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Found 9 papers, 2 papers with code
3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion
Point cloud completion aims to generate a complete and high-fidelity point cloud from an initially incomplete and low-quality input.
GetMesh: A Controllable Model for High-quality Mesh Generation and Manipulation
Mesh is a fundamental representation of 3D assets in various industrial applications, and is widely supported by professional softwares.
Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Then, another VFM trained on fine-grained 2D masks is adopted to guide the generation of semantically augmented images and point clouds to enhance the performance of neural networks, which mix the data from source and target domains like view frustums (FrustumMixing).
3D Gaussian as a New Vision Era: A Survey
3D Gaussian Splatting (3D-GS) has emerged as a significant advancement in the field of Computer Graphics, offering explicit scene representation and novel view synthesis without the reliance on neural networks, such as Neural Radiance Fields (NeRF).
Topology-Aware Latent Diffusion for 3D Shape Generation
By strategically incorporating topological features into the diffusion process, our generative module is able to produce a richer variety of 3D shapes with different topological structures.
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey
Finally, we share our thoughts on some of the challenges and potential issues that future research in self-supervised learning for pre-training 3D point clouds may encounter.
Generative Diffusion Prior for Unified Image Restoration and Enhancement
Besides, we devise hierarchical guidance and patch-based methods, enabling the GDP to generate images of arbitrary resolutions.
UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation Pipeline
Different from previous studies that only focus on a single adaptation task, UniDA3D can tackle several adaptation tasks in 3D segmentation field, by designing a unified source-and-target active sampling strategy, which selects a maximally-informative subset from both source and target domains for effective model adaptation.
Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis
Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision.
