24 citations · 59 across the 11 of their papers we have counts for
11 papers · 1 filter
ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
Chandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner +1
We present ScanNet++, a large-scale dataset that couples together capture of high-quality and commodity-level geometry and color of indoor scenes. Each scene is captured with a hig…
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning
Bolivar Solarte, Chin-Hsuan Wu, Yueh-Cheng Liu +2
We present 360-MLC, a self-training method based on multi-view layout consistency for finetuning monocular room-layout models using unlabeled 360-images only. This can be valuable…
360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation
Bolivar Solarte, Yueh-Cheng Liu, Chin-Hsuan Wu +2
We present 360-DFPE, a sequential floor plan estimation method that directly takes 360-images as input without relying on active sensors or 3D information. Our approach leverages a…
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data
Guan-Rong Lu, Yueh-Cheng Liu, Tung-I Chen +3
Anomaly awareness is an essential capability for safety-critical applications such as autonomous driving. While recent progress of robotics and computer vision has enabled anomaly…
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation
Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang +4
Despite the success of deep learning on supervised point cloud semantic segmentation, obtaining large-scale point-by-point manual annotations is still a significant challenge. To r…
Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining
Yueh-Cheng Liu, Yu-Kai Huang, Hung-Yueh Chiang +5
Most 3D neural networks are trained from scratch owing to the lack of large-scale labeled 3D datasets. In this paper, we present a novel 3D pretraining method by leveraging 2D netw…
: Learnable Sparse Signal Superdensity for Guided Depth Estimation
Yu-Kai Huang, Yueh-Cheng Liu, Tsung-Han Wu +5
Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality. While sparse signal, e.g., LiDAR and Radar, has been le…
Dual-Awareness Attention for Few-Shot Object Detection
Tung-I Chen, Yueh-Cheng Liu, Hung-Ting Su +5
While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existin…
Expanding Sparse Guidance for Stereo Matching
Yu-Kai Huang, Yueh-Cheng Liu, Tsung-Han Wu +2
The performance of image based stereo estimation suffers from lighting variations, repetitive patterns and homogeneous appearance. Moreover, to achieve good performance, stereo sup…
A Unified Point-Based Framework for 3D Segmentation
Hung-Yueh Chiang, Yen-Liang Lin, Yueh-Cheng Liu +1
3D point cloud segmentation remains challenging for structureless and textureless regions. We present a new unified point-based framework for 3D point cloud segmentation that effec…
Indoor Depth Completion with Boundary Consistency and Self-Attention
Yu-Kai Huang, Tsung-Han Wu, Yueh-Cheng Liu +1
Depth estimation features are helpful for 3D recognition. Commodity-grade depth cameras are able to capture depth and color image in real-time. However, glossy, transparent or dist…