  Exploring scientific datasets with billions of samples in real-time
visualization presents a challenge - balancing high-fidelity rendering with
speed. This work introduces a novel renderer - Neural Accelerated Renderer
(NAR), that uses the neural deferred rendering framework to visualize
large-scale scientific point cloud data. NAR augments a real-time point cloud
rendering pipeline with high-quality neural post-processing, making the
approach ideal for interactive visualization at scale. Specifically, we train a
neural network to learn the point cloud geometry from a high-performance
multi-stream rasterizer and capture the desired postprocessing effects from a
conventional high-quality renderer. We demonstrate the effectiveness of NAR by
visualizing complex multidimensional Lagrangian flow fields and photometric
scans of a large terrain and compare the renderings against the
state-of-the-art high-quality renderers. Through extensive evaluation, we
demonstrate that NAR prioritizes speed and scalability while retaining high
visual fidelity. We achieve competitive frame rates of $>$ 126 fps for
interactive rendering of $>$ 350M points (i.e., an effective throughput of $>$
44 billion points per second) using $\sim$12 GB of memory on RTX 2080 Ti GPU.
Furthermore, we show that NAR is generalizable across different point clouds
with similar visualization needs and the desired post-processing effects could
be obtained with substantial high quality even at lower resolutions of the
original point cloud, further reducing the memory requirements.
