• We developed a diffusion-based generative framework called SEN, designed for sketch-guided local shape editing. This framework ensures consistency in geometric coherence and preservation. • We introduced an attention fusion module that effectively connects the cross-modal conditions to produce the final shape editing results. • We developed a sketch generator to directly generate sketch images from point clouds to facilitate our SEN training. Modeling complex 3D shapes from 2D sketch images has gained significant attention due to recent advancements in deep generative models. While many efforts have focused on producing geometries that faithfully reflect sketches, there has been limited exploration of sketch-guided local editing. Existing shape generation techniques often modify not only the intended edited areas but also the unedited parts, compromising geometric coherence and fairness among different parts of the shape. This paper introduces a shape editing network (SEN) under the diffusion-based generative framework to specifically enhance consistency in sketch-guided local shape editing. Our core insight is capturing partial geometric structure correlations to achieve consistent editing results. To accomplish this, the SEN is designed to conditionally learn local edits of a partial shape while considering the geometries of the unedited parts and the sketch image. We also devise an attention fusion module that progressively blends and aligns cross-modal conditioning streams between the latent variables of the sketch and point cloud representations. Additionally, by incorporating a guided sampling strategy for diffusion modeling, our SEN allows for more flexible control, even with complex or limited conditioning patterns. Our method significantly improves the geometric coherence of the synthesized parts while preserving the shapes of unedited areas. We demonstrate the advantages of our approach qualitatively and quantitatively across various shape categories, outperforming state-of-the-art methodologies.

• Jointly models spatial, channel, and topological redundancies, moving beyond conventional spatial-only designs. • Embedding Mamba layers locally within specialized subcomponents instead of as a global backbone, enabling structured context modeling. • Achieves efficient long-range modeling with linear complexity, yielding a smaller model and faster decoding while outperforming baselines. Existing learned point cloud compression frameworks face two major limitations: (1) they focus almost exclusively on spatial redundancy and (2) rely on architectures built around local-global transformers or global Mamba blocks. Transformers incur quadratic complexity, while global Mamba lacks the granularity to capture structured correlations across multiple dimensions. We propose OctMamba, the first unified framework to jointly exploit spatial, channel, and topological redundancies, dimensions previously overlooked in point cloud geometry compression. Our approach introduces a new architectural principle: embedding Mamba modules within specialized subcomponents rather than applying them globally, challenging existing design paradigms. OctMamba combines two modules: Spatial-Channel Coupled Grouping Mamba (SCCGM) for spatial-channel fusion and Local Graph CNN-Mamba (LGCM) for topological encoding. This design enables efficient long-range modeling with linear complexity, delivering a smaller model and faster decoding while outperforming transformer-based and global Mamba baselines. On SemanticKITTI, OctMamba reduces bitrate by 60.2% over GPCC (D1 PSNR) and achieves state-of-the-art performance across LiDAR and dynamic human point cloud benchmarks with practical speed and scalability. By introducing multi-dimensional redundancy modeling, OctMamba has the potential to influence future research on efficient point cloud compression. Source code will be released.