3D Semantic Scene Completion
17 papers with code • 2 benchmarks • 2 datasets
This task was introduced in "Semantic Scene Completion from a Single Depth Image" (https://arxiv.org/abs/1611.08974) at CVPR 2017 . The target is to infer the dense 3D voxelized semantic scene from an incompleted 3D input (e.g. point cloud, depth map) and an optional RGB image. A recent summary can be found in the paper "3D Semantic Scene Completion: a Survey" (https://arxiv.org/abs/2103.07466), published at IJCV 2021.
Most implemented papers
Semantic Scene Completion from a Single Depth Image
This paper focuses on semantic scene completion, a task for producing a complete 3D voxel representation of volumetric occupancy and semantic labels for a scene from a single-view depth map observation.
3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior
To this end, we first propose a novel 3D sketch-aware feature embedding to explicitly encode geometric information effectively and efficiently.
LMSCNet: Lightweight Multiscale 3D Semantic Completion
We introduce a new approach for multiscale 3Dsemantic scene completion from voxelized sparse 3D LiDAR scans.
SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion
We propose a framework that ameliorates this issue by performing scene reconstruction and semantic scene completion jointly in an incremental and real-time manner, based on an input sequence of depth maps.
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input.
See and Think: Disentangling Semantic Scene Completion
Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings.
Efficient Semantic Scene Completion Network with Spatial Group Convolution
We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks.
EdgeNet: Semantic Scene Completion from a Single RGB-D Image
Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view.
Anisotropic Convolutional Networks for 3D Semantic Scene Completion
In contrast to the standard 3D convolution that is limited to a fixed 3D receptive field, our module is capable of modeling the dimensional anisotropy voxel-wisely.
Semantic Scene Completion via Integrating Instances and Scene in-the-Loop
The key insight is that we decouple the instances from a coarsely completed semantic scene instead of a raw input image to guide the reconstruction of instances and the overall scene.