7 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…