3 citations · 11 across the 16 of their papers we have counts for
16 papers
Non-Uniform Quantisation for 3DGS Compression
Bert Van hauwermeiren, Patrice Rondao Alface, Adrian Munteanu
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, yet its high bitrate requirements pose significant challenges for storage and transmissio…
Teger: Spatiotemporal Covariance for Probabilistic Traffic Forecasting
Seyed Mohamad Moghadas, Esther Rodrigo Bonet, Bruno Cornelis +1
Traffic conditions drift -- demand patterns, incident dynamics, and sensor behavior shift over a deployment's lifetime -- so a joint uncertainty estimate fit once at training time…
FlowS: One-Step Motion Prediction via Local Transport Conditioning
Leandro Di Bella, Adrian Munteanu, Bruno Cornelis
Generative motion prediction must satisfy three simultaneous requirements for real-world autonomy: high accuracy, diverse multimodal futures, and strictly bounded latency. Diffusio…
Fourier Splatting: Generalized Fourier encoded primitives for scalable radiance fields
Mihnea-Bogdan Jurca, Bert Van hauwermeiren, Adrian Munteanu
Novel view synthesis has recently been revolutionized by 3D Gaussian Splatting (3DGS), which enables real-time rendering through explicit primitive rasterization. However, existing…
FreqFlow: Long-term forecasting using lightweight flow matching
Seyed Mohamad Moghadas, Bruno Cornelis, Adrian Munteanu
Multivariate time-series (MTS) forecasting is fundamental to applications ranging from urban mobility and resource management to climate modeling. While recent generative models ba…
ReferGPT: Towards Zero-Shot Referring Multi-Object Tracking
Tzoulio Chamiti, Leandro Di Bella, Adrian Munteanu +1
Tracking multiple objects based on textual queries is a challenging task that requires linking language understanding with object association across frames. Previous works typicall…
HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking
Leandro Di Bella, Yangxintong Lyu, Bruno Cornelis +1
The evolution of Advanced Driver Assistance Systems (ADAS) has increased the need for robust and generalizable algorithms for multi-object tracking. Traditional statistical model-b…
ProtoSeg: A Prototype-Based Point Cloud Instance Segmentation Method
Remco Royen, Leon Denis, Adrian Munteanu
3D instance segmentation is crucial for obtaining an understanding of a point cloud scene. This paper presents a novel neural network architecture for performing instance segmentat…
RESSCAL3D++: Joint Acquisition and Semantic Segmentation of 3D Point Clouds
Remco Royen, Kostas Pataridis, Ward van der Tempel +1
3D scene understanding is crucial for facilitating seamless interaction between digital devices and the physical world. Real-time capturing and processing of the 3D scene are essen…
Strada-LLM: Graph LLM for traffic prediction
Seyed Mohamad Moghadas, Bruno Cornelis, Alexandre Alahi +1
Traffic forecasting is pivotal for intelligent transportation systems, where accurate and interpretable predictions can significantly enhance operational efficiency and safety. A k…
GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method
Ali Royat, Seyed Mohamad Moghadas, Lesley De Cruz +1
Deep neural networks (DNNs) have demonstrated remarkable performance across various domains, but their inherent complexity makes them challenging to interpret. This is especially t…
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…
RT-GS2: Real-Time Generalizable Semantic Segmentation for 3D Gaussian Representations of Radiance Fields
Mihnea-Bogdan Jurca, Remco Royen, Ion Giosan +1
Gaussian Splatting has revolutionized the world of novel view synthesis by achieving high rendering performance in real-time. Recently, studies have focused on enriching these 3D r…
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…