Weakly Supervised Point Cloud Semantic Segmentation via Artificial Oracle
Manual annotation of every point in a point cloud is a costly and labor-intensive process.
While weakly supervised point cloud semantic segmentation (WSPCSS) with sparse …
While weakly supervised point cloud semantic segmentation (WSPCSS) with sparse …
Self-supervised 3D Point Cloud Completion via Multi-view Adversarial Learning
In real-world scenarios, scanned point clouds are often incomplete due to occlusion issues.
The task of self-supervised point cloud completion involves reconstructing missing regions …
The task of self-supervised point cloud completion involves reconstructing missing regions …
Self-supervised Shape Completion via Involution and Implicit Correspondences
3D shape completion is traditionally solved using supervised training or by distribution
learning on complete shape examples. Recently self-supervised learning approaches that …
learning on complete shape examples. Recently self-supervised learning approaches that …
NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization
P Tu, X Zhou, M Wang, X Yang, B Peng, P Chen… - arXiv preprint arXiv …, 2024 - arxiv.org
Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the
photorealistic rendering of objects and environments, enabling the synthesis of novel …
photorealistic rendering of objects and environments, enabling the synthesis of novel …