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🚀 New paper: ConeGS Error-Guided Densification Using Pixel Cones. We improve 3D Gaussian Splatting by placing Gaussians where they matter most: ConeGS adds primitives along pixel-view cones guided by image error, boosting quality with fewer Gaussians. baranowskibrt.github.io/conegs/
Congratulations to our PhD student @takerumiyato.bsky.social for winning the Google PhD Fellowship in the category "Machine Learning and ML Foundations". Takeru is pioneering new neural architectures that improve generalization and efficiency. Check out his research: takerum.github.io
🚀Excited to share our recent work on test-time scaling for feed-forward Gaussian splatting:
we learn a recurrent model ReSplat that is able to iteratively improve the reconstruction quality in a feed-forward manner!
haofeixu.github.io/resplat/
Scholar Inbox in conference mode is soooo useful. Get your poster session recommendations with the poster number and access the paper....
Thanks @andreasgeiger.bsky.social and team!
I am so happy and excited that this project got funded!
Yes
EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis
Sheng Miao, Sijin Li, Pan Wang, Dongfeng Bai, Bingbing Liu, Yue Wang, @andreasgeiger.bsky.social, @yiyiliao.bsky.social
tl;dr: EVolSplat journal version->dynamic scene
arxiv.org/abs/2601.15951