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Found 14 papers, 7 papers with code
PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis
The DSE module, designed for real-world autonomous driving scenarios, enhances the semantic perception of point clouds, particularly for distant points.
Probing the Creativity of Large Language Models: Can models produce divergent semantic association?
Large language models possess remarkable capacity for processing language, but it remains unclear whether these models can further generate creative content.
SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator
In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in point cloud completion: understanding faithful global shapes from incomplete point clouds and generating high-accuracy local structures.
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising
We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD).
LBF:Learnable Bilateral Filter For Point Cloud Denoising
Bilateral filter (BF) is a fast, lightweight and effective tool for image denoising and well extended to point cloud denoising.
SPCNet: Stepwise Point Cloud Completion Network
We propose a novel stepwise point cloud completion network (SPCNet) for various 3D models with large missings.
Geometric and Learning-based Mesh Denoising: A Comprehensive Survey
In this work, we provide a comprehensive review of the advances in mesh denoising, containing both traditional geometric approaches and recent learning-based methods.
UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration
High-confidence overlap prediction and accurate correspondences are critical for cutting-edge models to align paired point clouds in a partial-to-partial manner.
CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completion
The first module transfers the intrinsic shape characteristics from single images to guide the geometry generation of the missing regions of point clouds, in which we propose IPAdaIN to embed the global features of both the image and the partial point cloud into completion.
Ranked #2 on
Point Cloud Completion
on ShapeNet-ViPC
GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling
Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding. Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity.
ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs
Low-overlap regions between paired point clouds make the captured features very low-confidence, leading cutting edge models to point cloud registration with poor quality.
Semi-MoreGAN: A New Semi-supervised Generative Adversarial Network for Mixture of Rain Removal
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions.
Deep Algebraic Fitting for Multiple Circle Primitives Extraction from Raw Point Clouds
The shape of circle is one of fundamental geometric primitives of man-made engineering objects.
Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds
Differently, our network is designed to refine the initial normal of each point by extracting additional information from multiple feature representations.
