SeqAfford: Sequential 3D affordance reasoning via Multimodal Large Language Model
Keywords: Multimodal Large Language Model, 3D Affordance Reasoning
TL;DR: This paper introduces Sequential 3D Affordance Reasoning, a task to handle complex user instructions; and presents the SeqAfford model and a 180K instruction-point cloud dataset for enhanced affordance segmentation and reasoning.
Abstract: 3D affordance segmentation aims to link human instructions to touchable regions of 3D objects for embodied manipulations. Existing efforts typically adhere to single-object, single-affordance paradigms, where each affordance type or explicit instruction strictly corresponds to a specific affordance region and are unable to handle long-horizon tasks. Such a paradigm cannot actively reason about complex user intentions that often imply sequential affordances with multiple objects involved. In this paper, we introduce the Sequential 3D Affordance Reasoning task, which extends the traditional paradigm by reasoning from cumbersome user intentions and then decomposing them into a sequence of segmentations. Toward this, we construct the first instruction-based affordance segmentation benchmark that includes reasoning over both single and sequential affordances, comprising 180K instruction-point cloud pairs. Based on the benchmark, we propose our model, SeqAfford, to unlock the 3D multi-modal large language model with additional affordance segmentation abilities, which ensures reasoning with world knowledge and fine-grained affordance grounding in a cohesive framework. We further introduce a multi-granular language-point integration module to endow 3D dense prediction. Extensive experimental evaluations show that our model excels over well-established methods and exhibits open-world generalization with sequential reasoning abilities.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 6165
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