MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models

Point cloud videos effectively capture real-world spatial geometries and temporal dynamics, which are essential for enabling intelligent agents to understand the dynamically changing 3D world we live in. Although static 3D point cloud processing has witnessed significant advancements, designing an effective 4D point cloud video backbone remains challenging, mainly due to the irregular and unordered distribution of points and temporal inconsistencies across frames. Moreover, recent state-of-the-art 4D backbones predominantly rely on transformer-based architectures, which commonly suffer from large computational costs due to their quadratic complexity, particularly when processing long video sequences. To address these challenges, we propose a novel 4D point cloud video understanding backbone based on the recently advanced State Space Models (SSMs). Specifically, our backbone begins by disentangling space and time in raw 4D sequences, and then establishing spatio-temporal correlations using our newly developed Intra-frame Spatial Mamba and Inter-frame Temporal Mamba blocks. The Intra-frame Spatial Mamba module is designed to encode locally similar or related geometric structures within a certain temporal searching stride, which can effectively capture short-term dynamics. Subsequently, these locally correlated tokens are delivered to the Inter-frame Temporal Mamba module, which globally integrates point features across the entire video with linear complexity, further establishing long-range motion dependencies. Experimental results on human action recognition and 4D semantic segmentation tasks demonstrate the superiority of our proposed method. Especially, for long video sequences, our proposed Mamba-based method has an 87.5% GPU memory reduction, 5.36 times speed-up, and much higher accuracy (up to +10.4%) compared with transformer-based counterparts on MSR-Action3D dataset.

DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion Model

Scene flow estimation, which aims to predict per-point 3D displacements of dynamic scenes, is a fundamental task in the computer vision field. However, previous works commonly suffer from unreliable correlation caused by locally constrained searching ranges, and struggle with accumulated inaccuracy arising from the coarse-to-fine structure. To alleviate these problems, we propose a novel uncertainty-aware scene flow estimation network (DifFlow3D) with the diffusion probabilistic model. Iterative diffusion-based refinement is designed to enhance the correlation robustness and resilience to challenging cases, e.g., dynamics, noisy inputs, repetitive patterns, etc. To restrain the generation diversity, three key flow-related features are leveraged as conditions in our diffusion model. Furthermore, we also develop an uncertainty estimation module within diffusion to evaluate the reliability of estimated scene flow. Our DifFlow3D achieves state-of-the-art performance, with 6.7\% and 19.1\% EPE3D reduction respectively on FlyingThings3D and KITTI 2015 datasets. Notably, our method achieves an unprecedented millimeter-level accuracy (0.0089m in EPE3D) on the KITTI dataset. Additionally, our diffusion-based refinement paradigm can be readily integrated as a plug-and-play module into existing scene flow networks, significantly increasing their estimation accuracy. Codes will be released later.