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I-Bin Liao

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AAAI Conference 2025 Conference Paper

Memory-Augmented Re-Completion for 3D Semantic Scene Completion

Yu-Wen Tseng
Sheng-Ping Yang
Jhih-Ciang Wu
I-Bin Liao
Yung-Hui Li
Hong-Han Shuai
Wen-Huang Cheng

Semantic Scene Completion (SSC) aims to reconstruct a 3D voxel representation occupied by semantic classes based on ordinary inputs such as 2D RGB images, depth maps, or point clouds. Given the cost-effective and promising applications in autonomous driving, camera-based SSC has attracted considerable attention to developing various approaches. However, current methods mainly focus on precise 2D-to-3D projection while overlooking the challenge of completing invisible regions, leading to numerous false negatives and suboptimal SSC performance. To address this issue, we propose a novel architecture, Memory-augmented Re-completion (MARE), designed to enhance completion capability. Our MARE model encapsulates regional relationships by incorporating a memory bank that stores vital region-tokens while two protocols concerning diversity and age are adopted to optimize the bank adversarially. Additionally, we introduce a Re-completion pipeline incorporated with an Information Spreading module to progressively complete the invisible regions while bridging the scale gap between region-level and voxel-level information. Extensive experiments conducted on the SSCBench-KITTI-360 and SemanticKITTI datasets validate the effectiveness of our approach.

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IROS Conference 2025 Conference Paper

PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model

Shang-Ching Liu
Van Nhiem Tran
Wenkai Chen
Wei-Lun Cheng
Yen-Lin Huang
I-Bin Liao
Yung-Hui Li
Jianwei Zhang

Affordance understanding, the task of identifying actionable regions on 3D objects, plays a vital role in allowing robotic systems to engage with and operate within the physical world. Although Visual Language Models (VLMs) have excelled in high-level reasoning and long-horizon planning for robotic manipulation, they still fall short in grasping the nuanced physical properties required for effective human-robot interaction. In this paper, we introduce PAVLM (Point cloud Affordance Vision-Language Model), an innovative framework that utilizes the extensive multimodal knowledge embedded in pre-trained language models to enhance 3D affordance understanding of point cloud. PAVLM is an approach to integrates a geometric-guided propagation module with hidden embeddings from large language models (LLMs) to enrich visual semantics. On the language side, we prompt Llama-3. 1 models to generate refined context-aware text, augmenting the instructional input with deeper semantic cues. Experimental results on the 3D-AffordanceNet benchmark demonstrate that PAVLM outperforms baseline methods for both full and partial point clouds, particularly excelling in its generalization to novel open-world affordance tasks of 3D objects. For more information, visit our project site: pavlm-source.github.io.

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