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Volume-based Semantic Labeling with Signed Distance Functions
[article]
2015
arXiv
pre-print
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Here, we link them quite tightly by delineating a category label fusion technique that allows for embedding semantic information into the dense map created by a volume-based SLAM algorithm such as KinectFusion ...
We validate our proposal using a publicly available semantically annotated RGB-D dataset and a) employing ground truth labels, b) corrupting such annotations with synthetic noise, c) deploying a state ...
Similarly to KinectFusion [11] , the map is represented by a Signed Distance Function [6] , but, peculiarly, we also provide each voxel with a label that specifies the type of object appearing in that ...
arXiv:1511.04242v1
fatcat:lbdnsyhsr5fr3feanab4etvm5u
Neural 3D Scene Reconstruction with the Manhattan-world Assumption
[article]
2022
arXiv
pre-print
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Specifically, we use an MLP network to represent the signed distance function as the scene geometry. ...
To resolve the inaccurate segmentation, we encode the semantics of 3D points with another MLP and design a novel loss that jointly optimizes the scene geometry and semantics in 3D space. ...
Other volume rendering based methods -UNISURF, NeuS and VolSDF perform better than NeRF as occupancy and signed distance function have better surface constraints. ...
arXiv:2205.02836v2
fatcat:u7fekb2kpvflhovl6vjbz3ehea
Deep semantic cross modal hashing based on graph similarity of modal-specific
2021
IEEE Access
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For image graph, we build the intra-modal similarity with Euclidean distance function. For text graph, we build the intra-modal similarity with cosine distance function. ...
Paying attention to the specifics of each modality, we build the images' similarity with Euclidean distance function and the texts' similarity with cosine distance function. ...
doi:10.1109/access.2021.3093357
fatcat:uyouxawgzbhzhlrsufj4iauiuy
SemanticFusion: Joint Labeling, Tracking and Mapping
[chapter]
2016
Lecture Notes in Computer Science
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Kick-started by deployment of the well-known KinectFusion, recent research on the task of RGBD-based dense volume reconstruction has focused on improving different shortcomings of the original algorithm ...
Accordingly, we present an extended KinectFusion pipeline which takes into account per-pixel semantic labels gathered from the input frames. ...
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. ...
doi:10.1007/978-3-319-49409-8_55
fatcat:s4e6ervplzce7akyzw4t7kkx6u
Two Stream 3D Semantic Scene Completion
[article]
2019
arXiv
pre-print
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The approach voxelizes the scene and predicts for each voxel if it is occupied and, if it is occupied, the semantic class label. ...
The approach constructs an incomplete 3D semantic tensor, which uses a compact three-channel encoding for the inferred semantic information, and uses a 3D CNN to infer the complete 3D semantic tensor. ...
To provide a more meaningful input signal, the signed distance function is transformed into a flipped TSDF [36] , where every signed distance value d is converted into a distance d f which is 1 or -1 ...
arXiv:1804.03550v4
fatcat:6bxtk7pcbfflhnirvsr5jrfhjq
Deep Multi-level Semantic Hashing for Cross-modal Retrieval
2019
IEEE Access
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In this paper, the multi-level semantic supervision generating approach is proposed by exploring the label relevance. ...
Most existing hashing methods are designed based on binary supervision, which transforms complex relationships of multi-label data into simple similar or dissimilar. ...
C (g) , C (x) ∈ {−1, +1} c×n F (g) = f (G; ϕ g ) F (x) = f (X ; ϕ x ) (8) where C (g) = sign(F (g) ), C (x) = sign(F (x) ), and sign(•) is a sign function defined as: sign(x) = 1, x 0 −1, x < 0 (9) • 2 ...
doi:10.1109/access.2019.2899536
fatcat:xynopqlgyfhe3ef6su55zqczim
A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor Scenes
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2108.05246v1 fatcat:hz24aswbvzfdxng2lit66v7cyy
Given noisy depth maps, a camera trajectory, and 2D semantic labels at train time, the proposed deep neural network based approach learns to fuse the depth over frames with suitable semantic labels in ...
This paper presents a real-time online vision framework to jointly recover an indoor scene's 3D structure and semantic label. ...
TSDF fusion [16] is an incremental method to integrate the depth maps over frames for each location x ∈ R 3 into a volume by averaging truncated signed distance functions (TSDF). ...
arXiv:2108.05246v2
fatcat:ux3hrwh75faynkmopirgoo7hdu
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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We propose a novel data-driven approach based on fully-convolutional neural networks that transforms incomplete signed distance functions (SDFs) into complete meshes at unprecedented spatial extents (middle ...
