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Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion
2019
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
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Semantic Scene Completion (SSC) aims to simultaneously predict the volumetric occupancy and semantic category of a 3D scene. ...
Our proposed framework has three outstanding advantages: (1) it explicitly models the 3D spatial context for performance improvement; (2) full-resolution 3D volumes are produced with structure-preserving ...
Our CCPNet is a self-cascaded pyramid structure to successively aggregate multi-scale 3D contexts and local geometry details for full-resolution scene completions. ...
doi:10.1109/iccv.2019.00789
dblp:conf/iccv/Zhang0LLY19
fatcat:b23nhwz4jrfvvirtca5til25me
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement
[article]
2021
arXiv
pre-print
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3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same semantic classes, using ...
Current methods use 2D features extracted from early-fused RGB-D images for 2D segmentation to improve 3D scene completion. ...
Acknowledgments This work was supported in part by Shenzhen Natural Science Foundation under Grant JCYJ20190813170601651, and in part by funding from Shenzhen Institute of Artificial Intelligence and Robotics for ...
arXiv:2106.15413v1
fatcat:i4cmmenxpjd2ngcc73dxyionoi
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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The resolution of input images is 1152 × 864. ...
HSM [48] builds a light model for high-resolution images with a hierarchical design. EMCUA [33] introduces an approach for multi-level context ultra-aggregation. ...
PSMNet [3] further introduces pyramid spatial pooling and 3D hourglass networks for cost volume regularization and yields better results. ...
doi:10.1109/cvpr42600.2020.00257
dblp:conf/cvpr/GuFZDTT20
fatcat:gyudjs657bdftgi4riswvrjxdu
Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
[article]
2020
arXiv
pre-print
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First, the proposed cost volume is built upon a standard feature pyramid encoding geometry and context at gradually finer scales. ...
These methods are limited when high-resolution outputs are needed since the memory and time costs grow cubically as the volume resolution increases. ...
HSM [48] builds a light model for high-resolution images with a hierarchical design. EMCUA [33] introduces an approach for multi-level context ultra-aggregation. ...
arXiv:1912.06378v3
fatcat:nalwio4wwrcebpel462r2kxhem
Survey on Semantic Segmentation using Deep Learning Techniques
2019
Neurocomputing
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Semantic segmentation is a challenging task in computer vision systems. ...
For this reason, we propose to survey these methods by, first categorizing them into ten different classes according to the common concepts underlying their architectures. ...
ACKNOWLEDGMENT The authors express their gratitude to University Technology Belfort-Montbeliard and Higher Education Commission of Pakistan for providing the support and necessary requirement for completion ...
doi:10.1016/j.neucom.2019.02.003
fatcat:aelsfl7unvdw5j2rtyqhtgqrsm
Semantic Scene Segmentation for Robotics
[article]
2024
arXiv
pre-print
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Therefore, semantic segmentation considers the full scene context, incorporating the object category, location, and shape of all the scene elements, including the background. ...
Numerous algorithms have been proposed for semantic segmentation over the years. ...
Therefore, as we show in Figure 1 .2, semantic segmentation models output a full-resolution semantic prediction that contains scene contexts such as the object category, location, and shape of all the ...
arXiv:2401.07589v1
fatcat:y4zfhkdjgzam5fhae3zwyr6ngu
A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving
2021
Electronics
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We categorize these works into four work streams, including object detection, full scene semantic segmentation, instance segmentation, and lane line segmentation. ...
As a prerequisite for autonomous driving, scene understanding has attracted extensive research. ...
Full-Scene Semantic Segmentation Full-scene semantic segmentation is segmenting object categories at the pixel level in a full image. ...
doi:10.3390/electronics10040471
fatcat:gyloykg24nbqvlw4ujiiagoneq
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation
[article]
2019
arXiv
pre-print
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Further, large motion and occlusions are well-known problems in scene flow estimation. PWOC-3D employs specialized design decisions to explicitly model these challenges. ...
The work presented in this paper overcomes these drawbacks efficiently (in terms of speed and accuracy) by proposing PWOC-3D, a compact CNN architecture to predict scene flow from stereo image sequences ...
