19 citations · 36 across the 7 of their papers we have counts for
9 papers
Vision Pretraining for Dense Spatial Perception
Zelin Fu, Bin Tan, Changjiang Sun +6
Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structured, metric, and actionable representations from pixel observat…
Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression
Chang Sun, Hui Yuan, Shiqi Jiang +3
Because LiDAR sensors acquire point clouds with a fixed angular resolution, the resulting data can be systematically parameterized and efficiently compressed in the spherical coord…
PQuantML: A Tool for End-to-End Hardware-aware Model Compression
Roope Niemi, Anastasiia Petrovych, Arghya Ranjan Das +9
PQuantML is a new open-source, hardware-aware neural network model compression library tailored to end-to-end workflows. Motivated by the need to deploy performant models to enviro…
DUGAE: Unified Geometry and Attribute Enhancement via Spatiotemporal Correlations for G-PCC Compressed Dynamic Point Clouds
Pan Zhao, Hui Yuan, Chang Sun +3
Existing post-decoding quality enhancement methods for point clouds are designed for static data and typically process each frame independently. As a result, they cannot effectivel…
Point Cloud Feature Coding for Object Detection over an Error-Prone Cloud-Edge Collaborative System
Chongzhen Tian, Hui Yuan, Pan Zhao +3
Cloud-edge collaboration enhances machine perception by combining the strengths of edge and cloud computing. Edge devices capture raw data (e.g., 3D point clouds) and extract salie…
Masked Depth Modeling for Spatial Perception
Bin Tan, Changjiang Sun, Xiage Qin +8
Spatial visual perception is a fundamental requirement in physical-world applications like autonomous driving and robotic manipulation, driven by the need to interact with 3D envir…
LPCM: Learning-based Predictive Coding for LiDAR Point Cloud Compression
Chang Sun, Hui Yuan, Shiqi Jiang +3
Since the data volume of LiDAR point clouds is very huge, efficient compression is necessary to reduce their storage and transmission costs. However, existing learning-based compre…
Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction
Chang Sun, Hui Yuan, Xiaolong Mao +2
In point cloud geometry compression, most octreebased context models use the cross-entropy between the onehot encoding of node occupancy and the probability distribution predicted…
Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss
Chang Sun, Hui Yuan, Shuai Li +2
In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label, and the cross-entropy between the one-hot encoding and the prob…