13 papers
White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation
Jiyun Im, SuBeen Lee, Miso Lee +1
Few-shot 3D Point Cloud Semantic Segmentation (FS-PCS) aims to predict per-point labels for an unlabeled point cloud, given only a few labeled examples. To extract representations…
Selective Synergistic Learning for Video Object-Centric Learning
WonJun Moon, Jae-Pil Heo
Typical video object-centric learning (VOCL) approaches employ slot-based frameworks that rely on reconstruction-driven encoder-decoder architectures, where learning is mediated by…
Temporally Consistent Long-Term Memory for 3D Single Object Tracking
Jaejoon Yoo, SuBeen Lee, Yerim Jeon +2
3D Single Object Tracking (3D-SOT) aims to localize a target object across a sequence of LiDAR point clouds, given its 3D bounding box in the first frame. Recent methods have adopt…
Looking Beyond the Window: Global-Local Aligned CLIP for Training-free Open-Vocabulary Semantic Segmentation
ByeongCheol Lee, Hyun Seok Seong, Sangeek Hyun +3
A sliding-window inference strategy is commonly adopted in recent training-free open-vocabulary semantic segmentation methods to overcome limitation of the CLIP in processing high-…
Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning
WonJun Moon, Hyun Seok Seong, Jae-Pil Heo
Video Object-Centric Learning seeks to decompose raw videos into a small set of object slots, but existing slot-attention models often suffer from severe over-fragmentation. This i…
Masking Matters: Unlocking the Spatial Reasoning Capabilities of LLMs for 3D Scene-Language Understanding
Yerim Jeon, Miso Lee, WonJun Moon +1
Recent advances in 3D scene-language understanding have leveraged Large Language Models (LLMs) for 3D reasoning by transferring their general reasoning ability to 3D multi-modal co…