We lifted 4 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| yyxssm/pointssc | canonical | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| index_points | Ran | yyxssm/pointssc/models/Transformer_utils.py pointer only (licence: NONE) · get_code("449a0265144f6530") |
| points2uv_batch | Ran | yyxssm/pointssc/models/deform_attn_utils.py pointer only (licence: NONE) · get_code("786516dc92c7396e") |
| square_distance | Ran | yyxssm/pointssc/models/dgcnn_group.py pointer only (licence: NONE) · get_code("cc7ff7ad964fd55f") |
| square_distance | Not yet run | yyxssm/pointssc/models/Transformer.py pointer only (licence: NONE) · get_code("6ddec81b1d23c787") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Semantic Scene Completion (SSC) aims to jointly generate space occupancies and semantic labels for complex 3D scenes. Most existing SSC models focus on volumetric representations, which are memory-inefficient for large outdoor spaces. Point clouds provide a lightweight alternative but existing benchmarks lack outdoor point cloud scenes with semantic labels. To address this, we introduce PointSSC, the first cooperative vehicle-infrastructure point cloud benchmark for semantic scene completion. These scenes exhibit long-range perception and minimal occlusion. We develop an automated annotation pipeline leveraging Semantic Segment Anything to efficiently assign semantics. To benchmark progress, we propose a LiDAR-based model with a Spatial-Aware Transformer for global and local feature extraction and a Completion and Segmentation Cooperative Module for joint completion and segmentation. PointSSC provides a challenging testbed to drive advances in semantic point cloud completion for real-world navigation. The code and datasets are available at https://github.com/yyxssm/PointSSC.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.12708")
get_code_for_paper("2309.12708")
have("2309.12708")
Connect an agent — have() is free.