Scene segmentation is the task of splitting a scene into its various object components.
Image adapted from Temporally coherent 4D reconstruction of complex dynamic scenes.
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Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively.
#2 best model for Semantic Segmentation on COCO-Stuff test
We present 3DMV, a novel method for 3D semantic scene segmentation of RGB-D scans in indoor environments using a joint 3D-multi-view prediction network.
SOTA for Scene Segmentation on ScanNet
Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results.
SOTA for 3D Object Detection on NYU Depth v2
With the introduction of Fully Convolutional Neural Network, which uses finer strides and utilizes deconvolutional layers for upsampling, it has been a go to for any image segmentation task.
In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors.
Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or a simulation environment.
Point cloud is an important type of geometric data structure.
#2 best model for Scene Segmentation on ScanNet