3 citations · 8 across the 5 of their papers we have counts for
5 papers
Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models
Ioannis Romanelis, Vlassios Fotis, Athanasios Kalogeras +3
We propose a novel point cloud U-Net diffusion architecture for 3D generative modeling capable of generating high-quality and diverse 3D shapes while maintaining fast generation ti…
LAM3D: Leveraging Attention for Monocular 3D Object Detection
Diana-Alexandra Sas, Leandro Di Bella, Yangxintong Lyu +2
Since the introduction of the self-attention mechanism and the adoption of the Transformer architecture for Computer Vision tasks, the Vision Transformer-based architectures gained…
Joint prototype and coefficient prediction for 3D instance segmentation
Remco Royen, Leon Denis, Adrian Munteanu
3D instance segmentation is crucial for applications demanding comprehensive 3D scene understanding. In this paper, we introduce a novel method that simultaneously learns coefficie…
DeepKalPose: An Enhanced Deep-Learning Kalman Filter for Temporally Consistent Monocular Vehicle Pose Estimation
Leandro Di Bella, Yangxintong Lyu, Adrian Munteanu
This paper presents DeepKalPose, a novel approach for enhancing temporal consistency in monocular vehicle pose estimation applied on video through a deep-learning-based Kalman Filt…
RESSCAL3D: Resolution Scalable 3D Semantic Segmentation of Point Clouds
Remco Royen, Adrian Munteanu
While deep learning-based methods have demonstrated outstanding results in numerous domains, some important functionalities are missing. Resolution scalability is one of them. In t…