3 citations · 10 across the 13 of their papers we have counts for
13 papers
6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model
Matteo Bortolon, Theodore Tsesmelis, Stuart James +2
We propose 6DGS to estimate the camera pose of a target RGB image given a 3D Gaussian Splatting (3DGS) model representing the scene. 6DGS avoids the iterative process typical of an…
High-resolution open-vocabulary object 6D pose estimation
Jaime Corsetti, Davide Boscaini, Francesco Giuliari +3
The generalisation to unseen objects in the 6D pose estimation task is very challenging. While Vision-Language Models (VLMs) enable using natural language descriptions to support 6…
Light-weight Retinal Layer Segmentation with Global Reasoning
Xiang He, Weiye Song, Yiming Wang +6
Automatic retinal layer segmentation with medical images, such as optical coherence tomography (OCT) images, serves as an important tool for diagnosing ophthalmic diseases. However…
IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model
Matteo Bortolon, Theodore Tsesmelis, Stuart James +2
We introduce IFFNeRF to estimate the six degrees-of-freedom (6DoF) camera pose of a given image, building on the Neural Radiance Fields (NeRF) formulation. IFFNeRF is specifically…
Delving into CLIP latent space for Video Anomaly Recognition
Luca Zanella, Benedetta Liberatori, Willi Menapace +3
We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision. We introduce the novel metho…
Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection
Mohamed L. Mekhalfi, Davide Boscaini, Fabio Poiesi
Unsupervised domain adaptation (UDA) plays a crucial role in object detection when adapting a source-trained detector to a target domain without annotated data. In this paper, we p…