LiDAR-based 3D object detection is widely used in high-level autonomous driving schemes. However, the cumbersome modules in most 3D detectors lead to substantial computational overhead. Despite knowledge distillation (KD) is an effective approach for compressing models, previous methods cannot be extended to the dense-to-sparse paradigm. To this end, we propose a simple yet effective Dense to Sparse Knowledge Distillation (D2S) framework for accelerating 3D detectors. Firstly, to compensate for the difference in predicted location between dense and sparse detectors, we introduce a lightweight feature diffusion (FeaD) module for spreading important features. Secondly, to achieve high performance, we propose a dual-stream distillation scheme to transfer knowledge. In this scheme, we align both of the feature and category prediction between distillation pairs at important positions. Extensive experiments on KITTI and Waymo Open Dataset demonstrate the effectiveness of our method. For example, on KITTI dataset, the sparse detector we obtained surpasses VoxelNeXt with around 2.0× fewer parameters and 1.6× fewer FLOPs.

Accurate and high-fidelity driving scene reconstruction relies on fully leveraging scene information as conditioning. However, existing approaches, which primarily use 3D bounding boxes and binary maps for foreground and background control, fall short in capturing the complexity of the scene and integrating multi-modal information. In this paper, we propose DualDiff, a dual-branch conditional diffusion model designed to enhance multi-view driving scene generation. We introduce Occupancy Ray Sampling (ORS), a semantic-rich 3D representation, alongside numerical driving scene representation, for comprehensive foreground and background control. To improve cross-modal information integration, we propose a Semantic Fusion Attention (SFA) mechanism that aligns and fuses features across modalities. Furthermore, we design a foreground-aware masked (FGM) loss to enhance the generation of tiny objects. DualDiff achieves state-of-the-art performance in FID score, as well as consistently better results in downstream BEV segmentation and 3D object detection tasks.

3D Gaussian Splatting (3DGS) achieves remarkable speed and performance in novel view synthesis but suffers from overfitting and degraded reconstruction when handling sparse-view inputs. This paper innovatively addresses this challenge from a learning-to-optimize perspective by leveraging a learned optimizer (i.e., a multi-layer perceptron, MLP) to update the relevant parameters of 3DGS during the optimization process. Evidently, using a single MLP to handle all optimization variables, whose numbers may even vary during the optimization process, is impossible. Therefore, we present a point-wise position-aware optimizer that updates the parameters for each 3DGS point individually. Specifically, it takes the point coordinates and corresponding parameter values as input to predict the updates, thereby allowing efficient and adaptive optimization. In the case of sparse view modeling, the learned optimizer imposes position-aware constraints on the parameter updates during optimization. This effectively encourages the relevant parameters to converge stably to better solutions. To update the optimizer's parameters, we propose a dynamic gradient update strategy based on spatial perturbation and weighted fusion, enabling the optimizer to capture broader contextual information. Experiments demonstrate that our method effectively addresses the problem of modeling 3DGS from sparse training views, achieving state-of-the-art results across multiple datasets.