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Marc Stamminger

7 accepted papers

2025

FruitNeRF++: A Generalized Multi-Fruit Counting Method Utilizing Contrastive Learning and Neural Radiance Fields

IROS 2025

We introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orchards. Our work is based on FruitNeRF [6], which employs a neural semantic field combined with a fruit-specific clusterin

Cited by 3SourcecodeScholar
2024

Automatic Spatial Calibration of Near-Field MIMO Radar With Respect to Optical Depth Sensors

IROS 2024poster

Despite an emerging interest in MIMO radar, the utilization of its complementary strengths in combination with optical depth sensors has so far been limited to far-field applications, due to the challenges that arise from mutual sensor calibration in the near field. In fact, most related approaches…

Cited by 2SourceScholar
2024

FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework

IROS 2024poster

We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system ind…

Cited by 9SourcecodeScholar
2024

PEGASUS: Physically Enhanced Gaussian Splatting Simulation System for 6DoF Object Pose Dataset Generation

IROS 2024poster

We introduce Physically Enhanced Gaussian Splatting Simulation System (PEGASUS) for 6DoF object pose dataset generation, a versatile dataset generator based on 3D Gaussian Splatting. Environment and object representations can be easily obtained using commodity cameras to reconstruct with Gaussian Sp…

Cited by 8SourcecodeScholar
2020

Image-guided Neural Object Rendering

ICLR 2020poster

We propose a learned image-guided rendering technique that combines the benefits of image-based rendering and GAN-based image synthesis. The goal of our method is to generate photo-realistic re-renderings of reconstructed objects for virtual and augmented reality applications (e.g., virtual showroom…

Cited by 71SourceScholar
2016

Face2Face: Real-Time Face Capture and Reenactment of RGB Videos

CVPR 2016oral

We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor an…

Cited by 2654PDFScholar