An explicit model using learned 3D Gaussians for volume compression encodes geometry explicitly and outperforms implicit neural representations on unstructured volumes with faster training.
Nerfacc: Efficient sampling accelerates nerfs
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
BulletGen enhances 4D dynamic scene reconstruction from monocular videos by supervising Gaussian optimization with diffusion-generated frames aligned at a bullet-time step, achieving SOTA on novel-view synthesis and tracking.
A disentangled mesh-vertex neural radiance field enables mesh-guided geometry edits, texture swap/fill/paint, and semantic-guided edits with claimed efficiency and quality gains.
Develops a game-theoretic estimator for true information gain in active perception that achieves sub-linear regret and shows average gains of 7% information gain and 42% error reduction across simulated and real robot experiments.
citing papers explorer
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Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
An explicit model using learned 3D Gaussians for volume compression encodes geometry explicitly and outperforms implicit neural representations on unstructured volumes with faster training.
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BulletGen: Improving 4D Reconstruction with Bullet-Time Generation
BulletGen enhances 4D dynamic scene reconstruction from monocular videos by supervising Gaussian optimization with diffusion-generated frames aligned at a bullet-time step, achieving SOTA on novel-view synthesis and tracking.
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NeuMesh++: Towards Versatile and Efficient Volumetric Editing with Disentangled Neural Mesh-based Implicit Field
A disentangled mesh-vertex neural radiance field enables mesh-guided geometry edits, texture swap/fill/paint, and semantic-guided edits with claimed efficiency and quality gains.
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An Active Perception Game for Robust Exploration
Develops a game-theoretic estimator for true information gain in active perception that achieves sub-linear regret and shows average gains of 7% information gain and 42% error reduction across simulated and real robot experiments.