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Found 5 papers, 2 papers with code
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.
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.
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.
Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks
Furthermore, we train the network with a novel embedding loss based on cosine similarities, such that the network predicts unique embeddings for every instance throughout videos.
