LiDAR semantic segmentation frameworks predominantly leverage geometry-based features to differentiate objects within a scan. While these methods excel in scenarios with clear boundaries and distinct shapes, their performance declines in environments where boundaries are blurred, particularly in off-road contexts. To address this, recent strides in 3D segmentation algorithms have focused on harnessing raw LiDAR intensity measurements to improve prediction accuracy. Despite these efforts, current learning-based models struggle to correlate the intricate connections between raw intensity and factors such as distance, incidence angle, material reflectivity, and atmospheric conditions. Building upon our prior work, this paper delves into the advantages of employing calibrated intensity (also referred to as reflectivity) within learning-based LiDAR semantic segmentation frameworks. We initially establish that incorporating reflectivity as an input enhances the existing LiDAR semantic segmentation model. Furthermore, we present findings that enable the model to learn to calibrate intensity can boost its performance. Through extensive experimentation on the off-road dataset Rellis-3D, we demonstrate notable improvements. Specifically, converting intensity to reflectivity results in a 4% increase in mean Intersection over Union (mIoU) when compared to using raw intensity in Off-road scenarios. Additionally, we also investigate the possible benefits of using calibrated intensity in semantic segmentation in urban environments (SemanticKITTI) and cross-sensor domain adaptation.
This paper presents a novel system designed for 3D mapping and visual relocalization using 3D Gaussian Splatting. Our proposed method uses LiDAR and camera data to create accurate and visually plausible representations of the environment. By leveraging LiDAR data to initiate the training of the 3D Gaussian Splatting map, our system constructs maps that are both detailed and geometrically accurate. To mitigate excessive GPU memory usage and facilitate rapid spatial queries, we employ a combination of a 2D voxel map and a KD-tree. This preparation makes our method well-suited for visual localization tasks, enabling efficient identification of correspondences between the query image and the rendered image from the Gaussian Splatting map via normalized cross-correlation (NCC). Additionally, we refine the camera pose of the query image using feature-based matching and the Perspective-n-Point (PnP) technique. The effectiveness, adaptability, and precision of our system are demonstrated through extensive evaluation on the KITTI360 dataset.
Fairness of recommender systems (RS) has attracted increasing attention recently. Based on the involved stakeholders, the fairness of RS can be divided into user fairness, item fairness, and two-sided fairness which considers both user and item fairness simultaneously. However, we argue that the intersectional two-sided unfairness may still exist even if the RS is two-sided fair, which is observed and shown by empirical studies on real-world data in this paper, and has not been well-studied previously. To mitigate this problem, we propose a novel approach called Intersectional Two-sided Fairness Recommendation (ITFR). Our method utilizes a sharpness-aware loss to perceive disadvantaged groups, and then uses collaborative loss balance to develop consistent distinguishing abilities for different intersectional groups. Additionally, predicted score normalization is leveraged to align positive predicted scores to fairly treat positives in different intersectional groups. Extensive experiments and analyses on three public datasets show that our proposed approach effectively alleviates the intersectional two-sided unfairness and consistently outperforms previous state-of-the-art methods.
In recommender systems, reinforcement learning solutions have shown promising results in optimizing the interaction sequence between users and the system over the long-term performance. For practical reasons, the policy's actions are typically designed as recommending a list of items to handle users' frequent and continuous browsing requests more efficiently. In this list-wise recommendation scenario, the user state is updated upon every request in the corresponding MDP formulation. However, this request-level formulation is essentially inconsistent with the user's item-level behavior. In this study, we demonstrate that an item-level optimization approach can better utilize item characteristics and optimize the policy's performance even under the request-level MDP. We support this claim by comparing the performance of standard request-level methods with the proposed item-level actor-critic framework in both simulation and online experiments. Furthermore, we show that a reward-based future decomposition strategy can better express the item-wise future impact and improve the recommendation accuracy in the long term. To achieve a more thorough understanding of the decomposition strategy, we propose a model-based re-weighting framework with adversarial learning that further boost the performance and investigate its correlation with the reward-based strategy.
