Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Reiser, Christian"'
Autor:
Esposito, Stefano, Chen, Anpei, Reiser, Christian, Bulò, Samuel Rota, Porzi, Lorenzo, Schwarz, Katja, Richardt, Christian, Zollhöfer, Michael, Kontschieder, Peter, Geiger, Andreas
High-quality real-time view synthesis methods are based on volume rendering, splatting, or surface rendering. While surface-based methods generally are the fastest, they cannot faithfully model fuzzy geometry like hair. In turn, alpha-blending techni
Externí odkaz:
http://arxiv.org/abs/2409.02482
Autor:
Reiser, Christian, Garbin, Stephan, Srinivasan, Pratul P., Verbin, Dor, Szeliski, Richard, Mildenhall, Ben, Barron, Jonathan T., Hedman, Peter, Geiger, Andreas
While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensive methods that model the scene's geometry as a volumetric density fie
Externí odkaz:
http://arxiv.org/abs/2402.12377
Autor:
Duckworth, Daniel, Hedman, Peter, Reiser, Christian, Zhizhin, Peter, Thibert, Jean-François, Lučić, Mario, Szeliski, Richard, Barron, Jonathan T.
Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed, and modern methods are capable of rendering near-photorealistic scenes at interactive frame rates. At the same time, a tension has arisen between explicit sce
Externí odkaz:
http://arxiv.org/abs/2312.07541
Autor:
Yariv, Lior, Hedman, Peter, Reiser, Christian, Verbin, Dor, Srinivasan, Pratul P., Szeliski, Richard, Barron, Jonathan T., Mildenhall, Ben
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural volume-surface scene representation designed to have well-behaved leve
Externí odkaz:
http://arxiv.org/abs/2302.14859
Autor:
Reiser, Christian, Szeliski, Richard, Verbin, Dor, Srinivasan, Pratul P., Mildenhall, Ben, Geiger, Andreas, Barron, Jonathan T., Hedman, Peter
Neural radiance fields enable state-of-the-art photorealistic view synthesis. However, existing radiance field representations are either too compute-intensive for real-time rendering or require too much memory to scale to large scenes. We present a
Externí odkaz:
http://arxiv.org/abs/2302.12249
Autor:
Reiser, Christian
We explore how observational and interventional causal discovery methods can be combined. A state-of-the-art observational causal discovery algorithm for time series capable of handling latent confounders and contemporaneous effects, called LPCMCI, i
Externí odkaz:
http://arxiv.org/abs/2212.02435
Autor:
Reiser, Christian
Reconstructing the causal relationships behind the phenomena we observe is a fundamental challenge in all areas of science. Discovering causal relationships through experiments is often infeasible, unethical, or expensive in complex systems. However,
Externí odkaz:
http://arxiv.org/abs/2209.03427
Predicting and Visualizing Daily Mood of People Using Tracking Data of Consumer Devices and Services
Autor:
Reiser, Christian
Users can easily export personal data from devices (e.g., weather station and fitness tracker) and services (e.g., screentime tracker and commits on GitHub) they use but struggle to gain valuable insights. To tackle this problem, we present the self-
Externí odkaz:
http://arxiv.org/abs/2202.03721
NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural radiance field to RGB images. However, NeRF requires querying a deep Multi-Layer Perceptron (MLP) millions of times, leading to slow rendering times, even on moder
Externí odkaz:
http://arxiv.org/abs/2103.13744
We consider the initial situation where a dataset has been over-partitioned into $k$ clusters and seek a domain independent way to merge those initial clusters. We identify the total variation distance (TVD) as suitable for this goal. By exploiting t
Externí odkaz:
http://arxiv.org/abs/1912.04022