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Tuan Anh Tran

2 accepted papers

2025

How Many Tokens Do 3D Point Cloud Transformer Architectures Really Need?

NeurIPS 2025poster

Recent advances in 3D point cloud transformers have led to state-of-the-art results in tasks such as semantic segmentation and reconstruction. However, these models typically rely on dense token representations, incurring high computational and memory costs during training and inference. In this wor…

Cited by 0SourcecodeScholar
2022

Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes

ECCV 2022poster

"Current 3D segmentation methods heavily rely on large-scale point-cloud datasets, which are notoriously laborious to annotate. Few attempts have been made to circumvent the need for dense per-point annotations. In this work, we look at weakly-supervised 3D semantic instance segmentation. The key id…

Cited by 76SourcePDFScholar