Search Results for author:
Found 10 papers, 3 papers with code
StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering
Consequently, rendering performance is nearly doubled, making our approach 1. 6x faster than the original Gaussian Splatting, with a 50% reduction in memory requirements.
LAENeRF: Local Appearance Editing for Neural Radiance Fields
We address these limitations with LAENeRF, a unified framework for photorealistic and non-photorealistic appearance editing of NeRFs.
Analyzing the Internals of Neural Radiance Fields
Modern Neural Radiance Fields (NeRFs) learn a mapping from position to volumetric density leveraging proposal network samplers.
MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos with Spherical Buffers and Padded Convolutions
Moving cameras add new challenges in how to fuse newly unveiled image regions with already processed regions efficiently to minimize the update rate - without increasing memory overhead and without knowing the camera extrinsics of future frames.
Gradient-based Weight Density Balancing for Robust Dynamic Sparse Training
Typically, the weights are redistributed after a predefined number of weight updates, removing a fraction of the parameters of each layer and inserting them at different locations in the same layers.
MotionDeltaCNN: Sparse CNN Inference of Frame Differences in Moving Camera Videos
Moving cameras add new challenges in how to fuse newly unveiled image regions with already processed regions efficiently to minimize the update rate - without increasing memory overhead and without knowing the camera extrinsics of future frames.
AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields
However, rendering images with this new paradigm is slow due to the fact that an accurate quadrature of the volume rendering equation requires a large number of samples for each ray.
DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos
With DeltaCNN, we present a sparse convolutional neural network framework that enables sparse frame-by-frame updates to accelerate video inference in practice.
DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks
In this work, we bring compact neural representations closer to practical rendering of synthetic content in real-time applications, such as games and virtual reality.
UNOC: Understanding Occlusion for Embodied Presence in Virtual Reality
Tracking body and hand motions in the 3D space is essential for social and self-presence in augmented and virtual environments.
