Qianyi Wu, Zhengzhe Liu, Chi-Wing Fu, Pheng-Ann Heng, Shi Qiu, Runsong Zhu, Ka-Hei Hui
We lifted 11 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 |
|---|---|---|
| runsong123/unified-lift | canonical | 5 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| boundary_iou | Ran | runsong123/unified-lift/script/eval_lerf_mask_unified_lift.py code served (permissive licence) · get_code("c20005191e0618bd") |
| get_confience_map | Ran | runsong123/unified-lift/train_unified_lift.py code served (permissive licence) · get_code("5f91cd17bff041c5") |
| l1_loss | Ran | runsong123/unified-lift/utils/loss_utils.py code served (permissive licence) · get_code("ac0e42d6fbcfbbe6") |
| load_mask | Ran | runsong123/unified-lift/script/eval_lerf_mask_unified_lift.py code served (permissive licence) · get_code("bf2d28223bdf332b") |
| mask_to_boundary | Ran | runsong123/unified-lift/script/eval_lerf_mask_unified_lift.py code served (permissive licence) · get_code("b79a90dcf06de145") |
| feature_to_rgb | Not yet run | runsong123/unified-lift/render.py code served (permissive licence) · get_code("955c607d3685039a") |
| id2rgb | Not yet run | runsong123/unified-lift/train_unified_lift.py code served (permissive licence) · get_code("9acdd81f4e30bd46") |
| masked_l1_loss | Not yet run | runsong123/unified-lift/utils/loss_utils.py code served (permissive licence) · get_code("0ac7968bd7da45d3") |
| show_mask | Not yet run | runsong123/unified-lift/ext/grounded_sam.py code served (permissive licence) · get_code("eb026ce356b1b155") |
| visualize_obj | Not yet run | runsong123/unified-lift/train_unified_lift.py code served (permissive licence) · get_code("74195777dbe870e4") |
| weighted_l1_loss | Not yet run | runsong123/unified-lift/utils/loss_utils.py code served (permissive licence) · get_code("e14045a7568f1af5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Lifting multi-view 2D instance segmentation to a radiance field has proven effective to enhance 3D understanding. Existing works rely on direct matching for end-to-end lifting, yielding inferior results, or employ a two-stage solution constrained by complex pre-or post-processing. In this work, we design Unified-Lift, a new end-to-end objectaware lifting approach that aims for high-quality 3D segmentation based on our object-aware 3D Gaussian representation. To start, we augment each Gaussian point with a Gaussian-level feature learned using a contrastive loss to encode instance information. Importantly, we introduce a learnable object-level codebook to account for individual objects in the scene for an explicit object-level understanding and associate the encoded object-level features with the Gaussian-level point features for segmentation predictions. While promising, achieving effective codebook learning is nontrivial and a naive solution leads to degraded performance. Hence, we formulate the association learning module and the noisy label filtering module for effective and robust codebook learning. We conduct experiments on three benchmarks LERF-Masked, Replica, and Messy Rooms. Both qualitative and quantitative results manifest that our Unified-Lift clearly outperforms existing methods in terms of segmentation quality and time efficiency.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2503.14029")
get_code_for_paper("2503.14029")
have("2503.14029")
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