GaussianDet3D: Bridging Gaussian Splatting and Sparse LiDAR Detection for Multi-View 3D Object Detection

Ecf3dmot: enhanced centerpoint framework for 3D object detection and tracking with LiDAR

VLGS-SLAM: Visual–Lidar Three-Dimensional Gaussian Splatting Simultaneous Localization and Mapping

Recent studies highlight the effectiveness of 3D Gaussian splatting (3DGS) in visual simultaneous localization and mapping (SLAM) systems, which work well indoors but struggle in large-scale outdoor environments. Typically, lidar data are used to address this issue; however, current multi-modal SLAM systems use 3DGS mainly for mapping, leaving its potential to enhance tracking unexplored. In this paper, we present VLGS-SLAM, a novel visual–lidar SLAM pipeline that leverages lidar data and 3DGS for both pose estimation and mapping. Our approach integrates lidar points as 3D Gaussian primitives, ensuring precise scene geometry and reducing pose estimation errors caused by floating Gaussians. To enhance tracking performance, we apply regularization to Gaussian scaling, which constrains the shape of each Gaussian ellipsoid. For loop closure, we combine image similarity with lidar cloud distance to effectively detect and close loops. Our experiments demonstrate that VLGS-SLAM achieves state-of-the-art accuracy in the 3DGS-based SLAM field, outperforming many traditional SLAM algorithms

LV-GS SLAM: A Decoupled LiDAR–Visual 3D Gaussian Splatting SLAM in a Large-Scale Environment

This paper proposes LV-GS SLAM, a novel system that integrates LiDAR and visual data for incremental, large-scale reconstruction with real-time tracking, and develops a keyframe-based submap management framework that dynamically adjusts memory allocation based on both primitive density and inter-frame overlap ratio, effectively preventing GPU memory overflow.

PES-PointPillars: LiDAR-Based 3D Object Detection for Autonomous Driving with Directional Convolution, Adaptive Feature Fusion, and Decoupled Regression

LiDAR-based 3D object detection for autonomous driving must balance localization accuracy with real-time inference, while sparse point measurements make small-scale objects such as pedestrians and cyclists particularly challenging to represent at long range. This paper presents PES-PointPillars, an enhanced PointPillars detector with three coordinated design changes. First, pinwheel-shaped convolution (PConv) replaces selected backbone convolutions to expand horizontal and vertical receptive fields for sparse structural patterns. Second, an Improved Inter-Layer Feature Correlation (I-EFC) module uses soft gating and adaptive thresholding to fuse multi-level features through continuous, input-dependent weights. Third, a Smooth L1-NWD (SNWD) loss applies normalized Wasserstein distance to planar position and scale while retaining Smooth L1 regression for vertical position, height, and orientation. Using the parameter settings and configuration of the original PointPillars implementation, the locally executed PES-PointPillars experiment achieves Moderate 3D average precision values of 77.1% for cars, 46.7% for pedestrians, and 62.9% for cyclists at 68.3 FPS on the KITTI validation split. Relative to the source-reported PointPillars reference, the corresponding numerical differences are 2.1, 3.2, and 3.8 percentage points. The reported component-wise and staged ablations show category-dependent gains, with the complete model providing the strongest aggregate performance among the evaluated configurations.

3D Object Detection Based on Polar Representation for Better Comprehensive Performances

Multi-modal 3D object detection is an important task in autonomous driving systems, where cameras and LiDAR provide complementary semantic and geometric information. Most existing BEV fusion methods are designed based on the Cartesian representation space, which does not fully match the sensing geometry of camera and LiDAR. This generally leads to redundant computation in distant regions. To address this issue, GARF, a geometry-aware polar BEV framework, is presented for multi-modal 3D object detection. GARF organizes camera and LiDAR features in a unified polar BEV space, which can represent spatial resolution more compactly. For the camera branch, the uncertainty-guided transformation of the polar view is designed to improve the reliability of depth estimation. Then, the generated polar BEV feature is further refined to attenuate radial noise and angular discontinuity. For the LiDAR branch, the polar-aware sparse feature extraction and distortion correction modules are designed to deal with the anisotropic structure and geometric distortion caused by polar voxelization. For multi-modal fusion, the region-aware cross-modal fusion strategy and polar detection head with anisotropic Gaussian center response map are developed, which achieve effective feature interaction and consistent geometry supervision. The experimental results on nuScenes show that GARF achieves 71.8% mAP and 73.7% NDS, improving the baseline by 3.3% mAP and 2.3% NDS. Meanwhile, the inference speed increases from 7.1 FPS to 8.9 FPS, and the consumption of GPU memory decreases from 41,114 MiB to 33,346 MiB.

Decoupling Mask Quality From Completion Design: A Diagnostic Framework for Occlusion-Aware LiDAR 3-D Object Detection

LiDAR-based 3D object detection is sensitive to sparse point support and occlusion-induced incomplete bird’s-eye-view (BEV) representations, especially for pedestrians, cyclists, and distant objects. This paper asks how much accurate, target-aligned occlusion guidance can help BEV feature completion and why a geometrically estimated region-of-occlusion map (ROM) fails to reproduce that benefit. We introduce OccBEV-Oracle, a lightweight completion neck inserted between the BEV backbone and dense head of a CenterPoint-style detector. Given an occlusion mask and a density map, it selects low-density occluded tokens, aggregates visible-token context through density-weighted cross-attention, and applies spatially constrained weak-residual fusion. On the KITTI validation split, full-map completion at a strong residual coefficient reduces mean Moderate 3D AP_R40 from 59.42% to 56.65%, whereas oracle GT-ROM-guided masked completion raises mean Moderate and Hard AP_R40 to 64.31% and 60.63%. A matched-strength control shows that weakening the full-map residual recovers only part of this gain (60.97% mean Moderate), so spatial restriction contributes a further 3.34 points that residual strength alone cannot supply. The gains concentrate on Pedestrian and Cyclist and on partly occluded and middle/far-range objects. Raycasting-based estimated ROM variants remain below the baseline. Mismatch, shifted/shuffled, zero-context and mask-only controls, and a direct measurement of where the neck edits the BEV map, show that the aligned GT-ROM mask itself supplies a strong localization prior. A mask-target analysis on nuScenes confirms the failure mode is dataset-independent. OccBEV-Oracle is therefore an upper-bound analysis, not a deployable detector: accurate target-related occlusion localization remains the main bottleneck.