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Found 18 papers, 7 papers with code
Visual SLAM with Graph-Cut Optimized Multi-Plane Reconstruction
This paper presents a semantic planar SLAM system that improves pose estimation and mapping using cues from an instance planar segmentation network.
PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Image
Piece-wise 3D planar reconstruction provides holistic scene understanding of man-made environments, especially for indoor scenarios.
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Dense methods also improved pose estimation in the presence of occlusion.
IKEA Object State Dataset: A 6DoF object pose estimation dataset and benchmark for multi-state assembly objects
Utilizing 6DoF(Degrees of Freedom) pose information of an object and its components is critical for object state detection tasks.
U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target.
Single Frame Semantic Segmentation Using Multi-Modal Spherical Images
In recent years, the research community has shown a lot of interest to panoramic images that offer a 360-degree directional perspective.
Ranked #1 on
Semantic Segmentation
on Matterport3D
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image.
A survey on applications of augmented, mixed and virtual reality for nature and environment
Augmented reality (AR), virtual reality (VR) and mixed reality (MR) are technologies of great potential due to the engaging and enriching experiences they are capable of providing.
TGA: Two-level Group Attention for Assembly State Detection
Assembly state detection, i. e., object state detection, has a critical meaning in computer vision tasks, especially in AR assisted assembly.
TICaM: A Time-of-flight In-car Cabin Monitoring Dataset
We present TICaM, a Time-of-flight In-car Cabin Monitoring dataset for vehicle interior monitoring using a single wide-angle depth camera.
PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN
Our model employs a variant of a fast single-stage CNN architecture to segment plane instances.
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization
We therefore perform a case study of the deployment of two representative object detection networks on an edge AI platform.
TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera
Most existing surveillance video datasets provide either grayscale or RGB videos.
Unsupervised Anomaly Detection from Time-of-Flight Depth Images
Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data.
Ranked #1 on
Unsupervised Anomaly Detection
on TIMo
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection
Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage methods typically suffer from overfitting and are incapable of explicitly encapsulating the geometric relation between depth and object bounding box.
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion
Radars and cameras belong to the most frequently used sensors for advanced driver assistance systems and automated driving research.
Achieving RGB-D level Segmentation Performance from a Single ToF Camera
Depth is a very important modality in computer vision, typically used as complementary information to RGB, provided by RGB-D cameras.
Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View
By exploiting complementary sensor information, radar and camera fusion systems have the potential to provide a highly robust and reliable perception system for advanced driver assistance systems and automated driving functions.
