68 citations · 184 across the 19 of their papers we have counts for
23 papers
P-JEPA: Procedural Video Representation Learning via Joint Embedding Predictive Architecture
Felix Tristram, Stefano Gasperini, Benjamin Killeen +4
The increasing maturity of embodied AI platforms has driven a growing interest in procedural video representation learning to support intelligent assistance systems for complex, mu…
Prompting Diffusion Models for Zero-Shot Instance Segmentation
Irem Zeynep Alagöz, Nils Morbitzer, Andrea Ramazzina +3
Several disruptive research directions have recently emerged in computer vision, including foundation models achieving previously unseen zero-shot performance in scene understandin…
SA4Depth: Consistent Pose-Depth Scale Alignment for Self-Supervised Monocular Depth Estimation
Changxuan Li, Nadine Berner, Nassir Navab +2
Self-supervised depth estimation from monocular sequences relies on the joint learning of a depth and a pose network. Despite abundant research done to improve the depth network, e…
Future Dynamic 3D Reconstruction: Toward 3D World Modeling with Disentangled Ego-Motion
Nils Morbitzer, Jonathan Evers, Artem Savkin +4
Forecasting the evolution of dynamic environments is crucial for autonomous agents. While generative world models have achieved high photorealism in 2D video synthesis by mixing eg…
OpenGaFF: Open-Vocabulary Gaussian Feature Field with Codebook Attention
Kunyi Li, Michael Niemeyer, Sen Wang +3
Understanding open-vocabulary 3D scenes with Gaussian-based representations remains challenging due to fragmented and spatially inconsistent semantic predictions across multi-view…
SING3R-SLAM: Submap-based Indoor Monocular Gaussian SLAM with 3D Reconstruction Priors
Kunyi Li, Michael Niemeyer, Sen Wang +3
Recent advances in dense 3D reconstruction have demonstrated strong capability in accurately capturing local geometry. However, extending these methods to incremental global recons…
Visibility-Aware Language Aggregation for Open-Vocabulary Segmentation in 3D Gaussian Splatting
Sen Wang, Kunyi Li, Siyun Liang +4
Recently, distilling open-vocabulary language features from 2D images into 3D Gaussians has attracted significant attention. Although existing methods achieve impressive language-b…
GALA: Guided Attention with Language Alignment for Open Vocabulary Gaussian Splatting
Elena Alegret, Kunyi Li, Sen Wang +5
3D scene reconstruction and understanding have gained increasing popularity, yet existing methods still struggle to capture fine-grained, language-aware 3D representations from 2D…
Prior2Former -- Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation
Sebastian Schmidt, Julius Körner, Dominik Fuchsgruber +3
In panoptic segmentation, individual instances must be separated within semantic classes. As state-of-the-art methods rely on a pre-defined set of classes, they struggle with novel…
From Open-Vocabulary to Vocabulary-Free Semantic Segmentation
Klara Reichard, Giulia Rizzoli, Stefano Gasperini +4
Open-vocabulary semantic segmentation enables models to identify novel object categories beyond their training data. While this flexibility represents a significant advancement, cu…
SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians
Siyun Liang, Sen Wang, Kunyi Li +5
3D Gaussian Splatting has recently gained traction for its efficient training and real-time rendering. While its vanilla representation is mainly designed for view synthesis, recen…
Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation
Francesco Di Felice, Alberto Remus, Stefano Gasperini +5
Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment. Yet, it presents many challenges, often related to the lack of f…
Re-Nerfing: Improving Novel View Synthesis through Novel View Synthesis
Felix Tristram, Stefano Gasperini, Nassir Navab +1
Recent neural rendering and reconstruction techniques, such as NeRFs or Gaussian Splatting, have shown remarkable novel view synthesis capabilities but require hundreds of images o…
VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis
Sen Wang, Qing Cheng, Stefano Gasperini +5
The generation of high-fidelity view synthesis is essential for robotic navigation and interaction but remains challenging, particularly in indoor environments and real-time scenar…
3D Adversarial Augmentations for Robust Out-of-Domain Predictions
Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro +4
Since real-world training datasets cannot properly sample the long tail of the underlying data distribution, corner cases and rare out-of-domain samples can severely hinder the per…
Robust Monocular Depth Estimation under Challenging Conditions
Stefano Gasperini, Nils Morbitzer, HyunJun Jung +2
While state-of-the-art monocular depth estimation approaches achieve impressive results in ideal settings, they are highly unreliable under challenging illumination and weather con…
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt +3
Panoptic segmentation methods assign a known class to each pixel given in input. Even for state-of-the-art approaches, this inevitably enforces decisions that systematically lead t…
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection
Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro +5
As 3D object detection on point clouds relies on the geometrical relationships between the points, non-standard object shapes can hinder a method's detection capability. However, i…
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
Stefano Gasperini, Jan Haug, Mohammad-Ali Nikouei Mahani +4
Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In perception for autonomous driving, measuring the uncertainty means providing…
R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes
Stefano Gasperini, Patrick Koch, Vinzenz Dallabetta +3
While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can st…
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds
Stefano Gasperini, Mohammad-Ali Nikouei Mahani, Alvaro Marcos-Ramiro +2
Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and e…
Signal Clustering with Class-independent Segmentation
Stefano Gasperini, Magdalini Paschali, Carsten Hopke +2
Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches. In this paper we propose a Deep Learning-based clus…
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Magdalini Paschali, Stefano Gasperini, Abhijit Guha Roy +2
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements. In this paper we propose 3DQ, a ternary quantization meth…