Abstract-We propose a view-based approach for labeling objects in 3D scenes reconstructed from RGB-D (color+depth) videos. We utilize sliding window detectors trained from object views to assign class probabilities to pixels in every RGB-D frame. These probabilities are projected into the reconstructed 3D scene and integrated using a voxel representation. We perform efficient inference on a Markov Random Field over the voxels, combining cues from view-based detection and 3D shape, to label the scene. Our detection-based approach produces accurate scene labeling on the RGB-D Scenes Dataset and improves the robustness of object detection.
Another study REF detected objects by combining sliding window detectors and 3D shape.
Detection-based object labeling in 3D scenes
2012 IEEE International Conference on Robotics and Automation
2012 IEEE International Conference on Robotics and Automation
Computer Science