Memory-based Adapters for Online 3D Scene Perception Framework

Core Concepts

The author proposes memory-based adapters to empower offline models with online perception ability by inserting plug-and-play modules and finetuning on RGB-D videos.

Abstract

Memory-based adapters are introduced to enhance temporal modeling in online 3D scene perception tasks. By caching and aggregating features over time, the proposed framework achieves leading performance compared to state-of-the-art methods on various datasets.
Content includes a detailed explanation of the proposed framework, its components, and how they are integrated into existing models. The approach is validated through extensive experiments on ScanNet and SceneNN datasets, showcasing significant improvements in semantic segmentation, object detection, and instance segmentation tasks.
Key points include the need for online 3D scene perception due to streaming RGB-D video inputs, the design of memory-based adapters for image and point cloud backbones, and the successful application of these adapters to boost performance across different tasks.
The study highlights the importance of temporal information in processing 3D scenes frame by frame and leveraging memory mechanisms for efficient aggregation. The results demonstrate superior performance compared to both offline and online methods without requiring model-specific designs or additional loss functions.

Stats

We propose a general framework for online 3D scene perception.
Our approach achieves leading performance on three 3D scene perception tasks.
Extensive experiments on ScanNet and SceneNN datasets demonstrate our approach's success.
Our method significantly boosts accuracy compared to state-of-the-art online methods.
Equipped with our memory-based adapters, offline models achieve better performance on online tasks.

Quotes

Key Insights Distilled From

Memory-based Adapters for Online 3D Scene Perception

by Xiuwei Xu,Ch... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06974.pdf

Deeper Inquiries

How can memory-based adapters be further optimized for real-time applications

Memory-based adapters can be further optimized for real-time applications by focusing on reducing computational complexity and memory usage. One way to achieve this is by implementing more efficient data structures and algorithms for caching and aggregating features in the memory. Additionally, optimizing the adapter modules themselves to minimize redundant computations and streamline information flow can improve the overall efficiency of the framework. Furthermore, exploring hardware acceleration techniques such as GPU parallelization or specialized accelerators can help speed up the processing of temporal information in real-time scenarios.

What challenges might arise when implementing this framework in complex environments

Implementing this framework in complex environments may pose several challenges. One challenge could be handling dynamic scenes with rapidly changing objects or backgrounds, which may require adaptive strategies for updating the memory and adapting to new contexts efficiently. Another challenge could be dealing with noisy or incomplete input data, which might impact the quality of temporal information stored in the memory. Ensuring robustness to variations in lighting conditions, occlusions, or sensor noise is crucial for maintaining accurate online 3D scene perception in complex environments.

How can this research impact other fields beyond computer vision

This research on memory-based adapters for online 3D scene perception has implications beyond computer vision into various fields such as robotics, augmented reality/virtual reality (AR/VR), autonomous navigation systems, and spatial mapping technologies. In robotics applications, real-time 3D scene understanding is essential for tasks like object manipulation, obstacle avoidance, and environment exploration. AR/VR experiences can benefit from improved online perception capabilities for creating immersive virtual environments based on live RGB-D video streams. Autonomous navigation systems can leverage these advancements to enhance localization accuracy and path planning efficiency based on dynamic surroundings. Spatial mapping technologies stand to gain from more effective methods of capturing detailed 3D representations of indoor spaces using streaming RGB-D data.