93 citations · 96 across the 5 of their papers we have counts for
8 papers
ZeroComp: Zero-shot Object Compositing from Image Intrinsics via Diffusion
Zitian Zhang, Frédéric Fortier-Chouinard, Mathieu Garon +2
We present ZeroComp, an effective zero-shot 3D object compositing approach that does not require paired composite-scene images during training. Our method leverages ControlNet to c…
Editable Indoor Lighting Estimation
Henrique Weber, Mathieu Garon, Jean-François Lalonde
We present a method for estimating lighting from a single perspective image of an indoor scene. Previous methods for predicting indoor illumination usually focus on either simple,…
RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking
Etienne Dubeau, Mathieu Garon, Benoit Debaque +2
Augmented reality devices require multiple sensors to perform various tasks such as localization and tracking. Currently, popular cameras are mostly frame-based (e.g. RGB and Depth…
Input Dropout for Spatially Aligned Modalities
Sébastien de Blois, Mathieu Garon, Christian Gagné +1
Computer vision datasets containing multiple modalities such as color, depth, and thermal properties are now commonly accessible and useful for solving a wide array of challenging…
Deep Template-based Object Instance Detection
Jean-Philippe Mercier, Mathieu Garon, Philippe Giguère +1
Much of the focus in the object detection literature has been on the problem of identifying the bounding box of a particular class of object in an image. Yet, in contexts such as r…
Fast Spatially-Varying Indoor Lighting Estimation
Mathieu Garon, Kalyan Sunkavalli, Sunil Hadap +2
We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image. Given an image and a 2D location in that image, our CNN estimates a 5th order sp…