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neg_loss_cornernet

Syntologyentry name in harvested coderead from the graph 2026-09-24

neg_loss_cornernet appears in the code Syntology harvested for 36 papers, as 4 distinct code bodies found in 36 places (a place is one code body under one paper). At least one of them ran in 2 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named neg_loss_cornernet do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 2 of the 4 distinct code bodies named neg_loss_cornernet; 2 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
2ran
2unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 3 of the 36 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

36 papers shown of 36, newest first; 36 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive; 8 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection 6 Jul 2025 AutoLab-SAI-SJTU/MambaFusion/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
arXiv:2506.03714 2025-06 (from id) Say2L/FSHNet/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
arXiv:2503.08639 2025-03 (from id) open-mmlab/OpenPCDet/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
OV-SCAN: Semantically Consistent Alignment for Novel Object Discovery in Open-Vocabulary 3D Object Detection 9 Mar 2025 ahtchow/OV-SCAN/OV-SCAN/pcdet/utils/loss_utils.py 488b91d67a807558 unverified MIT (permissive)
L4DR: LiDAR-4DRadar Fusion for Weather-Robust 3D Object Detection 7 Aug 2024 ylwhxht/l4dr/K-Radar-main-repo/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
LION: Linear Group RNN for 3D Object Detection in Point Clouds 25 Jul 2024 happinesslz/LION/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
SEED: A Simple and Effective 3D DETR in Point Clouds 15 Jul 2024 happinesslz/SEED/pcdet/utils/loss_utils.py 488b91d67a807558 unverified no licence file found · pointer only
Semi-supervised 3D Object Detection with PatchTeacher and PillarMix 13 Jul 2024 LittlePey/PTPM/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
Commonsense Prototype for Outdoor Unsupervised 3D Object Detection 25 Apr 2024 hailanyi/CPD/cpd/utils/loss_utils.py 488b91d67a807558 unverified no licence file found · pointer only
Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments 20 Mar 2024 djamahl99/findnpropagate/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection 9 Mar 2024 zhanggang001/HEDNet/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection 29 Jan 2024 BraveGroup/PointSAM-for-MixSup/OpenPCDet/pcdet/utils/loss_utils.py 3ecff2f9630bd90f ran Apache-2.0 (permissive)
DCDet: Dynamic Cross-based 3D Object Detector 14 Jan 2024 Say2L/DCDet/pcdet/utils/loss_utils.py 5e1dc41d084fa131 ran Apache-2.0 (permissive)
ScatterFormer: Efficient Voxel Transformer with Scattered Linear Attention 1 Jan 2024 skyhehe123/ScatterFormer/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation 29 Sep 2023 djning/see_beyond_seeing/pcdet/utils/loss_utils.py 488b91d67a807558 unverified no licence file found · pointer only
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation 11 Sep 2023 PJLab-ADG/3DTrans/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation 15 Aug 2023 haiyang-w/dsvt/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds 9 Jun 2023 pjlab-adg/detzero/detection/detzero_det/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences 5 Jun 2023 quan-dao/practical-collab-perception/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
Density-Insensitive Unsupervised Domain Adaption on 3D Object Detection 19 Apr 2023 woodwindhu/dts/pcdet/utils/loss_utils.py 488b91d67a807558 unverified MIT (permissive)
Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection 4 Apr 2023 azhuantou/HSSDA/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking 20 Mar 2023 dvlab-research/VoxelNeXt/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
Exploring Active 3D Object Detection from a Generalization Perspective 23 Jan 2023 Luoyadan/CRB-active-3Ddet/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks 20 Dec 2022 Eaphan/Robust3DOD/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs 21 Jun 2022 dvlab-research/focalsconv/OpenPCDet/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders 20 Jun 2022 chaytonmin/occupancy-mae/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data 13 Apr 2022 dschinagl/occam/pcdet/utils/loss_utils.py 488b91d67a807558 unverified BSD-3-Clause (permissive)
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds 19 Mar 2022 skyhehe123/VoxSeT/pcdet/utils/loss_utils.py 488b91d67a807558 unverified MIT (permissive)
M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers 24 Apr 2021 rayguan97/M3DeTR/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network 8 Jul 2019 sshaoshuai/PointCloudDet3D/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
arXiv:aaai_28141 Nightmare-n/GD-MAE/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
arXiv:aaai_25380 hailanyi/TED/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
arXiv:aaai_25370 yinjunbo/SSDA3D/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
arXiv:Liu_MonoTAKD_Teaching_Assistant_Knowledge_Distillation_for_Monocular_3D_Object_Detection_CVPR_2025_paper hoiliu-0801/MonoTAKD/pcdet/utils/loss_utils.py 08c2e1e2c13772e8 unverified Apache-2.0 (permissive)
arXiv:Dang_FASTer_Focal_token_Acquiring-and-Scaling_Transformer_for_Long-term_3D_Objection_Detection_CVPR_2025_paper MSunDYY/FASTer/pcdet/utils/loss_utils.py 488b91d67a807558 unverified Apache-2.0 (permissive)
arXiv:03732 jskvrna/TCC-Det/modified_openpcdet/pcdet/utils/loss_utils.py 488b91d67a807558 unverified MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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