Perception Method Library Overview
This directory is the method-level perception library. Each page should represent one technique, method, benchmark, or dataset-backed evaluation primitive. Broad synthesis pages in 30-autonomy-stack/perception/overview/ remain useful for system design, but this library is where individual methods get enough space for architecture, data, benchmarks, failure modes, deployment fit, Domain Fit, transfer notes for explicitly scoped ODDs, and sources.
Priority Ratings
Priority ratings are editorial reading and deployment triage signals. Learning answers what to read early for general autonomy understanding. Deployment answers what to evaluate early for AV deployment in the tagged context; it is not a certification, product-readiness, or all-domain average claim. If a method's deployment score is driven by a specific domain or stack role, the reason text should name that context.
| Method | Rating | Stage | Maturity | Reason |
|---|---|---|---|---|
| Availability-Aware Sensor Fusion | Learning: ★★★★☆ Deployment: ★★★★★ | deployment-pattern | prototype | Directly targets sensor degradation and availability-aware fusion. |
| LiDAR-MOS | Learning: ★★★★☆ Deployment: ★★★★★ | deployment-pattern | prototype | Moving-object segmentation is central to map hygiene and dynamic-scene handling. |
| 4DSegStreamer | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | 4DSegStreamer is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| AutoOcc | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | AutoOcc is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| BEVDepth | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Important depth-aware BEV bridge for camera-only 3D perception. |
| BEVDet | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Baseline camera BEV detector that organizes many later BEV methods. |
| BEVStereo | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | BEVStereo is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| Cam4DOcc | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Cam4DOcc is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| Conformal Boxes | Learning: ★★★★☆ Deployment: ★★★★☆ | deployment-pattern | prototype | Practical uncertainty wrapper for detection risk and release gates. |
| Cross-Domain LiDAR Scene Flow | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Cross-Domain LiDAR Scene Flow is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| Dynamic Occupancy Freespace | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Dynamic Occupancy Freespace is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| FlashOcc | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | FlashOcc is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| GaussianOcc | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | GaussianOcc is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| GraphBEV | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | GraphBEV is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| InsMOS | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | InsMOS is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| Instantaneous Motion Perception | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Instantaneous Motion Perception is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| LiDAR-Camera Occupancy Fusion | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | LiDAR-Camera Occupancy Fusion is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| M2-Occ | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | M2-Occ is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| MambaMOS | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | MambaMOS is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| Mask4D | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Mask4D is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| MotionSeg3D | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | MotionSeg3D is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| Neural Scene Flow Priors | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Neural Scene Flow Priors is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| Open-Vocabulary Panoptic Occupancy | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Open-Vocabulary Panoptic Occupancy is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| OpenAD | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | fielded-pattern | Open-world benchmark for corner cases and unseen categories. |
| RadarPillars | Learning: ★★★★☆ Deployment: ★★★★☆ | classic-baseline | prototype | Core radar-native detection baseline for weather-robust perception. |
| RenderOcc | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | RenderOcc is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| SegNet4D | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | SegNet4D is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| SelfOcc | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | SelfOcc is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| SOLOFusion | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | SOLOFusion is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| SparseOcc | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | SparseOcc is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| Spatiotemporal Memory Occupancy Flow | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Spatiotemporal Memory Occupancy Flow is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| Streaming Gaussian Occupancy | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Streaming Gaussian Occupancy is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| StreamingFlow | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | StreamingFlow is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| StreamMOS | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | StreamMOS is rated for motion segmentation, scene flow, or dynamic-object perception workflows. |
| SurroundOcc | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | Foundational camera occupancy reference for planning-facing perception. |
