Fri, Nov 11, 2022 @ 11:00 AM - 12:30 PM
Phd Candidate Name; Qiangeng Xu
Committee Chair: Prof. Ulrich Neumann from the CS department.
Committee Member: Prof. Jernej Barbic from the CS department.
Committee Member: Prof. Justin Haldar from the EE department.
Topic: Point-based Neural Radiance Fields
3D scene reconstruction is one of the core problems for 3D understanding. Reconstructing 3D scenes from 2D Images are among the hardest but most useful tasks for autonomous agents. In contrast to rendering, which obtains 2D images from 3D scenes, this task is a reverse rendering problem and can be solved by optimizing a differentiable rendering model with backpropagation.
The current state-of-the-art reverse rendering model utilizes implicit functions such as neural radiance fields (NeRF) to represent the 3D scene. However, it can only be optimized per-scene and not scalable. On the other hand, deep multi-view stereo methods can quickly reconstruct scene geometry via direct network inference. We propose point-based neural radiance fields that combine explicit (points) and Implicit (neural radiance fields) representation by using neural 3D point clouds, with associated neural features, to model a radiance field. Our model has potential to be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline.
Zoom link: https://usc.zoom.us/j/7125769726
WebCast Link: https://usc.zoom.us/j/7125769726
Audiences: Everyone Is Invited
Contact: Lizsl De Leon