Search Results for author:
Found 9 papers, 4 papers with code
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs.
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass.
Deep Auxiliary Learning for Visual Localization and Odometry
We evaluate our proposed VLocNet on indoor as well as outdoor datasets and show that even our single task model exceeds the performance of state-of-the-art deep architectures for global localization, while achieving competitive performance for visual odometry estimation.
VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
Semantic understanding and localization are fundamental enablers of robot autonomy that have for the most part been tackled as disjoint problems.
Topometric Localization with Deep Learning
Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective while their accuracy and reliability typically is inferior to LiDAR-based methods.
Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing
Learned representations from the traffic light recognition stream are fused with the estimated trajectories from the motion prediction stream to learn the crossing decision.
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes
We present NeSF, a method for producing 3D semantic fields from posed RGB images alone.
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training.