| TPVFormer | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | TPVFormer is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| TrackOcc | Learning: ★★★★☆ Deployment: ★★★★☆ | modern-core | prototype | TrackOcc is rated for BEV, occupancy, or freespace modeling that feeds planning-facing autonomy stacks. |
| 3D-KNN Blind-Spot Desnowing | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | 3D-KNN Blind-Spot Desnowing is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| 4D Radar Road Boundaries and Freespace | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | prototype | 4D Radar Road Boundaries and Freespace is rated for alternative-sensor perception and adverse-weather fallback evaluation. |
| 4D Radar-Camera Occupancy | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | prototype | 4D Radar-Camera Occupancy is rated for alternative-sensor perception and adverse-weather fallback evaluation. |
| 4DMOS | Learning: ★★★☆☆ Deployment: ★★★★☆ | modern-core | prototype | Extends LiDAR motion segmentation with temporal 4D reasoning. |
| Adverse-Weather Radar-LiDAR 3D Detection | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | prototype | Adverse-Weather Radar-LiDAR 3D Detection is rated for alternative-sensor perception and adverse-weather fallback evaluation. |
| AdverseNet | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | AdverseNet is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| AevaScenes | Learning: ★★★☆☆ Deployment: ★★★★☆ | reference | fielded-pattern | AevaScenes is rated as a benchmark or dataset reference for perception robustness and validation coverage. |
| AIDE | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | AIDE is rated for operational perception validation, calibration, or safety-screening workflows. |
| Classical LiDAR Outlier Removal | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | Classical LiDAR Outlier Removal is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| CVFusion | Learning: ★★★☆☆ Deployment: ★★★★☆ | modern-core | prototype | Important radar-camera fusion method for degraded visual conditions. |
| DenoiseCP-Net | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | DenoiseCP-Net is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| Ev-3DOD | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | prototype | Ev-3DOD is rated for alternative-sensor perception and adverse-weather fallback evaluation. |
| EvOcc | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | prototype | EvOcc is rated for alternative-sensor perception and adverse-weather fallback evaluation. |
| Fail2Drive | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | Fail2Drive is rated for operational perception validation, calibration, or safety-screening workflows. |
| K-Radar | Learning: ★★★☆☆ Deployment: ★★★★☆ | modern-core | fielded-pattern | Key 4D radar dataset and benchmark for all-weather perception evaluation. |
| LASP | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | LASP is rated for operational perception validation, calibration, or safety-screening workflows. |
| LiDAR Weather Artifact Removal | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | LiDAR Weather Artifact Removal is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| LIORNet | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | LIORNet is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| LiSnowNet | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | LiSnowNet is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| M-detector LiDAR Point-Stream MED | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | M-detector LiDAR Point-Stream MED is rated for operational perception validation, calibration, or safety-screening workflows. |
| MoME | Learning: ★★★☆☆ Deployment: ★★★★☆ | modern-core | prototype | Useful resilient fusion pattern for adverse sensor failure cases. |
| MSC-Bench | Learning: ★★★☆☆ Deployment: ★★★★☆ | reference | fielded-pattern | MSC-Bench is rated as a benchmark or dataset reference for perception robustness and validation coverage. |
| MultiCorrupt | Learning: ★★★☆☆ Deployment: ★★★★☆ | reference | fielded-pattern | MultiCorrupt is rated as a benchmark or dataset reference for perception robustness and validation coverage. |
| Occluded nuScenes | Learning: ★★★☆☆ Deployment: ★★★★☆ | reference | fielded-pattern | Occluded nuScenes is rated as a benchmark or dataset reference for perception robustness and validation coverage. |
| POD FMCW LiDAR Predictive Detection | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | prototype | POD FMCW LiDAR Predictive Detection is rated for alternative-sensor perception and adverse-weather fallback evaluation. |
| ProOOD | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | ProOOD is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| RaCFormer | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | prototype | RaCFormer is rated for alternative-sensor perception and adverse-weather fallback evaluation. |
| RC-AutoCalib | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | RC-AutoCalib is rated for operational perception validation, calibration, or safety-screening workflows. |
| RobuRCDet | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | prototype | RobuRCDet is rated for alternative-sensor perception and adverse-weather fallback evaluation. |
| S2R-Bench | Learning: ★★★☆☆ Deployment: ★★★★☆ | reference | fielded-pattern | S2R-Bench is rated as a benchmark or dataset reference for perception robustness and validation coverage. |
| SLiDE | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | SLiDE is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| Sparse4D | Learning: ★★★☆☆ Deployment: ★★★★☆ | modern-core | prototype | Practical sparse-query direction for camera 3D detection and tracking. |
| TripleMixer | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | pilot-proven | TripleMixer is rated for cleaning, stress testing, or failure detection in degraded perception conditions. |
| V2X-Radar | Learning: ★★★☆☆ Deployment: ★★★★☆ | deployment-pattern | prototype | V2X-Radar is rated for alternative-sensor perception and adverse-weather fallback evaluation. |
