Signed distance function
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DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation Open
Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilitie…
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ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields Open
We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene repre…
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Shape-Aware Organ Segmentation by Predicting Signed Distance Maps Open
In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness. Since the…
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Differentiable signed distance function rendering Open
Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images. The inversion of shape parameters is of particular inter…
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Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis Open
We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of implicit and explicit 3D representations by leveraging a nove…
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Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps Open
ISSN:2377-3766
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Reachable Distance Function for KNN Classification Open
Distance function is a main metrics of measuring the affinity between two\ndata points in machine learning. Extant distance functions often provide\nunreachable distance values in real applications. This can lead to incorrect\nmeasure of t…
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TSDF-based change detection for consistent long-term dense reconstruction and dynamic object discovery Open
Robots that are operating for extended periods of time need to be able to deal with changes in their environment and represent them adequately in their maps. In this paper, we present a novel 3D reconstruction algorithm based on an extende…
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SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static\n Images Open
Dense 3D object reconstruction from a single image has recently witnessed\nremarkable advances, but supervising neural networks with ground-truth 3D\nshapes is impractical due to the laborious process of creating paired\nimage-shape datase…
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A Novel Active Contour Model Guided by Global and Local Signed Energy-Based Pressure Force Open
Active contour models (ACMs) have been widely applied in the field of image segmentation. However, it is still very challenging to construct an efficient ACM to segment images with intensity inhomogeneity. In this paper, a novel ACM guided…
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Voxfield: Non-Projective Signed Distance Fields for Online Planning and 3D Reconstruction Open
Creating accurate maps of complex, unknown environments is of utmost importance for truly autonomous navigation robot. However, building these maps online is far from trivial, especially when dealing with large amounts of raw sensor readin…
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HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details Open
Neural rendering can be used to reconstruct implicit representations of shapes without 3D supervision. However, current neural surface reconstruction methods have difficulty learning high-frequency geometry details, so the reconstructed sh…
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Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces Open
Reconstructing continuous surfaces from 3D point clouds is a fundamental operation in 3D geometry processing. Several recent state-of-the-art methods address this problem using neural networks to learn signed distance functions (SDFs). In …
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Neural Joint Space Implicit Signed Distance Functions for Reactive Robot Manipulator Control Open
In this paper, we present an approach for learning a neural implicit signed distance function expressed in joint space coordinates, that efficiently computes distance-to-collisions for arbitrary robotic manipulator configurations. Computin…
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Active Contour Driven by Weighted Hybrid Signed Pressure Force for Image Segmentation Open
This study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global …
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Fuzzy Inventory Model without Shortages Using Signed Distance Method Open
In this paper an inventory model without shortage is considered under fuzzy environment.Our objective is to determine the optimal total cost and optimal order quantity for proposed inventory model.The optimum order quantity is calculated u…
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Real-Time Tracking of Single and Multiple Objects from Depth-Colour Imagery Using 3D Signed Distance Functions Open
We describe a novel probabilistic framework for real-time tracking of multiple objects from combined depth-colour imagery. Object shape is represented implicitly using 3D signed distance functions. Probabilistic generative models based on …
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Non-linear sphere tracing for rendering deformed signed distance fields Open
Signed distance fields (SDFs) are a powerful implicit representation for modeling solids, volumes and surfaces. Their infinite resolution, controllable continuity and robust constructive solid geometry operations, coupled with smooth blend…
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A Characteristic Function-Based Algorithm for Geodesic Active Contours Open
Active contour models have been widely used in image segmentation, and the level set method (LSM) is the most popular approach for solving the models, via implicitly representing the contour by a level set function. However, the LSM suffer…
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3D level set methods for evolving fronts on tetrahedral meshes with adaptive mesh refinement Open
The level set method is commonly used to model dynamically evolving fronts and interfaces. In this work, we present new methods for evolving fronts with a specified velocity field or in the surface normal direction on 3D unstructured tetra…
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Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields Open
Multilayer perceptrons (MLPs) have been successfully used to represent 3D shapes implicitly and compactly, by mapping 3D coordinates to the corresponding signed distance values or occupancy values. In this paper, we propose a novel positio…
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A Level Set Method for Infrared Image Segmentation Using Global and Local Information Open
Infrared image segmentation plays a significant role in many burgeoning applications of remote sensing, such as environmental monitoring, traffic surveillance, air navigation and so on. However, the precision is limited due to the blurred …
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A Fuzzy Environment Inventory Model with Partial Backlogging under Learning Effect Open
In this article we developed an inventory model for noninstantaneous decaying items is considered under crisp and fuzzy environment.In this study we have considered stock dependent demand rate and variable deterioration.It is supposed that…
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Computing the Hausdorff Distance of Two Sets from Their Distance Functions Open
The Hausdorff distance is a measure of (dis-)similarity between two sets which is widely used in various applications. Most of the applied literature is devoted to the computation for sets consisting of a finite number of points. This has …
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Constructive Solid Geometry on Neural Signed Distance Fields Open
Signed Distance Fields (SDFs) parameterized by neural networks have recently gained popularity as a fundamental geometric representation. However, editing the shape encoded by a neural SDF remains an open challenge. A tempting approach is …
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3D Reconstruction of Ancient Buildings Using UAV Images and Neural Radiation Field with Depth Supervision Open
The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique ch…
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Reach For the Spheres: Tangency-aware surface reconstruction of SDFs Open
Signed distance fields (SDFs) are a widely used implicit surface representation, with broad applications in computer graphics, computer vision, and applied mathematics. To reconstruct an explicit triangle mesh surface corresponding to an S…
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Boundary-wise loss for medical image segmentation based on fuzzy rough sets Open
The loss function plays an important role in deep learning models as it determines the model convergence behavior and performance. In semantic segmentation, many methods utilize pixel-wise (e.g. cross-entropy) and region-wise (e.g. dice) l…
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A method for solving linear programming with interval-valued trapezoidal fuzzy variables Open
An efficient method to handle the uncertain parameters of a linear programming (LP) problem is to express the uncertain parameters by fuzzy numbers which are more realistic, and create a conceptual and theoretical framework for dealing wit…
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A Study of an EOQ Model of Growing Items with Parabolic Dense Fuzzy Lock Demand Rate Open
In this article, the parabolic dense fuzzy set is defined, and its basic arithmetic operations are studied with graphical illustration. The lock set concept is incorporated in a parabolic dense fuzzy set. Then, it is applied to the problem…