Results 21 to 30 of about 5,609,782 (360)
Stratified Transformer for 3D Point Cloud Segmentation [PDF]
Computer Vision and Pattern Recognition, 2022 3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies.
Xin Lai +7 more
semanticscholar +1 more source
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training [PDF]
Neural Information Processing Systems, 2022 Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for language and 2D image transformers. However, it still remains an open question on how to exploit masked autoencoding for learning 3D representations of irregular ...
Renrui Zhang +7 more
semanticscholar +1 more source
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following [PDF]
arXiv.org, 2023 We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video. Guided by ImageBind, we construct a joint embedding space between 3D and multi-modalities, enabling many promising applications, e.g., any-
Ziyu Guo +10 more
semanticscholar +1 more source
Iraqi Journal of Science, 2023 The cloud point extraction technique has become increasingly popular in recent years for trace metal separation and preconcentration. When heated to a specific temperature, cloud point extraction utilizes the property of nonionic surfactants in ...
Fatimah Abd Wannas +3 more
semanticscholar +1 more source
REGTR: End-to-end Point Cloud Correspondences with Transformers [PDF]
Computer Vision and Pattern Recognition, 2022 Despite recent success in incorporating learning into point cloud registration, many works focus on learning feature descriptors and continue to rely on nearest-neighbor feature matching and outlier filtering through RANSAC to obtain the final set of ...
Zi Jian Yew, Gim Hee Lee
semanticscholar +1 more source
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling [PDF]
Computer Vision and Pattern Recognition, 2021 We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT [8] to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically, we first divide a
Xumin Yu +5 more
semanticscholar +1 more source
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.
Yin Zhou, Oncel Tuzel
semanticscholar +1 more source
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [PDF]
IEEE International Conference on Computer Vision, 2021 Point clouds captured in real-world applications are of-ten incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many ...
Xumin Yu +5 more
semanticscholar +1 more source
Georeferenced Point Clouds: A Survey of Features and Point Cloud Management [PDF]
ISPRS International Journal of Geo-Information, 2013 This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed.
Johannes Otepka +4 more
openaire +5 more sources
magazén, 2022 The Space, Place, Sound, and Memory: Immersive Experiences of the Past project was led by dr James Cook, in collaboration with the Digital Documentation and Innovation team at Historic Environment Scotland, Soluis Heritage, the Binchois Consort, and ...
Cook, James, Mirashrafi, Sophia
doaj +1 more source