arXiv Computer Vision
Oct 9

CoCam4D: Geometry-Aware Cooperative 4D Perception for Camera-Only Autonomous Driving

CoCam4D is a Bayesian framework for camera-only autonomous driving that models geometric uncertainty in collaborative perception. It employs a VGGT-based network to produce 3D Gaussian scene representations with uncertainty estimates, allowing multiple vehicles to fuse observations without LiDAR. The system introduces Dynamic Object Primitives, a 35‑byte format for efficient C‑V2X communication, and shows significant performance gains over recent vision-only methods on OPV2V+ and DAIR‑V2X‑C benchmarks.

By Soham Pahari, Sudip Das, Arindam Das, Ujjwal Bhattacharya
Hugging Face Trending Papers
Sep 8

Dual-Layer Semantic-Spatial Belief Mapping for Aerial Object Goal Navigation

The paper introduces AeroBelief, a dual‑layer semantic‑spatial belief mapping framework for aerial object goal navigation. It separates broad contextual plausibility (intuition layer) from target‑specific evidence (evidence layer) and fuses them into persistent spatial belief hotspots, while also employing object‑conditioned visual reasoning and temporally stable regional guidance. Experiments on the UAV‑ON benchmark show AeroBelief outperforms prior methods in success rate, object success rate, and SPL.