Abstract We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels. ...
We would also like to thank Shuran Song for helping with the SSCNet comparison. ...
doi:10.1109/cvpr.2018.00481
dblp:conf/cvpr/DaiRBRSN18
fatcat:is4fexgq55gl3nyuqchg5o2m3a
Deep Attention-Guided Hashing
2019
IEEE Access
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The loss function we propose contains two components: the semantic loss and the attention loss. ...
With the rapid growth of multimedia data (e.g., image, audio and video etc.) on the web, learning-based hashing techniques such as Deep Supervised Hashing (DSH) have proven to be very efficient for large-scale ...
[55] proposed a justifiable approach based on the continuation of the tanh function, which approaches the sign function with the scale parameter β in its limit: lim β→∞ tanh(βx) = sign(x), they prove ...
doi:10.1109/access.2019.2891894
fatcat:edzv4jcp5zarzkv4qmltalcgje
SEMANTIC LABELLING OF ROAD FURNITURE IN MOBILE LASER SCANNING DATA
2017
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Road furniture semantic labelling is vital for large scale mapping and autonomous driving systems. ...
In this paper, a novel method is proposed to interpret road furniture based on their logical relations and functionalities. ...
However, there is little attention on interpreting road furniture at a functional component level, namely semantically labelling of road furniture based on their functionalities. ...
doi:10.5194/isprs-archives-xlii-2-w7-247-2017
fatcat:ldlkgfvu45eebf3zwwsjxussg4
imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose
[article]
2021
arXiv
pre-print
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We present imGHUM, the first holistic generative model of 3D human shape and articulated pose, represented as a signed distance function. ...
We propose a novel network architecture and a learning paradigm, which make it possible to learn a detailed implicit generative model of human pose, shape, and semantics, on par with state-of-the-art mesh-based ...
equation ∥∇pS(p, α; ω)∥ = 1, (1) where S is a signed distance function that vanishes at the surface Y with gradients equal to surface normals. ...
arXiv:2108.10842v1
fatcat:4aoggdkt5ba75fbzlg7ji7h7au
Urban 3D semantic modelling using stereo vision
2013
2013 IEEE International Conference on Robotics and Automation
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In this paper we propose a robust algorithm that generates an efficient and accurate dense 3D reconstruction with associated semantic labellings. ...
The street level images are automatically labelled using a Conditional Random Field (CRF) framework exploiting stereo images, and label estimates are aggregated to annotate the 3D volume. ...
A signed distance function corresponds to the distance to the closest surface interface (zero crossing), with positive values corresponding to free space, and negative values corresponding to points behind ...
doi:10.1109/icra.2013.6630632
dblp:conf/icra/SenguptaGST13
fatcat:fnkmujibxbhkfa2qavaoddmble
BrainGazer - Visual Queries for Neurobiology Research
2009
IEEE Transactions on Visualization and Computer Graphics
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We focus on the ability to visually query the data based on semantic as well as spatial relationships. ...
We have designed and implemented BrainGazer, a system which integrates visualization techniques for volume data acquired through confocal microscopy as well as annotated anatomical structures with an intuitive ...
We use signed distance volumes generated for all objects in the database. ...
doi:10.1109/tvcg.2009.121
pmid:19834226
fatcat:pusgaju775ftjjmtbto2duzsui
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
[article]
2018
arXiv
pre-print
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We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels. ...
Our results show that we outperform other methods not only in the size of the environments handled and processing efficiency, but also with regard to completion quality and semantic segmentation performance ...
We would also like to thank Shuran Song for helping with the SSCNet comparison. ...
arXiv:1712.10215v2
fatcat:6mfauuwj5rathpmobktp2htubi
PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomics
[article]
2021
arXiv
pre-print
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We present PyTorch Connectomics (PyTC), an open-source deep-learning framework for the semantic and instance segmentation of volumetric microscopy images, built upon PyTorch. ...
Those functionalities can be easily realized in PyTC by changing the configuration options without coding and adapted to other 2D and 3D segmentation tasks for different tissues and imaging modalities. ...
Thus the loader samples fewer data points from volumes with sparse labels by expectation. ...
arXiv:2112.05754v1
fatcat:jjdatdvbr5eypmxmqbvujmarc4
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