34 citations · 34 across the 1 of their papers we have counts for
4 papers · 1 filter
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives
Tom Monnier, Jake Austin, Angjoo Kanazawa +2
Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many ap…
Unsupervised Layered Image Decomposition into Object Prototypes
Tom Monnier, Elliot Vincent, Jean Ponce +1
We present an unsupervised learning framework for decomposing images into layers of automatically discovered object models. Contrary to recent approaches that model image layers wi…
docExtractor: An off-the-shelf historical document element extraction
Tom Monnier, Mathieu Aubry
We present docExtractor, a generic approach for extracting visual elements such as text lines or illustrations from historical documents without requiring any real data annotation.…
Deep Transformation-Invariant Clustering
Tom Monnier, Thibault Groueix, Mathieu Aubry
Recent advances in image clustering typically focus on learning better deep representations. In contrast, we present an orthogonal approach that does not rely on abstract features…