LP-3DGS: Learning to Prune 3D Gaussian Splatting

Recently, 3D Gaussian Splatting (3DGS) has become one of the mainstream methodologies for novel view synthesis (NVS) due to its high quality and fast rendering speed. However, as a point-based scene representation, 3DGS potentially generates a large number of Gaussians to fit the scene, leading to high memory usage. Improvements that have been proposed require either an empirical and preset pruning ratio or importance score threshold to prune the point cloud. Such hyperparamter requires multiple rounds of training to optimize and achieve the maximum pruning ratio, while maintaining the rendering quality for each scene. In this work, we propose learning-to-prune 3DGS (LP-3DGS), where a trainable binary mask is applied to the importance score that can find optimal pruning ratio automatically. Instead of using the traditional straight-through estimator (STE) method to approximate the binary mask gradient, we redesign the masking function to leverage the Gumbel-Sigmoid method, making it differentiable and compatible with the existing training process of 3DGS. Extensive experiments have shown that LP-3DGS consistently produces a good balance that is both efficient and high quality.

SafeguardGS: 3D Gaussian Primitive Pruning While Avoiding Catastrophic Scene Destruction

3D Gaussian Splatting (3DGS) has made a significant stride in novel view synthesis, demonstrating top-notch rendering quality while achieving real-time rendering speed. However, the excessively large number of Gaussian primitives resulting from 3DGS' suboptimal densification process poses a major challenge, slowing down frame-per-second (FPS) and demanding considerable memory cost, making it unfavorable for low-end devices. To cope with this issue, many follow-up studies have suggested various pruning techniques, often in combination with different score functions, to optimize rendering performance. Nonetheless, a comprehensive discussion regarding their effectiveness and implications across all techniques is missing. In this paper, we first categorize 3DGS pruning techniques into two types: Cross-view pruning and pixel-wise pruning, which differ in their approaches to rank primitives. Our subsequent experiments reveal that while cross-view pruning leads to disastrous quality drops under extreme Gaussian primitives decimation, the pixel-wise pruning technique not only sustains relatively high rendering quality with minuscule performance degradation but also provides a reasonable minimum boundary for pruning. Building on this observation, we further propose multiple variations of score functions and empirically discover that the color-weighted score function outperforms others for discriminating insignificant primitives for rendering. We believe our research provides valuable insights for optimizing 3DGS pruning strategies for future works.

Automated Driving Systems Data Acquisition and Processing Platform

This paper presents an automated driving system (ADS) data acquisition and processing platform for vehicle trajectory extraction, reconstruction, and evaluation based on connected automated vehicle (CAV) cooperative perception. This platform presents a holistic pipeline from the raw advanced sensory data collection to data processing, which can process the sensor data from multiple CAVs and extract the objects' Identity (ID) number, position, speed, and orientation information in the map and Frenet coordinates. First, the ADS data acquisition and analytics platform are presented. Specifically, the experimental CAVs platform and sensor configuration are shown, and the processing software, including a deep-learning-based object detection algorithm using LiDAR information, a late fusion scheme to leverage cooperative perception to fuse the detected objects from multiple CAVs, and a multi-object tracking method is introduced. To further enhance the object detection and tracking results, high definition maps consisting of point cloud and vector maps are generated and forwarded to a world model to filter out the objects off the road and extract the objects' coordinates in Frenet coordinates and the lane information. In addition, a post-processing method is proposed to refine trajectories from the object tracking algorithms. Aiming to tackle the ID switch issue of the object tracking algorithm, a fuzzy-logic-based approach is proposed to detect the discontinuous trajectories of the same object. Finally, results, including object detection and tracking and a late fusion scheme, are presented, and the post-processing algorithm's improvements in noise level and outlier removal are discussed, confirming the functionality and effectiveness of the proposed holistic data collection and processing platform.