実世界におけるカテゴリレベル関節物体ポーズ推定に向けて
Towards Real-World Category-level Articulation Pose Estimation
複数の関節物体が共存し関節構造も多様な実環境向けにCAPERタスクを定義し、大規模混合現実データセットとRGB-D入力から複数物体の部位ポーズを一度に推定するReArtNOCSを提案した論文。
著者: Liu Liu, Han Xue, Wenqiang Xu, Haoyuan Fu, Cewu Lu
分類: cs.CV, cs.RO
原文アブストラクト
Human life is populated with articulated objects. Current Category-level Articulation Pose Estimation (CAPE) methods are studied under the single-instance setting with a fixed kinematic structure for each category. Considering these limitations, we reform this problem setting for real-world environments and suggest a CAPE-Real (CAPER) task setting. This setting allows varied kinematic structures within a semantic category, and multiple instances to co-exist in an observation of real world. To support this task, we build an articulated model repository ReArt-48 and present an efficient dataset generation pipeline, which contains Fast Articulated Object Modeling (FAOM) and Semi-Authentic MixEd Reality Technique (SAMERT). Accompanying the pipeline, we build a large-scale mixed reality dataset ReArtMix and a real world dataset ReArtVal. We also propose an effective framework ReArtNOCS that exploits RGB-D input to estimate part-level pose for multiple instances in a single forward pass. Extensive experiments demonstrate that the proposed ReArtNOCS can achieve good performance on both CAPER and CAPE settings. We believe it could serve as a strong baseline for future research on the CAPER task.
