70 citations · 102 across the 10 of their papers we have counts for
10 papers · 1 filter
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data
David Schinagl, Georg Krispel, Horst Possegger +2
While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we pr…
Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild
Alexander Grabner, Yaming Wang, Peizhao Zhang +5
We present a novel 3D pose refinement approach based on differentiable rendering for objects of arbitrary categories in the wild. In contrast to previous methods, we make two main…
ALCN: Adaptive Local Contrast Normalization
Mahdi Rad, Peter M. Roth, Vincent Lepetit
To make Robotics and Augmented Reality applications robust to illumination changes, the current trend is to train a Deep Network with training images captured under many different…
Patch augmentation: Towards efficient decision boundaries for neural networks
Marcus D. Bloice, Peter M. Roth, Andreas Holzinger
In this paper we propose a new augmentation technique, called patch augmentation, that, in our experiments, improves model accuracy and makes networks more robust to adversarial at…
Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation
Martin Hirzer, Peter M. Roth, Vincent Lepetit
We propose a novel method to efficiently estimate the spatial layout of a room from a single monocular RGB image. As existing approaches based on low-level feature extraction, foll…
Location Field Descriptors: Single Image 3D Model Retrieval in the Wild
Alexander Grabner, Peter M. Roth, Vincent Lepetit
We present Location Field Descriptors, a novel approach for single image 3D model retrieval in the wild. In contrast to previous methods that directly map 3D models and RGB images…