GSM-GS: Geometry-Constrained Single and Multi-view Gaussian Splatting for Surface Reconstruction

Recently, 3D Gaussian Splatting has emerged as a prominent research direction owing to its ultrarapid training speed and high-fidelity rendering capabilities. However, the unstructured and irregular nature of Gaussian point clouds poses challenges to reconstruction accuracy. This limitation frequently causes high-frequency detail loss in complex surface microstructures when relying solely on routine strategies. To address this limitation, we propose GSM-GS: a synergistic optimization framework integrating single-view adaptive sub-region weighting constraints and multi-view spatial structure refinement. For single-view optimization, we leverage image gradient features to partition scenes into texture-rich and texture-less sub-regions. The reconstruction quality is enhanced through adaptive filtering mechanisms guided by depth discrepancy features. This preserves high-weight regions while implementing a dual-branch constraint strategy tailored to regional texture variations, thereby improving geometric detail characterization. For multi-view optimization, we introduce a geometry-guided cross-view point cloud association method combined with a dynamic weight sampling strategy. This constructs 3D structural normal constraints across adjacent point cloud frames, effectively reinforcing multi-view consistency and reconstruction fidelity. Extensive experiments on public datasets demonstrate that our method achieves both competitive rendering quality and geometric reconstruction. See our interactive project page

Intention-Aware Diffusion Model for Pedestrian Trajectory Prediction

Predicting pedestrian motion trajectories is critical for the path planning and motion control of autonomous vehicles. Recent diffusion-based models have shown promising results in capturing the inherent stochasticity of pedestrian behavior for trajectory prediction. However, the absence of explicit semantic modelling of pedestrian intent in many diffusion-based methods may result in misinterpreted behaviors and reduced prediction accuracy. To address the above challenges, we propose a diffusion-based pedestrian trajectory prediction framework that incorporates both short-term and long-term motion intentions. Short-term intent is modelled using a residual polar representation, which decouples direction and magnitude to capture fine-grained local motion patterns. Long-term intent is estimated through a learnable, token-based endpoint predictor that generates multiple candidate goals with associated probabilities, enabling multimodal and context-aware intention modelling. Furthermore, we enhance the diffusion process by incorporating adaptive guidance and a residual noise predictor that dynamically refines denoising accuracy. The proposed framework is evaluated on the widely used ETH, UCY, and SDD benchmarks, demonstrating competitive results against state-of-the-art methods.

Intention Enhanced Diffusion Model for Multimodal Pedestrian Trajectory Prediction

Predicting pedestrian motion trajectories is critical for path planning and motion control of autonomous vehicles. However, accurately forecasting crowd trajectories remains a challenging task due to the inherently multimodal and uncertain nature of human motion. Recent diffusion-based models have shown promising results in capturing the stochasticity of pedestrian behavior for trajectory prediction. However, few diffusion-based approaches explicitly incorporate the underlying motion intentions of pedestrians, which can limit the interpretability and precision of prediction models. In this work, we propose a diffusion-based multimodal trajectory prediction model that incorporates pedestrians' motion intentions into the prediction framework. The motion intentions are decomposed into lateral and longitudinal components, and a pedestrian intention recognition module is introduced to enable the model to effectively capture these intentions. Furthermore, we adopt an efficient guidance mechanism that facilitates the generation of interpretable trajectories. The proposed framework is evaluated on two widely used human trajectory prediction benchmarks, ETH and UCY, on which it is compared against state-of-the-art methods. The experimental results demonstrate that our method achieves competitive performance.