: per-image depth estimation, followed by depth merging and surface reconstruction. Recently, a…
Single Image Depth Prediction with Wavelet Decomposition
Michaël Ramamonjisoa, Michael Firman, Jamie Watson +2
We present a novel method for predicting accurate depths from monocular images with high efficiency. This optimal efficiency is achieved by exploiting wavelet decomposition, which…
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
Jamie Watson, Oisin Mac Aodha, Victor Prisacariu +2
Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications,…
Learning Stereo from Single Images
Jamie Watson, Oisin Mac Aodha, Daniyar Turmukhambetov +2
Supervised deep networks are among the best methods for finding correspondences in stereo image pairs. Like all supervised approaches, these networks require ground truth data duri…
Footprints and Free Space from a Single Color Image
Jamie Watson, Michael Firman, Aron Monszpart +1
Understanding the shape of a scene from a single color image is a formidable computer vision task. However, most methods aim to predict the geometry of surfaces that are visible to…
Self-Supervised Monocular Depth Hints
Jamie Watson, Michael Firman, Gabriel J. Brostow +1
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-trut…