3D Gaussian Splatting (3DGS) has gained significant attention due to its high-quality novel view rendering, motivating research to address real-world challenges. A critical issue is the camera motion blur caused by movement during exposure, which hinders accurate 3D scene reconstruction. In this study, we propose CoMoGaussian, a Continuous Motion-Aware Gaussian Splatting that reconstructs precise 3D scenes from motion-blurred images while maintaining real-time rendering speed. Considering the complex motion patterns inherent in real-world camera movements, we predict continuous camera trajectories using neural ordinary differential equations (ODEs). To ensure accurate modeling, we employ rigid body transformations, preserving the shape and size of the object but rely on the discrete integration of sampled frames. To better approximate the continuous nature of motion blur, we introduce a continuous motion refinement (CMR) transformation that refines rigid transformations by incorporating additional learnable parameters. By revisiting fundamental camera theory and leveraging advanced neural ODE techniques, we achieve precise modeling of continuous camera trajectories, leading to improved reconstruction accuracy. Extensive experiments demonstrate state-of-the-art performance both quantitatively and qualitatively on benchmark datasets, which include a wide range of motion blur scenarios, from moderate to extreme blur.

This paper proposes a two-stage 3D object detection framework, multiscale voxel graph neural network (MSV-RGNN) which aims to fully exploit multiple scale graph features by establishing global and local relationships between voxel features at different 3D convolutional neural network (CNN) layers. In contrast to conventional graph-based methods, our proposed multiscale-voxel-graph region-of-interest (RoI) pooling module constructs graphs across diverse voxel resolutions to obtain geometric structure information on voxel features. Initially, our multiscale-voxel-graph RoI pooling module sample voxel center points with voxel-wise feature vectors and 3D region proposals from backbone network. Subsequently, graphs are constructed at different scales and graph features are aggregated for second-stage refinement. The experimental results demonstrate the potential of using multiscale graphs across different voxel resolutions for 3D object detection, achieving decent experimental results with state-of-the-art methods.