1 citations · 1 across the 7 of their papers we have counts for
11 papers
Curating Synthetic Data for Task-Specific Visual Perception
Saptarshi Neil Sinha, Paul Julius Kühn, Michael Weinmann
Synthetic data are most valuable where general-purpose datasets cannot provide the domain-specific priors a task requires, and where manual annotation is expensive, imprecise, or i…
SpectralCTGaussians: Projection-Domain Reconstruction and Basis Material Decomposition for Spectral CT using 3D Gaussian Splatting
Reinout Vos, Saptarshi Neil Sinha, Michael Weinmann
Spectral computed tomography (CT) extends conventional CT by measuring attenuation across multiple energy channels, allowing improved modeling of physical X-ray interactions and en…
AgenticCADedit: A Stateful, Tool-Mediated Agentic Approach to Multimodal 3D CAD Editing
Saptarshi Neil Sinha, Mika Silvan Goschke, Paul Julius Kühn +2
Computer-aided design is central to industrial manufacturing, and much of a designer's daily work consists of editing existing models from multimodal requests involving speech, ske…
TinyDETR-Pose: Towards End-to-End Real-Time Single-Stage 6DoF Object Pose Estimation with Lightweight Transformers
Paul Julius Kühn, Duc Anh Nguyen, Saptarshi Neil Sinha +2
Real-time 6DoF object pose estimation on resource-constrained hardware remains challenging, as accurate correspondence-based and refinement pipelines typically rely on non-differen…
ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection
Paul Julius Kühn, Saptarshi Neil Sinha, Tiago Kleist +3
While automated defect detection such as the detection of surface scratched is an important aspect in industrial quality control, the scarcity of annotated defect data make this ta…
WildFireGS: Physics-Based Wildfire Simulation in Large-Scale Semantics-Enriched Gaussian Splatting Forest Scenes
Nienke Driessen, Joris Rijsdijk, Sören Pirk +3
Climate-driven environmental change is driving an increase in both the frequency and severity of wildfire events, making accurate simulation and prediction critical for effective r…
Optimizing Multi-Modal Models for Image-Based Shape Retrieval: The Role of Pre-Alignment and Hard Contrastive Learning
Paul Julius Kühn, Cedric Spengler, Michael Weinmann +2
Image-based shape retrieval (IBSR) aims to retrieve 3D models from a database given a query image, hence addressing a classical task in computer vision, computer graphics, and robo…
6D Strawberry Pose Estimation: Real-time and Edge AI Solutions Using Purely Synthetic Training Data
Saptarshi Neil Sinha, Julius Kühn, Mika Silvan Goschke +1
Automated and selective harvesting of fruits has become an important area of research, particularly due to challenges such as high costs and a shortage of seasonal labor in advance…
Neural Restoration of Greening Defects in Historical Autochrome Photographs Based on Purely Synthetic Data
Saptarshi Neil Sinha, P. Julius Kuehn, Johannes Koppe +2
The preservation of early visual arts, particularly color photographs, is challenged by deterioration caused by aging and improper storage, leading to issues like blurring, scratch…
SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysis
Saptarshi Neil Sinha, Holger Graf, Michael Weinmann
We propose a novel cross-spectral rendering framework based on 3D Gaussian Splatting (3DGS) that generates realistic and semantically meaningful splats from registered multi-view s…
RANRAC: Robust Neural Scene Representations via Random Ray Consensus
Benno Buschmann, Andreea Dogaru, Elmar Eisemann +2
Learning-based scene representations such as neural radiance fields or light field networks, that rely on fitting a scene model to image observations, commonly encounter challenges…