Papers Object Detection In Indoor Scenes
“Object Detection In Indoor Scenes” 태그가 달린 논문 10편 · 필터 해제
Lookalike3D: Seeing Double in 3D
3D object understanding and generation methods produce impressive results, yet they often overlook a pervasive source of information in real-world scenes: repeated objects. We introduce the task of lookalike object detec…
Object Detection In Indoor Scenes3D Object ReconstructionUnified Object Detector for Different Modalities based on Vision Transformers
Traditional systems typically require different models for processing different modalities, such as one model for RGB images and another for depth images. Recent research has demonstrated that a single model for one moda…
Object Detection In Indoor ScenesTransfer LearningsimCrossTrans: A Simple Cross-Modality Transfer Learning for Object Detection with ConvNets or Vision Transformers
Transfer learning is widely used in computer vision (CV), natural language processing (NLP) and achieves great success. Most transfer learning systems are based on the same modality (e.g. RGB image in CV and text in NLP)…
object-detectionObject DetectionObject Detection In Indoor ScenesTransfer LearningFrustum VoxNet for 3D object detection from RGB-D or Depth images
Recently, there have been a plethora of classification and detection systems from RGB as well as 3D images. In this work, we describe a new 3D object detection system from an RGB-D or depth-only point cloud. Our system f…
3D Object DetectionObject DetectionObject Detection In Indoor ScenesThe effect of changing training data on a fixed deep learning detection model
Within the lack of accurate data, for some computer vision applications, researchers usually use other pictures collected from different sources for the training. To know the effect of these added data, we compare the de…
Object DetectionObject Detection In Indoor ScenesFaster Bounding Box Annotation for Object Detection in Indoor Scenes
This paper proposes an approach for rapid bounding box annotation for object detection datasets. The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step pro…
Objectobject-detectionObject DetectionObject Detection In Indoor ScenesFrustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we direct…
3D Object DetectionObjectObject DetectionObject Detection In Indoor Scenes+22D-Driven 3D Object Detection in RGB-D Images
In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-t…
2D Object Detection3D Object DetectionObjectobject-detection+2Synthesizing Training Data for Object Detection in Indoor Scenes
Detection of objects in cluttered indoor environments is one of the key enabling functionalities for service robots. The best performing object detection approaches in computer vision exploit deep Convolutional Neural Ne…
Objectobject-detectionObject DetectionObject Detection In Indoor ScenesLearning Rich Features from RGB-D Images for Object Detection and Segmentation
In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle …
Instance SegmentationObjectobject-detectionObject Detection+3