Point cloud (PC) processing tasks—such as completion, up-sampling, denoising, and colorization—are crucial in applications like autonomous driving and 3D reconstruction. Despite substantial advancements, prior approaches often address each of these tasks independently, with separate models focused on individual issues. However, this isolated approach fails to account for the fact that defects like incompleteness, low resolution, noise, and lack of color frequently coexist, with each defect influencing and correlating with the others. Simply applying these models sequentially can lead to error accumulation from each model, along with increased computational costs. To address these challenges, we introduce SuperPC, the first unified diffusion model capable of concurrently handling all four tasks. Our approach employs a three-level-conditioned diffusion framework, enhanced by a novel spatial-mix-fusion strategy, to leverage the correlations among these four defects for simultaneous, efficient processing. We show that SuperPC outperforms the state-of-the-art specialized models as well as their combination on all four individual tasks. Project website: https://sairlab.org/superpc/.

Motion prediction is critical for autonomous off-road driving, however, it presents significantly more challenges than on-road driving because of the complex interaction between the vehicle and the terrain. Traditional physics-based approaches encounter difficulties in accurately modeling dynamic systems and external disturbance. In contrast, data-driven neural networks require extensive datasets and struggle with explicitly capturing the fundamental physical laws, which can easily lead to poor generalization. By merging the advantages of both methods, neuro-symbolic approaches present a promising direction. These methods embed physical laws into neural models, potentially significantly improving generalization capabilities. However, no prior works were evaluated in real-world settings for off-road driving. To bridge this gap, we present PhysORD, a neural-symbolic approach integrating the conservation law, i.e., the Euler-Lagrange equation, into data-driven neural models for motion prediction in off-road driving. Our experiments showed that PhysORD can accurately predict vehicle motion and tolerate external disturbance by modeling uncertainties. It outperforms existing methods both in accuracy and efficiency and demonstrates data-efficient learning and generalization ability in long-term prediction.

The 360-depth estimation with spherical images and LiDAR data has recently become increasingly popular in autonomous driving and scene reconstruction. Compared with perspective images, spherical images have omnidirectional FoV, which exceedingly matches LiDAR data. However, the spherical distortion makes the 360-depth estimation a great challenge. To address this problem, we propose a self-supervised 360 depth estimation network in this paper. The network consists of a spherical convolution branch to extract panoramic image features and a ResNet branch to extract LiDAR features. Then an attention-based decoder is designed to estimate the depth. The reprojection error is used to self-supervise the network training. Experiments on the KITTI-360 dataset demonstrate the effectiveness of the proposed method.

Global visual localization is an important task in geoscience with a plethora of applications such as SLAM and autonomous navigation. Current place recognition approaches restrict the modality of the query data which relies on the database data modality. However, real-world robots are equipped with different sensors in different application scenarios and it is difficult for data from a single fixed modality to accommodate all challenging environments. To overcome this limitation, we propose to build a generalized model that allows spherical images and point clouds to be retrieved under any single-modal query. Our 2D-3D dataset is created based on the KITTI360 dataset with spherical images and corresponding point clouds for training and evaluation. Extensive experimental results demonstrate the effectiveness of our proposed approach.