34 citations · 36 across the 7 of their papers we have counts for
9 papers
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…
MACARONS: Mapping And Coverage Anticipation with RGB Online Self-Supervision
Antoine Guédon, Tom Monnier, Pascal Monasse +1
We introduce a method that simultaneously learns to explore new large environments and to reconstruct them in 3D from color images only. This is closely related to the Next Best Vi…
The Learnable Typewriter: A Generative Approach to Text Analysis
Ioannis Siglidis, Nicolas Gonthier, Julien Gaubil +2
We present a generative document-specific approach to character analysis and recognition in text lines. Our main idea is to build on unsupervised multi-object segmentation methods…
Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?
Monika Wysoczańska, Tom Monnier, Tomasz Trzciński +1
Recent advances in visual representation learning allowed to build an abundance of powerful off-the-shelf features that are ready-to-use for numerous downstream tasks. This work ai…
Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
Tom Monnier, Matthew Fisher, Alexei A. Efros +1
Approaches for single-view reconstruction typically rely on viewpoint annotations, silhouettes, the absence of background, multiple views of the same instance, a template shape, or…
Representing Shape Collections with Alignment-Aware Linear Models
Romain Loiseau, Tom Monnier, Mathieu Aubry +1
In this paper, we revisit the classical representation of 3D point clouds as linear shape models. Our key insight is to leverage deep learning to represent a collection of shapes a…
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docExtractor: An off-the-shelf historical document element extraction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We present docExtractor, a generic approach for extracting visual elements such as text lines or illustrations from historical documents without requiring any real data annotation.…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…