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Radu Alexandru Rosu

7 accepted papers

2025

DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models

CVPR 2025poster

We address the task of generating 3D hair geometry from a single image, which is challenging due to the diversity of hairstyles and the lack of paired image-to-3D hair data. Previous methods are primarily trained on synthetic data and cope with the limited amount of such data by using low-dimensiona…

2023

PermutoSDF: Fast Multi-View Reconstruction With Implicit Surfaces Using Permutohedral Lattices

CVPR 2023poster

Neural radiance-density field methods have become increasingly popular for the task of novel-view rendering. Their recent extension to hash-based positional encoding ensures fast training and inference with visually pleasing results. However, density-based methods struggle with recovering accurate s…

2022

Abstract Flow for Temporal Semantic Segmentation on the Permutohedral Lattice

ICRA 2022poster

Semantic segmentation is a core ability required by autonomous agents, as being able to distinguish which parts of the scene belong to which object class is crucial for navigation and interaction with the environment. Approaches which use only one time-step of data cannot distinguish between moving…

Cited by 18SourcecodeScholar
2022

Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images

ECCV 2022poster

"We present Neural Strands, a novel learning framework for modeling accurate hair geometry and appearance from multi-view image inputs. The learned hair model can be rendered in real-time from any viewpoint with high-fidelity view-dependent effects. Our model achieves intuitive shape and style contr…

Cited by 44SourcePDFScholar
2020

Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching

ICRA 2020poster

Pose estimation and map building are central ingredients of autonomous robots and typically rely on the registration of sensor data. In this paper, we investigate a new metric for registering images that builds upon on the idea of the photometric error. Our approach combines a gradient orientation-b…

Cited by 6SourceScholar
2020

LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices

RSS 2020poster

Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images. Applying the same methods on 3D data still poses challenges due to the heavy memory requirements and the lack of structured data. Here, we propose LatticeNet, a novel approach…

2017

Online depth calibration for RGB-D cameras using visual SLAM

IROS 2017poster

Modern consumer RGB-D cameras are affordable and provide dense depth estimates at high frame rates. Hence, they are popular for building dense environment representations. Yet, the sensors often do not provide accurate depth estimates since the factory calibration exhibits a static deformation. We p…

Cited by 9SourceScholar