◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Thomas Müller

5 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author4

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • astro-ph.IM1
ORCID 0000-0001-7577-755X
same name
  • Thomas Müller — 11 papers
  • Thomas Müller — 9 papers
  • Thomas Müller — 8 papers
  • Thomas Müller — 6 papers
  • Thomas Müller — 5 papers, h 15
  • Thomas Müller — 3 papers, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedInstant Neural Graphics Primitives with a Multiresolution Hash Encoding

4k citations · 4k across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023

Compact Neural Graphics Primitives with Learned Hash Probing

Towaki Takikawa, Thomas Müller, Merlin Nimier-David +4

Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid…

cs.CV2023

Adaptive Shells for Efficient Neural Radiance Field Rendering

Zian Wang, Tianchang Shen, Merlin Nimier-David +6

Neural radiance fields achieve unprecedented quality for novel view synthesis, but their volumetric formulation remains expensive, requiring a huge number of samples to render high…

cs.CV2023★ 5 cited

BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

Bowen Wen, Jonathan Tremblay, Valts Blukis +6

We present a near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence, while simultaneously performing neural 3D reconstruction of the obj…

cs.CV2022★ 4k cited

Instant Neural Graphics Primitives with a Multiresolution Hash Encoding

Thomas Müller, Alex Evans, Christoph Schied +1

Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that perm…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.