LiDAR is used in autonomous driving to provide 3D spatial information and enable accurate perception in off-road environments, aiding in obstacle detection, mapping, and path planning. Learning-based LiDAR semantic segmentation utilizes machine learning techniques to automatically classify objects and regions in LiDAR point clouds. Learning-based models struggle in off-road environments due to the presence of diverse objects with varying colors, textures, and undefined boundaries, which can lead to difficulties in accurately classifying and segmenting objects using traditional geometric-based features. In this paper, we address this problem by harnessing the LiDAR intensity parameter to enhance object segmentation in off-road environments. Our approach was evaluated in the RELLIS-3D data set and yielded promising results as a preliminary analysis with improved mIoU for classes "puddle" and "grass" compared to more complex deep learning-based benchmarks. The methodology was evaluated for compatibility across both Velodyne and Ouster LiDAR systems, assuring its cross-platform applicability. This analysis advocates for the incorporation of calibrated intensity as a supplementary input, aiming to enhance the prediction accuracy of learning based semantic segmentation frameworks. https://github.com/MOONLABIISERB/lidar-intensity-predictor/tree/main
Existing pedestrian behavior prediction methods rely primarily on deep neural networks that utilize features extracted from video frame sequences. Although these vision-based models have shown promising results, they face limitations in effectively capturing and utilizing the dynamic spatio-temporal interactions between the target pedestrian and its surrounding traffic elements, crucial for accurate reasoning. Additionally, training these models requires manually annotating domain-specific datasets, a process that is expensive, time-consuming, and difficult to generalize to new environments and scenarios. The recent emergence of Large Multimodal Models (LMMs) offers potential solutions to these limitations due to their superior visual understanding and causal reasoning capabilities, which can be harnessed through semi-supervised training. GPT-4V(ision), the latest iteration of the state-of-the-art Large-Language Model GPTs, now incorporates vision input capabilities. This report provides a comprehensive evaluation of the potential of GPT-4V for pedestrian behavior prediction in autonomous driving using publicly available datasets: JAAD, PIE, and WiDEVIEW. Quantitative and qualitative evaluations demonstrate GPT-4V(ision)'s promise in zero-shot pedestrian behavior prediction and driving scene understanding ability for autonomous driving. However, it still falls short of the state-of-the-art traditional domain-specific models. Challenges include difficulties in handling small pedestrians and vehicles in motion. These limitations highlight the need for further research and development in this area.
As the demand for autonomous navigation in off-road environments increases, the need for effective solutions to understand these surroundings becomes essential. In this study, we confront the inherent complexities of semantic segmentation in RADAR data for off-road scenarios. We present a novel pipeline that utilizes LIDAR data and an existing annotated off-road LIDAR dataset for generating RADAR labels, in which the RADAR data are represented as images. Validated with real-world datasets, our pragmatic approach underscores the potential of RADAR technology for navigation applications in off-road environments.
Growing attention has been paid to Reinforcement Learning (RL) algorithms when optimizing long-term user engagement in sequential recommendation tasks. One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies. When formulated as a Markov Decision Process (MDP), the dynamics and reward functions of the recommendation system are continuously affected by these changes. Existing RL algorithms for recommendation systems will suffer from distribution shift and struggle to adapt in such an MDP. In this paper, we introduce a novel paradigm called Adaptive Sequential Recommendation (AdaRec) to address this issue. AdaRec proposes a new distance-based representation loss to extract latent information from users' interaction trajectories. Such information reflects how RL policy fits to current user behavior patterns, and helps the policy to identify subtle changes in the recommendation system. To make rapid adaptation to these changes, AdaRec encourages exploration with the idea of optimism under uncertainty. The exploration is further guarded by zero-order action optimization to ensure stable recommendation quality in complicated environments. We conduct extensive empirical analyses in both simulator-based and live sequential recommendation tasks, where AdaRec exhibits superior long-term performance compared to all baseline algorithms.