Search Results for author:
Found 12 papers, 8 papers with code
RETR: Multi-View Radar Detection Transformer for Indoor Perception
RETR inherits the advantages of DETR, eliminating the need for hand-crafted components for object detection and segmentation in the image plane.
MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception
In this paper, we scale up indoor radar data collection using multi-view high-resolution radar heatmap in a multi-day, multi-room, and multi-subject setting, with an emphasis on the diversity of environment and subjects.
Deep Learning on Object-centric 3D Neural Fields
In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes.
Neural Processing of Tri-Plane Hybrid Neural Fields
While processing a field with the same reconstruction quality, we achieve task performance far superior to frameworks that process large MLPs and, for the first time, almost on par with architectures handling explicit representations.
Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation
3D semantic segmentation is a critical task in many real-world applications, such as autonomous driving, robotics, and mixed reality.
Permutation Equivariant Neural Functionals
The key building blocks of this framework are NF-Layers (neural functional layers) that we constrain to be permutation equivariant through an appropriate parameter sharing scheme.
Deep Learning on Implicit Neural Representations of Shapes
Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D shapes.
Learning Good Features to Transfer Across Tasks and Domains
Besides, we propose a set of strategies to constrain the learned feature spaces, to ease learning and increase the generalization capability of the mapping network, thereby considerably improving the final performance of our framework.
Self-Distillation for Unsupervised 3D Domain Adaptation
In contrast, in this work, we focus on obtaining a discriminative feature space for the target domain enforcing consistency between a point cloud and its augmented version.
RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation
Unsupervised Domain Adaptation (UDA) for point cloud classification is an emerging research problem with relevant practical motivations.
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation
We further rely on depth to generate a large and varied set of samples to Self-Train the final model.
Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries
Although deep neural networks have achieved remarkable results for the task of semantic segmentation, they usually fail to generalize towards new domains, especially when performing synthetic-to-real adaptation.
