Signals 4 · free daily AI digest

M3GD: Multi-Modal Multi-View Geometric Diffusion for Camera--LiDAR Novel View Synthesis

Paper recorded by Signals 4 on 2026-09-24 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-24 on arXiv · recorded by Signals 4 on 2026-09-25

Category: cs.CV · 计算机视觉 · first seen 2026-09-25

Abstract

Robotic novel view synthesis (NVS) must recover both visual appearance and metric 3D structure, yet most generative NVS methods rely only on images, overlooking LiDAR, a complementary sensor common on robotic platforms. We present M3GD, a Camera--LiDAR multimodal representation for generative NVS that composes independently pretrained 2D image and 3D point-cloud foundation models without separatel

Read on arXiv →

#10 most recent of 313 cs.CV papers we have recorded · ↑ newer: Can Frozen Hyperspherical Features Guide the Selection of Pseudo Masks · ↓ older: ConPro: Contrast Projection Pretraining for Label-Efficient Vessel Seg
Cite this page: M3GD: Multi-Modal Multi-View Geometric Diffusion for Camera--LiDAR Novel View Synthesis: the #10 most recent of 313 cs.CV papers we have recorded (as of 2026-09-24). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/m3gd-multi-modal-multi-view-geometric-diffusion-for-camera-lidar-novel-view-synt.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions