Subhankar Mishra
We lifted 8 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| smlab-niser/clean-gs | — | 5 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| color_distance | Ran | smlab-niser/clean-gs/clean-gs.py code served (permissive licence) · get_code("3e4803afea10e2f3") |
| load_cameras | Ran | smlab-niser/clean-gs/clean-gs.py code served (permissive licence) · get_code("121894d7292adaaa") |
| process_view_color_validate | Ran | smlab-niser/clean-gs/clean-gs.py code served (permissive licence) · get_code("37d4301a1ae72fb5") |
| process_view_whitelist | Ran | smlab-niser/clean-gs/clean-gs.py code served (permissive licence) · get_code("35fe4fd13ff42d66") |
| sh_to_rgb | Ran | smlab-niser/clean-gs/clean-gs.py code served (permissive licence) · get_code("f9255293c69be068") |
| Camera | Not yet run | smlab-niser/clean-gs/clean-gs.py code served (permissive licence) · get_code("6bf87d8b88ecef01") |
| clean_gs | Not yet run | smlab-niser/clean-gs/clean-gs.py code served (permissive licence) · get_code("576068a2cb4beb36") |
| project_gaussian_with_depth | Not yet run | smlab-niser/clean-gs/clean-gs.py code served (permissive licence) · get_code("be3807ed77a72136") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
3D Gaussian Splatting produces high-quality scene reconstructions but generates hundreds of thousands of spurious Gaussians (floaters) scattered throughout the environment. These artifacts obscure objects of interest and inflate model sizes, hindering deployment in bandwidth-constrained applications. We present Clean-GS, a method for removing background clutter and floaters from 3DGS reconstructions using sparse semantic masks. Our approach combines whitelist-based spatial filtering with color-guided validation and outlier removal to achieve 60-80% model compression while preserving object quality. Unlike existing 3DGS pruning methods that rely on global importance metrics, Clean-GS uses semantic information from as few as 3 segmentation masks (1% of views) to identify and remove Gaussians not belonging to the target object. Our multi-stage approach consisting of (1) whitelist filtering via projection to masked regions, (2) depth-buffered color validation, and (3) neighbor-based outlier removal isolates monuments and objects from complex outdoor scenes. Experiments on Tanks and Temples show that Clean-GS reduces file sizes from 125MB to 47MB while maintaining rendering quality, making 3DGS models practical for web deployment and AR/VR applications. Our code is available at https://github.com/smlab-niser/clean-gs
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2601.00913")
get_code_for_paper("2601.00913")
have("2601.00913")
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