Kavli Affiliate: Yi Zhou
| First 5 Authors: Yinlong Bai, Yinlong Bai, , ,
| Summary:
Recent advancements in 3D Gaussian Splatting (3DGS) have made a significant
impact on rendering and reconstruction techniques. Current research
predominantly focuses on improving rendering performance and reconstruction
quality using high-performance desktop GPUs, largely overlooking applications
for embedded platforms like micro air vehicles (MAVs). These devices, with
their limited computational resources and memory, often face a trade-off
between system performance and reconstruction quality. In this paper, we
improve existing methods in terms of GPU memory usage while enhancing rendering
quality. Specifically, to address redundant 3D Gaussian primitives in SLAM, we
propose merging them in voxel space based on geometric similarity. This reduces
GPU memory usage without impacting system runtime performance. Furthermore,
rendering quality is improved by initializing 3D Gaussian primitives via
Patch-Grid (PG) point sampling, enabling more accurate modeling of the entire
scene. Quantitative and qualitative evaluations on publicly available datasets
demonstrate the effectiveness of our improvements.
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