Results 11 to 20 of about 6,440,801 (359)
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
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
Diffusion Probabilistic Models for 3D Point Cloud Generation [PDF]
Computer Vision and Pattern Recognition, 2021 We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.
Shitong Luo, Wei Hu
semanticscholar +1 more source
PointCLIP: Point Cloud Understanding by CLIP [PDF]
Computer Vision and Pattern Recognition, 2021 Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training (CLIP) have shown inspirational performance on 2D visual recognition, which learns to match images with their corresponding texts in open-vocabulary settings. However,
Renrui Zhang +8 more
semanticscholar +1 more source
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency [PDF]
Computer Vision and Pattern Recognition, 2021 Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning techniques in this field, spatial consistency, which is essentially ...
Xuyang Bai +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
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
Self-Supervised Pretraining of 3D Features on any Point-Cloud [PDF]
IEEE International Conference on Computer Vision, 2021 Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like image recognition, video understanding etc.
Zaiwei Zhang +3 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