DOCTR: Disentangled Object-Centric Transformer for Point Scene Understanding

• URL: http://arxiv.org/abs/2403.16431v1
• Date: Mon, 25 Mar 2024 05:22:34 GMT
• Title: DOCTR: Disentangled Object-Centric Transformer for Point Scene Understanding
• Authors: Xiaoxuan Yu, Hao Wang, Weiming Li, Qiang Wang, Soonyong Cho, Younghun Sung,
• Abstract summary: Point scene understanding is a challenging task to process real-world scene point cloud. Recent state-of-the-art method first segments each object and then processes them independently with multiple stages for the different sub-tasks. We propose a novel Disentangled Object-Centric TRansformer (DOCTR) that explores object-centric representation.
• Score: 7.470587868134298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point scene understanding is a challenging task to process real-world scene point cloud, which aims at segmenting each object, estimating its pose, and reconstructing its mesh simultaneously. Recent state-of-the-art method first segments each object and then processes them independently with multiple stages for the different sub-tasks. This leads to a complex pipeline to optimize and makes it hard to leverage the relationship constraints between multiple objects. In this work, we propose a novel Disentangled Object-Centric TRansformer (DOCTR) that explores object-centric representation to facilitate learning with multiple objects for the multiple sub-tasks in a unified manner. Each object is represented as a query, and a Transformer decoder is adapted to iteratively optimize all the queries involving their relationship. In particular, we introduce a semantic-geometry disentangled query (SGDQ) design that enables the query features to attend separately to semantic information and geometric information relevant to the corresponding sub-tasks. A hybrid bipartite matching module is employed to well use the supervisions from all the sub-tasks during training. Qualitative and quantitative experimental results demonstrate that our method achieves state-of-the-art performance on the challenging ScanNet dataset. Code is available at https://github.com/SAITPublic/DOCTR.

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