P
            <scp>aparazzi</scp>

Liu, Hsueh-Ti Derek; Tao, Michael; Jacobson, Alec

doi:10.1145/3272127.3275047

Cited by 41 publications

(5 citation statements)

References 45 publications

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“…Neural mesh renderer (NMR) [KUH18] approximates the backward gradient for the rasterization operation using a handcrafted function for visibility changes. [LTJ18] proposed Paparazzi, an analytic differentiable renderer for mesh geometry processing using image filters. Petersen et al [PBDCO19] presented Pix2Vex , a C ∞ differentiable renderer via soft blending schemes of nearby triangles, and [LLCL19] introduced Soft Rasterizer , which renders and aggregates the probabilistic maps of mesh triangles, allowing gradient flow from the rendered pixels to the occluded and far‐range vertices.…”

Section: Fundamentals Of Neural Renderingmentioning

confidence: 99%

Advances in Neural Rendering

Tewari

Thies²,

Mildenhall

et al. 2022

Computer Graphics Forum

176

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Section: Fundamentals Of Neural Renderingmentioning

confidence: 99%

Advances in Neural Rendering

Tewari

Thies²,

Mildenhall

et al. 2022

Computer Graphics Forum

176

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show abstract

“…the parameters can be processed. Mesh‐based differentiable renderers [ 26 , 27 , 28 , 29 ] have been proposed to optimize various scene parameters such as geometry, illumination, textures, or materials.…”

Section: Related Workmentioning

confidence: 99%

Calibration‐free structured‐light‐based 3D scanning system in laparoscope for robotic surgery

Furukawa,

Chen,

Sagawa

et al. 2024

Healthcare Tech Letters

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Accurate 3D shape measurement is crucial for surgical support and alignment in robotic surgery systems. Stereo cameras in laparoscopes offer a potential solution; however, their accuracy in stereo image matching diminishes when the target image has few textures. Although stereo matching with deep learning has gained significant attention, supervised learning requires a large dataset of images with depth annotations, which are scarce for laparoscopes. Thus, there is a strong demand to explore alternative methods for depth reconstruction or annotation for laparoscopes. Active stereo techniques are a promising approach for achieving 3D reconstruction without textures. In this study, a 3D shape reconstruction method is proposed using an ultra‐small patterned projector attached to a laparoscopic arm to address these issues. The pattern projector emits a structured light with a grid‐like pattern that features node‐wise modulation for positional encoding. To scan the target object, multiple images are taken while the projector is in motion, and the relative poses of the projector and a camera are auto‐calibrated using a differential rendering technique. In the experiment, the proposed method is evaluated by performing 3D reconstruction using images obtained from a surgical robot and comparing the results with a ground‐truth shape obtained from X‐ray CT.

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“…With the advent of techniques adopting neural fields to reconstruct 3D representations [25][26][27][28][29], Neural Radiance Field (NeRF) [1] proposed by Mildenhall et al showed a promising result without the requirement of exact 3D geometry of each object in the scene. Compared to previous approaches which require a pre-defined representation of the scene by meshes [30,31], voxels [25,26], or point clouds [27,32], NeRF only requires a set of densely captured 2D images and corresponding camera parameters of each image. Despite its light requirements, it demonstrates quality reconstruction of the complex geometry of scenes.…”

Section: Related Workmentioning

confidence: 99%

A Critical Debate on the Project of the (Post)Modern University: University of Ritual or University of Excellence?

Kim¹

2016

Crit Theory Soc Korea

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To improve the environmental implications of the growing demand of computing, future applications need to improve the carbon-efficiency of computing infrastructures. State-of-theart approaches, however, do not consider the intermittent nature of renewable energy. The time and location-based carbon intensity of energy fueling computing has been ignored when determining how computation is carried out. This poses a new challenge -deciding when and where to run applications across consumer devices at the edge and servers in the cloud. Such scheduling decisions become more complicated with the stochastic runtime variance and the amortization of the rising embodied emissions. This work proposes GreenScale, a framework to understand the design and optimization space of carbon-aware scheduling for green applications across the edge-cloud infrastructure. Based on the quantified carbon output of the infrastructure components, we demonstrate that optimizing for carbon, compared to performance and energy efficiency, yields unique scheduling solutions. Our evaluation with three representative categories of applications (i.e., AI, Game, and AR/VR) demonstrate that the carbon emissions of the applications can be reduced by up to 29.1% with the GreenScale -with the scale of edge-cloud application users (in the order of millions), the yearly reduced carbon emission is 232.7tCO 2 , on average, which is on par the average yearly carbon emissions of 55 vehicles. The analysis in this work further provides a detailed road map for edge-cloud application developers to build green applications.

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P aparazzi

Cited by 41 publications

References 45 publications

Advances in Neural Rendering

Advances in Neural Rendering

Calibration‐free structured‐light‐based 3D scanning system in laparoscope for robotic surgery

A Critical Debate on the Project of the (Post)Modern University: University of Ritual or University of Excellence?

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