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Found 7 papers, 3 papers with code
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition
Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem.
ProSLAM: Graph SLAM from a Programmer's Perspective
In this paper we present ProSLAM, a lightweight stereo visual SLAM system designed with simplicity in mind.
Robotics
Least Squares Optimization: from Theory to Practice
In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain.
Robotics
Plug-and-Play SLAM: A Unified SLAM Architecture for Modularity and Ease of Use
Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the Robotics community to be a mature field.
Robotics
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction
In the field of 3D scene understanding, 3D scene graphs have emerged as a new scene representation that combines geometric and semantic information about objects and their relationships.
Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction
While it is widely accepted that pre-training is an effective approach to improve model performance in low data regimes, in this paper, we find that existing pre-training methods are ill-suited for 3D scene graphs.
Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships
We co-embed the features from a 3D scene graph prediction backbone with the feature space of powerful open world 2D vision language foundation models.
