no code implementations • 22 Jan 2025 • Akshay Krishnan, Xinchen Yan, Vincent Casser, Abhijit Kundu
We introduce a diffusion model Orchid, comprising a Variational Autoencoder (VAE) to encode color, depth, and surface normals to a latent space, and a Latent Diffusion Model (LDM) for generating these joint latents.
no code implementations • 14 Oct 2022 • Alex Zihao Zhu, Vincent Casser, Reza Mahjourian, Henrik Kretzschmar, Sören Pirk
We demonstrate that this formulation encourages the models to learn embeddings that are invariant to viewpoint variations and consistent across sensor modalities.
1 code implementation • 15 Jun 2022 • Wei-Chih Hung, Vincent Casser, Henrik Kretzschmar, Jyh-Jing Hwang, Dragomir Anguelov
However, camera-only detectors have limited depth accuracy, which may cause otherwise reasonable predictions that suffer from such longitudinal localization errors to be treated as false positives.
2 code implementations • CVPR 2022 • Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P. Srinivasan, Jonathan T. Barron, Henrik Kretzschmar
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments.
no code implementations • 16 Jan 2022 • Zhao Chen, Vincent Casser, Henrik Kretzschmar, Dragomir Anguelov
We propose GradTail, an algorithm that uses gradients to improve model performance on the fly in the face of long-tailed training data distributions.
1 code implementation • ICCV 2021 • AJ Piergiovanni, Vincent Casser, Michael S. Ryoo, Anelia Angelova
We present 4D-Net, a 3D object detection approach, which utilizes 3D Point Cloud and RGB sensing information, both in time.
5 code implementations • 30 Oct 2020 • Hanhan Li, Ariel Gordon, Hang Zhao, Vincent Casser, Anelia Angelova
We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D translation field of objects relative to the scene, with monocular photometric consistency being the sole source of supervision.
Ranked #10 on
Unsupervised Monocular Depth Estimation
on Cityscapes
1 code implementation • ECCV 2020 • Anelise Newman, Camilo Fosco, Vincent Casser, Allen Lee, Barry McNamara, Aude Oliva
Based on our findings we propose a new mathematical formulation of memorability decay, resulting in a model that is able to produce the first quantitative estimation of how a video decays in memory over time.
no code implementations • 7 Aug 2020 • Camilo Fosco, Vincent Casser, Amish Kumar Bedi, Peter O'Donovan, Aaron Hertzmann, Zoya Bylinskii
This paper introduces a Unified Model of Saliency and Importance (UMSI), which learns to predict visual importance in input graphic designs, and saliency in natural images, along with a new dataset and applications.
no code implementations • CVPR 2021 • Yao Lu, Sören Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad, Zhao Chen, Vincent Casser, Anelia Angelova, Ariel Gordon
Many computer vision tasks address the problem of scene understanding and are naturally interrelated e. g. object classification, detection, scene segmentation, depth estimation, etc.
no code implementations • 12 Jun 2019 • Vincent Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova
We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion.
no code implementations • 18 Apr 2019 • Matthias Müller, Guohao Li, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert.
no code implementations • MIDL 2019 • Vincent Casser, Kai Kang, Hanspeter Pfister, Daniel Haehn
High-resolution connectomics data allows for the identification of dysfunctional mitochondria which are linked to a variety of diseases such as autism or bipolar.
11 code implementations • 15 Nov 2018 • Vincent Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova
Models and examples built with TensorFlow
Ranked #12 on
Unsupervised Monocular Depth Estimation
on Cityscapes
no code implementations • 3 Mar 2018 • Guohao Li, Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images.
no code implementations • 19 Aug 2017 • Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years.
no code implementations • 19 Aug 2017 • Matthias Müller, Vincent Casser, Jean Lahoud, Neil Smith, Bernard Ghanem
We present a photo-realistic training and evaluation simulator (Sim4CV) with extensive applications across various fields of computer vision.