Beyond Viewpoint: Robust 3D Object Recognition under Arbitrary Views through Joint Multi-Part Representation
Existing view-based methods excel at recognizing 3D objects from predefined viewpoints, but their exploration of recognition under arbitrary views is limited. This is a challenging and realistic setting because each object has different viewpoint positions and quantities, and their poses are not aligned. However, most view-based methods, which aggregate multiple view features to obtain a global feature representation, hard to address 3D object recognition under arbitrary views. Due to the unaligned inputs from arbitrary views, it is challenging to robustly aggregate features, leading to performance degradation. In this paper, we introduce a novel Part-aware Network (PANet), which is a part-based representation, to address these issues. This part-based representation aims to localize and understand different parts of 3D objects, such as airplane wings and tails. It has properties such as viewpoint invariance and rotation robustness, which give it an advantage in addressing the 3D object recognition problem under arbitrary views. Our results on benchmark datasets clearly demonstrate that our proposed method outperforms existing view-based aggregation baselines for the task of 3D object recognition under arbitrary views, even surpassing most fixed viewpoint methods.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Object RecognitionObjectObject RecognitionSimilar Papers 제목 키워드 기반
Learning Canonical View Representation for 3D Shape Recognition with Arbitrary Views
In this paper, we focus on recognizing 3D shapes from arbitrary views, i.e., arbitrary numbers and positions of viewpoints. It is a challenging and realistic setting for view-based 3D shape recognition. We propose a cano…
3D Shape Recognition3D Shape RepresentationRecognizing Objects From Any View With Object and Viewer-Centered Representations
In this paper, we tackle an important task in computer vision: any view object recognition. In both training and testing, for each object instance, we are only given its 2D image viewed from an unknown angle. We propose …
ObjectObject RecognitionA Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition
Current researches of action recognition mainly focus on single-view and multi-view recognition, which can hardly satisfies the requirements of human-robot interaction (HRI) applications to recognize actions from arbitra…
Action AnalysisAction RecognitionSkeleton Based Action RecognitionTemporal Action LocalizationBowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object …
Data AugmentationMulti-Task LearningNovel View SynthesisObjectARGUS: Aligning Robot Scene Geometry Under Shifting Views with Large 3D Vision Models
Large-scale visuomotor policies have demonstrated impressive performance across a wide range of robot manipulation tasks. However, despite this success, manipulation polices often entangle scene geometry with the corresp…
Robot Manipulation