Fusion of LiDAR and RGB data has the potential to enhance outdoor 3D object detection accuracy. To address real-world challenges in outdoor 3D object detection, fusion of LiDAR and RGB input has started gaining traction. However, effective integration of these modalities for precise object detection tasks still remains a largely open problem. To address that, we propose a MultiStream Detection (MuStD) network, which meticulously extracts task-relevant information from both data modalities. The network follows a three-stream structure. Its LiDAR-PillarNet stream extracts sparse 2D pillar features from the LiDAR input while the LiDAR-Height Compression stream computes Bird’s-Eye View features. An additional 3D Multimodal stream combines RGB and LiDAR features using UV mapping and polar coordinate indexing. Eventually, the features containing comprehensive spatial, textural, and geometric information are carefully fused and fed to a detection head for 3D object detection. We evaluate our method on the challenging KITTI Object Detection Benchmark, with results available on the official evaluation server.1. Our approach achieves strong performance, with an average precision (AP) of 85.39% in 3D detection, 91.34% in Bird’s Eye View (BEV) detection, and 96.39% in 2D detection. These results match or surpass existing state-of-the-art methods. In the difficult "Hard" category, our method attains 80.78% AP in 3D detection and 94.04% AP in 2D detection, highlighting its robustness in challenging scenarios. Furthermore, our method runs at 67 ms, demonstrating efficiency and real-time capability. Our code will be released through the MuStD GitHub repository at https://github.com/IbrahimUWA/MuStD.

Precise localization is critical for autonomous vehicles. We present a self-supervised learning method that employs transformers for the first time for the task of outdoor localization using LiDAR data. We propose a pre-text task that reorganizes the slices of a 360° LiDAR scan to leverage its axial properties. Our model, called Slice Transformer, employs multi-head attention while systematically processing the slices. To the best of our knowledge, this is the first instance of leveraging multi-head attention for outdoor point clouds. We additionally introduce the Perth-Wadataset, which provides a large-scale LiDAR map of Perth city in Western Australia, covering ~4km2area. Localization annotations are provided for Perth - Wa.The proposed localization method is thoroughly evaluated on Perth-WA and Appollo-SouthBay datasets. We also establish the efficacy of our self-supervised learning approach for the common downstream task of object classification using ModelNet40 and ScanNN datasets. The code and Perth-WA data will be publicly released.

Localization is a fundamental task in robotics for autonomous navigation. Existing localization methods rely on a single input data modality or train several computational models to process different modalities. This leads to stringent computational requirements and sub-optimal results that fail to capitalize on the complementary information in other data streams. This paper proposes UnLoc, a novel unified neural modeling approach for localization with multi-sensor input in all weather conditions. Our multi-stream network can handle LiDAR, Camera and RADAR inputs for localization on demand, i.e., it can work with one or more input sensors, making it robust to sensor failure. UnLoc uses 3D sparse convolutions and cylindrical partitioning of the space to process LiDAR frames and implements ResNet blocks with a slot attention-based feature filtering module for the Radar and image modalities. We introduce a unique learnable modality encoding scheme to distinguish between the input sensor data. Our method is extensively evaluated on Oxford Radar RobotCar, ApolloSouthBay and Perth-WA datasets. The results ascertain the efficacy of our technique. The dataset, results, and codes are available at https://github.com/IbrahimUWA/UnLoc

Semantic segmentation of 3D point cloud is a key task in numerous intelligent transportation system applications, e.g., self-driving vehicles, traffic monitoring. Due to the sparsity and varying density of points in the outdoor point clouds, it becomes particularly challenging to extract object-centric features from data. This leads to poor semantic segmentation, especially for the rare object classes. To address that, we introduce the first-ever Slot Attention Transformer based technique to effectively model object-centric features in point cloud data. Our method uses cylindrical splits of space for voxelization and computes channel-wise positional embeddings before repetitively encoding the point cloud with slot attentions. Our second major contribution is a Large-Scale Outdoor Point Cloud dataset (SWAN), collected in a dense urban environment, driving 150km distance. It provides 16 billion points in more than 200K frames. The dataset also provides annotations for 10K frames for 24 classes. We also contribute a data augmentation scheme to handle rare object classes in real-world point clouds. Besides benchmarking popular existing methods on SWAN for the first time, we thoroughly evaluate our technique on the existing large-scale datasets, Semantic KITTI and nuScenes. Our results demonstrate a consistent performance gain for our technique, and verify the need of the more challenging SWAN dataset.