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Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for the goal of accurate and dense monocular reconstruction. We propose a method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM, based on a...
We present an architecture for online, incremental scene modeling which combines a SLAM-based scene understanding framework with semantic segmentation and object pose estimation. The core of this approach comprises a probabilistic inference scheme that predicts semantic labels for object hypotheses at each new frame. From these hypotheses, recognized scene structures are incrementally constructed...
This work proposes a real-time segmentation method for 3D point clouds obtained via Simultaneous Localization And Mapping (SLAM). The proposed method incrementally merges segments obtained from each input depth image in a unified global model using a SLAM framework. Differently from all other approaches, our method is able to yield segmentation of scenes reconstructed from multiple views in real-time,...
This paper describes an extension to the Monocular Simultaneous Localization and Mapping (MonoSLAM) method that relies on the images provided by a combined high resolution Time of Flight (HR-ToF) sensor. In its standard formulation MonoSLAM estimates the depth of each tracked feature as the camera moves. This depth estimation depends both on the quality of the feature tracking and the previous camera...
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