| 3D-AVS | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | 3D-AVS is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| 3D-OutDet | Learning: ★★★☆☆ Deployment: ★★★☆☆ | modern-core | prototype | 3D-OutDet is rated as a supporting perception method for autonomy-stack triage and follow-up reading. |
| Clipomaly | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | Useful anomaly-detection reference for long-tail discovery workflows. |
| CoHFF | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | prototype | CoHFF is rated for cooperative perception and infrastructure-assisted sensing evaluation. |
| CoInfra | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | prototype | CoInfra is rated for cooperative perception and infrastructure-assisted sensing evaluation. |
| CoopTrack | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | prototype | CoopTrack is rated for cooperative perception and infrastructure-assisted sensing evaluation. |
| CoSDH | Learning: ★★★☆☆ Deployment: ★★★☆☆ | modern-core | prototype | CoSDH is rated as a supporting perception method for autonomy-stack triage and follow-up reading. |
| DetAny3D | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | DetAny3D is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| DistillNeRF | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | DistillNeRF is rated for neural scene representation learning and simulation-oriented perception research. |
| DrivingGaussian | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | DrivingGaussian is rated for neural scene representation learning and simulation-oriented perception research. |
| ForeSight | Learning: ★★★☆☆ Deployment: ★★★☆☆ | modern-core | prototype | ForeSight is rated as a supporting perception method for autonomy-stack triage and follow-up reading. |
| GaussianFormer | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | GaussianFormer is rated for neural scene representation learning and simulation-oriented perception research. |
| HoloVIC | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | prototype | HoloVIC is rated for cooperative perception and infrastructure-assisted sensing evaluation. |
| HUGS Urban Gaussians | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | HUGS Urban Gaussians is rated for neural scene representation learning and simulation-oriented perception research. |
| Mosaic3D | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | Mosaic3D is rated for open-vocabulary 3D segmentation, dataset leverage, and long-tail perception validation. |
| OP3Det | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | OP3Det is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| Open3DTrack | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | Open3DTrack is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| OpenVox | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | OpenVox is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| OVAD And OVODA Open-Vocabulary 3D Attributes | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | OVAD And OVODA Open-Vocabulary 3D Attributes is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| OW-OVD | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | OW-OVD is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| RCooper | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | fielded-pattern | Cooperative-perception dataset relevant to infrastructure-assisted sensing. |
| S2M | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | S2M is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| SAM 3 | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | SAM 3 is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| SAM4D | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | SAM4D is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| SAMFusion | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | SAMFusion is rated for open-world perception, annotation leverage, and long-tail validation workflows. |
| SOAC | Learning: ★★★☆☆ Deployment: ★★★☆☆ | modern-core | prototype | SOAC is rated as a supporting perception method for autonomy-stack triage and follow-up reading. |
| SplatAD | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | SplatAD is rated for neural scene representation learning and simulation-oriented perception research. |
| SplatFlow | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | research | SplatFlow is rated for neural scene representation learning and simulation-oriented perception research. |
| TacoDepth | Learning: ★★★☆☆ Deployment: ★★★☆☆ | modern-core | prototype | TacoDepth is rated as a supporting perception method for autonomy-stack triage and follow-up reading. |
| V2X-ReaLO | Learning: ★★★☆☆ Deployment: ★★★☆☆ | frontier | prototype | V2X-ReaLO is rated for cooperative perception and infrastructure-assisted sensing evaluation. |
| WildDet3D | Learning: ★★★☆☆ Deployment: ★★★☆☆ | modern-core | prototype | WildDet3D is rated as a supporting perception method for autonomy-stack triage and follow-up reading. |
Domain Fit Guidance
Generic method pages should use Domain Fit, not Airside Fit, as the default deployment lens. Use three to six compact rows or bullets rather than a large matrix.
| Domain | Fit | Note |
|---|---|---|
| Road AV | strong / conditional / weak / insufficient evidence | State whether the method has road-scale evidence, actor coverage, and runtime maturity. |
| Airside | strong / conditional / weak / insufficient evidence | Include apron, GSE, FOD, aircraft-proximity, and weather relevance only when supported by the method evidence. |
| Warehouse / logistics yard / port / mining / construction / agriculture / delivery robot / outdoor campus | strong / conditional / weak / insufficient evidence | Add only the domains where the method assumptions or validation signals materially transfer. |
Airside-specific pages may stay airside-first, but generic pages should not make airside the only deployment lens.
How to Use This Library
For loss and residual foundations behind perception methods, use 3D Object Detection Losses and Assignment for detector training objectives and Robust Losses and M-Estimators for outlier-heavy geometric residuals that connect perception outputs to calibration, tracking, and SLAM.
