High-precision 3D environmental perception is crucial for autonomous systems to
navigate and interact effectively within complex indoor environments. However,
achieving high-granularity 3D reconstruction and high-fidelity rendering remains
challenging. Although 3D Gaussian Splatting (3DGS) enables efficient
photorealistic rendering, its geometric inconsistency limits its ability to
reconstruct high-accuracy scenes. Standard 3DGS relies on sparse
Structure-from-Motion (SfM) initialization, which often leads to uneven point
distributions and floaters, particularly in weak-texture areas like plain walls.
Furthermore, existing methods based on depth regularization or geometric
constraints usually fail to address the fundamental sparsity of the
initialization. To address these issues, we propose a lightweight
gradient-driven point allocation strategy for depth-guided Gaussian splatting
that bypasses traditional SfM. By utilizing RGB-D priors, we obtain a
high-quality, structurally continuous initial Gaussian distribution through
multi-view depth back-projection and texture-aware density control. Our method
incorporates a bidirectional re-projection check to eliminate transient noise
and occlusions, ensuring geometric consistency. We introduce a dual-grid
allocation scheme that adaptively distributes Gaussians based on spatial
frequency, mandating density in high-gradient regions while enforcing sparsity
in flat areas. Quantitative and qualitative experiments on the ScanNet++ dataset
demonstrate that our method achieves superior reconstruction accuracy and
rendering quality. Notably, our approach achieves state-of-the-art performance
with a significantly reduced Gaussian count, decreasing from 1.70M to 0.39M in
representative scenes, thereby mitigating unnecessary point cloud bloat and
reducing memory overhead without sacrificing structural integrity.