iMAP: Implicit Mapping and Positioning in Real-Time
This addresses the problem of efficient and detailed 3D mapping in robotics and AR/VR, representing a novel paradigm rather than an incremental improvement.
The paper tackles real-time SLAM for handheld RGB-D cameras by using a multilayer perceptron (MLP) as the sole scene representation, achieving tracking at 10 Hz and map updates at 2 Hz without prior data.
We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera. Our network is trained in live operation without prior data, building a dense, scene-specific implicit 3D model of occupancy and colour which is also immediately used for tracking. Achieving real-time SLAM via continual training of a neural network against a live image stream requires significant innovation. Our iMAP algorithm uses a keyframe structure and multi-processing computation flow, with dynamic information-guided pixel sampling for speed, with tracking at 10 Hz and global map updating at 2 Hz. The advantages of an implicit MLP over standard dense SLAM techniques include efficient geometry representation with automatic detail control and smooth, plausible filling-in of unobserved regions such as the back surfaces of objects.