Cody Reading — accepted AI-conference papers

2024

BANF: Band-Limited Neural Fields for Levels of Detail Reconstruction

Akhmedkhan Shabanov, Shrisudhan Govindarajan, Cody Reading, Lily Goli, Daniel Rebain, Kwang Moo Yi +1

CVPR 2024poster

Largely due to their implicit nature neural fields lack a direct mechanism for filtering as Fourier analysis from discrete signal processing is not directly applicable to these representations. Effective filtering of neural fields is critical to enable level-of-detail processing in downstream applic…

Cited by 3 Source ↗PDF ↗Scholar ↗

2024

Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields

Lily Goli, Cody Reading, Silvia Sellán, Alec Jacobson, Andrea Tagliasacchi

CVPR 2024highlight

Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation but learning from multiview images faces inherent uncertainties. Current methods to quantify them are either heuristic or computationally demanding. We introduce BayesRays a post-hoc framework…

Cited by 65 Source ↗PDF ↗code ↗Scholar ↗

2021

Categorical Depth Distribution Network for Monocular 3D Object Detection

Cody Reading, Ali Harakeh, Julia Chae, Steven L. Waslander

CVPR 2021poster

Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a solution with simple configuration compared to typical multi-sensor systems. The main challenge in monocular 3D detection lies in accurately predicting object depth, which must be inferred from object and scene…

Cited by 621 PDF ↗code ↗Scholar ↗