Spatial 3D Semantic Understanding for Perception in the Wild

Introduction

Understanding the 3D spatial semantics of the world around us is core to visual perception and digitization. Real-world environments are spatially three-dimensional and must be understood in their 3D context, even from 2D image observations.

Importance of 3D Perception

This understanding will lead to spatially-grounded reasoning and higher-level perception of the world around us. Such 3D perception will provide the foundation for transformative, next-generation technology across various fields, including:

• Machine perception
• Immersive communications
• Mixed reality
• Architectural modeling
• Industrial modeling
• And more

New Paradigm in Semantic Understanding

This will enable a new paradigm in semantic understanding that derives primarily from a spatially-consistent, 3D representation rather than relying on image-based reasoning that captures only projections of the world.

Challenges in 3D Semantic Reasoning

However, 3D semantic reasoning from visual data such as RGB or RGB-D observations remains in its infancy. This is due to challenges in:

1. Learning from limited amounts of real-world 3D data
2. The complex, high-dimensional nature of the problem

Proposed Solutions

In this proposal, we will develop new algorithmic approaches to effectively learn robust visual 3D perception. This will include new learning paradigms for:

• Features
• Representations
• Operators

These approaches aim to encompass 3D semantic understanding.