Search Results for author:
Found 9 papers, 6 papers with code
Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives
We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio.
MACARONS: Mapping And Coverage Anticipation with RGB Online Self-Supervision
We introduce a method that simultaneously learns to explore new large environments and to reconstruct them in 3D from color images only.
The Learnable Typewriter: A Generative Approach to Text Analysis
Second, we show the potential of our method for new applications, more specifically in the field of paleography, which studies the history and variations of handwriting, and for cipher analysis.
Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?
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.
Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency
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 symmetry.
3D Object Reconstruction From A Single Image
3D Reconstruction
+2
Representing Shape Collections with Alignment-Aware Linear Models
In this paper, we revisit the classical representation of 3D point clouds as linear shape models.
Unsupervised Layered Image Decomposition into Object Prototypes
We present an unsupervised learning framework for decomposing images into layers of automatically discovered object models.
docExtractor: An off-the-shelf historical document element extraction
We present docExtractor, a generic approach for extracting visual elements such as text lines or illustrations from historical documents without requiring any real data annotation.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
In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space.
Ranked #2 on
Unsupervised Image Classification
on SVHN
(using extra training data)
