177 citations · 421 across the 8 of their papers we have counts for
7 papers · 1 filter
Lifting by Gaussians: A Simple, Fast and Flexible Method for 3D Instance Segmentation
Rohan Chacko, Nicolai Haeni, Eldar Khaliullin +2
We introduce Lifting By Gaussians (LBG), a novel approach for open-world instance segmentation of 3D Gaussian Splatted Radiance Fields (3DGS). Recently, 3DGS Fields have emerged as…
Apple Counting using Convolutional Neural Networks
Nicolai Häni, Pravakar Roy, Volkan Isler
Estimating accurate and reliable fruit and vegetable counts from images in real-world settings, such as orchards, is a challenging problem that has received significant recent atte…
Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision
Nicolai Häni, Selim Engin, Jun-Jee Chao +1
Novel View Synthesis (NVS) is concerned with synthesizing views under camera viewpoint transformations from one or multiple input images. NVS requires explicit reasoning about 3D o…
MinneApple: A Benchmark Dataset for Apple Detection and Segmentation
Nicolai Häni, Pravakar Roy, Volkan Isler
In this work, we present a new dataset to advance the state-of-the-art in fruit detection, segmentation, and counting in orchard environments. While there has been significant rece…
Semantics-Aware Image to Image Translation and Domain Transfer
Pravakar Roy, Nicolai Häni, Jun-Jee Chao +1
Image to image translation is the problem of transferring an image from a source domain to a different (but related) target domain. We present a new unsupervised image to image tra…
A Comparative Study of Fruit Detection and Counting Methods for Yield Mapping in Apple Orchards
Nicolai Häni, Pravakar Roy, Volkan Isler
We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods. Our goal…
Classification of Aerial Photogrammetric 3D Point Clouds
Carlos Becker, Nicolai Häni, Elena Rosinskaya +2
We present a powerful method to extract per-point semantic class labels from aerialphotogrammetry data. Labeling this kind of data is important for tasks such as environmental mode…