Pith. sign in

REVIEW 9 cited by

GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2412.04384 v2 pith:QWGPQRXC submitted 2024-12-05 cs.CV cs.AIcs.LG

Yuanhui Huang , Amonnut Thammatadatrakoon , Wenzhao Zheng , Yunpeng Zhang , Dalong Du , Jiwen Lu This is my paper
classification cs.CVcs.AIcs.LG
keywords gaussiangaussiansoccupancyprobabilisticdistributiondrivingefficiencygaussianformer-2
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

3D semantic occupancy prediction is an important task for robust vision-centric autonomous driving, which predicts fine-grained geometry and semantics of the surrounding scene. Most existing methods leverage dense grid-based scene representations, overlooking the spatial sparsity of the driving scenes. Although 3D semantic Gaussian serves as an object-centric sparse alternative, most of the Gaussians still describe the empty region with low efficiency. To address this, we propose a probabilistic Gaussian superposition model which interprets each Gaussian as a probability distribution of its neighborhood being occupied and conforms to probabilistic multiplication to derive the overall geometry. Furthermore, we adopt the exact Gaussian mixture model for semantics calculation to avoid unnecessary overlapping of Gaussians. To effectively initialize Gaussians in non-empty region, we design a distribution-based initialization module which learns the pixel-aligned occupancy distribution instead of the depth of surfaces. We conduct extensive experiments on nuScenes and KITTI-360 datasets and our GaussianFormer-2 achieves state-of-the-art performance with high efficiency. Code: https://github.com/huang-yh/GaussianFormer.

Discussion (0). Sign in to comment.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    VISA improves closed-set 3D occupancy mIoU on nuScenes by using VLM instance audits as reliability-weighted semantic supervisors during training of existing world models.

  2. FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows

    cs.CV 2026-07 conditional novelty 6.5 of 10

    A two-stage flow-matching model that seeds point-cloud generation from a generated BEV density map, using teacher-estimated point pairings to keep transport paths straight, achieves SOTA JSD/IoU on SemanticKITTI compl...

  3. SparseOcc++: Geometry-Aware Sparse Latent Representation for Semantic Occupancy Prediction

    cs.CV 2026-07 accept novelty 6.5 of 10

    SparseOcc++ decouples geometry completion (via orthogonal SCF regression on sparse anchors) from semantics, improving IoU 2.3 points and running 3.9 imes faster than SparseOcc on nuScenes while 5.9 imes faster than Oc...

  4. GaussianSeed: Hierarchical Gaussian Seeding for High-Resolution 3D Occupancy Prediction

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A hierarchical Gaussian occupancy representation with regression-based seeding predicts high-resolution 3D occupancy at lower latency than prior sparse baselines, validated on nuScenes and a new 0.1m campus dataset.

  5. RayOcc: Occlusion-Aware Ray Occupancy Estimation via Gaussian Mixture Intensity

    cs.CV 2026-07 conditional novelty 6.0 of 10

    RayOcc models each camera ray as a non-normalized Gaussian mixture with Poisson-based occupancy probabilities, allowing multiple depth hypotheses per ray and improving Gaussian-initialized 3D occupancy prediction on nuScenes.

  6. S2GO: Streaming Sparse Gaussian Occupancy Prediction

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A sparse-query, streaming Gaussian occupancy predictor achieves state-of-the-art 3D semantic occupancy on nuScenes and KITTI with real-time inference.

  7. GaussianFusionOcc: A Seamless Sensor Fusion Approach for 3D Occupancy Prediction Using 3D Gaussians

    cs.CV 2025-07 conditional novelty 5.0 of 10

    GaussianFusionOcc fuses camera, LiDAR, and radar features through deformable attention to refine semantic 3D Gaussians, improving 3D occupancy prediction on nuScenes while lowering memory use and latency.

  8. QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction

    cs.CV 2025-06 conditional novelty 5.0 of 10

    QuadricFormer represents 3D scenes as a probabilistic mixture of superquadrics, improving accuracy and efficiency over Gaussian-based occupancy prediction on nuScenes.

  9. SHTOcc: Effective 3D Occupancy Prediction with Sparse Head and Tail Voxels

    cs.CV 2025-05 reject novelty 5.0 of 10

    SHTOcc combines attention-based sparse voxel selection with decoupled classifier retraining for 3D occupancy prediction, reporting efficiency gains and small, partly inconsistent accuracy improvements.

Pith tools