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Found 14 papers, 5 papers with code
Leveraging Large Language Model-based Room-Object Relationships Knowledge for Enhancing Multimodal-Input Object Goal Navigation
In this study, we propose a data-driven, modular-based approach, trained on a dataset that incorporates common-sense knowledge of object-to-room relationships extracted from a large language model.
PEGASUS: Physically Enhanced Gaussian Splatting Simulation System for 6DOF Object Pose Dataset Generation
We introduce Physically Enhanced Gaussian Splatting Simulation System (PEGASUS) for 6DOF object pose dataset generation, a versatile dataset generator based on 3D Gaussian Splatting.
NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields
We present NeuralLabeling, a labeling approach and toolset for annotating a scene using either bounding boxes or meshes and generating segmentation masks, affordance maps, 2D bounding boxes, 3D bounding boxes, 6DOF object poses, depth maps and object meshes.
TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation
A synthetic multi-modal dataset is made public to validate the generalization ability of the proposed fusion strategy, which also works for other combinations of different modalities.
Deformable Mesh Transformer for 3D Human Mesh Recovery
DeFormer iteratively fits a body mesh model to an input image via a mesh alignment feedback loop formed within a transformer decoder that is equipped with efficient body mesh driven attention modules: 1) body sparse self-attention and 2) deformable mesh cross attention.
Ranked #21 on
3D Human Pose Estimation
on 3DPW
Instance-specific 6-DoF Object Pose Estimation from Minimal Annotations
In many robotic applications, the environment setting in which the 6-DoF pose estimation of a known, rigid object and its subsequent grasping is to be performed, remains nearly unchanging and might even be known to the robot in advance.
Object Memory Transformer for Object Goal Navigation
This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes.
Rapid Pose Label Generation through Sparse Representation of Unknown Objects
The sparse representation leads to the development of a dense model and the pose labels for each image frame in the set of scenes.
Deep Reactive Planning in Dynamic Environments
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path
Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path.
Learning Body Shape and Pose from Dense Correspondences
In this paper, we address the problem of learning 3D human pose and body shape from 2D image dataset, without having to use 3D dataset (body shape and pose).
Skeleton Transformer Networks: 3D Human Pose and Skinned Mesh from Single RGB Image
In this paper, we present Skeleton Transformer Networks (SkeletonNet), an end-to-end framework that can predict not only 3D joint positions but also 3D angular pose (bone rotations) of a human skeleton from a single color image.
Ranked #279 on
3D Human Pose Estimation
on Human3.6M
Understanding and Exploiting Object Interaction Landscapes
We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved.
Symmetry-Aware Nonrigid Matching of Incomplete 3D Surfaces
We present a nonrigid shape matching technique for establishing correspondences of incomplete 3D surfaces that exhibit intrinsic reflectional symmetry.
