Convolutional Neural Network-based Efficient Dense Point Cloud Generation using Unsigned Distance Fields

Dense point cloud generation from a sparse or incomplete point cloud is a crucial and challenging problem in 3D computer vision and computer graphics. So far, the existing methods are either computationally too expensive, suffer from limited resolution, or both. In addition, some methods are strictly limited to watertight surfaces -- another major obstacle for a number of applications. To address these issues, we propose a lightweight Convolutional Neural Network that learns and predicts the unsigned distance field for arbitrary 3D shapes for dense point cloud generation using the recently emerged concept of implicit function learning. Experiments demonstrate that the proposed architecture outperforms the state of the art by 7.8x less model parameters, 2.4x faster inference time and up to 24.8% improved generation quality compared to the state-of-the-art

DC-DFFN: Densely Connected Deep Feature Fusion Network With Sign Agnostic Learning for Implicit Shape Representation

Reconstructing 3D surfaces from raw point cloud data is still a challenging and complex problem in computer vision and graphics. Recently emerged neural implicit representations model 3D surfaces implicitly in arbitrary resolution and diverse topologies. In this domain, most of the studies have so far used a single latent code-based variational auto-encoder (VAE) or auto-decoder (AD) architectures, or architectures similar to UNets. Due to the deep architectures of the existing approaches, gradients and/or input information can vanish while passing through the layers, which can cause suboptimal learning at training time and consequently low performance at test time. As a countermeasure, skip connections and feature fusion have been used in related application fields of convolutional neural networks. In this study, we embrace this idea and propose a novel densely connected deep feature fusion network, DC-DFFN, architecture for implicit shape representation. In the experimental results we show that DC-DFFN outperforms baseline approaches in terms visual reconstruction quality and quantitatively based on several measures. In addition, the proposed approach provides faster convergence during training compared to the baseline approaches. The DC-DFFN architecture has been implemented in PyTorch and is available as open source.©2023 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Scheduling of CAL actor networks based on dynamic code analysis

International audienceCAL is a dataflow oriented language for writing high-level specifications of signal processing applications. The language has recently been standardized and selected for the new MPEG Reconfigurable Video Coding standard. Application specifications written in CAL can be transformed into executable implementations through development tools. Unfortunately, the present tools provide no way to schedule the CAL entities efficiently at run-time. This paper proposes an automated approach to analyze specifications written in CAL, and produce run-time schedules that perform on average 1.45 #x00D7; faster than implementations relying on default scheduling. The approach is based on quasi-static scheduling, which reduces conditional execution in the run-time system

Automatic synthesis of TTA processor networks from RVC-CAL dataflow programs

International audienceThe RVC-CAL dataflow language has recently become standardized through its use as the official language of Reconfigurable Video Coding (RVC), a recent standard by MPEG. The tools developed for RVC-CAL have enabled the transformation of RVC-CAL dataflow programs into C language and VHDL (among others), enabling implementations for instruction processors and HDL synthesis. This paper introduces new tools that enable automatic creation of heterogeneous multiprocessor networks out of RVC-CAL dataflow programs. Each processor in the network performs the functionality of one RVC-CAL actor. The processors are of the Transport Triggered Architecture (TTA) type, for which a complete co-design toolset exists. The existing tools enable customizing the processors according to the requirements of individual dataflow actors. The functionality of the tool chain has been demonstrated by synthesizing an MPEG-4 Simple Profile video decoder to an FPGA. This particular decoder is automatically realized into 21 tiny, heterogeneous processors

Sensor Networks TDOA Self-Calibration: 2D Complexity Analysis and Solutions

Given a network of receivers and transmitters, the process of determining their positions from measured pseudo-ranges is known as network self-calibration. In this paper we consider 2D networks with synchronized receivers but unsynchronized transmitters and the corresponding calibration techniques,known as TDOA techniques. Despite previous work, TDOA self-calibration is computationally challenging. Iterative algorithms are very sensitive to the initialization, causing convergence issues.In this paper, we present a novel approach, which gives an algebraic solution to three previously unsolved scenarios. Our solvers can lead to a position error <1.2% and are robust to noise

Automatic Hierarchical Discovery of Quasi-Static Schedules of RVC-CAL Dataflow Programs

International audienceRVC-CAL is an actor-based dataflow language that enables concurrent, modular and portable description of signal processing algorithms. RVC-CAL programs can be compiled to implementation languages such as C/C++ and VHDL for producing software or hardware implementations. This paper presents a methodology for automatic discovery of piecewise-deterministic (quasi-static) execution schedules for RVC-CAL program software implementations. Quasi-static scheduling moves computational burden from the implementable run-time system to design-time compilation and thus enables making signal processing systems more efficient. The presented methodology divides the RVC-CAL program into segments and hierarchically detects quasi-static behavior from each segment: first at the level of actors and later at the level of the whole segment. Finally, a code generator creates a quasi-statically scheduled version of the program. The impact of segment based quasi-static scheduling is demonstrated by applying the methodology to several RVC-CAL programs that execute up to 58 % faster after applying the presented methodology

I Trust You Dr. Researcher, but not the Company that Handles My Data –Trust in the Data Economy

In the rising era of artificial intelligence (AI), learning machinery and hyper surveillance, trust is a sought-after attribute. The General Data Protection Regulation (GDPR) was introduced to increase individuals’ control over their own personal data, yet proof of its effectiveness is still lacking. Indeed, contrary to the intentions of the GDPR recent studies have shown numerous flaws in the regulation including issues from user negligence and ignorance to manipulation via dark design patterns etc. Even informed through the compulsory privacy notices and consent, people are experiencing less trust than ever. This is impacting every area of human society. This paper reports two interview studies (N=31) that probed individuals’ trust company-driven data handling practice and communication. The results demonstrate low to no trust in the perception of data-related information given by companies, rather perceiving researchers as trustworthy in terms of correspondence between data-handling related communication and the applied reality

I Trust You Dr. Researcher, but not the Company that Handles My Data –Trust in the Data Economy

In the rising era of artificial intelligence (AI), learning machinery and hyper surveillance, trust is a sought-after attribute. The General Data Protection Regulation (GDPR) was introduced to increase individuals’ control over their own personal data, yet proof of its effectiveness is still lacking. Indeed, contrary to the intentions of the GDPR recent studies have shown numerous flaws in the regulation including issues from user negligence and ignorance to manipulation via dark design patterns etc. Even informed through the compulsory privacy notices and consent, people are experiencing less trust than ever. This is impacting every area of human society. This paper reports two interview studies (N=31) that probed individuals’ trust company-driven data handling practice and communication. The results demonstrate low to no trust in the perception of data-related information given by companies, rather perceiving researchers as trustworthy in terms of correspondence between data-handling related communication and the applied reality.Papers published as part of the Proceedings of the 57th Annual Hawaii International Conference on System Sciences are under Creative Commons licenses (CC-BY-NC-ND 4.0). https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed