1 code implementation • 27 Nov 2023 • Lukas Hoyer, David Joseph Tan, Muhammad Ferjad Naeem, Luc van Gool, Federico Tombari
In SemiVL, we propose to integrate rich priors from VLM pre-training into semi-supervised semantic segmentation to learn better semantic decision boundaries.
Ranked #1 on Semi-Supervised Semantic Segmentation on PASCAL VOC 2012 732 labeled (using extra training data)
1 code implementation • CVPR 2023 • Dario Pavllo, David Joseph Tan, Marie-Julie Rakotosaona, Federico Tombari
Neural Radiance Fields (NeRF) coupled with GANs represent a promising direction in the area of 3D reconstruction from a single view, owing to their ability to efficiently model arbitrary topologies.
no code implementations • 8 May 2022 • Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari
We propose a novel convolutional operator for the task of point cloud completion.
no code implementations • CVPR 2022 • Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari
To this aim, we introduce a second model that assembles our layers within a transformer architecture.
no code implementations • 14 Jan 2022 • John Ridley, Huseyin Coskun, David Joseph Tan, Nassir Navab, Federico Tombari
The video action segmentation task is regularly explored under weaker forms of supervision, such as transcript supervision, where a list of actions is easier to obtain than dense frame-wise labels.
no code implementations • 17 Nov 2020 • Riccardo Spezialetti, David Joseph Tan, Alessio Tonioni, Keisuke Tateno, Federico Tombari
Estimating the 3D shape of an object from a single or multiple images has gained popularity thanks to the recent breakthroughs powered by deep learning.
1 code implementation • ECCV 2020 • Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari
In this paper, we propose a method for 3D object completion and classification based on point clouds.
no code implementations • ICCV 2019 • Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari
We propose a novel model for 3D semantic completion from a single depth image, based on a single encoder and three separate generators used to reconstruct different geometric and semantic representations of the original and completed scene, all sharing the same latent space.
Ranked #7 on 3D Semantic Scene Completion on NYUv2 (using extra training data)
no code implementations • 25 Oct 2018 • Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari
We propose a method to reconstruct, complete and semantically label a 3D scene from a single input depth image.
2 code implementations • ECCV 2018 • Huseyin Coskun, David Joseph Tan, Sailesh Conjeti, Nassir Navab, Federico Tombari
Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks.
no code implementations • 5 Sep 2017 • David Joseph Tan, Nassir Navab, Federico Tombari
To determine the 3D orientation and 3D location of objects in the surroundings of a camera mounted on a robot or mobile device, we developed two powerful algorithms in object detection and temporal tracking that are combined seamlessly for robotic perception and interaction as well as Augmented Reality (AR).
no code implementations • CVPR 2016 • David Joseph Tan, Thomas Cashman, Jonathan Taylor, Andrew Fitzgibbon, Daniel Tarlow, Sameh Khamis, Shahram Izadi, Jamie Shotton
We present a fast, practical method for personalizing a hand shape basis to an individual user's detailed hand shape using only a small set of depth images.
no code implementations • ICCV 2015 • David Joseph Tan, Federico Tombari, Slobodan Ilic, Nassir Navab
This paper proposes a temporal tracking algorithm based on Random Forest that uses depth images to estimate and track the 3D pose of a rigid object in real-time.