Papers Amodal Instance Segmentation
“Amodal Instance Segmentation” 태그가 달린 논문 26편 · 필터 해제
Learnable Shape Prototypes with Occlusion-Geometry-Guided Injection for Amodal Instance Segmentation
Amodal instance segmentation aims to predict the complete object mask including occluded regions that lack pixel-level observations and must be inferred with the aid of shape priors. Existing methods acquire shape priors…
Amodal Instance SegmentationGDA-YOLO11: Amodal Instance Segmentation for Occlusion-Robust Robotic Fruit Harvesting
Occlusion remains a critical challenge in robotic fruit harvesting, as undetected or inaccurately localised fruits often results in substantial crop losses. To mitigate this issue, we propose a harvesting framework using…
Amodal Instance SegmentationSegment Anything, Even Occluded
Amodal instance segmentation, which aims to detect and segment both visible and invisible parts of objects in images, plays a crucial role in various applications including autonomous driving, robotic manipulation, and s…
Amodal Instance SegmentationAutonomous DrivingDecoderInstance Segmentation+3Amodal Instance Segmentation with Diffusion Shape Prior Estimation
Amodal Instance Segmentation (AIS) presents an intriguing challenge, including the segmentation prediction of both visible and occluded parts of objects within images. Previous methods have often relied on shape prior in…
Amodal Instance SegmentationInstance SegmentationObjectPrediction+2Foundation Models for Amodal Video Instance Segmentation in Automated Driving
In this work, we study amodal video instance segmentation for automated driving. Previous works perform amodal video instance segmentation relying on methods trained on entirely labeled video data with techniques borrowe…
Amodal Instance SegmentationInstance SegmentationPoint TrackingSegmentation+2ShapeFormer: Shape Prior Visible-to-Amodal Transformer-based Amodal Instance Segmentation
Amodal Instance Segmentation (AIS) presents a challenging task as it involves predicting both visible and occluded parts of objects within images. Existing AIS methods rely on a bidirectional approach, encompassing both …
Amodal Instance SegmentationInstance SegmentationSemantic SegmentationOBSeg: Accurate and Fast Instance Segmentation Framework Using Segmentation Foundation Models with Oriented Bounding Box Prompts
Instance segmentation in remote sensing images is a long-standing challenge. Since horizontal bounding boxes introduce many interference objects, oriented bounding boxes (OBBs) are usually used for instance identificatio…
Amodal Instance SegmentationInstance SegmentationKnowledge DistillationObject+2BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion
Perceiving the complete shape of occluded objects is essential for human and machine intelligence. While the amodal segmentation task is to predict the complete mask of partially occluded objects, it is time-consuming an…
Amodal Instance SegmentationSegmentationGIN: Generative INvariant Shape Prior for Amodal Instance Segmentation
Amodal instance segmentation (AIS) predicts the complete shape of the occluded object, including both visible and occluded regions. Because visual clues are lacking, the occluded region is difficult to segment accurately…
Amodal Instance SegmentationInstance SegmentationSegmentationSemantic SegmentationSynTable: A Synthetic Data Generation Pipeline for Unseen Object Amodal Instance Segmentation of Cluttered Tabletop Scenes
In this work, we present SynTable, a unified and flexible Python-based dataset generator built using NVIDIA's Isaac Sim Replicator Composer for generating high-quality synthetic datasets for unseen object amodal instance…
Amodal Instance SegmentationDataset GenerationInstance SegmentationSemantic Segmentation+1OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation
The Amodal Instance Segmentation (AIS) task aims to infer the complete mask of occluded instance. Under many circumstances, existing methods treat occluded objects as unoccluded ones, and vice versa, leading to inaccurat…
Amodal Instance SegmentationInstance SegmentationSegmentationSemantic SegmentationAmodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark
Images of realistic scenes often contain intra-class objects that are heavily occluded from each other, making the amodal perception task that requires parsing the occluded parts of the objects challenging. Although impo…
Amodal Instance SegmentationInstance SegmentationRobotic GraspingSemantic SegmentationMUVA: A New Large-Scale Benchmark for Multi-View Amodal Instance Segmentation in the Shopping Scenario
Amodal Instance Segmentation (AIS) endeavors to accurately deduce complete object shapes that are partially or fully occluded. However, the inherent ill-posed nature of single-view datasets poses challenges in determ…
Amodal Instance SegmentationInstance SegmentationSegmentationSemantic SegmentationAISFormer: Amodal Instance Segmentation with Transformer
Amodal Instance Segmentation (AIS) aims to segment the region of both visible and possible occluded parts of an object instance. While Mask R-CNN-based AIS approaches have shown promising results, they are unable to mode…
Amodal Instance SegmentationDecoderInstance SegmentationSegmentation+12D Amodal Instance Segmentation Guided by 3D Shape Prior
Amodal instance segmentation aims to predict the complete mask of the occluded instance, including both visible and invisible regions. Existing 2D AIS methods learn and predict the complete silhouettes of target instance…
3D ReconstructionAmodal Instance SegmentationInstance SegmentationSegmentation+1WALT: Watch and Learn 2D Amodal Representation From Time-Lapse Imagery
Current methods for object detection, segmentation, and tracking fail in the presence of severe occlusions in busy urban environments. Labeled real data of occlusions is scarce (even in large datasets) and synthetic …
2D Object Detection4kAmodal Instance SegmentationAmodal Tracking+5Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment. Although previous works achieved encouraging results, they were limited to segmenting the only visible regio…
Amodal Instance SegmentationInstance SegmentationObjectSegmentation+2A Weakly Supervised Amodal Segmenter with Boundary Uncertainty Estimation
This paper addresses weakly supervised amodal instance segmentation, where the goal is to segment both visible and occluded (amodal) object parts, while training provides only ground-truth visible (modal) segmentations. …
Amodal Instance SegmentationInstance SegmentationSegmentationSemantic SegmentationDeep Occlusion-Aware Instance Segmentation with Overlapping BiLayers
Segmenting highly-overlapping objects is challenging, because typically no distinction is made between real object contours and occlusion boundaries. Unlike previous two-stage instance segmentation methods, we model imag…
Amodal Instance SegmentationBoundary DetectionInstance SegmentationObject Detection+3Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation
Blastomere instance segmentation is important for analyzing embryos' abnormality. To measure the accurate shapes and sizes of blastomeres, their amodal segmentation is necessary. Amodal instance segmentation aims to reco…
Amodal Instance SegmentationInstance SegmentationObjectSegmentation+1