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Found 20 papers, 9 papers with code
Free-form language-based robotic reasoning and grasping
Our method detects all objects as keypoints and uses these keypoints to annotate marks on images, aiming to facilitate GPT-4o's zero-shot spatial reasoning.
Fully-Geometric Cross-Attention for Point Cloud Registration
At the point level, we also devise a self-attention mechanism that aggregates the local geometric structure information into point features for fine matching.
PerLA: Perceptive 3D Language Assistant
Enabling Large Language Models (LLMs) to understand the 3D physical world is an emerging yet challenging research direction.
GSTran: Joint Geometric and Semantic Coherence for Point Cloud Segmentation
The proposed network mainly consists of two principal components: a local geometric transformer and a global semantic transformer.
Vocabulary-Free 3D Instance Segmentation with Vision and Language Assistant
We evaluate our method using ScanNet200 and Replica, outperforming existing methods in both vocabulary-free and open-vocabulary settings.
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning
To answer this question, we first empirically validate that integrating MAE-based point cloud pre-training with the standard contrastive learning paradigm, even with meticulous design, can lead to a decrease in performance.
ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models
To address this, we construct scene graphs to capture spatial relationships among objects and apply a graph matching algorithm to these graphs to accurately identify matched objects.
Geometrically-driven Aggregation for Zero-shot 3D Point Cloud Understanding
Zero-shot 3D point cloud understanding can be achieved via 2D Vision-Language Models (VLMs).
Point Cloud Pre-training with Diffusion Models
This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud, thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object.
Attentive Multimodal Fusion for Optical and Scene Flow
This paper presents an investigation into the estimation of optical and scene flow using RGBD information in scenarios where the RGB modality is affected by noise or captured in dark environments.
Cross-source Point Cloud Registration: Challenges, Progress and Prospects
The emerging topic of cross-source point cloud (CSPC) registration has attracted increasing attention with the fast development background of 3D sensor technologies.
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data.
Graph Matching Optimization Network for Point Cloud Registration
Point Cloud Registration is a fundamental and challenging problem in 3D computer vision.
Overlap-guided Gaussian Mixture Models for Point Cloud Registration
Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations.
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting
This paper proposes a general unsupervised approach, named \textbf{ConClu}, to perform the learning of point-wise and global features by jointly leveraging point-level clustering and instance-level contrasting.
COTReg:Coupled Optimal Transport based Point Cloud Registration
Generating a set of high-quality correspondences or matches is one of the most critical steps in point cloud registration.
GenReg: Deep Generative Method for Fast Point Cloud Registration
To solve this challenge, we propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration.
A comprehensive survey on point cloud registration
This survey conducts a comprehensive survey, including both same-source and cross-source registration methods, and summarize the connections between optimization-based and deep learning methods, to provide further research insight.
Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences
We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection error without correspondences.
