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Progressively Optimized Local Radiance Fields for Robust View Synthesis paper page: present an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video. The task poses two core challenges. First, most existing radiance field reconstruction approaches rely on accurate pre-estimated camera poses from...

140,616 views • 3 years ago •via X (Twitter)

10 Comments

Jia-Bin Huang's profile picture
Jia-Bin Huang3 years ago

Code available (MIT license 🔥): High-res explainer video here:

Shawn Fumo's profile picture
Shawn Fumo3 years ago

FYI, this page has more videos and links (including to code):

GUST's profile picture
GUST3 years ago

Is there any kind of test open to the public foreseen?

Yann Masoch's profile picture
Yann Masoch3 years ago

I love it!

Aitherious One. 💫's profile picture
Aitherious One. 💫3 years ago

Would it be possible to have us moving in & out of the tree line & go forward/ backward up & down like a dragonfly? I would prefer it didn’t feel like a video game always going into a place that I didn’t want to go. Maybe I want to slow down. & look over there at some thing.🤔

Aitherious One. 💫's profile picture
Aitherious One. 💫3 years ago

💫 You made me feel like I was a dragonfly flying through the wilderness! Going through the corridors & stairway is most unique. & if on campus you won’t have to ask for directions if you can pre-view,..🤔 Immersive is the word I will think of when viewing AI.

Aitherious One. 💫's profile picture
Aitherious One. 💫3 years ago

It’s really beautiful! 💜🤗💫

Mustafa Basrai's profile picture
Mustafa Basrai3 years ago

@SaveToNotion

kps's profile picture
kps3 years ago

This is brilliant.

balala's profile picture
balala3 years ago

Looks like you can get a more steady rendered video form the original video with much jitter. But what are the differences between the result video with a original video with high fps and smoothing moving?

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Davide Scaramuzza

10,839 views • 3 months ago

FAU Erlangen-Nürnberg presents TRIPS Trilinear Point Splatting for Real-Time Radiance Field Rendering paper page: Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [Kerbl and Kopanas et al. 2023] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [R\"uckert et al. 2022] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen-space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole-free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions. Our evaluation demonstrate that TRIPS surpasses existing state-of-the-art methods in terms of rendering quality while maintaining a real-time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto-exposed footage.

AK

45,458 views • 2 years ago