L
Leonidas J. Guibas
Researcher at Stanford University
Publications - 736
Citations - 99526
Leonidas J. Guibas is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Point cloud. The author has an hindex of 124, co-authored 691 publications receiving 79200 citations. Previous affiliations of Leonidas J. Guibas include PARC & Association for Computing Machinery.
Papers
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VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization
TL;DR: Zhang et al. as discussed by the authors proposed VDN-NeRF, a method to train neural radiance fields (NeRFs) for better geometry under non-Lambertian surface and dynamic lighting conditions that cause significant variation in the radiance of a point when viewed from different angles.
Posted Content
HuMoR: 3D Human Motion Model for Robust Pose Estimation
TL;DR: HuMoR as discussed by the authors uses a conditional variational autoencoder to learn a distribution of the change in pose at each step of a motion sequence, which can be used to estimate plausible pose and shape from ambiguous observations.
Journal ArticleDOI
From Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds
TL;DR: This work proposes a new method for segmentation-free joint estimation of orthogonal planes, their intersection lines, relationship graph and corners lying at the intersection of three orthogonality planes, and forms a graph of these primitives, paving the way to the extraction of further reliable features: lines and corners.
Book
Robust and compact geographic/topological structures for routing and information dissemination in wireless sensor networks
Leonidas J. Guibas,Qing Fang +1 more
TL;DR: This dissertation describes a new approach in organizing hundreds or more of autonomous nodes, each with sensing, computing and wireless communication capabilities, and proposes GLIDER, a scheme that uses a two-tier combinatorial structure for network addressing and routing.
Kinetic Collision Detection: Algorithms and
TL;DR: A number of collision detection algorithms formulated under the Kinetic Data Structures (KDS) framework, a framework for designing and analyzing algorithms for objects in motion, are presented and their practical efficiency is demonstrated.