Survey on Modeling of Articulated Objects: Understanding Shape and Motion

Belangrijkste concepten

Comprehensive overview of 3D modeling of articulated objects, highlighting progress, challenges, and future research directions.

Samenvatting

This content provides a detailed survey on the modeling of articulated objects within computer vision, graphics, and robotics. It covers topics such as geometry processing, articulation modeling, datasets, and methodologies used in analyzing articulated part perception and object creation. The survey emphasizes the importance of understanding shape and motion in creating realistic models of articulated objects.
Directory:

Introduction

Articulated objects in daily life.
Importance in various applications.

Background & Scope

Definition of articulated objects.
Representation methods.

Datasets

Challenges in creating datasets for articulated objects.
Overview of existing datasets for object-level and scene-level data.

Geometry Processing

Methods for analyzing articulated part perception.
Intermediate representations used (surface patch, 3D trajectory, NAOCS/NPCS).

Methodology

Strategies for analyzing part structure (handcrafted methods, supervised learning).

Data Extraction

Statistieken

"3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics."
"Its objective is to understand the shape and motion of the articulated components."
"This survey provides a comprehensive overview of the current state-of-the-art in 3D modeling of articulated objects."

Citaten

"Noisy input data can affect the performance of handcrafted methods."
"Supervised learning is commonly used for predicting part labels in point cloud data."

Belangrijkste Inzichten Gedestilleerd Uit

Survey on Modeling of Articulated Objects

by Jiayi Liu,Ma... om arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14937.pdf

Diepere vragen

How do real-world datasets differ from synthetic datasets in terms of complexity?

Real-world datasets differ from synthetic datasets in several ways, especially in terms of complexity:

Data Collection: Real-world datasets are collected using sensors like RGB-D cameras, resulting in data that may be incomplete or noisy due to the limitations of sensor technology. In contrast, synthetic datasets are generated artificially and can be more controlled and precise.

Diversity: Real-world datasets tend to have more diversity in terms of object shapes, textures, and articulation types compared to synthetic datasets which may have limited variations based on the design choices made during generation.

Annotation Process: Creating annotations for real-world objects is labor-intensive as it requires detailed modeling of each part's geometry and careful annotation of articulation parameters. Synthetic dataset creation involves manual design but can lack the complexity found in real objects.

Accessibility: Real-world data is often harder to obtain due to logistical challenges such as access to scanning equipment or permissions needed for data collection. Synthetic data is more accessible as it can be generated programmatically.

Noise Levels: Real-world data tends to have higher levels of noise due to environmental factors or sensor inaccuracies, while synthetic data can be cleaner and more controlled.

What are the implications of using neural fields as an intermediate representation for object generation?

Using neural fields as an intermediate representation for object generation has several implications:

Continuous Representation: Neural fields provide a continuous representation of 3D geometry, allowing for smooth transitions between different states or configurations of an object without discretization errors.

Flexibility: Neural fields offer flexibility in representing complex shapes and structures that may not conform easily to traditional geometric primitives or parametric models.

Learning-based Generation: By training neural networks on large amounts of data, neural fields can learn intricate patterns and details present in objects' geometries, enabling realistic synthesis even with limited input information.

Implicit Surface Representation: Neural fields represent surfaces implicitly rather than explicitly defining them through mesh vertices or points, offering advantages such as adaptive resolution where detail is needed most.

5 .Generative Applications: The use of neural fields allows for generative applications where new samples can be synthesized by sampling from the learned distribution represented by the network weights.

How can advancements in deep learning impact the future development articulated object modeling?

Advancements in deep learning could significantly impact future developments articulated object modeling:
1 .Improved Accuracy: Deep learning techniques enable better accuracy when analyzing complex structures within articulated objects by automatically learning features from raw input data instead relying on handcrafted rules or descriptors
2 .Enhanced Generalization: Deep learning models trained on diverse sets could improve generalization across various categories and instances articulated objects leading robust performance across different scenarios
3 .Efficient Learning: Deep learning algorithms allow systems learn representations directly from raw sensory inputs reducing reliance human intervention making process efficient scalable
4 .Complexity Handling: With ability handle high-dimensional non-linear relationships deep learning models capable capturing intricacies involved understanding motion structure within articulated objects
5 .Interdisciplinary Integration: Advancements deep earning facilitate integration multiple disciplines computer vision graphics robotics creating holistic approach towards solving challenges related articulated object modeling