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Found 21 papers, 9 papers with code
Shonan Rotation Averaging: Global Optimality by Surfing SO(p)(n)
Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise.
Distributed Global Structure-from-Motion with a Deep Front-End
While initial approaches to Structure-from-Motion (SfM) revolved around both global and incremental methods, most recent applications rely on incremental systems to estimate camera poses due to their superior robustness.
im2nerf: Image to Neural Radiance Field in the Wild
We take a step towards addressing this shortcoming by introducing a model that encodes the input image into a disentangled object representation that contains a code for object shape, a code for object appearance, and an estimated camera pose from which the object image is captured.
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
Our model builds a panoptic radiance field representation of any scene from just color images.
Probabilistic Tracking with Deep Factors
In this paper, we show how to use a deep feature encoding in conjunction with generative densities over the features in a factor-graph based, probabilistic tracking framework.
Imitation Learning via Simultaneous Optimization of Policies and Auxiliary Trajectories
Many IL methods, such as Dataset Aggregation (DAgger), combat challenges like distributional shift by interacting with oracular experts.
Neural Volume Rendering: NeRF And Beyond
Besides the COVID-19 pandemic and political upheaval in the US, 2020 was also the year in which neural volume rendering exploded onto the scene, triggered by the impressive NeRF paper by Mildenhall et al. (2020).
Shonan Rotation Averaging: Global Optimality by Surfing $SO(p)^n$
Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise.
Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Novel Dynamic Brush Model
Chinese calligraphy is a unique art form with great artistic value but difficult to master.
Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Simulated Brush Model
Chinese calligraphy is a unique form of art that has great artistic value but is difficult to master.
Robotics
Fast 3D Pose Refinement with RGB Images
Pose estimation is a vital step in many robotics and perception tasks such as robotic manipulation, autonomous vehicle navigation, etc.
Taking a Deeper Look at the Inverse Compositional Algorithm
In this paper, we provide a modern synthesis of the classic inverse compositional algorithm for dense image alignment.
Learning to Align Images using Weak Geometric Supervision
Such descriptors are often derived using supervised learning on existing datasets with ground truth correspondences.
Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference
We benchmark our algorithms against several sampling-based and trajectory optimization-based motion planning algorithms on planning problems in multiple environments.
Robotics
Sparse Gaussian Processes for Continuous-Time Trajectory Estimation on Matrix Lie Groups
Continuous-time trajectory representations are a powerful tool that can be used to address several issues in many practical simultaneous localization and mapping (SLAM) scenarios, like continuously collected measurements distorted by robot motion, or during with asynchronous sensor measurements.
Robotics
Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models
Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems.
4D Crop Monitoring: Spatio-Temporal Reconstruction for Agriculture
Autonomous crop monitoring at high spatial and temporal resolution is a critical problem in precision agriculture.
A Continuous Optimization Approach for Efficient and Accurate Scene Flow
We propose a continuous optimization method for solving dense 3D scene flow problems from stereo imagery.
On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
However, real-time optimization quickly becomes infeasible as the trajectory grows over time, this problem is further emphasized by the fact that inertial measurements come at high rate, hence leading to fast growth of the number of variables in the optimization.
Robotics
Dataset Fingerprints: Exploring Image Collections Through Data Mining
As the amount of visual data increases, so does the need for summarization tools that can be used to explore large image collections and to quickly get familiar with their content.
Pose Graph Optimization in the Complex Domain: Lagrangian Duality, Conditions For Zero Duality Gap, and Optimal Solutions
Our analysis shows that the duality gap is connected to the number of eigenvalues of the penalized pose graph matrix, which arises from the solution of the dual.
Robotics 68W01, 68W40, 68W25, 49K30 I.2.9; G.1.6
