Emerging Trends in 3D Scene Understanding and Mesh Generation

The field of 3D scene understanding and mesh generation is rapidly advancing, with a focus on developing innovative methods for effective 3D representation and reconstruction. Researchers are exploring new approaches, such as reinforcement learning, autoregressive generative models, and self-supervised learning, to improve the accuracy and efficiency of 3D scene understanding and mesh generation. These advancements have the potential to enable a wide range of applications, including computer vision, robotics, and graphics.

Noteworthy papers in this area include: The paper on Local Random Access Sequence modeling, which achieves state-of-the-art novel view synthesis and 3D object manipulation capabilities. The paper on PRISM, a novel compositional approach for 3D shape generation that integrates categorical diffusion models with Statistical Shape Models and Gaussian Mixture Models, enabling high fidelity and diversity in generated shapes.

Sources

Optimization of a Triangular Delaunay Mesh Generator using Reinforcement Learning

3D Scene Understanding Through Local Random Access Sequence Modeling

Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

Interpretable Single-View 3D Gaussian Splatting using Unsupervised Hierarchical Disentangled Representation Learning

PRISM: Probabilistic Representation for Integrated Shape Modeling and Generation

DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation

QEMesh: Employing A Quadric Error Metrics-Based Representation for Mesh Generation

Human Activity Recognition using RGB-Event based Sensors: A Multi-modal Heat Conduction Model and A Benchmark Dataset

KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection

Masked Scene Modeling: Narrowing the Gap Between Supervised and Self-Supervised Learning in 3D Scene Understanding