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Talent Scout

M

Maximilian Luz

University of Freiburg

R

Rohit Mohan

University of Freiburg

T

Thomas Nürnberg

Bosch Research, Robert Bosch GmbH

Y

Yakov Miron

University of Haifa

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References (42)

[1]
ForecastOcc: Vision-based Semantic Occupancy Forecasting
2026Riya Mohan, Juana Valeria Hurtado et al.
[2]
Bridging Perspectives: Foundation Model Guided BEV Maps for 3D Object Detection and Tracking
2025Markus Kappeler, Ozgun cCiccek et al.
[3]
TrackOcc: Camera-Based 4D Panoptic Occupancy Tracking
2025Zhuoguang Chen, Kenan Li et al.
[4]
BEVFormer: Learning Bird’s-Eye-View Representation From LiDAR-Camera via Spatiotemporal Transformers
2024Zhiqi Li, Wenhai Wang et al.
[5]
GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction
2024Yuanhui Huang, Amonnut Thammatadatrakoon et al.
[6]
Progressive Multi-Modal Fusion for Robust 3D Object Detection
2024Rohit Mohan, Daniele Cattaneo et al.
[7]
OPUS: Occupancy Prediction Using a Sparse Set
2024Jiabao Wang, Zhaojiang Liu et al.
[8]
GaussianOcc: Fully Self-supervised and Efficient 3D Occupancy Estimation with Gaussian Splatting
2024Wanshui Gan, Fang Liu et al.
[9]
LION: Linear Group RNN for 3D Object Detection in Point Clouds
2024Zhe Liu, Jinghua Hou et al.
[10]
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Prediction
2024Yuanhui Huang, Wenzhao Zheng et al.
[11]
SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction
2024Pin Tang, Zhongdao Wang et al.
[12]
Point Transformer V3: Simpler, Faster, Stronger
2023Xiaoyang Wu, Li Jiang et al.
[13]
COTR: Compact Occupancy TRansformer for Vision-Based 3D Occupancy Prediction
2023Qihang Ma, Xin Tan et al.
[14]
PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness
2023Anh-Quan Cao, Angela Dai et al.
[15]
Amodal Optical Flow
2023Maximilian Luz, Rohit Mohan et al.
[16]
CTVIS: Consistent Training for Online Video Instance Segmentation
2023Kaining Ying, Qing Zhong et al.
[17]
Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving
2023Xiaoyu Tian, Tao Jiang et al.
[18]
Self-Supervised Multi-Object Tracking for Autonomous Driving From Consistency Across Timescales
2023Christopher Lang, Alexander Braun et al.
[19]
OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction
2023Yunpeng Zhang, Zhengbiao Zhu et al.
[20]
VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Tracking
2023Yukang Chen, Jianhui Liu et al.

Showing 20 of 42 references

Founder's Pitch

"Innovative 4D panoptic occupancy tracking system for enhanced robotic perception in dynamic environments."

4D Panoptic Occupancy TrackingScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

2/4 signals

5

Series A Potential

4/4 signals

10

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Why It Matters

Dynamic scene understanding is essential for autonomous systems to operate safely in complex environments. This research provides a unified, temporally consistent framework that improves tracking and occupancy mapping, addressing a significant gap in current robotic perception capabilities.

Product Angle

Develop a plug-and-play AI module for autonomous fleets that integrates with existing vehicle vision systems, enhancing visual perception and decision-making.

Disruption

The solution could replace less sophisticated occupancy and tracking mechanisms currently in use, especially those that do not integrate temporal element associations.

Product Opportunity

Autonomous vehicle manufacturers and robotics companies needing reliable scene understanding tools for safety and efficiency will benefit from this technology. As the AV market grows, demand for improved perception systems increases.

Use Case Idea

Deploy this model in autonomous vehicles for improved navigation and safety, particularly in environments with unpredictable moving objects and varying temporal elements.

Science

Latent Gaussian Splatting (LaGS) uses 3D Gaussians as a sparse intermediaterepresentation to enhance 3D voxel grids' temporal structure for perception systems. By employing mask-based segmentation married with end-to-end tracking, LaGS efficiently aggregates multi-view data into a unified scene representation, showing significant performance gains.

Method & Eval

The approach was evaluated on the Occ3D nuScenes and Waymo datasets, outperforming existing models on various metrics like occupancy segmentation and tracking quality by over 18 percentage points.

Caveats

The approach might require high computational resources, potentially limiting real-time application. Additionally, the integration into existing systems could face compatibility challenges.

Author Intelligence

Maximilian Luz

University of Freiburg

Rohit Mohan

University of Freiburg

Thomas Nürnberg

Bosch Research, Robert Bosch GmbH

Yakov Miron

University of Haifa

Daniele Cattaneo

University of Freiburg

Abhinav Valada

University of Freiburg