ECCV 2026 | 浙大提出CoGoal3D：协同3D目标检测，刷新多个SOTA！

TL;DR - CoGoal3D, an ECCV 2026 paper from Zhejiang University's Machine Vision & Autonomous Navigation Lab, is a two-stage collaborative (vehicle-to-everything) 3D object detection framework that fixes 3D spatial misalignment left unaddressed by conventional 2D BEV feature fusion, reporting ~10-point 3D AP@0.7 gains across three real-world datasets.

• Problem framing: Broadcast-style collaborative perception aligns agent features in 2D BEV space, implicitly assuming all sensors share one horizontal plane; differing mounting heights/poses and sloped roads leave residual 3D misalignment that degrades 3D box accuracy.
• Stage 1 — 3D-AGF: A multi-scale 3D-aware global fusion module encodes collaborator BEV grid positions into ego coordinates, adds positional encoding, and uses deformable attention to sample/align collaborator features before multi-scale fusion feeding an RPN.
• Stage 2 — RGLR: BEV RoI pooling plus self-attention feeds a 3D point reconstruction auxiliary task (training only) to learn fine object geometry; a Ground-Truth Optimization step transfers collaborator in-box points into ego boxes to reduce calibration/sync/scan noise. Inference uses only the detection branch.
• Results: DAIR-V2X 72.16 BEV / 50.55 3D AP@0.7 (+6.77 / +10.86); V2V4Real 59.72 / 31.50 (+6.65 / +10.34); V2X-Real 76.98 / 54.64 (+6.96 / +10.18). Stage-1-only runs 24.8 FPS, full model 16.8 FPS. Also proposes MCDA multi-agent collaborative data augmentation (local rotation + global scaling/flip) to cut information loss. Code: github.com/Megalo-f/CoGoal3D.