RGBD Semantic Segmentation
9 papers with code • 1 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in RGBD Semantic Segmentation
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Most implemented papers
3D Graph Neural Networks for RGBD Semantic Segmentation
Each node in the graph corresponds to a set of points and is associated with a hidden representation vector initialized with an appearance feature extracted by a unary CNN from 2D images.
STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling
The proposed network produces a high quality segmentation of a single image by leveraging information from additional views of the same scene.
ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
The main contributions lie in the Attention Complementary Module (ACM) and the architecture with three parallel branches.
Scene Completeness-Aware Lidar Depth Completion for Driving Scenario
choyingw/SCADC-DepthCompletion
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Recent sparse depth completion for lidars only focuses on the lower scenes and produces irregular estimations on the upper because existing datasets, such as KITTI, do not provide groundtruth for upper areas.
Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation
S-Conv is competent to infer the sampling offset of the convolution kernel guided by the 3D spatial information, helping the convolutional layer adjust the receptive field and adapt to geometric transformations.
Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation
harshm121/m3l
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We create the first benchmark for semi-supervised multi-modal semantic segmentation and also report the robustness to missing modalities.
DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation
We present DFormer, a novel RGB-D pretraining framework to learn transferable representations for RGB-D segmentation tasks.
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion.
FusionVision: A comprehensive approach of 3D object reconstruction and segmentation from RGB-D cameras using YOLO and fast segment anything
Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery.
