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Aligning cyber space with physical world: A comprehensive survey on embodied ai
Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General
Intelligence (AGI) and serves as a foundation for various applications that bridge cyberspace …
Intelligence (AGI) and serves as a foundation for various applications that bridge cyberspace …
Pointmamba: A simple state space model for point cloud analysis
Transformers have become one of the foundational architectures in point cloud analysis
tasks due to their excellent global modeling ability. However, the attention mechanism has …
tasks due to their excellent global modeling ability. However, the attention mechanism has …
Oa-cnns: Omni-adaptive sparse cnns for 3d semantic segmentation
The booming of 3D recognition in the 2020s began with the introduction of point cloud
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …
transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models …
Unlearnable 3D point clouds: Class-wise transformation is all you need
Traditional unlearnable strategies have been proposed to prevent unauthorized users from
training on the 2D image data. With more 3D point cloud data containing sensitivity …
training on the 2D image data. With more 3D point cloud data containing sensitivity …
Voxel mamba: Group-free state space models for point cloud based 3d object detection
Serialization-based methods, which serialize the 3D voxels and group them into multiple
sequences before inputting to Transformers, have demonstrated their effectiveness in 3D …
sequences before inputting to Transformers, have demonstrated their effectiveness in 3D …
Point mamba: A novel point cloud backbone based on state space model with octree-based ordering strategy
Recently, state space model (SSM) has gained great attention due to its promising
performance, linear complexity, and long sequence modeling ability in both language and …
performance, linear complexity, and long sequence modeling ability in both language and …
Groupcontrast: Semantic-aware self-supervised representation learning for 3d understanding
Self-supervised 3D representation learning aims to learn effective representations from
large-scale unlabeled point clouds. Most existing approaches adopt point discrimination as …
large-scale unlabeled point clouds. Most existing approaches adopt point discrimination as …
Large spatial model: End-to-end unposed images to semantic 3d
Reconstructing and understanding 3D structures from a limited number of images is a
classical problem in computer vision. Traditional approaches typically decompose this task …
classical problem in computer vision. Traditional approaches typically decompose this task …
Deep learning based 3D segmentation: A survey
3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …
applications in autonomous driving and robotics. It has received significant attention from the …
Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model
Existing Transformer-based models for point cloud analysis suffer from quadratic complexity,
leading to compromised point cloud resolution and information loss. In contrast, the newly …
leading to compromised point cloud resolution and information loss. In contrast, the newly …