Scene flow is one such reconstruction of the complete 3D motion of objects in the world. ...
arXiv:1904.06116v1
fatcat:rsvgzb3erbe23mcvt4ekus3moe
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes
[article]
2020
arXiv
pre-print
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Extensive experimental results demonstrate the effectiveness of our 2D supervised framework, which achieves comparable results with the state-of-the-art approaches trained with full 3D labels, for semantic ...
3D semantic segmentation models of natural scene point clouds while not explicitly capturing their inherent structures, even with only single view per training sample. ...
Other methods such as [33] and [8] tackled the semantic scene completion from the 3D volume perspective, as well as explored the relationship between scene completion and semantic scene parsing. ...
arXiv:2004.12498v2
fatcat:5mr6uuli6baixai7bjud24heda
Group-Based Atrous Convolution Stereo Matching Network
2021
Wireless Communications and Mobile Computing
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Moreover, we introduce a tailored cascade cost volume in a pyramid form to reduce memory, so as to meet real-time performance. ...
The group-based atrous convolution stereo matching network is evaluated on the street scene benchmark KITTI 2015 and Scene Flow and achieves state-of-the-art performance. ...
A lower resolution disparity map is constructed by a smaller cost volume to complete the first estimate. ...
doi:10.1155/2021/7386280
fatcat:dmnxhftlqjgzfevui6qtncf7uq
Deep Learning-Based Frameworks for Semantic Segmentation of Road Scenes
2022
Electronics
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This paper presents a detailed review of deep learning-based frameworks used for semantic segmentation of road scenes, highlighting their architectures and tasks. ...
With the revolution of deep learning, the need for more efficient and accurate segmentation systems has increased. ...
Semantic segmentation means a complete scene understanding and is applied to images, videos, and 3D data. ...
doi:10.3390/electronics11121884
fatcat:ekykzfnqtjcbla3fh4vlwhkvgu
3D Semantic Scene Completion: a Survey
[article]
2021
arXiv
pre-print
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Semantic Scene Completion (SSC) aims to jointly estimate the complete geometry and semantics of a scene, assuming partial sparse input. ...
In the last years following the multiplication of large-scale 3D datasets, SSC has gained significant momentum in the research community because it holds unresolved challenges. ...
(LW)-ASPP, (Lightweight) Atrous Spatial Pyramid Pooling. CCP, Cascaded Context Pyramid. FAM, Feature Aggregation Module. AIC, Anisotropic Convolutional Module. GA, Global Aggregation. ...
arXiv:2103.07466v3
fatcat:swz4azlznre3laziatls6sdrfm
Semantic Scene Completion via Integrating Instances and Scene in-the-Loop
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2104.03640v1 fatcat:n42suwxacffjhahj6wulenefmu
Semantic Scene Completion aims at reconstructing a complete 3D scene with precise voxel-wise semantics from a single-view depth or RGBD image. ...
Specifically, the SI is able to encode objects' surrounding context for effectively decoupling instances from the scene and each instance could be voxelized into higher resolution to capture finer details ...
CCPNet [49] proposes a cascaded context pyramid which progressively restores details of objects and improves the labeling coherence. ...
arXiv:2104.03640v2
fatcat:2d6frzqqxnc47bplddxwgjkg5q
A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques
2022
Sensors
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We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for. ...
This is due to it outputting the image at full resolution without the need to upsample from a lower-resolution prediction. ...
The left image is fed through a context network to extract the context features. ...
doi:10.3390/s22239364
pmid:36502065
pmcid:PMC9737943
fatcat:6qiunw54knbnlmkdlrxn64rucq
Photographic Image Synthesis with Cascaded Refinement Networks
[article]
2017
arXiv
pre-print
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The presented approach scales seamlessly to high resolutions; we demonstrate this by synthesizing photographic images at 2-megapixel resolution, the full resolution of our training data. ...
The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image. ...
Semantic layout
GAN+semantic segmenation Full-resolution network Our result Isola et al. [16] Encoder-decoder Semantic layout Our result Isola et al. ...
arXiv:1707.09405v1
fatcat:bd7mzuimvvgylaeuj35ct7fjzi
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