Position Extrapolation

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9 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Extrapolation Matters: Sparse-Only Depth Completion Beyond LiDAR Coverage
Nov 5, 2026Maximilian Jansen, Gasser Elazab, Praveen KulkarniDepth Completion Camera-LiDAR Fusion
CARIAD SE · Bosch
Learning to Extrapolate in Depth with Looped Transformers
Oct 10, 2026Huitao Yang, Jianhao Huang, Zixuan Wang +1Position Extrapolation Recurrent Transformers
University of California, Los Angeles · Princeton University
Storing Is Not Remembering: LSTM-UT and Bounded Gated Memory for Looped Transformers
Sep 17, 2026Aras Kavuncu, Muhammad Burhan HafezMemory-Augmented Neural Networks Recurrent Transformers
University of Southampton, Electronics and Computer Science
Coupled-cluster molecular properties across the main group that extrapolate beyond training size
Aug 18, 2026Wenhao He, Xu Chen, Noah Song +10Hamiltonian Learning Quantum Chemistry
Center for Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemistry, Emory University, Atlanta, GA 30322, USA · Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA +2
SEGA: Spectral-Energy Guided Attention for Resolution Extrapolation in Diffusion Transformers
May 21, 2026Javad Rajabi, Kimia Shaban, Koorosh Roohi +2Rotary Positional Embeddings Diffusion Model Guidance
University of Toronto · Vector Institute
NeuroLiDAR: Adaptive Frame Rate Depth Sensing via Neuromorphic Event-LiDAR Fusion
May 16, 2026Darshana Rathnayake, Dulanga Weerakoon, Meera Radhakrishnan +1Depth Estimation Camera-LiDAR Fusion
Singapore Management University, Singapore · Singapore-MIT Alliance for Research and Technology Centre, Singapore · University of Technology Sydney, Australia
Decouple and Cache: KV Cache Construction for Streaming Video Understanding
May 3, 2026Zhanzhong Pang, Dibyadip Chatterjee, Fadime Sener +1Streaming Video Understanding KV Caching
National University of Singapore.
Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformers
Apr 9, 2026Harsh Kohli, Srinivasan Parthasarathy, Huan Sun +1Position Extrapolation Implicit Reasoning in Language Models
The Ohio State University
Distilling Latent Manifolds: Resolution Extrapolation by Variational Autoencoders
Mar 15, 2026Jiaming Chu, Tao Wang, Lei JinPosition Extrapolation Variational Autoencoders
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China

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Extrapolation Matters: Sparse-Only Depth Completion Beyond LiDAR Coverage

Maximilian Jansen, Gasser Elazab, Praveen Kulkarni

CARIAD SE · Bosch

Depth Completion Camera-LiDAR Fusion Position Extrapolation

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Extrapolation beyond LiDAR coverage is an important yet under-evaluated challenge of depth completion. In real camera--LiDAR systems, partial sensor overlap, occlusions, and limited field coverage often leave large image regions weakly observed or completely unobserved by LiDAR. Thus, we investigate depth completion in this beyond-coverage regime using only sparse supervision from raw single-sweep LiDAR, without dense depth labels, LiDAR accumulation, or pseudo-LiDAR generation. Our approach leverages a frozen monocular depth foundation model, lightweight per-frame alignment to sparse LiDAR geometry, and a refinement module that fuses the aligned prior with sparse LiDAR anchors to predict dense metric depth. To regularize learning under raw sparse supervision, we introduce a high-frequency normal-consistency loss defined as the image-space gradient of the per-pixel normal cosine discrepancy. This term penalizes spatial variation in scalar angular error; it does not directly match changes in neighboring normal vectors. We further introduce an evaluation protocol that explicitly separates LiDAR-supported and beyond-coverage regions, revealing extrapolative behavior that conventional depth-completion metrics can obscure. Our method achieves strong depth accuracy beyond LiDAR coverage while remaining competitive in LiDAR-covered regions, providing a practical way to deploy monocular foundation priors in multi-sensor systems.