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Found 2 papers, 1 papers with code
GVGEN: Text-to-3D Generation with Volumetric Representation
To simplify the generation of GaussianVolume and empower the model to generate instances with detailed 3D geometry, we propose a coarse-to-fine pipeline.
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
In this paper, we introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation, thereby establishing a pathway to 3D foundational models.
Ranked #1 on
3D Semantic Segmentation
on ScanNet++
(using extra training data)
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