LIFT-GS: Cross-Scene Render-Supervised Distillation for 3D Language Grounding

• URL: http://arxiv.org/abs/2502.20389v1
• Date: Thu, 27 Feb 2025 18:59:11 GMT
• Title: LIFT-GS: Cross-Scene Render-Supervised Distillation for 3D Language Grounding
• Authors: Ang Cao, Sergio Arnaud, Oleksandr Maksymets, Jianing Yang, Ayush Jain, Sriram Yenamandra, Ada Martin, Vincent-Pierre Berges, Paul McVay, Ruslan Partsey, Aravind Rajeswaran, Franziska Meier, Justin Johnson, Jeong Joon Park, Alexander Sax,
• Abstract summary: We train a feedforward model that makes predictions in 3D, but never requires 3D labels and is supervised only in 2D.<n>For training, only need images and camera pose, and 2D labels.<n>We show that we can even remove the need for 2D labels by using pseudo-labels from pretrained 2D models.
• Score: 64.28181017898369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our approach to training 3D vision-language understanding models is to train a feedforward model that makes predictions in 3D, but never requires 3D labels and is supervised only in 2D, using 2D losses and differentiable rendering. The approach is new for vision-language understanding. By treating the reconstruction as a ``latent variable'', we can render the outputs without placing unnecessary constraints on the network architecture (e.g. can be used with decoder-only models). For training, only need images and camera pose, and 2D labels. We show that we can even remove the need for 2D labels by using pseudo-labels from pretrained 2D models. We demonstrate this to pretrain a network, and we finetune it for 3D vision-language understanding tasks. We show this approach outperforms baselines/sota for 3D vision-language grounding, and also outperforms other 3D pretraining techniques. Project page: https://liftgs.github.io.

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