File Boundary Rules
| Rule | Practical meaning |
|---|---|
| One file, one method | A page should not bundle multiple unrelated methods just because they share a modality. If two papers solve the same exact technique lineage, the page can compare versions, but the title must still name the primary method. |
| Overview pages link out | Existing files such as BEV Encoding Architectures, Streaming Temporal Perception, and Infrastructure Cooperative Perception should summarize families and point here for method-level details. |
| Benchmarks count as methods when they shape evaluation | Pages such as MSC-Bench, S2R-Bench, LASP, OpenAD, and Fail2Drive deserve first-class treatment because they define what a deployment team measures. |
| Domain fit is mandatory for generic pages | Generic method pages should state the domains where the method is a strong, conditional, weak, or insufficient-evidence fit. Airside-specific pages may use a transfer note instead. |
| Sources stay close to claims | Each method page must include primary paper, project, dataset, or repository links so future refreshes can verify claims quickly. |
Standard Page Shape
Each method page should include:
- What the method is.
- Core technical idea.
- Inputs, outputs, and model/data assumptions.
- Architecture or pipeline.
- Training/evaluation setup and benchmark signals.
- Strengths.
- Failure modes and deployment risks.
- Domain Fit, or an airside transfer note when the page is explicitly airside-specific.
- Implementation notes.
- Sources.
Relationship to the Perception Stack
| Existing synthesis page | Method-library role |
|---|---|
| BEV Encoding Architectures | Explains the BEV design space, then links to BEVDet/BEVDepth/BEVStereo/SOLOFusion and camera occupancy methods. |
| Camera-Only Degraded Perception | Uses camera BEV, occupancy, depth, and open-vocabulary method pages to define fallback modes. |
| LiDAR Semantic Segmentation | Summarizes segmentation architecture choices, then links to LiDAR-MOS, 4DMOS, SegNet4D, Mask4D, MotionSeg3D, MambaMOS, neural scene-flow priors, and HeLiMOS-style evaluation. |
| LiDAR Artifact Removal Techniques | Synthesizes learned denoisers, classical filters, weather artifact handling, ghost/multipath failures, validation, datasets, and map-cleaning links. |
| Streaming Temporal Perception | Connects StreamMOS, 4DSegStreamer, MotionSeg3D, MambaMOS, LASP, sparse-query detection, scene flow, and temporal occupancy into a runtime stack. |
| Open-Vocabulary and Zero-Shot Detection | Stays as the broad open-vocabulary primer; OpenAD, OP3Det, WildDet3D, DetAny3D, OW-OVD, Clipomaly, S2M, and SAM 3 get individual pages here. |
| Infrastructure Cooperative Perception | Synthesizes V2X deployment tradeoffs; RCooper, HoloVIC, CoInfra, V2X-ReaLO, CoHFF, CoSDH, and CoopTrack live here as atomic references. |
| Production Perception Systems | Uses this library as the evidence base for validation matrices, degradation policies, and sensor-suite decisions. |
Expansion Backlog
The first waves focused on methods already identified as P0/P1 in the Perception Coverage Audit. The 2026-05-09 loops promoted SplatAD, GaussianFormer, GaussianOcc, streaming Gaussian occupancy, Cam4DOcc, StreamingFlow, Sparse4D, TacoDepth, RaCFormer, LIORNet, learned LiDAR desnowing/denoising, broad artifact removal, classical outlier filtering, MotionSeg3D, MambaMOS, neural scene-flow priors, CVFusion, 4D radar-camera occupancy, POD/FMCW LiDAR, DrivingGaussian, HUGS, SplatFlow, DistillNeRF, TrackOcc, cross-domain scene flow, LiDAR-camera occupancy fusion, dynamic occupancy/free-space, radar-LiDAR adverse-weather detection, RobuRCDet, SAMFusion, spatiotemporal memory occupancy flow, OVAD/OVODA, and open-vocabulary panoptic occupancy into atomic files. Future waves should split remaining grouped rows into atomic pages, especially:
- VEON, EvOcc, ProOOD, SA-Occ, DR-REMOVER, and ExelMap.
- Drive-OccWorld and DFIT-OccWorld where they need separate world-model or planning-facing treatment beyond the dynamic occupancy page.
- SparseBEV, DETR4D, DySS, and ForeSight.
- DepthOcc, LinkOcc, missing-view occupancy, Gaussian-rendered occupancy, SAM4D, and related 2026 radar/occupancy follow-ons.
- SparseCoop, CoDS, JigsawComm, QuantV2X, TruckV2X, and collaborative Gaussian occupancy.
- DriveBench, Airport-FOD3S data-engine pages, DSERT-RoLL, CMHT, embodied robotics 3D perception, indoor open-vocabulary 3D instance segmentation, and airside-specific dust/de-icing-mist datasets.
Sources
- Perception coverage audit and backlog: coverage-audit-2026.md
- Existing perception synthesis index: Research Index - Perception