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CodecSplat: Ultra-Compact Latent Coding for Feed-Forward 3D Gaussian Splatting
Pengpeng Yu, Runqing Jiang, Qi Zhang +3
While feed-forward 3D Gaussian splatting reconstructs renderable Gaussian primitives from sparse context views without per-scene optimization, existing pipelines do not provide a c…
SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation
Yun Wang, Zhengjie Yang, Jiahao Zheng +3
Recent self-supervised stereo matching methods have made significant progress. They typically rely on the photometric consistency assumption, which presumes corresponding points ac…
Towards Practical Lossless Neural Compression for LiDAR Point Clouds
Pengpeng Yu, Haoran Li, Runqing Jiang +4
LiDAR point clouds are fundamental to various applications, yet the extreme sparsity of high-precision geometric details hinders efficient context modeling, thereby limiting the co…
Learning Hierarchical Color Guidance for Depth Map Super-Resolution
Runmin Cong, Ronghui Sheng, Hao Wu +5
Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However…
Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision
Haojie Liu, Kang Liao, Chunyu Lin +2
Pseudo-LiDAR point cloud interpolation is a novel and challenging task in the field of autonomous driving, which aims to address the frequency mismatching problem between camera an…
PLIN: A Network for Pseudo-LiDAR Point Cloud Interpolation
Haojie Liu, Kang Liao, Chunyu Lin +2
LiDAR sensors can provide dependable 3D spatial information at a low frequency (around 10Hz) and have been widely applied in the field of autonomous driving and UAV. However, the c…