3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification
This work provides an incremental improvement for researchers and practitioners using 3DGS in real-world scenarios by mitigating the impact of transient distractors.
This paper addresses the degradation of 3D Gaussian Splatting (3DGS) quality in real-world environments due to transient distractors like moving objects and varying shadows. The authors propose 3DGS-HPC, which uses a patch-wise classification strategy and a hybrid classification metric to robustly identify and suppress these distractors, leading to improved novel view synthesis.
3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and 3D scene reconstruction, yet its quality often degrades in real-world environments due to transient distractors, such as moving objects and varying shadows. Existing methods commonly rely on semantic cues extracted from pre-trained vision models to identify and suppress these distractors, but such semantics are misaligned with the binary distinction between static and transient regions and remain fragile under the appearance perturbations introduced during 3DGS optimization. We propose 3DGS-HPC, a framework that circumvents these limitations by combining two complementary principles: a patch-wise classification strategy that leverages local spatial consistency for robust region-level decisions, and a hybrid classification metric that adaptively integrates photometric and perceptual cues for more reliable separation. Extensive experiments demonstrate the superiority and robustness of our method in mitigating distractors to improve 3DGS-based novel view synthesis.