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Found 8 papers, 1 papers with code
S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM
With the emergence of Neural Radiance Fields (NeRF), neural implicit representations have gained widespread applications across various domains, including simultaneous localization and mapping.
A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems
To address these challenges, our research introduces a parallel spatial transformation (PST)-based framework for large-scale, multi-view, multi-sensor scenarios.
UniQuadric: A SLAM Backend for Unknown Rigid Object 3D Tracking and Light-Weight Modeling
Subsequently, in the part of object state estimation, we propose a tightly coupled optimization model for object pose and scale estimation, incorporating hybrids constraints into a novel dual sliding window optimization framework for joint estimation.
ResFormer: Scaling ViTs with Multi-Resolution Training
We introduce, ResFormer, a framework that is built upon the seminal idea of multi-resolution training for improved performance on a wide spectrum of, mostly unseen, testing resolutions.
Rethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation
Skip connections are fundamental units in encoder-decoder networks, which are able to improve the feature propagtion of the neural networks.
Deeper Insights into the Robustness of ViTs towards Common Corruptions
With Vision Transformers (ViTs) making great advances in a variety of computer vision tasks, recent literature have proposed various variants of vanilla ViTs to achieve better efficiency and efficacy.
Accurate and Robust Object-oriented SLAM with 3D Quadric Landmark Construction in Outdoor Environment
To solve this problem, we propose a quadric initialization method based on the decoupling of the quadric parameters method, which improves the robustness to observation noise.
Accurate and Robust Scale Recovery for Monocular Visual Odometry Based on Plane Geometry
In this paper, with the assumption of a constant height of the camera above the ground, we develop a light-weight scale recovery framework leveraging an accurate and robust estimation of the ground plane.
