NeuralBlox: 逐次観測から大規模3D地図をリアルタイム構築するニューラル暗黙表現フュージョン
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping
シーンを潜在コードのグリッドで表現し潜在空間で直接更新することで、逐次部分観測から大規模シーンの占有マップをCPUでもリアルタイムに構築・更新できる手法を提案した論文。
著者: Stefan Lionar, Lukas Schmid, Cesar Cadena, Roland Siegwart, Andrei Cramariuc
分類: cs.CV, cs.RO
原文アブストラクト
We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction. Most existing state-of-the-art neural implicit representation methods are limited to object-level reconstructions and can not incrementally perform updates given new data. In this work, we propose a fusion strategy and training pipeline to incrementally build and update neural implicit representations that enable the reconstruction of large scenes from sequential partial observations. By representing an arbitrarily sized scene as a grid of latent codes and performing updates directly in latent space, we show that incrementally built occupancy maps can be obtained in real-time even on a CPU. Compared to traditional approaches such as Truncated Signed Distance Fields (TSDFs), our map representation is significantly more robust in yielding a better scene completeness given noisy inputs. We demonstrate the performance of our approach in thorough experimental validation on real-world datasets with varying degrees of added pose noise.
