7 papers
Exploiting Semantic and Pixel Representations for Ultra-Low Bitrate Image Compression
Hao Wei, Yanhui Zhou, Chenyang Ge +2
Most existing extreme compression methods fail to achieve an optimal rate-distortion-perception trade-off, as they typically prioritize perceptual fidelity and visual realism over…
Faithful Extreme Image Rescaling with Learnable Reversible Transformation and Semantic Priors
Hao Wei, Yanhui Zhou, Chenyang Ge +2
Most recent extreme rescaling methods struggle to preserve semantically consistent structures and produce realistic details, due to the severely ill-posed nature of low- to high-re…
FLaTEC: Frequency-Disentangled Latent Triplanes for Efficient Compression of LiDAR Point Clouds
Xiaoge Zhang, Zijie Wu, Mingtao Feng +4
Point cloud compression methods jointly optimize bitrates and reconstruction distortion. However, balancing compression ratio and reconstruction quality is difficult because low-fr…
DiffCom: Decoupled Sparse Priors Guided Diffusion Compression for Point Clouds
Xiaoge Zhang, Zijie Wu, Mehwish Nasim +3
Lossy compression relies on an autoencoder to transform a point cloud into latent points for storage, leaving the inherent redundancy of latent representations unexplored. To reduc…
Multistream Network for LiDAR and Camera-based 3D Object Detection in Outdoor Scenes
Muhammad Ibrahim, Naveed Akhtar, Haitian Wang +2
Fusion of LiDAR and RGB data has the potential to enhance outdoor 3D object detection accuracy. To address real-world challenges in outdoor 3D object detection, fusion of LiDAR and…
PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation
Yong He, Hongshan Yu, Mingtao Feng +5
Diffusion probabilistic models are traditionally used to generate colors at fixed pixel positions in 2D images. Building on this, we extend diffusion models to point cloud semantic…