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Found 14 papers, 7 papers with code
ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
Each scene is captured with a high-end laser scanner at sub-millimeter resolution, along with registered 33-megapixel images from a DSLR camera, and RGB-D streams from an iPhone.
360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning
In addition, since ground truth annotations are not available during training nor in testing, we leverage the entropy information in multiple layout estimations as a quantitative metric to measure the geometry consistency of the scene, allowing us to evaluate any layout estimator for hyper-parameter tuning, including model selection without ground truth annotations.
360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation
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.
Anomaly-Aware Semantic Segmentation by Leveraging Synthetic-Unknown Data
We design a new Masked Gradient Update (MGU) module to generate auxiliary data along the boundary of in-distribution data points.
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation
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.
S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation
Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality.
Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining
Most 3D neural networks are trained from scratch owing to the lack of large-scale labeled 3D datasets.
$S^3$: Learnable Sparse Signal Superdensity for Guided Depth Estimation
Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality.
Dual-Awareness Attention for Few-Shot Object Detection
While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems.
Ranked #9 on
Few-Shot Object Detection
on MS-COCO (10-shot)
Raw Image Deblurring
Therefore, we built a new dataset containing both RAW images and processed sRGB images and design a new model to utilize the unique characteristics of RAW images.
GDN: A Coarse-To-Fine (C2F) Representation for End-To-End 6-DoF Grasp Detection
We proposed an end-to-end grasp detection network, Grasp Detection Network (GDN), cooperated with a novel coarse-to-fine (C2F) grasp representation design to detect diverse and accurate 6-DoF grasps based on point clouds.
Expanding Sparse Guidance for Stereo Matching
The performance of image based stereo estimation suffers from lighting variations, repetitive patterns and homogeneous appearance.
Indoor Depth Completion with Boundary Consistency and Self-Attention
We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced.
Ranked #2 on
Depth Completion
on Matterport3D
A Unified Point-Based Framework for 3D Segmentation
We present a new unified point-based framework for 3D point cloud segmentation that effectively optimizes pixel-level features, geometrical structures and global context priors of an entire scene.
