  We present an approach for recognizing all objects in a scene and estimating
their full pose from an accurate 3D instance-aware semantic reconstruction
using an RGB-D camera. Our framework couples convolutional neural networks
(CNNs) and a state-of-the-art dense Simultaneous Localisation and Mapping
(SLAM) system, ElasticFusion, to achieve both high-quality semantic
reconstruction as well as robust 6D pose estimation for relevant objects. While
the main trend in CNN-based 6D pose estimation has been to infer object's
position and orientation from single views of the scene, our approach explores
performing pose estimation from multiple viewpoints, under the conjecture that
combining multiple predictions can improve the robustness of an object
detection system. The resulting system is capable of producing high-quality
object-aware semantic reconstructions of room-sized environments, as well as
accurately detecting objects and their 6D poses. The developed method has been
verified through experimental validation on the YCB-Video dataset and a newly
collected warehouse object dataset. Experimental results confirmed that the
proposed system achieves improvements over state-of-the-art methods in terms of
surface reconstruction and object pose prediction. Our code and video are
available at https://sites.google.com/view/object-rpe.
