Papers 3D Object Recognition
“3D Object Recognition” 태그가 달린 논문 100편 · 필터 해제
SlimEdge: Performance and Device Aware Distributed DNN Deployment on Resource-Constrained Edge Hardware
Distributed deep neural networks (DNNs) have become central to modern computer vision, yet their deployment on resource-constrained edge devices remains hindered by substantial parameter counts, computational demands, an…
3D Object RecognitionAugmenting cobots for sheet-metal SMEs with 3D object recognition and localisation
Due to high-mix-low-volume production, sheet-metal workshops today are challenged by small series and varying orders. As standard automation solutions tend to fall short, SMEs resort to repetitive manual labour impacting…
3D Object RecognitionHierarchical Abstraction Enables Human-Like 3D Object Recognition in Deep Learning Models
Both humans and deep learning models can recognize objects from 3D shapes depicted with sparse visual information, such as a set of points randomly sampled from the surfaces of 3D objects (termed a point cloud). Although…
3D Object RecognitionSASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
Recent advancements in deep learning have greatly enhanced 3D object recognition, but most models are limited to closed-set scenarios, unable to handle unknown samples in real-world applications. Open-set recognition (OS…
3D Object RecognitionObject RecognitionOpen Set LearningAligning Text, Images, and 3D Structure Token-by-Token
Creating machines capable of understanding the world in 3D is essential in assisting designers that build and edit 3D environments and robots navigating and interacting within a three-dimensional space. Inspired by advan…
3D Object RecognitionInstruction FollowingObject RecognitionQuestion AnsweringTopology-Guided Knowledge Distillation for Efficient Point Cloud Processing
Point cloud processing has gained significant attention due to its critical role in applications such as autonomous driving and 3D object recognition. However, deploying high-performance models like Point Transformer V3 …
3D Object RecognitionAutonomous DrivingKnowledge DistillationObject RecognitionLM-MCVT: A Lightweight Multi-modal Multi-view Convolutional-Vision Transformer Approach for 3D Object Recognition
In human-centered environments such as restaurants, homes, and warehouses, robots often face challenges in accurately recognizing 3D objects. These challenges stem from the complexity and variability of these environment…
3D Object RecognitionObjectObject RecognitionOcclusion-aware Text-Image-Point Cloud Pretraining for Open-World 3D Object Recognition
Recent open-world representation learning approaches have leveraged CLIP to enable zero-shot 3D object recognition. However, performance on real point clouds with occlusions still falls short due to the unrealistic pretr…
3D Object RecognitionObject RecognitionRepresentation LearningTargeted View-Invariant Adversarial Perturbations for 3D Object Recognition
Adversarial attacks pose significant challenges in 3D object recognition, especially in scenarios involving multi-view analysis where objects can be observed from varying angles. This paper introduces View-Invariant Adve…
3D Object RecognitionAdversarial RobustnessObject RecognitionImproving 3D Semi-supervised Learning by Effectively Utilizing All Unlabelled Data
Semi-supervised learning (SSL) has shown its effectiveness in learning effective 3D representation from a small amount of labelled data while utilizing large unlabelled data. Traditional semi-supervised approaches rely o…
3D Classification3D Object RecognitionFormula-Supervised Visual-Geometric Pre-training
Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object recognition have tended to process these mo…
3D Object Classification3D Object RecognitionObjectObject Recognition+1Beyond 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 obje…
3D Object RecognitionObjectObject Recognition3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data
3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor environments. It is critical for such systems to recognize and segment the 3D object instances that they encount…
3D Instance Segmentation3D Object RecognitionInstance SegmentationObject+3MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding
We introduce MM-Mixing, a multi-modal mixing alignment framework for 3D understanding. MM-Mixing applies mixing-based methods to multi-modal data, preserving and optimizing cross-modal connections while enhancing diversi…
3D Classification3D Object Recognition3D Shape RetrievalContrastive Learning+6Deep Models for Multi-View 3D Object Recognition: A Review
Human decision-making often relies on visual information from multiple perspectives or views. In contrast, machine learning-based object recognition utilizes information from a single image of the object. However, the in…
3D Classification3D Object RecognitionDecision MakingObject+2One Noise to Rule Them All: Multi-View Adversarial Attacks with Universal Perturbation
This paper presents a novel universal perturbation method for generating robust multi-view adversarial examples in 3D object recognition. Unlike conventional attacks limited to single views, our approach operates on mult…
3D Object RecognitionAllObjectObject RecognitionSUGAR: Pre-training 3D Visual Representations for Robotics
Learning generalizable visual representations from Internet data has yielded promising results for robotics. Yet, prevailing approaches focus on pre-training 2D representations, being sub-optimal to deal with occlusions …
3D Instance Segmentation3D Object RecognitionInstance SegmentationKnowledge Distillation+5Lifting Multi-View Detection and Tracking to the Bird's Eye View
Taking advantage of multi-view aggregation presents a promising solution to tackle challenges such as occlusion and missed detection in multi-object tracking and detection. Recent advancements in multi-view detection and…
3D Object RecognitionMulti-Object Trackingmulti-view detectionMultiview Detection+2FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects
In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data. Our goal is to predict the 3D shape, size, and 6D pose of objects within a single RGB-D image, op…
3D Object Recognition6D Pose EstimationObject RecognitionPose EstimationFine-grained 3D object recognition: an approach and experiments
Three-dimensional (3D) object recognition technology is being used as a core technology in advanced technologies such as autonomous driving of automobiles. There are two sets of approaches for 3D object recognition: (i) …
3D Object RecognitionAutonomous DrivingDeep LearningObject+1